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1
DeSordi, Steven Paul. "Utah local area model sensitivity to boundary conditions for summer rain simulations." Wright-Patterson AFB, Ohio : Dept. of the Air Force, 1996. http://stinet.dtic.mil/cgi-bin/fulcrum%5Fmain.pl?database=ft%5Fu2&searchid=0&keyfieldvalue=ADA319136&filename=%2Ffulcrum%2Fdata%2FTR%5Ffulltext%2Fdoc%2FADA319136.pdf.
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Thesis (M.S.)--University of Utah, 1996. Thesis from the University of Utah's Department of Meteorology explores the sensitivity of the pecipitation-predicting model known as the Utah Limited Area Model (LAM) to the way that the lateral and upper boundary conditions are applied. The approach is different from most past studies of LAM boundary specification because it is founded upon a medium-range simulation using real data. Many other studies of boundary conditions have used idealized cases or short-term (a few days or less) predictions.Title from web page (viewed Oct. 30, 2003). "96-084." "August 1996." Includes bibliographical references p. [110]-112. Also available in print version.
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Li, Jing. "Clustering and forecasting for rain attenuation time series data." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219615.
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Clustering is one of unsupervised learning algorithm to group similar objects into the same cluster and the objects in the same cluster are more similar to each other than those in the other clusters. Forecasting is making prediction based on the past data and efficient artificial intelligence models to predict data developing tendency, which can help to make appropriate decisions ahead. The datasets used in this thesis are the signal attenuation time series data from the microwave networks. Microwave networks are communication systems to transmit information between two fixed locations on the earth. They can support increasing capacity demands of mobile networks and play an important role in next generation wireless communication technology. But inherent vulnerability to random fluctuation such as rainfall will cause significant network performance degradation. In this thesis, K-means, Fuzzy c-means and 2-state Hidden Markov Model are used to develop one step and two step rain attenuation data clustering models. The forecasting models are designed based on k-nearest neighbor method and implemented with linear regression to predict the real-time rain attenuation in order to help microwave transport networks mitigate rain impact, make proper decisions ahead of time and improve the general performance.Clustering is een van de unsupervised learning algorithmen om groep soortgelijke objecten in dezelfde cluster en de objecten in dezelfde cluster zijn meer vergelijkbaar met elkaar dan die in de andere clusters. Prognoser är att göra förutspårningar baserade på övergående data och effektiva artificiella intelligensmodeller för att förutspå datautveckling, som kan hjälpa till att fatta lämpliga beslut. Dataseten som används i denna avhandling är signaldämpningstidsseriedata från mikrovågsnätverket. Mikrovågsnät är kommunikationssystem för att överföra information mellan två fasta platser på jorden. De kan stödja ökade kapacitetsbehov i mobilnät och spela en viktig roll i nästa generationens trådlösa kommunikationsteknik. Men inneboende sårbarhet för slumpmässig fluktuering som nedbörd kommer att orsaka betydande nätverksförstöring. I den här avhandlingen används K-medel, Fuzzy c-medel och 2-state Hidden Markov Model för att utveckla ett steg och tvåstegs regen dämpning dataklyvningsmodeller. Prognosmodellerna är utformade utifrån k-närmaste granne-metoden och implementeras med linjär regression för att förutsäga realtidsdämpning för att hjälpa mikrovågstransportnät att mildra regnpåverkan, göra rätt beslut före tid och förbättra den allmänna prestandan.
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Karlsson, Magnus Sven. "NEAREST NEIGHBOR REGRESSION ESTIMATORS IN RAINFALL-RUNOFF FORECASTING." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/282088.
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The subject of this study is rainfall-runoff forecasting and flood warning. Denote by (X(t),Y(t)) a sequence of equally spaced bivariate random variables representing rainfall and runoff, respectively. A flood is said to occur at time period (n + 1) if Y(n + 1) > T where T is a fixed number. The main task of flood warning is that of deciding whether or not to issue a flood alarm for the time period n + 1 on the basis of the past observations of rainfall and runoff up to and including time n. With each decision, warning or no warning, there is a certain probability of an error (false alarm or no alarm). Using notions from classical decision theory, the optimal solution is the decision that minimizes Bayes risk. In Chapter 1 a more precise definition of flood warning will be given. A critical review (Chapter 2) of classical methods for forecasting used in hydrology reveals that these methods are not adequate for flood warning and similar types of decision problems unless certain Gaussian assumptions are satisfied. The purpose of this study is to investigate the application of a nonparametric technique referred to as the k-nearest neighbor (k-NN) methods to flood warning and least squares forecasting. The motivation of this method stems from recent results in statistics which extends nonparametric methods for inferring regression functions in a time series setting. Assuming that the rainfall-runoff process can be cast in the framework of Markov processes then, with some additional assumptions, the k-NN technique will provide estimates that converge with an optimal rate to the correct decision function. With this in mind, and assuming that our assumptions are valid, then we can claim that this method will, as the historical record grows, provide the best possible estimate in the sense that no other method can do better. A detailed description of the k-NN estmator is provided along with a scheme for calibration. In the final chapters, the forecasts of this new method are compared with the forecasts of several other methods commonly used in hydrology, on both real and simulated data.
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Gorugantula, Srikanth V. L. "A GPS-IPW Based Methodology for Forecasting Heavy Rain Events." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/10145.
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The mountainous western Virginia is the source of the headwater streams for the New, the Roanoke, and the James rivers. The region is prone to flash flooding, typically the result of localized precipitation. Fortunately, within the region, there is an efficient system of instruments for real-time data gathering with IFLOWS (Integrated Flood Observing and Warning System) gages, WSR-88D Doppler radar, and high precision GPS (Global Positioning System) receiver. The focus of this research is to combine the measurements from these various sensors in an algorithmic framework to determine the flash flood magnitude. It has been found that the trend in the GPS signals serves as a precursor for rain events with a lead-time of 30 minutes to 2 hours. The methodology proposed herein takes advantage of this lead-time as the trigger to initiate alert related calculations. It is shown here that the sum of the rates of change of total cloud water, water vapor contents and logarithmic profiles of partial pressure of dry air and temperature in an atmospheric column is equal to the rain rate. The total water content is measurable as the profiles of integrated precipitable water (IPW) from the GPS, the vertically integrated liquid (VIL) from the radar (representing different phases of the atmospheric water) and the pressure and temperature profiles are available. An example problem is presented illustrating the involving the calculations.Master of Science
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Ryall, Gill. "An automated system for generating very-short-range forecasts of precipitation." Thesis, University of Sussex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284079.
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Pettegrew, Brian P. "On methods of precipitation efficiency estimation /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1422951.
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Michaud, Jene Diane. "RAINFALL-RUNOFF MODELING OF FLASH FLOODS IN SEMI-ARID WATERSHEDS." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1992. http://hdl.handle.net/10150/614156.
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Flash floods caused by localized thunderstorms are a natural hazard
of the semi -arid Southwest, and many communities have responded by
installing ALERT flood forecasting systems. This study explored a
rainfall- runoff modeling approach thought to be appropriate for
forecasting in such watersheds. The kinematic model KINEROS was
evaluated because it is a distributed model developed specifically for
desert regions, and can be applied to basins without historic data.
This study examined the accuracy of KINEROS under data constraints
that are typical of semi -arid ALERT watersheds. The model was validated
at the 150 km2, semi -arid Walnut Gulch experimental watershed. Under the
conditions examined, KINEROS provided poor simulations of runoff volume
and peak flow, but good simulations of time to peak. For peak flows, the
standard error of estimate was nearly 100% of the observed mean.
Surprisingly, when model parameters were based only on measurable
watershed properties, simulated peak flows were as accurate as when
parameters were calibrated on some historic data. The accuracy of
KINEROS was compared to that of the SCS model. When calibrated, a
distributed SCS model with a simple channel loss component was as
accurate as KINEROS.
Reasons for poor simulations were investigated by examining a)
rainfall sampling errors, b) model sensitivity and dynamics, and c)
trends in simulation accuracy. The cause of poor simulations was divided
between rainfall sampling errors and other problems. It was found that
when raingage densities are on the order of 1/20 km2, rainfall sampling errors preclude the consistent and reliable simulation of runoff from
localized thunderstorms. Even when rainfall errors were minimized,
accuracy of simulations were still poor. Good results, however, have
been obtained with KINEROS on small watersheds; the problem is not
KINEROS itself but its application at larger scales.
The study also examined the hydrology of thunderstorm -generated
floods at Walnut Gulch. The space -time dynamics of rainfall and runoff
were characterized and found to be of fundamental importance. Hillslope
infiltration was found to exert a dominant control on runoff, although
flow hydraulics, channel losses, and initial soil moisture are also
important. Watershed response was found to be nonlinear.
8
Tsang, Fan Cheong. "Advances in flood forecasting using radar rainfalls and time-series analysis." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.481184.
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This thesis reports the use of a time-series analysis approach to study the catchment hydrological system of the River Ribble. Rain gauge records, radar rainfall estimates and flow data are used in the analysis. The preliminary study consists of the flow forecasting at Reedyford, Pendle Water (82 km2). Flow forecasts generated from the rain gauge records are better than the radar rainfall estimates over this small catchment. However, the catchment response to rainfall is quick and no clear advantages in extending the lead-time of the forecast can be introduced by using an artificial time delayed rainfall input. A non-linear rainfall-flow relationship has been studied using the rain gauge rainfall and flow records at the River Hodder catchment (261 km2). A calibration scheme is used to identify the non-linear function of the catchment as well as the rainfall-flow system model. Although a better time-invariant system model can be identified, the non-linear rainfall-flow process cannot be fully explained by a power law function of effective rainfall. Assuming the dynamic, nonlinear system characteristics of the catchment can be reflected by a time-varying model gain parameter, relationships between the parameter and the flow, and between the parameter and the rainfall can be evaluated. These relationships have been used to improve the flow forecast during storm events. The results indicate, however, that the approach failed to improve the flow forecast near the peak flow condition. Radar data have been incorporated to forecast the flow at Jumbles Rock (1053 km2) and Samlesbury (1140 km2), River Ribble. The radar data calibrated by the Lancaster University Adaptive Radar Calibration System appears to produce better flow forecasts than the standard radar data product calibrated by the Meteorological Office. The proposed flow forecasting scheme generates better forecasts than the current system operated by the National Rivers Authority, North West Region.
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Cataldo, Edmund F. "Evaluation of the SSM/I rain analyses for selective storms in the ERICA project." Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA241321.
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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 1990.Thesis Advisor(s): Wash, Carlyle H. Second Reader: Nuss, Wendell A. "September 1990." Description based on title screen viewed on December 17, 2009. DTIC Descriptor(s): Weather forecasting, satellite meteorology, uncertainty, polarization, ships, coastal regions, light, rates, theses, radar, regression analysis, precipitation, solutions(general), rain, winter, rainfall intensity, storms, equations, cyclones, channels, corrections, temperate regions, cyclogenesis, algorithms, temperature. DTIC Identifier(s): Rainfall intensity, erica project, ssm/i(special sensor microwave/images). Author(s) subject terms: Microwave, ERICA, SSM/I, precipitation forecasting, rain. Includes bibliographical references (p. 81-82). Also available in print.
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Karnieli, Arnon 1952. "Storm runoff forecasting model incorporating spatial data." Diss., The University of Arizona, 1988. http://hdl.handle.net/10150/191138.
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This study is concerned with design forecasting of storm hydrographs with emphasis on runoff volume and peak discharge. The objective of the study was to develop, calibrate and test a method for forecasting storm runoff from small semi-arid watersheds using an available prediction model. In order to turn the selected prediction model into a forecasting model an objective procedure in terms of an API-type model was developed for evaluating the soil moisture deficit in the upper soil layer at the beginning of each storm. Distinction was made between the physically-based parameters and the other fitting parameters. The rainfall excess calculation was computed by solving the Green and Ampt equation for unsteady rainfall conditions using the physically-based parameters. For the physically-based parameters a geographic information system was developed in order to account for the variability in time and space of the input data and the watershed characteristics and to coregister parameters on a common basis. The fitting parameters were used to calibrate the model on one subwatershed in the Walnut Gulch Experimental Watershed while the physically-based parameters remained constant. Two objective functions were selected for the optimization procedure. These functions expressed the goodness of fit between the calculated hydrograph volume and peak discharge and the observed volume and peak discharge. Linear relationships between the effective matric potential parameter and the two objective functions obtained from the sensitivity analyses made it possible to develop a bilinear interpolation algorithm to minimize, simultaneously, the difference between the calculated and observed volume and peak discharge. The prediction mode of the model was tested both on different storm events on the same subwatershed and on another subwatershed with satisfactory results. In the prediction mode the effective matric potential parameter was allowed to vary from storm to storm, however, in the forecasting mode these values were obtained from the API model. Relatively poor results were obtained in testing the forecasting mode on another subwatershed. These errors were able to be corrected by changing the channel losses fitting parameters.
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Chapman, Matthew. "Spatial forecasting of air pollution in urban environments : a geographical information system approach." Thesis, University of Brighton, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271974.
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Hanni-Wells, Michael R. "A climatology of lower tropospheric environments during freezing rain events in the south-central United States." Virtual Press, 2004. http://liblink.bsu.edu/uhtbin/catkey/1286603.
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Intraregional variability in tropospheric environments during freezing rain events is investigated for the South Central United States. National Weather Service (NWS) Automated Surface Observing Stations (ASOS) are used to detect the occurrence of freezing rain, and rawinsonde observations (RAOB) employed to analyze lower tropospheric vertical profiles of temperature, dew point temperature, wind, and layer thicknesses during these periods. The study area consists of seven 100 mile radius RAOB proximity sub-regions centered around Peachtree City Georgia, Nashville Tennessee, Birmingham Alabama, Jackson Mississippi, Shreveport Louisiana, Little Rock Arkansas, and Springfield Missouri. A series of difference of means tests are performed to determine if statistically significant differences exist in mean values of selected tropospheric variables during periods of freezing rain between adjacent RAOB sites to determine the character of intraregional variability within the South Central United States. Results of these tests suggest 5 sub-regions exist in which freezing rain events can be forecast based upon thresholds and ranges of lower tropospheric environmental variables. As a final step, flow charts are developed for each of the 5 subregions to aid meteorologists in forecasting freezing rain within the Southeast United States.Department of Geography
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Hajjam, Sohrab. "Real-time flood forecasting model intercomparison and parameter updating rain gauge and weather radar data." Thesis, University of Salford, 1997. http://usir.salford.ac.uk/43019/.
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This thesis describes the development of real-time flood forecasting models at selected catchments in the three countries, using rain gauge and radar derived rainfall estimates and time-series analysis. An extended inter-comparison of real-time flood forecasting models has been carried out and an attempt has been made to rank the flood forecasting models. It was found that an increase in model complexity does not necessarily lead to an increase in forecast accuracy. An extensive analysis of group calibrated transfer function (TF) models on the basis of antecedent conditions of the catchment and storm characteristics has revealed that the use of group model resulted in a significant improvement in the quality of the forecast. A simple model to calculate the average pulse response has also been developed. The development of a hybrid genetic algorithm (HGA), applied to a physically realisable transfer function model is described. The techniques of interview selection and fitness scaling as well as random bit mutation and multiple crossover have been included, and both binary and real number encoding technique have been assessed. The HGA has been successfully applied for the identification and simulation of the dynamic TF model. Four software packages have been developed and extensive development and testing has proved the viability of the approach. Extensive research has been conducted to find the most important adjustment factor of the dynamic TF model. The impact of volume, shape and time adjustment factors on forecast quality has been evaluated. It has been concluded that the volume adjustment factor is the most important factor of the three. Furthermore, several attempts have been made to relate the adjustment factors to different elements. The interaction of adjustment factors has also been investigated. An autoregressive model has been used to develop a new updating technique for the dynamic TF model by the updating of the B parameters through the prediction of future volume adjustment factors over the forecast lead-time. An autoregressive error prediction model has also been combined with a static TF model. Testing has shown that the performance of both new TF models is superior to conventional procedures.
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Vavae, Hilia. "A simple forecasting scheme for predicting low rainfalls in Funafuti, Tuvalu." The University of Waikato, 2008. http://hdl.handle.net/10289/2435.
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The development of some ability for forecasting low rainfalls would be helpful in Tuvalu as rainwater is the only source of fresh water in the country. The subsurface water is brackish and saline so the entire country depends totally on rainwater for daily domestic supplies, agricultural and farming activities. More importantly, these atolls are often influenced by droughts which consequently make inadequate drinking water an issue. A simple graph-based forecasting scheme is developed and presented in this thesis for forecasting below average mean rainfall in Funafuti over the next n-month period. The approach uses precursor ocean surface temperature data to make predictions of below average rainfall for n = 1, 2 12. The simplicity of the approach makes it a suitable method for the country and thus for the Tuvalu Meteorological Service to use as an operational forecasting tool in the climate forecasting desk. The graphical method was derived from standardised monthly rainfalls from the Funafuti manual raingauge for the period January 1945 to July 2007. The method uses lag-1 and-lag 2 NINO4 sea surface temperatures to define whether prediction conditions hold. The persistence of predictability tends to be maintained when the observed NINO4 ocean surface temperatures fall below 26.0oC. Although the developed method has a high success probability of up to 80 percent, this can only be achieved when conditions are within the predictable field. A considerable number of below average rainfall periods are not within the predictable field and therefore cannot be forecast by this method. However, the graphical approach has particular value in warning when an existing drought is likely to continue.
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Dupigny-Giroux, Lesley-Ann. "Techniques for rainfall estimation and surface characterization over northern Brazil." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=40345.
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The sertao of northeast Brazil is a semiarid region characterized by recurring droughts. The vastness of the area (650,000 km$ sp2)$ poses a challenge to the effective monitoring of the impacts of drought at a scale that would be useful to the inhabitants of the sertao. Remote sensing data provide a viable way of assessing the extent and nature of drought across the landscape.The work present a more effective algorithm to estimate rainfall from both the cold and warm cloud types present. Using a decision-tree methodology, the analysis yields rainfall estimates over the 0-21 mm range. Because seasonal variations in rainfall produce differences in vegetation, soils and hydrologic responses, Principal Components Analysis was used to examine these land surface responses. Individual components and component pairings were useful in identifying variations in vegetation density, geobotanical differences and drainage characteristics. The presence of cloud cover was found to dampen the land surface information that could be extracted. Landsat Thematic Mapper (TM) imagery was then used to produce a moisture index which characterizes surface wetness in relation to other features present in a scene. The multispectral combination of TM bands 1, 4 and 6 allowed for the separation of the surface types present, in locational space. This space was defined by an open-ended triange made up of a vertical "water line", a horizontal line of equal vegetation density; and a negatively-slopping iso-moisture line. The stability of the moisture index was influenced by varying scale and seasonal conditions.
In the drought conditions that prevailed in 1991-1992, these methods provide important additions to existing drought monitoring approaches in the Brazilian northeast. Further calibration is required in order to extend their applicability to other geographical regions and time frames.
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Paduru, Anirudh. "Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/1097.
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Weather radar imagery is important for several remote sensing applications including tracking of storm fronts and radar echo classification. In particular, tracking of precipitation events is useful for both forecasting and classification of rain/non-rain events since non-rain events usually appear to be static compared to rain events. Recent weather radar imaging-based forecasting approaches [3] consider that precipitation events can be modeled as a combination of localized functions using Radial Basis Function Neural Networks (RBFNNs). Tracking of rain events can be performed by tracking the parameters of these localized functions. The RBFNN-based techniques used in forecasting are not only computationally expensive, but also moderately effective in modeling small size precipitation events. In this thesis, an existing RBFNN technique [3] was implemented to verify its computational efficiency and forecasting effectiveness. The feasibility of modeling precipitation events using RBFNN effectively was evaluated, and several modifications to the existing technique have been proposed.
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Heneker, Theresa M. "An improved engineering design flood estimation technique: removing the need to estimate initial loss /." Title page, abstract and table of contents only, 2002. http://web4.library.adelaide.edu.au/theses/09PH/09phh4989.pdf.
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Thesis (Ph.D.)--University of Adelaide, Dept. of Civil and Environmental Engineering, 2002."May 2002" Includes list of papers published during this study. Errata slip inserted inside back cover of v. 1. Includes bibliographical references (leaves 331-357).
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Dravitzki, Stacey Maree. "Precipitation in the Waikato River catchment : a thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy in Geophysics /." ResearchArchive@Victoria e-thesis, 2009. http://researcharchive.vuw.ac.nz/handle/10063/955.
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Hsu, Kuo-Lin, Soroosh Sorooshian, Xiaogang Gao, and Hoshin Vijai Gupta. "Rainfall estimation from satellite infrared imagery using artificial neural networks." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1997. http://hdl.handle.net/10150/615703.
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Infrared (IR) imagery collected by geostationary satellites provides useful information
about the dirunal evolution of cloud systems. These IR images can be analyzed to indicate
the location of clouds as well as the pattern of cloud top temperatures (Tbs). During the past
several decades, a number of different approaches for estimation of rainfall rate (RR) from
Tb have been explored and concluded that the Tb-RR relationship is (1) highly nonlinear,
and (2) seasonally and regionally dependent. Therefore, to properly model the relationship,
the model must be able to:
(1) detect and identify a non-linear mapping of the Tb-RR relationship;
(2) Incorporate information about various cloud properties extracted from IR image;
(3) Use feedback obtained from RR observations to adaptively adjust to seasonal and
regional variations; and
(4) Effectively and efficiently process large amounts of satellite image data in real -time.
In this study, a kind of artificial neural network (ANN), called Modified Counter
Propagation Network (MCPN), that incorporates these features, has been developed. The
model was calibrated using the data around the Japanese Islands provided by the Global
Precipitation Climatology Project (GPCP) First Algorithm Intercomparison Project (AIP-I).
Validation results over the Japanese Islands and Florida peninsula show that by providing
limited ground-truth observation, the MCPN model is effective in monthly and hourly
rainfall estimation. Comparison of results from MCPN model and GOES Precipitation Index
(GPI) approach is also provided in the study.
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Rodgers, William N. "Land Cover Change and its Impacts on a Flash Flood-Producing Rain Event in Eastern Kentucky." TopSCHOLAR®, 2014. http://digitalcommons.wku.edu/theses/1363.
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Eastern Kentucky is a 35-county region that is a part of the Cumberland Plateau of the Appalachian Mountains. With mountaintop removal and associated land cover change (LCC) (primarily deforestation), it is hypothesized that there would be changes in various atmospheric boundary layer parameters and precipitation. In this research, we have conducted sensitivity experiments of atmospheric response of a significant flash flood-producing rainfall event by modifying land cover and topography. These reflect recent LCC, including mountaintop removal (MTR). We have used the Weather Research and Forecasting (WRF) model for this purpose. The study found changes in amount, location, and timing of precipitation. LCC also modified various surface fluxes, moist static energy, planetary boundary layer height, and local-scale circulation wind circulation. The key findings were the modification in fluxes and precipitation totals. With respect to sensible heat flux (H), there was an increase to bare soil (post-MTR) in comparison to pre-MTR conditions (increased elevation with no altered land cover). Allowing for growth of vegetation, the grass simulation resulted in a decrease in H. H increased when permitting the growth of forest land cover (LC) but not to the degree of bare soil. In regards to latent heat flux (LE), there was a dramatic decrease transitioning from pre-MTR to post-MTR simulations. Then with the subsequent grass and forest simulations, there was an increase in LE comparable to the pre-MTR simulation. Under pre-MTR conditions, the total precipitation was at its highest level overall. Then with the simulated loss of vegetation and elevation, there was a dramatic decrease in precipitation. With the grass LC, the precipitation increased in all areas of interest. Then forest LC was simulated allowing overall slightly higher precipitation than grass.
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Hsu, Kuo-lin 1961. "Rainfall estimation from satellite infrared imagery using artificial neural networks." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/191209.
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Infrared (IR) imagery collected by geostationary satellites provides useful information about the dirunal evolution of cloud systems. These JR images can be analyzed to indicate the location of clouds as well as the pattern of cloud top temperatures (Tbs). During the past several decades, a number of different approaches for estimation of rainfall rate (RR) from Tb have been explored and concluded that the Tb-RR relationship is (1) highly nonlinear, and (2) seasonally and regionally dependent. Therefore, to properly model the relationship, the model must be able to: (1) detect and identify a non-linear mapping of the Tb-RR relationship; (2) Incorporate information about various cloud properties extracted from IR image; (3) Use feedback obtained from RR observations to adaptively adjust to seasonal and regional variations; and (4) Effectively and efficiently process large amounts of satellite image data in real-time. In this study, a kind of artificial neural network (ANN), called Modified Counter Propagation Network (MCPN), that incorporates these features, has been developed. The model was calibrated using the data around the Japanese Islands provided by the Global Precipitation Climatology Project (GPCP) First Algorithm Intercompari son Project (AIP-I). Validation results over the Japanese Islands and Florida peninsula show that by providing limited ground-truth observation, the MCPN model is effective in monthly and hourly rainfall estimation. Comparison of results from MCPN model and GOES Precipitation Index (GPI) approach is also provided in the study.
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Dolcine, Leslie. "Prévision quantitative à très courte échéance de la pluie : modèle global adapté à l'information radar." Grenoble 1, 1997. http://www.theses.fr/1997GRE10067.
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Une prevision quantitative a tres courte echeance des precipitations peut contribuer a ameliorer la prevision des crues des bassins versants a risque, ou la gestion des systemes d'assainissement pluvial urbain. Cette prevision est realisee, jusqu'a present, par simple advection des observations radar, et suppose que la dynamique du nuage precipitant est stationnaire. L'utilisation multi-site des radars permettant une exploration volumique de l'atmosphere, la possibilite de disposer en temps reel de donnees meteorologiques au sol et de donnees satellite, ont favorise le developpement d'une nouvelle approche de prevision quantitative de la pluie. Dans cette approche, le nuage precipitant est conceptualise comme une colonne atmospherique dont le temps de reponse depend des parametres microphysiques des precipitations et du profil vertical de contenu en eau precipitante de cette colonne. Les equations regissant l'evolution de cette colonne atmospherique sont deduites des equations de continuite pour l'air, la vapeur d'eau, l'eau nuageuse et l'eau precipitante ainsi que des lois de la thermodynamique et d'une microphysique simplifiee. Des ameliorations graduelles ont ete introduites dans le modele global de depart qui se ramenait a l'equation d'evolution de l'eau liquide precipitante. Une equation supplementaire pour la description de la vitesse verticale et la prise en compte du renforcement orographique de la pluie tres importante en region montagneuse a ete ajoutee. Ce modele, applique a des evenements pluvieux de l'experience radar des cevennes et a des evenements pluvieux simules, s'est montre superieur dans la majorite des cas a deux methodes de prevision plus simples : les methodes de persistance et d'advection. Une analyse de sensibilite a montre l'importance de la vitesse verticale et la faible influence des donnees meteorologiques au sol sur les resultats du modele. La qualite de la prevision dans l'approche globale depend de la vision tridimensionnelle du champ pluvieux, de la variabilite de la pluie et de la validite des hypotheses d'evolution. Pour une meilleure description des champs pluvieux fortement variables, la formulation du modele global a ete etendue afin d'inclure l'eau nuageuse. L'interet potentiel de ce modele a ete demontre par comparaison a un modele microphysique. L'estimation de l'eau nuageuse reste cependant un prealable a l'evaluation de cette nouvelle formulation sur des donnees reelles.
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Vilanculos, Agostinho Chuquelane Fadulo. "The use of hydrological information to improve flood management-integrated hydrological modelling of the Zambezi River basin." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1018915.
Full textAbstract:
The recent high profile flooding events – that have occurred in many parts of the world – have drawn attention to the need for new and improved methods for water resources assessment, water management and the modelling of large-scale flooding events. In the case of the Zambezi Basin, a review of the 2000 and 2001 floods identified the need for tools to enable hydrologists to assess and predict daily stream flow and identify the areas that are likely to be affected by flooding. As a way to address the problem, a methodology was set up to derive catchment soil moisture statistics from Earth Observation (EO) data and to study the improvements brought about by an assimilation of this information into hydrological models for improving reservoir management in a data scarce environment. Rainfall data were obtained from the FEWSNet Web site and computed by the National Oceanic and Atmospheric Administration Climatic Prediction Center (NOAA/CPC). These datasets were processed and used to monitor rainfall variability and subsequently fed into a hydrological model to predict the daily flows for the Zambezi River Basin. The hydrological model used was the Geospatial Stream Flow Model (GeoSFM), developed by the United States Geological Survey (USGS). GeoSFM is a spatially semi-distributed physically-based hydrological model, parameterised using spatially distributed topographic data, soil characteristics and land cover data sets available globally from both Remote Sensing and in situ sources. The Satellite rainfall data were validated against data from twenty (20) rainfall gauges located on the Lower Zambezi. However, at several rain gauge stations (especially those with complex topography, which tended to experience high rainfall spatial variability), there was no direct correlation between the satellite estimates and the ground data as recorded in daily time steps. The model was calibrated for seven gauging stations. The calibrated model performed quite well at seven selected locations (R2=0.66 to 0.90, CE=0.51 to 0.88, RSR=0.35 to 0.69, PBIAS=−4.5 to 7.5). The observed data were obtained from the National Water Agencies of the riparian countries. After GeoSFM calibration, the model generated an integration of the flows into a reservoir and hydropower model to optimise the operation of Kariba and Cahora Bassa dams. The Kariba and Cahora Bassa dams were selected because this study considers these two dams as the major infrastructures for controlling and alleviating floods in the Zambezi River Basin. Other dams (such as the Kafue and Itezhi-Thezi) were recognised in terms of their importance but including them was beyond the scope of this study because of financial and time constraints. The licence of the reservoir model was limited to one year for the same reason. The reservoir model used was the MIKE BASIN, a professional engineering software package and quasi-steady-state mass balance modelling tool for integrated river basin and management, developed by the Denmark Hydraulic Institute (DHI) in 2003. The model was parameterised by the geometry of the reservoir basin (level, area, volume relationships) and by the discharge-level (Q-h) relationship of the dam spillways. The integrated modelling system simulated the daily flow variation for all Zambezi River sub-basins between 1998 and 2008 and validated between 2009 and 2011. The resulting streamflows have been expressed in terms of hydrograph comparisons between simulated and observed flow values at the four gauging stations located downstream of Cahora Bassa dam. The integrated model performed well, between observed and forecast streamflows, at four selected gauging stations (R2=0.53 to 0.90, CE=0.50 to 0.80, RSR=0.49 to 0.69, PBIAS=−2.10 to 4.8). From the results of integrated modelling, it was observed that both Kariba and Cahora Bassa are currently being operated based on the maximum rule curve and both remain focused on maximising hydropower production and ensuring dam safety rather than other potential influences by the Zambezi River (such as flood control downstream – where the communities are located – and environmental issues). In addition, the flood mapping analysis demonstrated that the Cahora Bassa dam plays an important part in flood mitigation downstream of the dams. In the absence of optimisation of flow releases from both the Kariba and Cahora Bassa dams, in additional to the contribution of any other tributaries located downstream of the dams, the impact of flooding can be severe. As such, this study has developed new approaches for flood monitoring downstream of the Zambezi Basin, through the application of an integrated modelling system. The modelling system consists of: predicting daily streamflow (using the calibrated GeoSFM), then feeding the predicted streamflow into MIKE BASIN (for checking the operating rules) and to optimise the releases. Therefore, before releases are made, the flood maps can be used as a decision-making tool to both assess the impact of each level of release downstream and to identify the communities likely to be affected by the flood – this ensures that the necessary warnings can be issued before flooding occurs. Finally an integrated flood management tool was proposed – to host the results produced by the integrated system – which would then be accessible for assessment by the different users. These results were expressed in terms of water level (m). Four discharge-level (Q-h) relationships were developed for converting the simulated flow into water level at four selected sites downstream of Cahora Bassa dam – namely: Cahora Bassa dam site, Tete (E-320), Caia (E-291) and Marromeu (E-285). However, the uncertainties in these predictions suggested that improved monitoring systems may be achieved if data access at appropriate scale and quality was improved.
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Ramesh, Chirania Saloni. "Forecasting Model for High-Speed Rail in the United States." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76878.
Full textAbstract:
A tool to model both current rail and future high-speed rail (HSR) corridors has been presented in this work. The model is designed as an addition to the existing TSAM (Transportation System Analysis Model) capabilities of modeling commercial airline and automobile demand. TSAM is a nationwide county to county multimodal demand forecasting tool based on the classical four step process. A variation of the Box-Cox logit model is proposed to best capture the characteristic behavior of rail demand in US. The utility equation uses travel time and travel cost as the decision variables for each model. Additionally, a mode specific geographic constant is applied to the rail mode to model the North-East Corridor (NEC). NEC is of peculiar interest in modeling, as it accounts for most of the rail ridership. The coefficients are computed using Genetic Algorithms. A one county to one station assignment is employed for the station choice model. Modifications are made to the station choice model to replicate choices affected by the ease of access via driving and mass transit. The functions for time and cost inputs for the rail system were developed from the AMTRAK website. These changes and calibration coefficients are incorporated in TSAM. The TSAM model is executed for the present and future years and the predictions are discussed. Sensitivity analysis for cost and speed of the predicted HSR is shown. The model shows the market shift for different modes with the introduction of HSR. Limited data presents the most critical hindrance in improving the model further. The current validation process incorporates essential assumptions and approximations for transfer rates, short trip percentages, and access and egress distances. The challenges for the model posed by limited data are discussed in the model.Master of Science
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Maier, George. "Forecasting ridership impacts of transit oriented development at MARTA rail stations." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54477.
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The Metropolitan Atlanta Rapid Transit Authority (MARTA) Transit Oriented Development (TOD) program has been expanding the number of stations being considered for development of surface parking lots and into the air rights over certain rail stations. As of 2015, MARTA has six rail stations in various stages of TOD development, which will increase multi-modal options for metro Atlanta residents. The overarching goal of TOD development is to increase transit ridership and reduce auto-dependency; hence quantifying the potential benefits of TOD development in terms of ridership is paramount. Despite several drawbacks, travel demand models have historically been utilized to forecast ridership for land use changes and transit improvements. Direct ridership models (DRMs) are transit demand forecasting methods that can be applied to land development in cases where traditional travel demand models (TDMs) are not well suited. DRMs leverage geographic tools commonly used by planners to take advantage of small scale pedestrian environment factors immediately surrounding transit stations. Although DRM data and methods can achieve greater precision in predicting local walk-access transit trips, the lack of regional and large-scale datasets reduces the ability to model ridership generated from riders outside the immediate vicinity of the rail stations. Stations that have high multi-modal access trips, particularly via personal vehicle and connecting buses, are not typically accounted for by DRMs. Hence, this study focuses on pedestrian-based rail boardings only, a metric that also allows the use of a large scale onboard survey distributed by the Atlanta Regional Commission (ARC) in late 2009 and early 2010 in Atlanta, Georgia. Analysis of the large scale on-board ridership survey also reveals variables that may be useful in forecasting ridership at the station level when coupled with available census data. Comparison of variables such as income, age, gender, ethnicity, and race from census data with the large scale survey guided the selection of candidate variables to be included in a DRM for MARTA rail stations. Results from the comparison showed that using census data in DRMs does not always accurately reflect the ridership demographics. Notable differences in pedestrian-based ridership and transit catchments appear to occur in populations making less than $40,000, African American populations, and the young and elderly populations. Large differences in the survey and census data reported around the stations raise questions about the usability of census data in predicting ridership at rail stations. Despite the shortcomings of using census data to directly predict walk access transit ridership, an ordinary least squared (OLS) regression model predicts a high proportion of variance of pedestrian-based ridership in Atlanta, Georgia. A small number of variables were incorporated into a DRM to show the strong relationship of employment density with pedestrian based ridership. The number of low income residents was also influential in increasing ridership via walk access.
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PELZ, ZACHARY L. "A STATION LEVEL ANALYSIS OF COMPETING LIGHT- RAIL ALTERNATIVES IN CINCINNATI'S EASTERN CORRIDOR." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179851133.
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Yeun-Touh, Li. "High Speed Rail Demand Adaptation and Travellers' Long-term Usage Patterns." 京都大学 (Kyoto University), 2016. http://hdl.handle.net/2433/217154.
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Samuel, Jos Martinus. "Effects of multi-scale rainfall variability on flood frequency : a comparative study of catchments in Perth, Newcastle and Darwin, Australia." University of Western Australia. School of Environmental Systems Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0066.
Full textAbstract:
Issues arising from climate change and long-term natural climate variability have become the focus of much recent research. In this study, we specifically explore the impacts of long-term climate variability and climate changes upon flood frequencies. The analyses of the flood frequencies are carried out in a comparative manner in catchments located in semiarid-temperate and tropical landscapes in Australia, namely Perth, Newcastle and Darwin, using a process-based derived flood frequency approach. The derived flood frequency analyses are carried out using deterministic rainfall-runoff models that capture the intrinsic water balance variability in the study catchments, and driven by temporal rainfall event sequences that are generated by a stochastic rainfall model that incorporates temporal variabilities over a multiplicity of time scales, ranging from within-event, between-event to seasonal, multi-annual and multi-decadal time scales. Six climate scenarios are considered for Newcastle, that combine the ENSO (El Niño Southern Oscillation) and IPO (Inter-decadal Pacific Oscillation) modes of variability, and six different climate scenarios are considered for Perth and Darwin that combine these different ENSO modes and step changes in climate (upwards or downwards) that occurred in 1970 in both regions, which were identified through statistical analysis. The results of the analyses showed that La Niña years cause higher annual maximum floods compared to El Niño and Neutral years in all three catchments. The impact of ENSO on annual maximum floods in the Newcastle catchment is enhanced when the IPO is negative and for Perth, the impact of ENSO weakens in the post-1970 period, while it strengthens in Darwin in the same period. In addition, the results of sensitivity and scenario analyses with the derived flood frequency model explored the change of dominant runoff generation processes contributing to floods in each of the study catchments. These analyses highlighted a switch from subsurface stormflow to saturation excess runoff with a change of return period, which was much more pronounced in Perth and Darwin, and not so in Newcastle. In Perth and Darwin this switch was caused by the interactions between the out-of-phase seasonal variabilities of rainfall and potential evaporation, whereas the seasonality was much weaker in Newcastle. On the other hand, the combination of higher rainfall intensities and shallower soil depths led to saturation excess runoff being the dominant mechanism in Newcastle across the full range of return periods. Consequently, within-storm rainfall intensity patterns were important in Newcastle in all major flood producing events (all return periods), where they were only important in Perth and Darwin for floods of high return periods, which occur during wet months in wet years, when saturation excess runoff was the dominant mechanism. Additionally, due to the possibility of a change of process from subsurface stormflow to saturation excess when conditions suited this switch, the estimates of flood frequency are highly uncertain especially at high return periods (in Darwin and Perth) and much less in Newcastle (when no process change was involved).
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Forooqi, A. Masood. "Ridership studies for the proposed Florida high speed rail system." FIU Digital Commons, 1990. http://digitalcommons.fiu.edu/etd/3254.
Full textAbstract:
Florida, the fourth largest and sunshine state, is growing at the rate of 800 new residents daily.!! By the year 2000 the population is estimated to be 16 Million, and the annual tourists at 80 Million, generating 40 Million trips.
The proposed High Speed Rail will connect Miami and West Palm Beach to Orlando and Tampa. This 325-mile corridor represents 70 % of all the "Socio-Economic Resources" of the whole of Florida and the trend will continue well into the next century. The Miami-Orlando ride will reduce to 2 hours speeding at up to 150 mph. It will be operational by 1995 and the system is estimated to cost 4.6 Billion Dollars.
One of the major problems encountered by the new High Speed Rail (HSR) is the "RIDERSHIP FORECASTING," In the United States there is a lack of current information about the Total Volume of Intercity Trips and the Specific Characteristics of the Trips that determines a willingness to use HSR. The Quality, Comprehensiveness, and Acceptability, by the forecasts must be sufficient to generate Public Support, Confidence, and Response for the Implementation of HSR.
The THESIS discusses the various Ridership Forecasting Techniques and chooses the “Most Suitable Model” applicable to conditions in South and Central Florida.
A “Model Choice Based Model” is selected called, “THE LOGIT FUNCTION”, which takes into account, the Floridian Choice of available Travel Modes, and the Factors Affecting the Manner of the “Decision making Process”, in Favour of a Particular Mode. Evaluating Business and Non-Business Travel for the Internal Trips, (including the Induced Demand and the Short Trips) and the External Trips. The External and Short Trips were Not considered by Previous Studies. The standard guidelines for “Revenue and Ridership Forecasting,” by High Speed Rail Association are closely followed in this Study. Due consideration is also given to Socio-Economic data involving population, wealth, average per capita income, number of families, size of labor force, number of hotel / motel rooms and college enrollment. A Survey was carried out, to collect the data and to test the Sensitivity, under given set of conditions and scenarios.
The studies conclude that HSR is a Feasible Project and by the year 2000, the Ridership will be 3.8 Million Annual Trips. The future studies will continue to improve the results, as an individual’s attitude and response towards HSR Travel becomes better known and recorded in Florida
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Garcia, Jean Rodrigo 1980. "Estudo do comportamento carga VS recalque de estacas raiz carregadas a compressão." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/258763.
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Orientador: Paulo Jose Rocha de AlbuquerqueDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo
Made available in DSpace on 2018-08-09T00:23:04Z (GMT). No. of bitstreams: 1 Garcia_JeanRodrigo_M.pdf: 20549074 bytes, checksum: f2026225c4a01801d604ae04aad3497a (MD5) Previous issue date: 2006
Resumo: Nesta pesquisa analisa-se o comportamento da curva carga vs recalque de estaca isolada carregada à compressão, através do emprego de métodos de previsão. Foram ensaiadas duas estacas raiz, uma com 23m de comprimento e 31cm de diâmetro, e outra com 12m de comprimento e 41cm de diâmetro, no intuito de atribuir o comportamento da interação solo-estaca, a um ou outro fator característico do elemento de fundação. Para isso, foram realizadas provas de carga do tipo lenta. O subsolo local é composto por solo proveniente de diabásio, constituído basicamente de duas camadas, a primeira de argila silto-arenosa (O a 6,5m de profundidade) e a segunda de silte argilo-arenoso (6,5-23m de profundidade), ambas as camadas são predominantes da região de Campinas (SP) e de grande parte das regiões sul e sudeste do Brasil. A prova de carga foi instrumentada de maneira a se obter os dados do mecanismo de transferência de carga e de deslocamento em profundidade. Dessa forma, obteve-se o valor da carga de ruptura, bem como, da respectiva carga admissível (Qadm),através da completa solicitação por atrito lateral e por resistência de ponta, apresentados pela interação do sistema solo-estaca, ou convencionando-se uma ruptura em função de um recalque limite ou ainda de critérios de ruptura fisica, como o método da rigidez (Décourt), Chin e outros. De maneira geral, analisa-se, de maneira critica, os métodos de previsão de recalque e de curva carga vs recalque, comparando os resultados reais com os previstos, através dos métodos teóricos e empíricos para o recalque do elemento fundação quando submetido à carga admissível estimada (Qadm)e para a curva carga vs recalque. Dessa forma, pretende-se chegar a algum entendimento sobre a interação solo-estrutura e seu modelo de transferência de carga para o solo
Abstract: In this research, the behavior of the curve load versus settlement ofloaded isolated pile to the compression is analyzed, through forecast methods. Two root piles had been assayed, one with 23m oflength and 31 cm of diameter, and the other with length of 12 m and 41 cm of diameter, in order to attribute the behavior of the interaction ground-pile to one or another characteristic factor of the foundation elemento For this, load tests of the slow type had been carrried out. The local subsoil is composed of ground of diabásio, consisting basically of two layers: the first one of silt-sandy clay (6,5m - 23m of depth) and second silt clay-sandy (6,5 - 23m of depth), both layers are predominant in the region ofCampinas (SP) and in a great part ofthe southem and southeastem regions ofBrazil. The load test was instrumented to get the data of the mechanism of transference of load and displacement in depth. Thus, the value of the rupture load was obtained, as well as the respective permissible load (Qadm),through the complete request for lateral attrition and tip resistance, presented by the interaction of the ground-pile system or stipulating a rupture related to a stress limit or still of criteria of physical rupture, as the method of the rigidity (Décourt), Chin and others. In general, the methods of forecast of settlement and curve load versus stresses are analysed in a critical way, by comparing the real results with the foreseen ones, through theoretical by empirical methods for the settlement of the foundation element when. submitted to the esteemed permissible load (Qadm) and for the curve load versus settlement. Therefore, there is the intention to come to an agreement about the groundstructure interaction and its model of load transference to the ground
Mestrado
Geotecnia
Mestre em Engenharia Civil
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Tirivarombo, Sithabile. "Climate variability and climate change in water resources management of the Zambezi River basin." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1002955.
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Water is recognised as a key driver for social and economic development in the Zambezi basin. The basin is riparian to eight southern African countries and the transboundary nature of the basin’s water resources can be viewed as an agent of cooperation between the basin countries. It is possible, however, that the same water resource can lead to conflicts between water users. The southern African Water Vision for ‘equitable and sustainable utilisation of water for social, environmental justice and economic benefits for the present and future generations’ calls for an integrated and efficient management of water resources within the basin. Ensuring water and food security in the Zambezi basin is, however, faced with challenges due to high variability in climate and the available water resources. Water resources are under continuous threat from pollution, increased population growth, development and urbanisation as well as global climate change. These factors increase the demand for freshwater resources and have resulted in water being one of the major driving forces for development. The basin is also vulnerable due to lack of adequate financial resources and appropriate water resources infrastructure to enable viable, equitable and sustainable distribution of the water resources. This is in addition to the fact that the basin’s economic mainstay and social well-being are largely dependent on rainfed agriculture. There is also competition among the different water users and this has the potential to generate conflicts, which further hinder the development of water resources in the basin. This thesis has focused on the Zambezi River basin emphasising climate variability and climate change. It is now considered common knowledge that the global climate is changing and that many of the impacts will be felt through water resources. If these predictions are correct then the Zambezi basin is most likely to suffer under such impacts since its economic mainstay is largely determined by the availability of rainfall. It is the belief of this study that in order to ascertain the impacts of climate change, there should be a basis against which this change is evaluated. If we do not know the historical patterns of variability it may be difficult to predict changes in the future climate and in the hydrological resources and it will certainly be difficult to develop appropriate management strategies. Reliable quantitative estimates of water availability are a prerequisite for successful water resource plans. However, such initiatives have been hindered by paucity in data especially in a basin where gauging networks are inadequate and some of them have deteriorated. This is further compounded by shortages in resources, both human and financial, to ensure adequate monitoring. To address the data problems, this study largely relied on global data sets and the CRU TS2.1 rainfall grids were used for a large part of this study. The study starts by assessing the historical variability of rainfall and streamflow in the Zambezi basin and the results are used to inform the prediction of change in the future. Various methods of assessing historical trends were employed and regional drought indices were generated and evaluated against the historical rainfall trends. The study clearly demonstrates that the basin has a high degree of temporal and spatial variability in rainfall and streamflow at inter-annual and multi-decadal scales. The Standardised Precipitation Index, a rainfall based drought index, is used to assess historical drought events in the basin and it is shown that most of the droughts that have occurred were influenced by climatic and hydrological variability. It is concluded, through the evaluation of agricultural maize yields, that the basin’s food security is mostly constrained by the availability of rainfall. Comparing the viability of using a rainfall based index to a soil moisture based index as an agricultural drought indicator, this study concluded that a soil moisture based index is a better indicator since all of the water balance components are considered in the generation of the index. This index presents the actual amount of water available for the plant unlike purely rainfall based indices, that do not account for other components of the water budget that cause water losses. A number of challenges were, however, faced in assessing the variability and historical drought conditions, mainly due to the fact that most parts of the Zambezi basin are ungauged and available data are sparse, short and not continuous (with missing gaps). Hydrological modelling is frequently used to bridge the data gap and to facilitate the quantification of a basin’s hydrology for both gauged and ungauged catchments. The trend has been to use various methods of regionalisation to transfer information from gauged basins, or from basins with adequate physical basin data, to ungauged basins. All this is done to ensure that water resources are accounted for and that the future can be well planned. A number of approaches leading to the evaluation of the basin’s hydrological response to future climate change scenarios are taken. The Pitman rainfall-runoff model has enjoyed wide use as a water resources estimation tool in southern Africa. The model has been calibrated for the Zambezi basin but it should be acknowledged that any hydrological modelling process is characterised by many uncertainties arising from limitations in input data and inherent model structural uncertainty. The calibration process is thus carried out in a manner that embraces some of the uncertainties. Initial ranges of parameter values (maximum and minimum) that incorporate the possible parameter uncertainties are assigned in relation to physical basin properties. These parameter sets are used as input to the uncertainty version of the model to generate behavioural parameter space which is then further modified through manual calibration. The use of parameter ranges initially guided by the basin physical properties generates streamflows that adequately represent the historically observed amounts. This study concludes that the uncertainty framework and the Pitman model perform quite well in the Zambezi basin. Based on assumptions of an intensifying hydrological cycle, climate changes are frequently expected to result in negative impacts on water resources. However, it is important that basin scale assessments are undertaken so that appropriate future management strategies can be developed. To assess the likely changes in the Zambezi basin, the calibrated Pitman model was forced with downscaled and bias corrected GCM data. Three GCMs were used for this study, namely; ECHAM, GFDL and IPSL. The general observation made in this study is that the near future (2046-2065) conditions of the Zambezi basin are expected to remain within the ranges of historically observed variability. The differences between the predictions for the three GCMs are an indication of the uncertainties in the future and it has not been possible to make any firm conclusions about directions of change. It is therefore recommended that future water resources management strategies account for historical patterns of variability, but also for increased uncertainty. Any management strategies that are able to satisfactorily deal with the large variability that is evident from the historical data should be robust enough to account for the near future patterns of water availability predicted by this study. However, the uncertainties in these predictions suggest that improved monitoring systems are required to provide additional data against which future model outputs can be assessed.
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Wan, Been-Lih, and 萬本立. "Real-time Flood Forecasting by Considering the Rain-burst Effect." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/14111022041155049899.
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碩士國立臺灣大學
生物環境系統工程學研究所
100
In recent years, the extreme rainfall events become more and more so that result in many flood disasters that make residents’ lives and property suffered a serious threat. In order to reduce flood damage, real-time flood forecasting has become an important research topic. Research analysis was processed with flood events of Tseng-Wen Reservoir Watershed and Chi-Lan River basin. This study constituted several forecasting models of hourly stream discharge based on AR(2) model and Naïve model, and correct the problem of forecasting time lag phenomenon by considering rainfall data. The discussion of rainfall data is divided in two parts. First part is that discuss the relationship between increment of rainfall and increment of discharge. By identify the increment of rainfall (rain-burst) which can make discharge significantly increase in a short time, we can establish the function of relationship between increment of rainfall and increment of discharge and combined with AR(2) model to correct the problem of forecasting time lag phenomenon which result from rain-burst effect. Second part is that apply the concept of unit hydrograph to establish response function between rainfall difference and discharge difference by linear regression, use data of rainfall difference before prediction time to estimate discharge difference on prediction time and combined with Naïve model to forecast hourly discharge. By considering the trend of rainfall variations, significantly improve the problem of forecasting time lag phenomenon. The results of research shows that AR(2) model by considering the rain-burst effect can improve the problem of forecasting time lag phenomenon and enhance CP value by reducing the prediction error on peak time. And the performance of Naïve model which combined with response function is significantly better than other models. This result demonstrates that considering the trend of rainfall variations is very effective to improve the problem of forecasting time lag phenomenon.
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Adhikary, Sajal Kumar. "Optimal Design of a Rain Gauge Network to Improve Streamflow Forecasting." Thesis, 2017. https://vuir.vu.edu.au/35054/.
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Enhanced streamflow forecasting has always been an important task for researchers and water resources managers. However, streamflow forecasting is often challenging owing to the complexity of hydrologic systems. The accuracy of streamflow forecasting mainly depends on the input data, especially rainfall as it constitutes the key input in transforming rainfall into runoff. This emphasizes the need for incorporating accurate rainfall input in streamflow forecasting models in order to achieve enhanced streamflow forecasting. Based on past research, it is well-known that an optimal rain gauge network is necessary to provide high quality rainfall estimates. Therefore, this study focused on the optimal design of a rain gauge network and integration of the optimal network-based rainfall input in artificial neural network (ANN) models to enhance the accuracy of streamflow forecasting. The Middle Yarra River catchment in Victoria, Australia was selected as the case study catchment, since the management of water resources in the catchment is of great importance to the majority of Victorians.
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Pologne, Lawrence Cai Ming. "Spatiotemporal variability and prediction of rainfall over the eastern Caribbean." Diss., 2005. http://etd.lib.fsu.edu/theses/available/etd-07112005-162948/.
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Thesis (M. S.)--Florida State University, 2005.Advisor: Dr. Ming Cai, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Sept. 19, 2005). Document formatted into pages; contains x, 60 pages. Includes bibliographical references.
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Martinez, Carlos J. "Seasonal Climatology, Variability, Characteristics, and Prediction of the Caribbean Rainfall Cycle." Thesis, 2021. https://doi.org/10.7916/d8-byp7-1b34.
Full textAbstract:
The Caribbean is a complex region that heavily relies on its seasonal rainfall cycle for its economic and societal needs. This makes the Caribbean especially susceptible to hydro-meteorological disasters (e.g., droughts and floods), and other weather/climate risks. Therefore, effectively predicting the Caribbean rainfall cycle is valuable for the region. The efficacy of predicting the Caribbean rainfall cycle is largely dependent on effectively characterizing the climate dynamics of the region. However, the dynamical processes and climate drivers that shape the seasonal cycle are not fully understood, as previous observational studies show inconsistent findings as to what mechanisms influence the mean state and variability of the cycle. These inconsistencies can be attributed to the limitations previous studies have when investigating the Caribbean rainfall cycle, such as using monthly or longer resolutions in the data or analysis that often mask the seasonal transitions and regional differences of rainfall, and investigating the Caribbean under a basin-wide lens rather than a sub-regional lens. This inhibits the ability to accurately calculate and predict subseasonal-to-seasonal (S2S) rainfall characteristics in the region. To address these limitations and inconsistencies, the research in this thesis examines the seasonal climatology, variability, and characteristics of the Caribbean rainfall cycle under a sub-regional and temporally fine lens in order to investigate the prediction of the cycle.
Regional variations and dynamical processes of the Caribbean annual rainfall cycle are assessed using (1) a principal component analysis across Caribbean stations using daily observed precipitation data; and, (2) a moisture budget analysis. The results show that the seasonal cycle of rainfall in the Caribbean hinges on three main facilitators of moisture convergence: the Atlantic Intertropical Convergence Zone (ITCZ), the Eastern Pacific ITCZ, and the North Atlantic Subtropical High (NASH). A warm body of sea-surface temperatures (SSTs) in the Caribbean basin known as the Atlantic Warm Pool (AWP) and a low-level jet centered at 925hPa over the Caribbean Sea known as the Caribbean Low-Level Jet (CLLJ) modify the extent of moisture provided by these main facilitators. The interactions of these dynamical processes are responsible for shaping the seasonal components of the annual rainfall cycle: The Winter Dry Season (WDS; mid-November to April); the Early-Rainy Season (ERS; mid-April to mid-June); an intermittent relatively dry period known as the mid-summer drought, (MSD; mid-June to late August), and the Late-Rainy Season (LRS; late August to late November). Five geographical sub-regions are identified in the Caribbean Islands, each with its unique set of dynamical processes, and consequently, its unique pattern of rainfall distribution throughout the rainy season: Northwestern Caribbean, the Western Caribbean, the Central Caribbean, the Central and Southern Lesser Antilles, and Trinidad and Tobago and Guianas. Convergence by sub-monthly transients contributes little to Caribbean rainfall.
The wettest and driest Caribbean ERS and LRS years’ are then explored by conducting the following: (1) a spatial composite of rainfall using the daily rainfall data; and, (2) spatial composites of SSTs, sea-level pressure (SLP), and mean flow moisture convergence and transports using monthly data. The ERS and LRS are impacted in distinctly different ways by two different, and largely independent, large-scale phenomena, external to the region: a SLP dipole mode of variability in the North Atlantic known as the North Atlantic Oscillation (NAO), and the El Nino Southern Oscillation (ENSO). Dry ERS years are associated with a persistent dipole of cold and warm SSTs over the Caribbean Sea and Gulf of Mexico, respectively, that are caused by a preceding positive NAO state. This setting involves a wind-evaporation-SST (WES) feedback expressed in enhanced trade winds and consequently, moisture transport divergence over all of the Caribbean, except in portions of the Northwestern Caribbean in May. A contribution from the preceding winter cold ENSO event is also discernible during dry ERS years. Dry LRS years are due to the summertime onset of an El Niño event, developing an inter-basin SLP pattern that moves moisture out of the Caribbean, except in portions of the Northwestern Caribbean in November. Both large-scale climate drivers would have the opposite effect during their opposite phases leading to wet years in both seasons.
Existing methodologies that calculate S2S rainfall characteristics were not found to be suitable for a region like the Caribbean, given its complex rainfall pattern; therefore, a novel and comprehensive method is devised and utilized to calculate onset, demise, and MSD characteristics in the Caribbean. When applying the method to calculate S2S characteristics in the Caribbean, meteorological onsets and demises, which are calculated via each year’s ERS and LRS mean thresholds, effectively characterize the seasonal evolution of mean onsets and demises in the Caribbean. The year-to-year variability of MSD characteristics, and onsets and demises that are calculated by climatological ERS and LRS mean thresholds resemble the variability of seasonal rainfall totals in the Caribbean and are statistically significantly correlated with the identified dynamical processes that impact each seasonal component of the rainfall cycle.
Finally, the seasonal prediction of the Caribbean rainfall cycle is assessed using the identified variables that could provide predictive skill of S2S rainfall characteristics in the region. Canonical correlation analysis is used to predict seasonal rainfall characteristics of station-averaged sub-regional frequency and intensity of the ERS and LRS wet days, and magnitude of the MSD. Predictor fields are based on observations from the ERA-Interim reanalysis and GCM output from the North America Multi-Model Ensemble (NMME). Spearman Correlation and Relative Operating Characteristics are applied to assess the forecast skill. The use of SLP, 850-hPa zonal winds (u850), vertically integrated zonal (UQ), and meridional (VQ) moisture fluxes show comparable, if not better, forecast skill than SSTs, which is the most common predictor field for regional statistical prediction. Generally, the highest ERS predictive skill is found for the frequency of wet days, and the highest LRS predictive skill is found for the intensity of wet days. Rainfall characteristics in the Central and Eastern Caribbean have statistically significant predictive skill. Forecast skill of rainfall characteristics in the Northwestern and Western Caribbean are lower and less consistent. The sub-regional differences and consistently significant skill across lead times up to at least two months can be attributed to persistent SST/SLP anomalies during the ERS that resemble the North Atlantic Oscillation pattern, and the summer-time onset of the El Niño-Southern Oscillation during the LRS. The spatial pattern of anomalies during the MSD bears resemblance to both the ERS and LRS spatial patterns.
The findings from this thesis provide a more comprehensive and complete understanding of the climate dynamics, variability, and annual mean state of the Caribbean rainfall cycle. These results have important implications for prediction, decision-making, modeling capabilities, understanding the genesis of hydro-meteorological disasters, investigating rainfall under other modes of variability, and Caribbean impact studies regarding weather risks and future climate.
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"Long-range summer rainfall: forecast of Hong Kong." Chinese University of Hong Kong, 1990. http://library.cuhk.edu.hk/record=b5886567.
Full textAbstract:
Tung Wai Lan, Iris.Thesis (M.Phil.)--Chinese University of Hong Kong, 1990.
Bibliography: leaves 92-101.
ACKNOWLEDGEMENTS
ABSTRACT
LIST OF FIGURES --- p.iii
LIST OF TABLES --- p.iv
CHAPTER
Chapter I --- INTRODUCTION --- p.1
Chapter 1.1 --- Background --- p.1
Chapter 1.2 --- Significance of the Research --- p.3
Chapter 1.3 --- Objectives of the Research --- p.5
Chapter 1.4 --- Organization of the Research --- p.5
Chapter II --- LITERATURE REVIEW --- p.7
Chapter 2.1 --- Introduction --- p.7
Chapter 2.2 --- Development of Long-Range Forecasting Technique --- p.8
Chapter 2.3 --- Available Techniques of Long-Range Forecast --- p.9
Chapter 2.3.1 --- Analogs and persistence --- p.10
Chapter 2.3.2 --- Statistical modelling --- p.12
Chapter 2.3.3 --- Atmosphere-ocean interaction --- p.17
Chapter 2.3.4 --- Cycles and time series --- p.18
Chapter 2.3.5 --- Numerical modelling --- p.19
Chapter 2.4 --- Rainfall Prediction in Hong Kong --- p.21
Chapter III --- RAINFALL OF HONG KONG --- p.24
Chapter 3.1 --- Climatic Feature --- p.24
Chapter 3.2 --- The Causes of Hong Kong Rainfall --- p.26
Chapter 3.2.1 --- Tropical cyclone --- p.26
Chapter 3.2.2 --- Trough or front --- p.28
Chapter IV --- METHODOLOGY --- p.31
Chapter 4.1 --- Introduction --- p.31
Chapter 4.2 --- Empirical Orthogonal Function (EOF) Analysis --- p.32
Chapter 4.2.1 --- What's EOF --- p.32
Chapter 4.2.2 --- Why use EOF --- p.34
Chapter 4.3 --- Discriminant Analysis --- p.36
Chapter 4.4 --- Data Base --- p.37
Chapter 4.5 --- Computation Procedures --- p.40
Chapter 4.6 --- Analysis of Forecast Capability --- p.44
Chapter V --- THE RESULT AND ANALYSIS OF PREDICTION MODEL --- p.48
Chapter 5.1 --- The result of EOF analysis --- p.48
Chapter 5.1.1 --- Extraction of eigenvectors and eigenvalues --- p.48
Chapter 5.1.2 --- Spatial and Temporal variation of eigenvector pattern --- p.52
Chapter 5.2 --- Accuracy of the prediction model --- p.53
Chapter 5.2.1 --- Introduction --- p.53
Chapter 5.2.2 --- The forecast accuracy from each month --- p.54
Chapter 5.2.2.1 --- The forecast accuracy made by October --- p.54
Chapter 5.2.2.2 --- The forecast accuracy made by November --- p.56
Chapter 5.2.2.3 --- The forecast accuracy made by December --- p.58
Chapter 5.2.2.4 --- The forecast accuracy made by January --- p.58
Chapter 5.2.2.5 --- The forecast accuracy made by February --- p.61
Chapter 5.2.2.6 --- The forecast accuracy made by March --- p.61
Chapter 5.2.2.7 --- The forecast accuracy made by April --- p.64
Chapter 5.2.3 --- Optimal length of dependent data --- p.64
Chapter 5.2.4 --- Analysis the prediction results --- p.67
Chapter 5.2.5 --- Comparison between the method used in this study with those methods adopted by ROHK --- p.69
Chapter 5.2.5.1 --- Introduction --- p.69
Chapter 5.2.5.2 --- Comparison of the forecast accuracy between two studies --- p.70
Chapter VI --- CONCLUSION --- p.73
Chapter 6.1 --- Summary of Findings --- p.73
Chapter 6.2 --- Limitations of the Research --- p.75
Chapter 6.3 --- Prospects of the Research --- p.76
APPENDICES --- p.78
LIST OF CITED REFERENCES --- p.92
LIST OF READING MATERIALS --- p.97
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Dickey, Jeffrey James Elsner James B. "Improved flood prediction from basin elevation distribution." 2006. http://etd.lib.fsu.edu/theses/available/07092006-103933.
Full textAbstract:
Thesis (Ph. D.)--Florida State University, 2006.Advisor: James B. Elsner, Florida State University, College of Social Sciences, Dept. of Geography. Title and description from dissertation home page (Sept. 19, 2006). Document formatted into pages; contains x, 90 pages. Includes bibliographical references.
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"Improved estimation of catchment rainfall for continuous simulation modelling." Thesis, 2005. http://hdl.handle.net/10413/2685.
Full textAbstract:
Long sequences of rainfall at fme spatial and temporal details are increasingly required, not only for hydrological studies, but also to provide inputs for models of crop growth, land fills, tailing dams, disposal of liquid waste on land and other environmentally-sensitive projects. However, rainfall records from raingauges frequently fail to meet the requirements of the above studies. Therefore, it is important to improve the estimation of the depth and spatial distribution of rainfall falling over a catchment. A number of techniques have been developed to improve the estimation of the spatial distribution of rainfall from sparsely distributed raingauges. These techniques range from simple interpolation techniques developed to estimate areal rainfall from point rainfall measurements, to statistical and deterministic models, which generate rainfall values and downscale the rainfall values based on the physical properties of the clouds or rain cells. Furthermore, these techniques include different statistical methods, which combine the rainfall information gathered from radar, raingauges and satellites. Although merging the radar and raingauge rainfall fields gives a best estimate of the "true rainfall field", the length of the radar record and spatial coverage of the radar in a country such as South Africa is relatively short and hence is of limited use in hydrological studies. Therefore, the relationship between the average merged rainfall value for a catchment and a "driver" station, which is selected to represent rainfall in the catchment, is developed and assessed in this study. Rainfall data from the Liebenbergsvlei Catchment near Bethlehem in the Free State Province and a six-month record of radar data are used to develop relationships between the average merged subcatchment rainfall for each of the Liebenbergsvlei subcatchments and a representative raingauge selected to represent the rainfall in each of the subcatchments. The relationships between daily raingauges and the average rainfall depth of the subcatchments are generally good and in most of the subcatchments the correlation coefficient is greater than 0.5. It was also noted that, in most of the subcatchments, the daily raingauges overestimate the average areal rainfall depth of the subcatchments. In addition, the String of Beads Model (SBM) developed by Clothier and Pegram (2002) was used to generate synthetic rainfall series for the Liebenbergsvlei catchments. The SBM is able to produce rainfall values at a spatial resolution of IxI km with a 5 minute temporal resolution. The SBM is a high-resolution space-time model of radar rainfall images, which takes advantage of the detailed spatial and temporal information captured by weather radar and combines it with the long-term seasonal variation captured by a network of daily raingauges. Statistics from a 50 year period of generated rainfall values were compared with the statistics computed from a 50 year raingauge data series, and it was found that the generated rainfall values mimic the rainfall data from the raingauges reasonably well. The relationship developed between the merged catchment rainfall values and driver rainfall station values, which are selected to represent the mean areal rainfall of the subcatchment, was used to adjust the Conventional Driver rainfall Station (CDS) into Modified Driver Station (MDS) values. Streamflow was simulated using both the CDS and MDS rainfall compared against the observed streamflow from the Liebenbergsvlei catchment. In general, the streamflow simulated by the ACRU model do not correlate well with the observed streamflow, which is attributed to unrealistic observed flow and inter-catchments transfers of water. However, it is noted that the volume of streamflow simulated with the MDS rainfall is only 71 % of that simulated with the CDS rainfall, thus highlighting the limitation of using the CDS rainfall approach for modelling and the need to apply the methodology to improve the estimation of catchment rainfall developed in this study to other catchments in South Africa.Thesis (M.Sc.)-University of KwaZulu-Natal, 2005.
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Purdie, Jennifer. "Model development for seasonal forecasting of hydro lake inflows in the Upper Waitaki Basin, New Zealand /." 2005. http://adt.waikato.ac.nz/public/adt-uow20070223.140731/index.html.
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Hsu, Kuo-lin. "Rainfall estimation from satellite infrared imagery using artificial neural networks." 1996. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_e9791_1996_410_sip1_w.pdf&type=application/pdf.
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Hallowes, Jason Scott. "Evaluation of a methodology to translate rainfall forecasts into runoff forecasts for South Africa." Thesis, 2002. http://hdl.handle.net/10413/4513.
Full textAbstract:
South Africa experiences some of the lowest water resource system yields in the
world as a result of the high regional variability of rainfall and runoff. Population
growth and economic development are placing increasing demands on the nation's
scarce water resources. These factors, combined with some of the objectives of
the new National Water Act (1998), are highlighting the need for efficient
management of South Africa's water resources.
In South Africa's National Water Act (1998) it is stated that its purpose is to ensure
that the nation's water resources are protected, used, conserved, managed and
controlled in a way, which takes into account, inter alia,
i. promoting the efficient, sustainable and beneficial use of water in the public
interest, and
ii. managing floods and droughts.
Efficient and sustainable water resource and risk management can be aided by the
application of runoff forecasting. Forecasting thus fits into the ambit of the National
Water Act and, therefore, there is a need for its operational application to be
investigated. In this document an attempt is made to test the following hypotheses:
Hypothesis 1: Reliable and skilful hydrological forecasts have the ability to prevent
loss of life, spare considerable hardship and save affected
industries and commerce millions of Rands annually if applied
operationally within the context of water resources and risk
management.
Hypothesis 2: Long to medium term rainfall forecasts can be made with a degree
of confidence, and these rainfall forecasts can be converted into
runoff forecasts which, when applied within the framework of water
resources and risk management, are more useful to water resource
managers and users than rainfall forecasts by themselves.
The validity of Hypothesis 1 is investigated by means of a literature review. South
Africa's high climate variability and associated high levels of uncertainty as well as
its current and future water resources situation are reviewed in order to highlight the importance of runoff forecasting in South Africa. Hypothesis 1 is further
examined by reviewing the concepts of hazards and risk with a focus on the role of
effective risk management in preventing human, financial and infrastructural
losses.
A runoff forecasting technique using an indirect methodology, whereby rainfall
forecasts are translated into runoff forecasts, was developed in order to test
Hypothesis 2. The techniques developed are applied using probabilistic regional
rainfall forecasts supplied by the South African Weather Service for 30 day periods
and categorical regional forecasts for one, three and four month periods for
I
regions making up the study area of South Africa, Lesotho and Swaziland. These
forecasts where downscaled spatially for application to the 1946 Quaternary
Catchments making up the study area and temporally to give daily rainfall forecast
values.
Different runoff forecasting time spans produced varying levels of forecast
accuracy and skill, with the three month forecasts producing the worst results,
followed by the four month forecasts. The 30 day and one month forecasts for the
most part produced better results than the more extended forecast periods. In the
study it was found that hydrological forecast accuracy results seem to be inversely
correlated to the amount of rainfall received in a region, i.e. the wetter the region
the less accurate the runoff forecasts. This trend is reflected in both temporal and
spatial patterns where it would seem that variations in the antecedent moisture
conditions in wetter areas and wetter periods contribute to the overall variability,
rendering forecasts less accurate. In general, the runoff forecasts improve with
corresponding improvements in the rainfall forecast accuracy. There are, however,
runoff forecast periods and certain regions that produce poor runoff forecast
results even with improved rainfall forecasts. This would suggest that even perfect
rainfall forecasts still cannot capture all the local scale variability of persistence of
wet and dry days as well as magnitudes of rainfall on individual days and the effect
of catchment antecedent moisture conditions. More local scale rainfall forecasts
are thus still needed in the South African region.
In this particular study the methods used did not produce convincing results in
terms of runoff forecast accuracy and skill scores. The poor performance can
probably be attributed to the relatively unsophisticated nature of the downscaling
and interpolative techniques used to produce daily rainfall forecasts at a
Quaternary Catchment scale. It is the author's opinion that in the near future, with
newly focussed research efforts, and building on what has been learned in this
study, more reliable agrohydrological forecasts can be used within the framework
of water resources and risk management, preventing loss of life, saving
considerable hardship and saving affected industry and commerce millions of
rands annually.Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2002.
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Hajani, Evan. "Impact of climate change on design rainfall." Thesis, 2018. http://hdl.handle.net/1959.7/uws:49274.
Full textAbstract:
Quantification of rainfall is needed for planning, designing and operation of water engineering projects such as bridges, culverts, flood control levees, open channels, roof gutters, dykes and dams. The design rainfall in the form of intensity frequency duration (IFD) data is widely used in practice. IFD data is generally derived by applying a regional frequency analysis technique to a rainfall data set consisting of a large number of stations in the region. In Australia, new IFD curves have been developed in 2013 as a part of Australian Rainfall and Runoff (ARR) by the Australian Bureau of Meteorology (BOM). The BOM 2013 IFD data were derived without considering the impacts of climate change. This research focuses on the assessment of the impacts of climate change and variability on design rainfall using data from New South Wales (NSW), Australia.
A total of 60 pluviograph stations were used from NSW in the analysis of trends in the extreme rainfall events. A FORTRAN program was developed to extract annual maximum (AM) rainfall events of six sub-hourly durations (6, 12, 18, 24, 30 and 48-minute), six sub-daily durations (1, 2, 3, 6, 8 and 12-hour), and three daily durations (1, 2 and 3-day) from each of the selected pluviograph stations. Mann-Kendall (MK) and Spearman’s Rho (SR) tests were applied to assess trends at local stations. For regional trend analysis, the regional MK test was employed. The impacts of climatic variability modes (SAM, SOI and PDO) on the observed trends in the AM and seasonal maximum rainfall events were investigated.
For assessing changes in daily rainfall, a total of 200 daily rainfall stations were selected from NSW. The MK test was applied to identify trends in the selected rainfall indices, while the Pettitt change point test was employed to determine the direction and timing of a change point. Van Bell and Hughes homogeneity test was applied to examine homogeneity of the observed trends.
Using data from ten pluviography stations in NSW and adopting a non-stationary approach, the IFD curves generated by the two most commonly adopted probability distributions, Generalized Extreme Value (GEV) and Log Pearson Type 3 (LP3) distributions were compared. Three goodness-of-fit tests (i.e. Kolmogrove-Smirnov, Anderson-Darling and Chi-Square tests) were adopted to assess the goodness-of-fit of the GEV and LP3 distributions. Empirical and polynomial regression methods in smoothing the IFD curves were also compared. The latest IFD curves in Australia as a part of the new Australian Rainfall and Runoff (ARR) were also compared with the at-site IFD curves (derived by stationary approach) to examine the expected degree of variation between the at-site and regional IFD curves.
It has been found that when the MK and SR tests are applied at individual stations, the number of statistically significant positive trends in the annual maximum (AM) rainfall intensity data is greater than the statistically significant negative trends, especially in the case of rainfall intensity data of shorter durations. The regional MK test results show that there is no significant positive or negative trend in the AM rainfall intensity data when NSW State is considered as a single region.
The number of stations exhibiting statistically significant trends in rainfall intensity is decreased when the impact of climate indices (SAM, SOI and PDO) is accounted for through the use of partial MK test, suggesting that much of the observed trends in AM rainfall intensity and seasonal maximum rainfall data are associated with these climate indices. This indicates that variability described by these climate indices is able to explain a significant proportion of the observed trends in the AM rainfall intensity data in NSW State.
It has been found that for the annual total rainfall (ATR) and annual maximum daily rainfall (AMDR), a negative trend dominates NSW rainfall regime, while for annual total number of rainy days (ATRD), there is an overall positive trend. For ATR and AMDR, stations showing negative trends are concentrated to south-eastern NSW. However, for ATRD, north-eastern NSW is dominated by a positive trend, and south-eastern and central NSW are dominated by a negative trend. Based on the Pettitt test it is found that AMDR data in NSW is dominated by a negative shift. Furthermore, Van Belle and Hughes method shows that NSW is dominated by non-homogeneous trends in monthly maximum daily rainfall data. Based on the goodness-of-fit tests, it has been found that both the GEV and LP3 distributions fit the AM rainfall data (at 1% significance level) at the selected NSW stations. The developed IFD curves based on the second degree polynomial represent better fitting than the empirical method. The ARR87 and ARR13 IFD curves are generally higher than the at-site IFD curves derived in this study. The median difference between the at-site and regional ARR recommended IFD curves are in the range of 13-19%.
The comparison of the new IFD curves based on the stationary and non-stationary approaches with ARR IFD curves (ARR87 and ARR13) illustrates that there is a better match between the ARR IFD curves and the new stationary IFD curves compared to the non-stationary IFD curves. Both SAM and SOI climate indices produce nearly similar effects on non-stationary IFD curves.
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Jayathissa, Halvithana A. G. [Verfasser]. "Combined statistical and dynamic modeling for real time forecasting of rain induced landslides in Matara district, Sri Lanka : a case study / vorgelegt von Halvithana A. G. Jayathissa." 2010. http://d-nb.info/1010181009/34.
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Li, Liang-Hann, and 李亮翰. "Establishment of the Turbidity Forecasting Model Using the ANN Method to Investigate the Impact of Heavy Rain on the Water Treatment Plant Operation-Case Study on Jhihtan Water Treatment Plant." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/05218349966423434282.
Full textAbstract:
碩士明志科技大學
環境與資源工程研究所
101
In Taiwan, turbidities of rivers are getting high due to the unstable soil intrusion into water during rainy seasons which rainfall became more frequent and more intense. This situation impacts the operation of water treatment plants, which lower the amount of water supply or even influence the downstream users. The high turbidity occurred at upstream is caused by many factors, such as collapsed slope due to rainstorm, river regulation works at upstream, sediment releasing from reservoir, etc. The high turbidity usually causes water treatment plants unable to handle it due to too much sludge produced in the settling and dewatering processes or fail to meet the effluent standards. The Jhihtan Water Treatment Plant at the upstream of Xindan River was demonstrated as an example in the study. The factors such as flow rate, rainfall and cumulative rainfalls at upper streams of the watershed are analyzed by ANOVA method to find the higher correlated factors, and using them to build an ANN model to predict the turbidity of Jhihtan water intake point. Then, discuss what influences the turbidity caused to water treatment plant's operation are, and what kind of responses the water treatment plant can take under this situation. As the result, rainfall has a higher correlation coefficient to turbidity; therefore rainfall is chosen as the input factor of the turbidity predicting model. After the training and validation processes, the model is quite useful. During the rainstorm periods, the possible turbidity data will be predicted by inputing the instant rainfall data into the turbidity model and provide them to the water treatment plants. The operators in Jhihtan Treatment Plant use this information to control the chemical dosage and intake water based on the predicted turbidity. In this study, the mass balance method is used to calculate the sludge production under high turbidity conditions. According to the Design and Operation Experience in Water Supply Facilities, 4 times of maximum daily sludge volume as designed is used to adjust the intake water. Then a diagram of intake water versus sludge production under high turbidity situations was setup which provides as a control basis to the operators in Jhihtan Treatment Plant.
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Heneker, Theresa Michelle. "An improved engineering design flood estimation technique: removing the need to estimate initial loss." 2002. http://web4.library.adelaide.edu.au/theses/09PH/09phh4989.pdf.
Full textAbstract:
"May 2002" Includes list of papers published during this study Errata slip inserted inside back cover of v. 1 Includes bibliographical references (leaves 331-357) V. 1. [Text} -- v. 2. Appendices Develops an alternative design flood estimation methodology. Establishing a relationship between catchment characteristics and the rainfall excess frequency duration proportions enables the definition of these proportions for generic catchment types, increasing the potential for translation to catchments with limited data but similar hydrographic properties, thereby improving design process.
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"Rainfall derivatives for Hong Kong Disneyland." 2003. http://library.cuhk.edu.hk/record=b5891383.
Full textAbstract:
by Ng Wing-Sze Cecilia.Thesis (M.B.A.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (leaves 92-93).
ABSTRACT --- p.ii
TABLE OF CONTENT --- p.iii
CHAPTER
Chapter 1. --- COMPANY PROFILE --- p.1
The Walt Disney Parks --- p.1
Hong Kong Disneyland --- p.1
Location --- p.1
Park Developer & Operator --- p.2
Financing --- p.2
Infrastructure --- p.3
Schedule of Operation --- p.4
Chapter 2. --- HONG KONG DISNEYLAND BUSINESS MODEL --- p.6
Revenue Model --- p.7
Customer Base --- p.7
Pricing Strategy --- p.8
Financial Performance Variable --- p.9
Risk Management Program --- p.10
The Walt Disney Company Risk Management --- p.10
HKDL Risk Management --- p.13
Risk Management on Book Record --- p.13
Chapter 3. --- PRECIPITATION RISK EXPOSURE --- p.15
Introduction to Precipitation --- p.15
Distinguish between Weather and Climate --- p.16
Rainfall Risk Exposure --- p.16
Precipitation in Hong Kong --- p.17
Overview --- p.17
Rainstorm Warning System --- p.18
Practices on Rainy Days --- p.20
Theme Park Industry --- p.20
The Ocean Park --- p.21
Rainfall Risk Mitigation --- p.21
Chapter 4. --- WEATHER DERIVATIVES --- p.24
Evolution --- p.24
The Birth of Weather Derivatives --- p.24
Weather Risk Management Association --- p.24
Year 1999 --- p.25
Year 2000 --- p.25
Year 2001 --- p.26
Year 2002 --- p.26
Precipitation Derivatives --- p.27
Market & Market Players --- p.28
Types of Product --- p.30
Index Derivatives --- p.30
Event-Basis Derivatives --- p.32
Chapter 5. --- Hedging Against Rainfall Risk with Weather Derivatives --- p.33
Formation of Hedging Strategy --- p.34
Hedging Objectives --- p.34
Hedging Target --- p.35
Dimension of Precipitation Impacts --- p.35
Normal Revenue without Rainfall Risk --- p.40
Revenue Forecasting for Year 1 --- p.41
Specifications on the Contracts --- p.46
Chapter 6. --- General Recommendations to HKDL for hedging with all kinds of Rainfall Derivatives --- p.49
Choice of Market and Counter Parties --- p.49
Index Model Design --- p.50
Dimensions of Variables & Time Scale --- p.50
Accumulated Rainfall Index --- p.51
Methodologies of Rainfall Measurements --- p.54
Location of Rainfall Measuring Stations --- p.54
Measuring Instrument --- p.56
Historical Data Consistency --- p.58
Data Availability and Reliability --- p.59
Choice of Strike Level --- p.59
Tick Size and Maximum Payments --- p.62
Pricing Approach --- p.63
Chapter 7. --- Example of Rainfall Derivatives --- p.66
Black/Red Rainstorm Signal Call --- p.66
Specifications --- p.66
Revenue model under Different Scenario --- p.68
Chapter 8. --- Portfolio Management --- p.70
Risk Management Information System --- p.70
Issues on Book Keeping --- p.71
Chapter 9. --- CONCULSION --- p.72
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Knoesen, Darryn Marc. "Integrating hydro-climatic hazards and climate changes as a tool for adaptive water resources management in the Orange River Catchment." Thesis, 2012. http://hdl.handle.net/10413/8628.
Full textAbstract:
The world’s freshwater resources are being placed under increasing pressure owing
to growth in population, economic development, improved standards of living,
agricultural intensification (linked mainly to irrigation), pollution and mismanagement
of available freshwater resources. Already, in many parts of the Orange River
Catchment, water availability has reached a critical stage.
It has become increasingly evident that water related problems can no longer be
resolved by water managers alone, owing to the problems becoming more
interconnected with other development related issues, as well as with social,
economic, environmental, legal and political factors. With the advent of climate
change and the likelihood of increases in extreme events, water managers’
awareness of uncertainties and critical reflections on the adequacy of current
management approaches is increasing.
In order to manage water resources effectively a more holistic approach is required
than has hitherto been the case, in which technological, social and economic
development are linked with the protection of natural ecosystems and with
dependable projections of future climatic conditions. To assess the climate risk
connected with rural and urban water management, and to develop adaptive
strategies that can respond to an increasingly variable climate that is projected into
the future and help to reduce adverse impacts, it is necessary to make connections
between climate related hazards, climate forecasts as well as climate change, and
the planning, design, operation, maintenance, and rehabilitation of water related
infrastructure. Therefore, adaptive water resources management (AWRM), which in
essence is “learning by doing”, is believed to be a timely extension of the integrated
water resources management (IWRM) approach as it acknowledges uncertainty and
is flexible in that it allows for the adjustment of actions based on information learned
about the system. Furthermore, it is suggested that climate risk management be
imbedded within the AWRM framework.
The objective of the research presented in this thesis is to develop techniques to
integrate state-of-the-art climate projection scenarios – which forms part of the first
step of the adaptive management cycle – downscaled to the regional/local scale, with
hydro-climatic hazard determination – which forms part of the first step in the risk
management process – in order to simulate projected impacts of climate change on
hydro-climatic hazards in the Orange River Catchment (defined in this study as those
areas of the catchment that exist within South Africa and Lesotho). The techniques
developed and the results presented in this study can be used by decision-makers in
the water sector in order to make informed proactive decisions as a response to
projected future impacts of hydro-climatic hazards – all within a framework of AWRM.
Steps towards fulfilling the above-mentioned objective begins by way of a
comprehensive literature review; firstly of the study area, where it is identified that the
Orange River Catchment is, in hydro-climatic terms, already a high risk environment;
and secondly, of the relevant concepts involved which are, for this specific study,
those pertaining to climate change, and the associated potential hydro-climatic
impacts. These include risk management and its components, in order identify how
hazard identification fits into the broader concept of risk management; and water
resources management practices, in order to place the issues identified above within
the context of AWRM.
This study uses future projections of climate from five General Circulation Models, all
using the SRES A2 emission scenario. By and large, however, where techniques
developed in this study are demonstrated, this is done using the projections from the
ECHAM5/MPI-OM GCM which, relative to the other four available GCMs, is
considered to provide “middle of the road” projections of future climates over
southern Africa. These climate projections are used in conjunction with the locally
developed and widely verified ACRU hydrological model, as well as a newly
developed hydro-climatic database at a finer spatial resolution than was available
before, to make projections regarding the likelihood and severity of hydro-climatic
hazards that may occur in the Orange River Catchment. The impacts of climate
change on hydro-climatic hazards, viz. design rainfalls, design floods, droughts and
sediment yields are investigated, with the results including a quantitative uncertainty
analysis, by way of an index of concurrence from multiple GCM projections, for each
of the respective analyses.
A new methodology for the calculation of short duration (< 24 hour) design rainfalls
from daily GCM rainfall projections is developed in this study. The methodology
utilises an index storm approach and is based on L-moments, allowing for short
duration design rainfalls to be estimated at any location in South Africa for which
daily GCM rainfall projections exist.
The results from the five GCMs used in this study indicate the following possible
impacts of climate change on hydro-climatic hazards in the Orange River Catchment:
· Design rainfalls of both short and long duration are, by and large, projected to
increase by the intermediate future period represented by 2046 - 2065, and
even more so by the more distant future period 2081 - 2100.
· Design floods are, by and large, projected to increase into the intermediate
future, and even more into the more distant future; with these increases being
larger than those projected for design rainfalls.
· Both meteorological and hydrological droughts are projected to decrease, both
in terms of magnitude and frequency, by the period 2046 - 2065, with further
decreases projected for the period 2081 - 2100. Where increases in
meteorological and hydrological droughts are projected to occur, these are
most likely to be in the western, drier regions of the catchment.
· Annual sediment yields, as well as their year-to-year variability, are projected
to increase by the period 2046 - 2065, and even more so by the period 2081 -
2100. These increases are most likely to occur in the higher rainfall, and
especially in the steeper, regions in the east of the catchment.
Additionally, with respect to the above-mentioned hydro-climatic hazards, it was
found that:
· The statistic chosen to describe inter-annual variability of hydro-climatic
variables may create different perceptions of the projected future hydroclimatic
environment and, hence, whether or not the water manager would
decide whether adaptive action is necessary to manage future variability.
· There is greater uncertainty amongst the GCMs used in this study when
estimating design events (rainfall and streamflow) for shorter durations and
longer return periods, indicating that GCMs may still be failing to simulate
individual extreme events.
· The spatial distribution of projected changes in meteorological and
hydrological droughts are different, owing to the complexities introduced by
the hydrological system
· Many areas may be exposed to increases in hydrological hazards (i.e.
hydrological drought, floods and/or sediment yields) because, where one
extreme is projected to decrease, one of the others is often projected to
increase.
The thesis is concluded with recommendations for future research in the climate
change and hydrological fields, based on the experiences gained in undertaking this
study.Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2012.
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Ghile, Yonas Beyene. "Development of a framework for an integrated time-varying agrohydrological forecast system for southern Africa." Thesis, 2007. http://hdl.handle.net/10413/352.
Full textAbstract:
Policy makers, water managers, farmers and many other sectors of the society in southern Africa are confronting increasingly complex decisions as a result of the marked day-to-day, intra-seasonal and inter-annual variability of climate. Hence, forecasts of hydro-climatic variables with lead times of days to seasons ahead are becoming increasingly important to them in making more informed risk-based management decisions. With improved representations of atmospheric processes and advances in computer technology, a major improvement has been made by institutions such as the South African Weather Service, the University of Pretoria and the University of Cape Town in forecasting southern Africa’s weather at short lead times and its various climatic statistics for longer time ranges. In spite of these improvements, the operational utility of weather and climate forecasts, especially in agricultural and water management decision making, is still limited. This is so mainly because of a lack of reliability in their accuracy and the fact that they are not suited directly to the requirements of agrohydrological models with respect to their spatial and temporal scales and formats. As a result, the need has arisen to develop a GIS based framework in which the “translation” of weather and climate forecasts into more tangible agrohydrological forecasts such as streamflows, reservoir levels or crop yields is facilitated for enhanced economic, environmental and societal decision making over southern Africa in general, and in selected catchments in particular. This study focuses on the development of such a framework. As a precursor to describing and evaluating this framework, however, one important objective was to review the potential impacts of climate variability on water resources and agriculture, as well as assessing current approaches to managing climate variability and minimising risks from a hydrological perspective. With the aim of understanding the broad range of forecasting systems, the review was extended to the current state of hydro-climatic forecasting techniques and their potential applications in order to reduce vulnerability in the management of water resources and agricultural systems. This was followed by a brief review of some challenges and approaches to maximising benefits from these hydro-climatic forecasts. A GIS based framework has been developed to serve as an aid to process all the computations required to translate near real time rainfall fields estimated by remotely sensed tools, as well as daily rainfall forecasts with a range of lead times provided by Numerical Weather Prediction (NWP) models into daily quantitative values which are suitable for application with hydrological or crop models. Another major component of the framework was the development of two methodologies, viz. the Historical Sequence Method and the Ensemble Re-ordering Based Method for the translation of a triplet of categorical monthly and seasonal rainfall forecasts (i.e. Above, Near and Below Normal) into daily quantitative values, as such a triplet of probabilities cannot be applied in its original published form into hydrological/crop models which operate on a daily time step. The outputs of various near real time observations, of weather and climate models, as well as of downscaling methodologies were evaluated against observations in the Mgeni catchment in KwaZulu-Natal, South Africa, both in terms of rainfall characteristics as well as of streamflows simulated with the daily time step ACRU model. A comparative study of rainfall derived from daily reporting raingauges, ground based radars, satellites and merged fields indicated that the raingauge and merged rainfall fields displayed relatively realistic results and they may be used to simulate the “now state” of a catchment at the beginning of a forecast period. The performance of three NWP models, viz. the C-CAM, UM and NCEP-MRF, were found to vary from one event to another. However, the C-CAM model showed a general tendency of under-estimation whereas the UM and NCEP-MRF models suffered from significant over-estimation of the summer rainfall over the Mgeni catchment. Ensembles of simulated streamflows with the ACRU model using ensembles of rainfalls derived from both the Historical Sequence Method and the Ensemble Re-ordering Based Method showed reasonably good results for most of the selected months and seasons for which they were tested, which indicates that the two methods of transforming categorical seasonal forecasts into ensembles of daily quantitative rainfall values are useful for various agrohydrological applications in South Africa and possibly elsewhere. The use of the Ensemble Re-ordering Based Method was also found to be quite effective in generating the transitional probabilities of rain days and dry days as well as the persistence of dry and wet spells within forecast cycles, all of which are important in the evaluation and forecasting of streamflows and crop yields, as well as droughts and floods. Finally, future areas of research which could facilitate the practical implementation of the framework were identified.Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2007.
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Al-Mamoon, Abdullah. "Rainfall analysis under changing climate regime in Qatar." Thesis, 2018. http://hdl.handle.net/1959.7/uws:48858.
Full textAbstract:
Rainfall data is needed in the planning and design of storm water infrastructure, hydraulic structures, flood management works and various environmental assessment tasks. Design rainfall is generally expressed by intensity-duration-frequency (IDF) curves. This thesis focuses on rainfall analysis, in particular, trends and variability in rainfall indices, selection of probability distributions in frequency analysis of rainfall data, uncertainty assessment and evaluation of climate change impact on design rainfall. In this research, Qatar, located in the arid region of the Gulf has been selected as the study area. Rainfall data from a total of 35 rainfall stations from Qatar and nearby Gulf countries including Kingdom of Saudi Arabia, Bahrain, Oman and United Arab Emirates have been used in this study. A comprehensive quality check has been carried out in collating these rainfall data. Any station failing the quality assurance test is excluded from the analysis. It should be noted that different subsets of these stations have been used in the analysis and modelling presented in different thesis chapters. This research identified trends in rainfall data in Qatar using fifteen different rainfall indices by applying a combination of Mann-Kendall and Spearman’s Rho tests. It has been found that rainfall indices in Qatar have mixed trends (both positive and negative trends) throughout the country. Stations showing increasing trend in annual total rainfall are mainly located in the central part of Qatar. However, no relationship between spatial location and the elevation of rain gauges is found with the identified trends. Examination of trends in annual total rainfall during dry and rainy seasons shows that seasonal rainfall in Qatar is changing. This study identifies the best fit probability distribution for Qatar for annual maximum rainfall data based on fourteen different probability distributions and three goodness-of-fit tests. Based on a relative scoring method, the Generalized Extreme Value distribution is found to be the best fit distribution for majority of the selected stations. A modelling framework is also developed to quantify uncertainty in design rainfall estimation arising from limited data length using Monte Carlo simulation and bootstrapping techniques. Results from bootstrapping on the observed annual maximum rainfall data show that the estimate of the mean rainfall is associated with the smallest degree of standard error, whilst skewness has the highest error level. The coefficient of variation (CV) of standard deviation estimate is found to be 12 times higher than that of the mean. Furthermore, the CV of skewness estimate is found to be 26 times higher than that of the mean. Based on the results of Monte Carlo simulation, it has been found that the confidence band (measure of uncertainty) increases with increasing “average recurrence interval” (ARI). The 100 year ARI design rainfall intensity has the highest degree of uncertainty among the six ARIs (2 to 100 years) considered in this study. This study assesses the impacts of climate change on the design rainfall estimation in Qatar based on Intergovernmental Panel on Climate Change’s most recent new generation of climate models. A total of 61 Global Circulation Models with 609 emission scenarios are considered for the assessment. The results indicate an increase of up to 50% for the 100-year rainfall event from current to the intermediate scenario (2040-2069). The rate-of-change of the far future (2070-2100) is at similar level as the intermediate period.
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Lanka, Karthikeyan. "Predictability of Nonstationary Time Series using Wavelet and Empirical Mode Decomposition Based ARMA Models." Thesis, 2013. http://etd.iisc.ernet.in/2005/3363.
Full textAbstract:
The idea of time series forecasting techniques is that the past has certain information about future. So, the question of how the information is encoded in the past can be interpreted and later used to extrapolate events of future constitute the crux of time series analysis and forecasting. Several methods such as qualitative techniques (e.g., Delphi method), causal techniques (e.g., least squares regression), quantitative techniques (e.g., smoothing method, time series models) have been developed in the past in which the concept lies in establishing a model either theoretically or mathematically from past observations and estimate future from it. Of all the models, time series methods such as autoregressive moving average (ARMA) process have gained popularity because of their simplicity in implementation and accuracy in obtaining forecasts. But, these models were formulated based on certain properties that a time series is assumed to possess. Classical decomposition techniques were developed to supplement the requirements of time series models. These methods try to define a time series in terms of simple patterns called trend, cyclical and seasonal patterns along with noise. So, the idea of decomposing a time series into component patterns, later modeling each component using forecasting processes and finally combining the component forecasts to obtain actual time series predictions yielded superior performance over standard forecasting techniques. All these methods involve basic principle of moving average computation. But, the developed classical decomposition methods are disadvantageous in terms of containing fixed number of components for any time series, data independent decompositions. During moving average computation, edges of time series might not get modeled properly which affects long range forecasting. So, these issues are to be addressed by more efficient and advanced decomposition techniques such
as Wavelets and Empirical Mode Decomposition (EMD). Wavelets and EMD are some of the most innovative concepts considered in time series analysis and are focused on processing nonlinear and nonstationary time series. Hence, this research has been undertaken to ascertain the predictability of nonstationary time series using wavelet and Empirical Mode Decomposition (EMD) based ARMA models.
The development of wavelets has been made based on concepts of Fourier analysis and Window Fourier Transform. In accordance with this, initially, the necessity of involving the advent of wavelets has been presented. This is followed by the discussion regarding the advantages that are provided by wavelets. Primarily, the wavelets were defined in the sense of continuous time series. Later, in order to match the real world requirements, wavelets analysis has been defined in discrete scenario which is called as Discrete Wavelet Transform (DWT). The current thesis utilized DWT for performing time series decomposition. The detailed discussion regarding the theory behind time series decomposition is presented in the thesis. This is followed by description regarding mathematical viewpoint of time series decomposition using DWT, which involves decomposition algorithm.
EMD also comes under same class as wavelets in the consequence of time series decomposition. EMD is developed out of the fact that most of the time series in nature contain multiple frequencies leading to existence of different scales simultaneously. This method, when compared to standard Fourier analysis and wavelet algorithms, has greater scope of adaptation in processing various nonstationary time series. The method involves decomposing any complicated time series into a very small number of finite empirical modes (IMFs-Intrinsic Mode Functions), where each mode contains information of the original time series. The algorithm of time series decomposition using EMD is presented post conceptual elucidation in the current thesis. Later, the proposed time series forecasting algorithm that couples EMD and ARMA model is presented that even considers the number of time steps ahead of which forecasting needs to be performed.
In order to test the methodologies of wavelet and EMD based algorithms for prediction of time series with non stationarity, series of streamflow data from USA and rainfall data from India are used in the study. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability by the proposed algorithm is checked in two scenarios, first being six months ahead forecast and the second being twelve months ahead forecast. Normalized Root Mean Square Error (NRMSE) and Nash Sutcliffe Efficiency Index (Ef) are considered to evaluate the performance of the proposed techniques.
Based on the performance measures, the results indicate that wavelet based analyses generate good variations in the case of six months ahead forecast maintaining harmony with the observed values at most of the sites. Although the methods are observed to capture the minima of the time series effectively both in the case of six and twelve months ahead predictions, better forecasts are obtained with wavelet based method over EMD based method in the case of twelve months ahead predictions. It is therefore inferred that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place.
Finally, the study concludes that the wavelet based time series algorithm could be used to model events such as droughts with reasonable accuracy. Also, some modifications that could be made in the model have been suggested which can extend the scope of applicability to other areas in the field of hydrology.