Academic literature on the topic 'Precipitation forecasting Tasmania Mathematical models'
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Journal articles on the topic "Precipitation forecasting Tasmania Mathematical models"
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Santos, Douglas Matheus das Neves, Yuri Antônio da Silva Rocha, Danúbia Freitas, Paulo Beltrão, Paulo Santos Junior, Glauber Marques, Otavio Chase, and Pedro Campos. "Time-series forecasting models." International Journal for Innovation Education and Research 9, no. 8 (August 1, 2021): 24–47. http://dx.doi.org/10.31686/ijier.vol9.iss8.3239.
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Statistical and mathematical models of forecasting are of paramount importance for the understanding and study of databases, especially when applied to data of climatological variables, which enables the atmospheric study of a city or region, enabling greater management of the anthropic activities and actions that suffer the direct or indirect influence of meteorological parameters, such as precipitation and temperature. Therefore, this article aimed to analyze the behavior of monthly time series of Average Minimum Temperature, Average Maximum Temperature, Average Compensated Temperature, and Total Precipitation in Belém (Pará, Brazil) on data provided by INMET, for the production and application forecasting models. A 30-year time series was considered for the four variables, from January 1990 to December 2020. The Box and Jenkins methodology was used to determine the statistical models, and during their applications, models of the SARIMA and Holt-Winters class were estimated. For the selection of the models, analyzes of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Autocorrelation Correlogram (ACF), and Partial Autocorrelation (PACF) and tests such as Ljung-Box and Shapiro-Wilk were performed, in addition to Mean Square Error (NDE) and Absolute Percent Error Mean (MPAE) to find the best accuracy in the predictions. It was possible to find three SARIMA models: (0,1,2) (1,1,0) [12], (1,1,1) (0,0,1) [12], (0,1,2) (1,1,0) [12]; and a Holt-Winters model with additive seasonality. Thus, we found forecasts close to the real data for the four-time series worked from the SARIMA and Holt-Winters models, which indicates the feasibility of its applicability in the study of weather forecasting in the city of Belém. However, it is necessary to apply other possible statistical models, which may present more accurate forecasts.
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ALAM, MAHBOOB, and MOHD AMJAD. "A precipitation forecasting model using machine learning on big data in clouds environment." MAUSAM 72, no. 4 (November 1, 2021): 781–90. http://dx.doi.org/10.54302/mausam.v72i4.3546.
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Numerical weather prediction (NWP) has long been a difficult task for meteorologists. Atmospheric dynamics is extremely complicated to model, and chaos theory teaches us that the mathematical equations used to predict the weather are sensitive to initial conditions; that is, slightly perturbed initial conditions could yield very different forecasts. Over the years, meteorologists have developed a number of different mathematical models for atmospheric dynamics, each making slightly different assumptions and simplifications, and hence each yielding different forecasts. It has been noted that each model has its strengths and weaknesses forecasting in different situations, and hence to improve performance, scientists now use an ensemble forecast consisting of different models and running those models with different initial conditions. This ensemble method uses statistical post-processing; usually linear regression. Recently, machine learning techniques have started to be applied to NWP. Studies of neural networks, logistic regression, and genetic algorithms have shown improvements over standard linear regression for precipitation prediction. Gagne et al proposed using multiple machine learning techniques to improve precipitation forecasting. They used Breiman’s random forest technique, which had previously been applied to other areas of meteorology. Performance was verified using Next Generation Weather Radar (NEXRAD) data. Instead of using an ensemble forecast, it discusses the usage of techniques pertaining to machine learning to improve the precipitation forecast.
This paper is to present an approach for mapping of precipitation data. The project attempts to arrive at a machine learning method which is optimal and data driven for predicting precipitation levels that aids farmers thereby aiming to provide benefits to the agricultural domain.
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Kizilova, N. M., and N. L. Rychak. "Probabilistic models of water resources management on urbanized areas." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 4 (2020): 22–27. http://dx.doi.org/10.17721/1812-5409.2020/4.3.
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Gradual global climate change poses new challenges to the mathematical sciences, which are related to forecasting of meteorological conditions, preparing the infrastructure for possible rains, storms, droughts, and other climatic disasters. One of the most common approaches is synthetic regression-probability models, which use the spatio-temporal probability density functions of precipitation level. This approach is applied to the statistics of precipitation in the Kharkiv region, which shows the tendency to a gradual increase in air temperature, high indices of basic water stress, indices of drought and riverside flood threats. Open data on temperature distributions and precipitation were processed using various probability statistics. It is shown that the lognormal distribution most accurately describes the measurement data and allows making more accurate prognoses. Estimates of drought and flood probabilities in Kharkiv region under different scenarios of climate change dynamics have been carried out. The results of the study can be used for management of water resources on urban territories at global climate warming.
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Marquez, Adriana, Bettys Farias, and Edilberto Guevara. "Method for forecasting the flood risk in a tropical country." Water Supply 20, no. 6 (June 18, 2020): 2261–74. http://dx.doi.org/10.2166/ws.2020.129.
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Abstract In this study, a novel method for forecasting the flood risk in a tropical country is proposed, called CIHAM-UC-FFR. The method is based on the rainfall–runoff process. The CIHAM-UC-FFR method consists of three stages: (1) calibration and validation for the effective precipitation model, called CIHAM-UC-EP model, (2) calibration of forecasting models for components of the CIHAM-UC-EP model, (3) proposed model for forecasting of gridded flood risk called CIHAM-UC-FR. The CIHAM-UC-EP model has a mathematical structure derived from a conceptual model obtained by applying the principle of mass conservation combined with the adapted principle of Fick's law. The CIHAM-UC-FR model is a stochastic equation based on the exceedance probability of the forecast effective precipitation. Various scenarios are shown for a future time where the flood risk is progressively decreased as the expected life parameter of the hydraulic structure is increased.
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Duan, Q., Z. Di, J. Quan, C. Wang, W. Gong, Y. Gan, A. Ye, et al. "Automatic Model Calibration: A New Way to Improve Numerical Weather Forecasting." Bulletin of the American Meteorological Society 98, no. 5 (May 1, 2017): 959–70. http://dx.doi.org/10.1175/bams-d-15-00104.1.
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Abstract Weather forecasting skill has been improved over recent years owing to advances in the representation of physical processes by numerical weather prediction (NWP) models, observational systems, data assimilation and postprocessing, new computational capability, and effective communications and training. There is an area that has received less attention so far but can bring significant improvement to weather forecasting—the calibration of NWP models, a process in which model parameters are tuned using certain mathematical methods to minimize the difference between predictions and observations. Model calibration of the NWP models is difficult because 1) there are a formidable number of model parameters and meteorological variables to tune, and 2) a typical NWP model is very expensive to run, and conventional model calibration methods require many model runs (up to tens of thousands) or cannot handle the high dimensionality of NWP models. This study demonstrates that a newly developed automatic model calibration platform can overcome these difficulties and improve weather forecasting through parameter optimization. We illustrate how this is done with a case study involving 5-day weather forecasting during the summer monsoon in the greater Beijing region using the Weather Research and Forecasting Model. The keys to automatic model calibration are to use global sensitivity analysis to screen out the most important parameters influencing model performance and to employ surrogate models to reduce the need for a large number of model runs. Through several optimization and validation studies, we have shown that automatic model calibration can improve precipitation and temperature forecasting significantly according to a number of performance measures.
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Starchenko, A. V., A. A. Bart, L. I. Kizhner, and E. A. Danilkin. "MESOSCALE METEOROLOGICAL MODEL TSUNM3 FOR THE STUDY AND FORECAST OF METEOROLOGICAL PARAMETERS OF THE ATMOSPHERIC SURFACE LAYER OVER A MAJOR POPULATION CENTER." Vestnik Tomskogo gosudarstvennogo universiteta. Matematika i mekhanika, no. 66 (2020): 35–55. http://dx.doi.org/10.17223/19988621/66/3.
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The paper describes the mathematical formulation and numerical method of the TSUNM3 high-resolution mesoscale meteorological model being developed at Tomsk State University. The model is nonhydrostatic and includes three-dimensional nonstationary equations of hydrothermodynamics of the atmospheric boundary layer with parameterization of turbulence, moisture microphysics, long-wave and short-wave (solar) radiation, and advective and latent heat flows in the atmosphere and at the boundary of its interaction with the underlying surface. The numerical algorithm is constructed using structured grids with uniform spacing in horizontal directions and condensing to the Earth surface in the vertical direction. When approximating the differential formulation of the problem, the finite volume method with the second order approximation in the spatial variables is used. Explicit-implicit approximations in time (Adams–Bashforth and Crank–Nicolson) are used to achieve second-order accuracy in time. The paper presents results of numerical forecasting of the main meteorological parameters of the atmosphere (temperature, humidity, wind speed and direction) and precipitation in different seasons in the Siberian region. The models were tested with the help of observations obtained using the Volna-4M sodar, MTR-5 temperature profile meter, and Meteo-2 ultrasonic weather stations of the Atmosfera Collective Use Center. The improved TSUNM3 model is shown to adequately reflect the precipitation time and intensity. However, in some cases, the times of its beginning and end do not always coincide, the difference can reach several hours. The precipitation phase state is reflected reliably. Over 70% of precipitation cases are confirmed by numerical calculations. The model satisfactorily predicts temperature and humidity characteristics. The quality of the precipitation forecast model is comparable to the modern mesoscale models, such as the Weather Research and Forecasting (WRF) model.
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Rodríguez, Ricardo Osés. "Chaos Theory of Mathematics as seen from a New Perspective for Weather Forecasting." Bioscience Biotechnology Research Communications 15, no. 3 (September 30, 2022): 390–98. http://dx.doi.org/10.21786/bbrc/15.3.4.
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In this work, 8 meteorological variables were modeled in the Yabú station, Cuba, for which the daily database of this meteorological station was used, where the meteorological variables were taken into account are: extreme temperatures, extreme humidity and its average value, precipitation, wind force and cloudiness corresponding to the period 1977 to 2021. A linear mathematical model was obtained using the Objective Regressive Regression (ORR) methodology for each variable, which explains its behavior according to these variables, 15, 13, 10 and 8 years in advance. The calculation of the mean error with respect to the persistence forecast in temperatures, wind strength and cloudiness, as well as the persistence model was better with respect to humidity, this allows having valuable long-term information of the weather in a locality, which results in better decision making in the different aspects of the economy and society that are impacted by the weather forecast. It is concluded that these models allow long-term weather forecasting, opening a new possibility for forecasting, so that weather chaos can be overcome if this way of forecasting is used; moreover, it is the first time that an ORR model is applied to weather forecasting processes for a specific day so many years in advance.
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Smirnov, Anatolii. "DEVELOPMENT OF A METHODICAL APPROACH TO THE MAINTENANCE OF HIGHWAYS IN THE WINTER PERIOD TAKING INTO ACCOUNT WORLDWIDE EXPERIENCE." AUTOMOBILE ROADS AND ROAD CONSTRUCTION, no. 111 (June 30, 2022): 92–98. http://dx.doi.org/10.33744/0365-8171-2022-111-092-098.
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The article examines models and proposes a methodical approach to highway maintenance in the winter period, taking into account world experience. It was determined that the assessment of winter road maintenance measures performed in different countries takes different forms according to: quality indicator or standard; cost accounting and analysis; organized management system; standards for inspection and monitoring of works; measurement of conformity of service provision; analysis of complaints from users of the road network; methods of forecasting and prevention of certain winter phenomena. It is proposed to use the WSI (FHWA) index, which is calculated on the basis of the average value of daily snowfall and the recorded minimum and maximum temperature on average for the season, to assess the severity of the impact of weather on winter maintenance. It is recommended that the results of precipitation forecasting and the level of the WSI index be used as a basis for determining the operational level of service, which forms a set of potential measures for winter road maintenance. It is proposed to justify the level of service based on models of winter maintenance and forecasting measures that allow to form a methodical approach to highway maintenance. A methodological approach has been developed, which is based on the use of a mathematical model of road maintenance in the winter period, which is a function of minimizing the accumulated indicators of the quality of winter maintenance of road elements depending on their weight for maintenance.
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Dominguez, F., H. Hu, and J. A. Martinez. "Two-Layer Dynamic Recycling Model (2L-DRM): Learning from Moisture Tracking Models of Different Complexity." Journal of Hydrometeorology 21, no. 1 (January 2020): 3–16. http://dx.doi.org/10.1175/jhm-d-19-0101.1.
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AbstractAtmospheric moisture tracking models are used to identify and quantify sources and sinks of water in the atmospheric branch of the hydrologic cycle. These models are primarily used to investigate the origin of moisture resulting in precipitation for particular regions around the globe. Moisture tracking models vary widely in their level of complexity, depending on the number of physical processes represented. Complex models are comprehensive in their physical representation, but computationally much more expensive than simple models, which only focus on specific physical processes and use simplifying assumptions. We present the mathematical derivation of the new two-layer dynamical recycling model (2L-DRM), a simple analytical moisture tracking model that relaxes the vertically integrated formulation of the original one-layer DRM. By comparing the simple DRM to a very complex moisture tracking model that uses water vapor tracers embedded within the Weather Research and Forecasting regional climate model (WRF-WVT) for the North American monsoon region, we pinpoint the absence of vertical wind shear as the main deficiency in the simple DRM. When comparing both simple models (DRM and 2L-DRM) to the WRF-WVT (which we treat as “truth”), the 2L-DRM better captures the spatial extent, the net amount, and the temporal variability of precipitation that originates from oceanic and local terrestrial sources. The 2L-DRM is well suited to study the large-scale climatological sources of moisture, and for these applications, performs on par with the much more complex and computationally demanding WRF-WVT model.
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Лебедев, С. Н. "ПРОГНОЗ РАЗМНОЖЕНИЯ ВРЕДОНОСНЫХ ПОКОЛЕНИЙ ГРОЗДЕВОЙ ЛИСТОВЕРТКИ В УСЛОВИЯХ РАВНИННО-СТЕПНОГО КРЫМА." Вісник Полтавської державної аграрної академії, no. 1 (March 29, 2012): 84–87. http://dx.doi.org/10.31210/visnyk2012.01.20.
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Наводяться дані про залежність розвитку шкід-ливих поколінь ґронової листовійки на винограднихнасадженнях рівнинно-степового Криму від абіо-тичних чинників: середньодобової температуриповітря, суми опадів, відносної вологості повітря,а також площі листової поверхні куща винограду.На основі цих даних розроблені математичні мо-делі прогнозу розвитку фітофага, що дадуть змо-гу оптимізувати кратність і своєчасність захис-них заходів у боротьбі з зазначеним шкідником наконкретному сорті винограду.
Provides information on the intent of the development ofmalicious generations Lobesia botrana of the leaf rolleron vine plantations of plain-steppe Crimea from abioticfactors: the average daily air temperature, amount of precipitation,relative air humidity, as well as the area of leafsurface bush of grapes. On the basis of these datadeveloped mathematical models of forecasting of thedevelopment of the phytophage, that allows to optimizethe frequency and timeliness of protective measures inthe fight against this pest on a particular cultivar ofgrapes.
Dissertations / Theses on the topic "Precipitation forecasting Tasmania Mathematical models"
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Khajehei, Sepideh. "A Multivariate Modeling Approach for Generating Ensemble Climatology Forcing for Hydrologic Applications." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2403.
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Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach.
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Dars, Ghulam Hussain. "Climate Change Impacts on Precipitation Extremes over the Columbia River Basin Based on Downscaled CMIP5 Climate Scenarios." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/979.
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Hydro-climate extreme analysis helps understanding the process of spatio-temporal variation of extreme events due to climate change, and it is an important aspect in designing hydrological structures, forecasting floods and an effective decision making in the field of water resources design and management. The study evaluates extreme precipitation events over the Columbia River Basin (CRB), the fourth largest basin in the U.S., by simulating four CMIP5 global climate models (GCMs) for the historical period (1970-1999) and future period (2041-2070) under RCP85 GHG scenario.
We estimated the intensity of extreme and average precipitation for both winter (DJF) and summer (JJA) seasons by using the GEV distribution and multi-model ensemble average over the domain of the Columbia River Basin. The four CMIP5 models performed very well at simulating precipitation extremes in the winter season. The CMIP5 climate models showed heterogeneous spatial pattern of summer extreme precipitation over the CRB for the future period. It was noticed that multi-model ensemble mean outperformed compared to the individual performance of climate models for both seasons.
We have found that the multi-model ensemble shows a consistent and significant increase in the extreme precipitation events in the west of the Cascades Range, Coastal Ranges of Oregon and Washington State, the Canadian portion of the basin and over the Rocky Mountains. However, the mean precipitation is projected to decrease in both winter and summer seasons in the future period.
The Columbia River is dominated by the glacial snowmelt, so the increase in the intensity of extreme precipitation and decrease in mean precipitation in the future period, as simulated by four CMIP5 models, is expected to aggravate the earlier snowmelt and contribute to the flooding in the low lying areas especially in the west of the Cascades Range. In addition, the climate change shift could have serious implications on transboundary water issues in between the United States and Canada. Therefore, adaptation strategies should be devised to cope the possible adverse effects of the changing the future climate so that it could have minimal influence on hydrology, agriculture, aquatic species, hydro-power generation, human health and other water related infrastructure.
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Mohobane, Thabiso. "Water resources availability in the Caledon River basin : past, present and future." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1019802.
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The Caledon River Basin is located on one of the most water-scarce region on the African continent. The water resources of the Caledon River Basin play a pivotal role in socio-economic activities in both Lesotho and South Africa but the basin experiences recurrent severe droughts and frequent water shortages. The Caledon River is mostly used for commercial and subsistence agriculture, industrial and domestic supply. The resources are also important beyond the basin’s boundaries as the water is transferred to the nearby Modder River. The Caledon River is also a significant tributary to the Orange-Senqu Basin, which is shared by five southern African countries. However, the water resources in the basin are under continuous threat as a result of rapidly growing population, economic growth as well as changing climate, amongst others. It is therefore important that the hydrological regime and water resources of the basin are thoroughly evaluated and assessed so that they can be sustainably managed and utilised for maximum economic benefits. Climate change has been identified by the international community as one of the most prominent threats to peace, food security and livelihood and southern Africa as among the most vulnerable regions of the world. Water resources are perceived as a natural resource which will be affected the most by the changing climate conditions. Global warming is expected to bring more severe, prolonged droughts and exacerbate water shortages in this region. The current study is mainly focused on investigating the impacts of climate change on the water resources of the Caledon River Basin. The main objectives of the current study included assessing the past and current hydrological characteristics of the Caledon River Basin under current state of the physical environment, observed climate conditions and estimated water use; detecting any changes in the future rainfall and evaporative demands relative to present conditions and evaluating the impacts of climate on the basin’s hydrological regime and water resources availability for the future climate scenario, 2046-2065. To achieve these objectives the study used observed hydrological, meteorological data sets and the basin’s physical characteristics to establish parameters of the Pitman and WEAP hydrological models. Hydrological modelling is an integral part of hydrological investigations and evaluations. The various sources of uncertainties in the outputs of the climate and hydrological models were identified and quantified, as an integral part of the whole exercise. The 2-step approach of the uncertainty version of the model was used to estimate a range of parameters yielding behavioural natural flow ensembles. This approach uses the regional and local hydrological signals to constrain the model parameter ranges. The estimated parameters were also employed to guide the calibration process of the Water Evaluation And Planning (WEAP) model. The two models incorporated the estimated water uses within the basin to establish the present day flow simulations and they were found to sufficiently simulate the present day flows, as compared to the observed flows. There is an indication therefore, that WEAP can be successfully applied in other regions for hydrological investigations. Possible changes in future climate regime of the basin were evaluated by analysing downscaled temperature and rainfall outputs from a set of 9 climate models. The predictions are based on the A2 greenhouse gases emission scenario which assumes a continuous increase in emission rates. While the climate models agree that temperature, and hence, evapotranspiration will increase in the future, they demonstrate significant disagreement on whether rainfall will decrease or increase and by how much. The disagreement of the GCMs on projected future rainfall constitutes a major uncertainty in the prediction of water resources availability of the basin. This is to the extent that according to 7 out of 9 climate models used, the stream flow in four sub-basins (D21E, D22B, D23D and D23F) in the Caledon River Basin is projected to decrease below the present day flows, while two models (IPSL and MIUB) consistently project enhanced water resource availability in the basin in the future. The differences in the GCM projections highlight the margin of uncertainty involved predicting the future status of water resources in the basin. Such uncertainty should not be ignored and these results can be useful in aiding decision-makers to develop policies that are robust and that encompass all possibilities. In an attempt to reduce the known uncertainties, the study recommends upgrading of the hydrological monitoring network within the Caledon River Basin to facilitate improved hydrological evaluation and management. It also suggests the use of updated climate change data from the newest generation climate models, as well as integrating the findings of the current research into water resources decision making process.
Books on the topic "Precipitation forecasting Tasmania Mathematical models"
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Ceschia, Mario. La previsione delle precipitazioni a scala mensile: Confronto tra modelli statistici e a reti neurali, il caso di Udine. Udine: Forum, 2000.
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Ridge, Daniel. A candidate mesocale numerical cloud/precipitation model. Hanscom AFB, MA: Atmospheric Sciences Division, Air Force Geophysics Laboratory, 1985.
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David A. #q (David Alan) Unger. The moisture model for the local AFOS MOS Program. Silver Spring, Md: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, Techniques Development Laboratory, 1986.
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Unger, David A. The moisture model for the local AFOS MOS Program. Silver Spring, Md: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, Techniques Development Laboratory, 1986.
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Hayes, Pamela Speers. Prediction of precipitation in Western Washington State. Olympia, Wash: Washington State Dept. of Transportation, Planning, Research and Public Transportation Division, 1991.
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A, Matthews David. Nested model simulations of regional orographic precipitation. Denver, Colo: U.S. Dept. of the Interior, Bureau of Reclamation, Technical Service Center, 1997.
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A, Matthews David. Nested model simulations of regional orographic precipition. Denver, Colo: U.S. Dept. of the Interior, Bureau of Reclamation, Technical Service Center, 1997.
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(Korea), Han'gang Hongsu T'ongjeso. Han'gang hongsu yebo: Imjin'gang, Ansŏngch'ŏn p'oham. Sŏul-si: Kukt'o Haeyangbu Han'gang Hongsu T'ongjeso, 2011.
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Parrett, Charles. Characteristics of extreme storms in Montana and methods for constructing synthetic storm hyetographs. Helena, Mont: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.
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Müller, Klaus. Prognose von Niederschlagswahrscheinlichkeiten an einer Station (Berlin). Berlin: D. Reimer, 1986.
Find full textBook chapters on the topic "Precipitation forecasting Tasmania Mathematical models"
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Moreno, A., E. Soria, J. García, J. D. Martín, and R. Magdalena. "Neural Models for Rainfall Forecasting." In Soft Computing Methods for Practical Environment Solutions, 353–69. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-893-7.ch021.
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This chapter is focused on obtaining an optimal forecast of one month lagged rainfall in Spain. It is assessed by analyzing 22 years of both satellite observations of vegetation activity (e.g. NDVI) and climatic data (precipitation, temperature). The specific influence of non-spatial climatic indices such as NAO and SOI is also addressed. The approaches considered for rainfall forecasting include classical Auto-Regressive Moving-Average with Exogenous Inputs (ARMAX) models and Artificial Neural Networks (ANN), the so-called Multilayer Perceptron (MLP), in particular. The use of neural models is proven to be an adequate mathematical prediction tool in this problem due the non-linearity of the problem. These models enable us to predict, with one month foresight, the general rainfall dynamics, with average errors of 44 mm (RMSE) in a test series of 4 years with a rainfall standard deviation equal to 73 mm. Also, the sensitivity analysis in the neural network models reveals that observations in the status of the vegetation cover in previous months have a predictive power greater than other considered variables. Linear models yield average results of 55 mm (RMSE) although they need a large number of error terms (12) to obtain acceptable models. Nevertheless, they provide means for assessing the seasonal influence of the precipitation regime with the aid of linear dummy regression parameters, thereby offering an immediate interpretation (e.g. coherent maps) of the causality between vegetation cover and rainfall.