Journal articles on the topic 'Hydrometric approaches'
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1
Zvyagintseva, A. V., S. A. Sazonova, O. I. Marar, and L. V. Koval. "Environmental approaches when determining the calculated hydrological characteristics." IOP Conference Series: Earth and Environmental Science 839, no. 4 (September 1, 2021): 042079. http://dx.doi.org/10.1088/1755-1315/839/4/042079.
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Abstract The issues of forecasting dangerous hydrological phenomena in water bodies in the presence of hydrometrically observed data are considered. The analytical distribution functions of annual excess probabilities are applied - sufficiency curves. The features of calculating the empirical annual probability of exceeding hydrological characteristics, variation coefficients and asymmetries for distribution, the scattering of estimates and other distribution parameters are considered. In the case of heterogeneity of the initial data of hydrometric observations, when the series under consideration consists of heterogeneous elements of the hydrological regime, empirical and analytical distribution curves are set separately for each homogeneous totality. Based on the considered data, a system for monitoring and forecasting emergencies of a hydrological nature at water bodies is being constructed.
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DAWSON, CHRISTIAN W., MARTIN R. BROWN, and ROBERT L. WILBY. "INDUCTIVE LEARNING APPROACHES TO RAINFALL-RUNOFF MODELLING." International Journal of Neural Systems 10, no. 01 (February 2000): 43–57. http://dx.doi.org/10.1142/s0129065700000053.
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Trying to model the rainfall-runoff process is a complex activity as it is influenced by a number of implicit and explicit factors — for example, precipitation distribution, evaporation, transpiration, abstraction, watershed topography, and soil types. However, this kind of forecasting is particularly important as it is used to predict serious flooding, estimate erosion and identify problems associated with low flow. Inductive learning approaches (e.g. decision trees and artificial neural networks) are particularly well suited to problems of this nature as they can often interpret underlying factors (such as seasonal variations) which cannot be modelled by other techniques. In addition, these approaches can easily be trained on the explicit factors (e.g. rainfall) and the inexplicit factors (e.g. abstraction) that affect river flow. Inductive learning approaches can also be extended to account for new factors that emerge over a period of time. This paper evaluates the application of decision trees and two artificial neural network models (the multilayer perceptron and the radial basis function network) to river flow forecasting in two flood prone UK catchments using real hydrometric data. Comparisons are made between the performance of these approaches and conventional flood forecasting systems.
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Hock Hwee, Heng, Hii Ching Poon, Siaw Fei Lu, and Pan Wang Fook. "Dam Yield Assessment in Selangor 2016." Journal of Engineering & Technological Advances 2, no. 1 (2017): 57–77. http://dx.doi.org/10.35934/segi.v2i1.57.
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This paper presents a review of water yields for six reservoir schemes within Selangor and Kuala Lumpur. The study was carried out using up-to-date hydrometric database up to 2009. Three approaches were used for yield calculations; namely, (1) Drought Sequence, (2) Longterm records, and (3) Storage-yield-reliability model techniques/methodologies. It was found that the gross yields of various reservoir schemes were agreeable with one another, at least within the margin of difference which is about 5%.
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Sutanto, S. J., B. van den Hurk, G. Hoffmann, J. Wenninger, P. A. Dirmeyer, S. I. Seneviratne, T. Röckmann, K. E. Trenberth, and E. M. Blyth. "HESS Opinions: A perspective on different approaches to determine the contribution of transpiration to the surface moisture fluxes." Hydrology and Earth System Sciences Discussions 11, no. 3 (March 5, 2014): 2583–612. http://dx.doi.org/10.5194/hessd-11-2583-2014.
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Abstract. Current techniques to disentangle the total evaporative flux from the continental surface into a contribution evaporated from soils and canopy, or transpired by plants are under debate. Many isotope-based studies show that transpiration contributes generally more than 70% to the total moisture fluxes, while other isotope-independent techniques lead to considerably smaller transpiration fractions. This paper provides a perspective on isotope-based vs. non isotope-based partitioning studies. Some partitioning results from isotope-based methods, hydrometric measurements, and modeling are presented for comparison. Moreover, the methodological aspects of the analysis of partitioning are discussed including their limitations, and explanations of possible discrepancies between the methods are briefly discussed. We conclude that every method has its own uncertainties and these may lead to a high bias in the results, e.g. instruments inaccuracy and error, some assumptions used in analyses, parameters calibration. A number of comparison studies using isotope-based methods and hydrometric measurements in the same plants and climatic conditions are consistent within the errors, however, models tend to produce lower transpiration fractions. The relatively low transpiration fractions in current state of the art land surface models calls for a reassessment of the skill of the underlying model parameterizations. The scarcity of global evaporation data makes calibration and validation of global isotope-independent and isotope-based results difficult. However, isotope enabled land-surface and global climate modeling studies allow the evaluation of the parameterization of land surface models by comparing the computed water isotopologue signals in the atmosphere with the available remote sensing and flux-based data sets. Future studies that allow this evaluation could provide a better understanding of the hydrological cycle in vegetated regions.
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Sprenger, M., T. H. M. Volkmann, T. Blume, and M. Weiler. "Estimating flow and transport parameters in the unsaturated zone with pore water stable isotopes." Hydrology and Earth System Sciences Discussions 11, no. 10 (October 10, 2014): 11203–45. http://dx.doi.org/10.5194/hessd-11-11203-2014.
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Abstract. Determining the soil hydraulic properties is a prerequisite to physically model transient water flow and solute transport in the vadose zone. Estimating these properties by inverse modelling techniques has become more common within the last two decades. While these inverse approaches usually fit simulations to hydrometric data, we expanded the methodology by using independent information about the stable isotope composition of the soil pore water depth profile as a single or additional optimization target. To demonstrate the potential and limits of this approach, we compared the results of three inverse modelling strategies where the fitting targets were (a) pore water isotope concentrations, (b) a combination of pore water isotope concentrations and soil moisture time series, and (c) a two-step approach using first soil moisture data to determine water flow parameters and then the pore water stable isotope concentrations to estimate the solute transport parameters. The analyses were conducted at three study sites with different soil properties and vegetation. The transient unsaturated water flow was simulated by numerically solving the Richards equation with the finite-element code of Hydrus-1D. The transport of deuterium was simulated with the advection-dispersion equation, and the Hydrus code was modified to allow for deuterium loss during evaporation. The Mualem–van Genuchten and the longitudinal dispersivity parameters were determined for two major soil horizons at each site. The results show that approach (a) using only the pore water isotope content cannot substitute hydrometric information to derive parameter sets that reflect the observed soil moisture dynamics, but gives comparable results when the parameter space is constrained by pedotransfer functions. Approaches (b) and (c) using both, the isotope profiles and the soil moisture time series resulted in satisfying model performances and good parameter identifiability. However, approach (b) has the advantage that it considers the isotope data not only for the solute transport parameters, but also for water flow, and thus increases parameter realism. Approaches (b) and (c) both outcompeted simulations run with parameters derived from pedotransfer functions, which did not result in satisfying model efficiencies. Overall, parameters based on this new approach that includes isotope data lead to similar model performances regarding the water balance and soil moisture dynamics and better parameter identifiability than the conventional inverse model approaches limited to hydrometric fitting targets. If only data from isotope profiles in combination with textural information is available, the results are still satisfactory. This method has the additional advantage that it will not only allow us to estimate water balance and response times, but also site specific time variant transit times or solute breakthrough within the soil profile.
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Sutanto, S. J., B. van den Hurk, P. A. Dirmeyer, S. I. Seneviratne, T. Röckmann, K. E. Trenberth, E. M. Blyth, J. Wenninger, and G. Hoffmann. "HESS Opinions "A perspective on isotope versus non-isotope approaches to determine the contribution of transpiration to total evaporation"." Hydrology and Earth System Sciences 18, no. 8 (August 4, 2014): 2815–27. http://dx.doi.org/10.5194/hess-18-2815-2014.
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Abstract. Current techniques to disentangle the evaporative fluxes from the continental surface into a contribution evaporated from soils and canopy, or transpired by plants, are under debate. Many isotope-based studies show that transpiration contributes generally more than 70% to the total evaporation, while other isotope-independent techniques lead to considerably smaller transpiration fractions. This paper provides a perspective on isotope-based versus non-isotope-based partitioning studies. Some partitioning results from isotope-based methods, hydrometric measurements, and modeling are presented for comparison. Moreover, the methodological aspects of the partitioning analysis are considered, including their limitations, and explanations of possible discrepancies between the methods are discussed. We suggest sources of systematic error that may lead to biases in the results, e.g., instruments inaccuracy, assumptions used in analyses, and calibration parameters. A number of comparison studies using isotope-based methods and hydrometric measurements in the same plants and climatic conditions are consistent within the errors; however, models tend to produce lower transpiration fractions. The relatively low transpiration fraction in current state-of-the-art land-surface models calls for a reassessment of the skill of the underlying model parameterizations. The scarcity of global evaporation data makes calibration and validation of global isotope-independent and isotope-based results difficult. However, isotope-enabled land-surface and global climate modeling studies allow for the evaluation of the parameterization of land-surface models by comparing the computed water isotopologue signals in the atmosphere with the available remote sensing and flux-based data sets. Future studies that allow for this evaluation could provide a better understanding of the hydrological cycle in vegetated regions.
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Sprenger, M., T. H. M. Volkmann, T. Blume, and M. Weiler. "Estimating flow and transport parameters in the unsaturated zone with pore water stable isotopes." Hydrology and Earth System Sciences 19, no. 6 (June 3, 2015): 2617–35. http://dx.doi.org/10.5194/hess-19-2617-2015.
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Abstract. Determining the soil hydraulic properties is a prerequisite to physically model transient water flow and solute transport in the vadose zone. Estimating these properties by inverse modelling techniques has become more common within the last 2 decades. While these inverse approaches usually fit simulations to hydrometric data, we expanded the methodology by using independent information about the stable isotope composition of the soil pore water depth profile as a single or additional optimization target. To demonstrate the potential and limits of this approach, we compared the results of three inverse modelling strategies where the fitting targets were (a) pore water isotope concentrations, (b) a combination of pore water isotope concentrations and soil moisture time series, and (c) a two-step approach using first soil moisture data to determine water flow parameters and then the pore water stable isotope concentrations to estimate the solute transport parameters. The analyses were conducted at three study sites with different soil properties and vegetation. The transient unsaturated water flow was simulated by solving the Richards equation numerically with the finite-element code of HYDRUS-1D. The transport of deuterium was simulated with the advection-dispersion equation, and a modified version of HYDRUS was used, allowing deuterium loss during evaporation. The Mualem–van Genuchten and the longitudinal dispersivity parameters were determined for two major soil horizons at each site. The results show that approach (a), using only the pore water isotope content, cannot substitute hydrometric information to derive parameter sets that reflect the observed soil moisture dynamics but gives comparable results when the parameter space is constrained by pedotransfer functions. Approaches (b) and (c), using both the isotope profiles and the soil moisture time series, resulted in good simulation results with regard to the Kling–Gupta efficiency and good parameter identifiability. However, approach (b) has the advantage that it considers the isotope data not only for the solute transport parameters but also for water flow and root water uptake, and thus increases parameter realism. Approaches (b) and (c) both outcompeted simulations run with parameters derived from pedotransfer functions, which did not result in an acceptable representation of the soil moisture dynamics and pore water stable isotope composition. Overall, parameters based on this new approach that includes isotope data lead to similar model performances regarding the water balance and soil moisture dynamics and better parameter identifiability than the conventional inverse model approaches limited to hydrometric fitting targets. If only data from isotope profiles in combination with textural information is available, the results are still satisfactory. This method has the additional advantage that it will not only allow us to estimate water balance and response times but also site-specific time variant transit times or solute breakthrough within the soil profile.
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Azarpira, Fariba, and Sajad Shahabi. "Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables." Journal of Hydroinformatics 23, no. 6 (October 5, 2021): 1165–81. http://dx.doi.org/10.2166/hydro.2021.105.
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Abstract Streamflow forecasting, as one of the most important issues in hydrological studies, plays a vital role in several aspects of water resources management such as reservoir operation, water allocation, and flood forecasting. In this study, wavelet-gene expression programming (WGEP) and wavelet-M5 prime (WM5P) techniques, as two robust artificial intelligence (AI) models, were applied for forecasting the monthly streamflow in Khoshkroud and Polroud Rivers located in two basins with the same names. Results of hybrid AI techniques were compared with those achieved by two stand-alone models of GEP and M5P. Seven combinations of hydrological (H) and meteorological (M) variables were considered to investigate the effect of climatic variables on the performance of the proposed techniques. Moreover, the performance of both stand-alone and hybrid models were evaluated by statistical criteria of correlation of coefficient, root-mean-square error, index of agreement, the Nash–Sutcliffe model efficiency coefficient, and relative improvement. The statistical results revealed that there is a dependency between ‘the M5P and GEP performance’ and ‘the geometric properties of basins (e.g., area, shape, slope, and river network density)’. It was found that a preprocessed technique could increase the performance of M5P and GEP models. Compared to the stand-alone techniques, the hybrid AI models resulted in higher performance. For both basins, the performance of the WM5P model was higher than the WGEP model, especially for extreme events. Overall, the results demonstrated that the proposed hybrid AI approaches are reliable tools for forecasting the monthly streamflow, while the meteorological and hydrometric variables are taken into account.
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Azarpira, Fariba, and Sajad Shahabi. "Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables." Journal of Hydroinformatics 23, no. 6 (October 5, 2021): 1165–81. http://dx.doi.org/10.2166/hydro.2021.105.
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Abstract Streamflow forecasting, as one of the most important issues in hydrological studies, plays a vital role in several aspects of water resources management such as reservoir operation, water allocation, and flood forecasting. In this study, wavelet-gene expression programming (WGEP) and wavelet-M5 prime (WM5P) techniques, as two robust artificial intelligence (AI) models, were applied for forecasting the monthly streamflow in Khoshkroud and Polroud Rivers located in two basins with the same names. Results of hybrid AI techniques were compared with those achieved by two stand-alone models of GEP and M5P. Seven combinations of hydrological (H) and meteorological (M) variables were considered to investigate the effect of climatic variables on the performance of the proposed techniques. Moreover, the performance of both stand-alone and hybrid models were evaluated by statistical criteria of correlation of coefficient, root-mean-square error, index of agreement, the Nash–Sutcliffe model efficiency coefficient, and relative improvement. The statistical results revealed that there is a dependency between ‘the M5P and GEP performance’ and ‘the geometric properties of basins (e.g., area, shape, slope, and river network density)’. It was found that a preprocessed technique could increase the performance of M5P and GEP models. Compared to the stand-alone techniques, the hybrid AI models resulted in higher performance. For both basins, the performance of the WM5P model was higher than the WGEP model, especially for extreme events. Overall, the results demonstrated that the proposed hybrid AI approaches are reliable tools for forecasting the monthly streamflow, while the meteorological and hydrometric variables are taken into account.
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Obodovskyi, Oleksandr, Olga Lukianets, Oksana Konovalenko, and Valeriy Mykhaylenko. "Mapping the Mean Annual River Runoff in the Ukrainian Carpathian Region." Environmental Research, Engineering and Management 76, no. 2 (July 3, 2020): 22–33. http://dx.doi.org/10.5755/j01.erem.76.2.20916.
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The paper presents the spatial distribution of the mean annual river runoff in the Ukrainian Carpathians in the form of a map. The methodological approaches concerning the river runoff mapping and the technological stages of map creation by applying the geographic information system (GIS) analytical functions are considered. The accuracy assessment of the calculation of the mean annual river runoff water based on the data from the hydrometric stations for the whole observation period was performed. The mapping reliability of the mean annual runoff and their territorial variability over the main basins in the Ukrainian Carpathians are analysed.
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Shmakova, M. V. "GEOECOLOGICAL ASPECTS OF SEDIMENT TRANSPORT IN WATER OBJECTS: NEW APPROACHES AND FORMULAS." Izvestiya of Samara Scientific Center of the Russian Academy of Sciences 24, no. 5 (2022): 117–23. http://dx.doi.org/10.37313/1990-5378-2022-24-5-117-123.
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Calculations of the solid flow of water bodies in various geoecological applications are often complicated by irregular and poor-quality observational data. With the existing multitude of formulas for sediment flow, the results of calculations using them often show unsatisfactory quality, especially under conditions of an irregular, unsteady regime of river flow. The situation is aggravated by additional factors of solid matter inflow into the stream and the influence of hydraulic engineering measures and structures. In this case, the relevance of reliable calculations of solid flow (sediment load, turbidity and transporting capacity of the flow) is undoubtedly due to many areas of water management activities. It is quite obvious that calculations of solid flow should have a theoretical basis, unambiguously coupled with the processes of two-phase mass transfer in the river flow, as well as be based on standard hydrometric observation data. The conclusion of new and a review of previously developed methods for calculating solid runoff presented in this paper allows us to give a sufficiently reliable assessment of hydraulic variables of the state of two-phase mass transfer in a water body.
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Wolfe, Jared D., Kevin R. Shook, Chris Spence, and Colin J. Whitfield. "A watershed classification approach that looks beyond hydrology: application to a semi-arid, agricultural region in Canada." Hydrology and Earth System Sciences 23, no. 9 (September 25, 2019): 3945–67. http://dx.doi.org/10.5194/hess-23-3945-2019.
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Abstract. Classification and clustering approaches provide a means to group watersheds according to similar attributes, functions, or behaviours, and can aid in managing natural resources. Although they are widely used, approaches based on hydrological response parameters restrict analyses to regions where well-developed hydrological records exist, and overlook factors contributing to other management concerns, including biogeochemistry and ecology. In the Canadian Prairie, hydrometric gauging is sparse and often seasonal. Moreover, large areas are endorheic and the landscape is highly modified by human activity, complicating classification based solely on hydrological parameters. We compiled climate, geological, topographical, and land-cover data from the Prairie and conducted a classification of watersheds using a hierarchical clustering of principal components. Seven classes were identified based on the clustering of watersheds, including those distinguishing southern Manitoba, the pothole region, river valleys, and grasslands. Important defining variables were climate, elevation, surficial geology, wetland distribution, and land cover. In particular, three classes occur almost exclusively within regions that tend not to contribute to major river systems, and collectively encompass the majority of the study area. The gross difference in key characteristics across the classes suggests that future water management and climate change may carry with them heterogeneous sets of implications for water security across the Prairie. This emphasizes the importance of developing management strategies that target sub-regions expected to behave coherently as current human-induced changes to the landscape will affect how watersheds react to change. The study provides the first classification of watersheds within the Prairie based on climatic and biophysical attributes, with the framework used being applicable to other regions where hydrometric data are sparse. Our findings provide a foundation for addressing questions related to hydrological, biogeochemical, and ecological behaviours at a regional level, enhancing the capacity to address issues of water security.
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Roushangar, Kiyoumars, Nasrin Aghajani, Roghayeh Ghasempour, and Farhad Alizadeh. "The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river." Journal of Hydroinformatics 23, no. 3 (February 22, 2021): 655–70. http://dx.doi.org/10.2166/hydro.2021.146.
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Abstract Sediment transport is one of the most important issues in river engineering. In this study, the capability of the Kernel Extreme Learning Machine (KELM) approach for predicting the river daily Suspended Sediment Concentration (SSC) and Discharge (SSD) was assessed. Three successive hydrometric stations of Mississippi river were considered and based on the sediment and flow characteristics during the period of 2005–2008. Several models were developed and tested for SSC and SSD modeling. For improving the applied model efficiency, two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used. Also, two states of modeling based on stations' own data (state 1) and previous stations' data (state 2) were considered. The single and integrated KELM model results comparison indicated that the integrated WT and EEMD-KELM models resulted in more accurate outcomes. Results showed that data processing with WT was more effective than EEMD in increasing the models' efficiency. Data processing enhanced the models' capability by up to 15%. The results showed that the state 1 modeling led to better results, however, using the integrated KELM approaches the previous stations data could be applied successfully for SSC and SSD modeling when the stations' own data were not available. HIGHLIGHT The suspended sediment concentration (SSC) and suspended sediment discharge (SSD) were predicted via artificial intelligence approach in successive hydrometric stations. The data pre-processing impacts on models' efficiency improvement was assessed. The sensitivity analysis showed the most effective subseries was obtained from pre-processing models.
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Amininia, Karim, and Seyed Mahdi Saghebian. "Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models." Journal of Hydroinformatics 23, no. 4 (February 5, 2021): 897–913. http://dx.doi.org/10.2166/hydro.2021.142.
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Abstract The flow assessment in a river is of vital interest in hydraulic engineering for flood warning and evacuation measures. To operate water structures more efficiently, models that forecast river discharge are desired to be of high precision and certain degree of accuracy. Therefore, in this study, two artificial intelligence models, namely kernel extreme learning machine (KELM) and multivariate adaptive regression splines (MARS), were applied for the monthly river flow (MRF) modeling. For this aim, Mississippi river with three consecutive hydrometric stations was selected as case study. Using the previous MRF values during the period of 1950–2019, several models were developed and tested under two scenarios (i.e. modeling based on station's own data or previous station's data). Wavelet transform (WT) and ensemble empirical mode decomposition (EEMD) as data processing approaches were used for enhancing modeling capability. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the model's capability up to 25%. It was observed that the previous station's data could be applied successfully for MRF modeling when the station's own data were not available. The best-applied model dependability was assessed via uncertainty analysis, and an allowable degree of uncertainty was found in MRF modeling.
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Barker, Lucy J., Jamie Hannaford, Simon Parry, Katie A. Smith, Maliko Tanguy, and Christel Prudhomme. "Historic hydrological droughts 1891–2015: systematic characterisation for a diverse set of catchments across the UK." Hydrology and Earth System Sciences 23, no. 11 (November 15, 2019): 4583–602. http://dx.doi.org/10.5194/hess-23-4583-2019.
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Abstract. Hydrological droughts occur in all climate zones and can have severe impacts on society and the environment. Understanding historical drought occurrence and quantifying severity is crucial for underpinning drought risk assessments and developing drought management plans. However, hydrometric records are often short and capture only a limited range of variability. The UK is no exception: numerous severe droughts over the past 50 years have been well captured by observations from a dense hydrometric network. However, a lack of long-term observations means that our understanding of drought events in the early 20th century and late 19th century is limited. Here we take advantage of new reconstructed flow series for 1891 to 2015 to identify and characterise historic hydrological droughts for 108 near-natural catchments across the UK using the standardised streamflow index (SSI). The identified events are ranked according to four event characteristics (duration, accumulated deficit, mean deficit and maximum intensity), and their severity is reviewed in the context of events of the recent past (i.e. the last 50 years). This study represents the first national-scale assessment and ranking of hydrological droughts. Whilst known major drought events were identified, we also shed light on events which were regionally important, such as those in 1921 and 1984 (which were important in the south-east and north-west of the UK, respectively). Events which have been poorly documented, such as those of the 1940s in the post-war years or the early 1970s (prior to the landmark 1975–1976 event), were found to be important in terms of their spatial coverage and severity. This improved knowledge of historic events can support improved long-term water resource planning approaches. Given the universal importance of historical drought appraisal, our systematic approach to historical drought assessment provides a methodology that could be applied in other settings internationally.
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Iukhno, Artem, Sergei Buzmakov, and Alisa Zorina. "WATER DISCHARGE MEASURING INSTRUMENTS: AN UP TO DATE OVERVIEW." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2021): 116–23. http://dx.doi.org/10.17770/etr2021vol3.6613.
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Technological progress could not but affect the sphere of hydrometric measurements. New instruments have been implemented to add to such traditional measuring instruments as mechanical current meters or to replace them. Over the past 20 years, the number of different types measuring instruments has increased dramatically. That is why the analytical review and classification of these devices are needed to help with making appropriate management decisions in the field of streamflow monitoring and surveys. The article presents the multivariable classification of measuring instruments, based on such factors as: morphology scaling (channel width and depth), measuring conditions (open, weed or ice-covered channel), logistical factor (mobile or stationary) and required accuracy. Characteristics of each type of measuring instruments were also considered and the limitations of their applicability were described. The results presented in the paper are expected to expand the horizons of approaches used for estimation of water discharge.
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Benyahya, Loubna, Daniel Caissie, Fahim Ashkar, Nassir El-Jabi, and Mysore Satish. "Comparison of the annual minimum flow and the deficit below threshold approaches: case study for the province of New Brunswick, Canada." Canadian Journal of Civil Engineering 36, no. 9 (September 2009): 1421–34. http://dx.doi.org/10.1139/l09-077.
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A low-flow frequency analysis using the annual minimum flow (AMF) and the deficit below threshold (DBT) approaches was carried out for 30 hydrometric stations across the province of New Brunswick. The AMF method considers only the annual minimum events, and the DBT method considers all low flows below a certain threshold (or truncation level). In the present study, the DBT method characterizes low flow in terms of deficit intensity, which is becoming increasingly important in both water and aquatic resources management. The annual minimum series were fitted using the three-parameter Weibull distribution (AMF–WEI3), and the intensity data series were fitted using the two-parameter Weibull distribution (DBT–WEI2) and the generalized Pareto distribution (DBT–GP). All parameter estimates were obtained using the maximum likelihood method. The AMF–WEI3 and DBT–GP approaches provided a good fit to at-site data in terms of annual minimum flow and deficit intensity, respectively. However, the present study showed that the DBT–WEI2 approach underestimated the right tail of low-flow intensity. The Anderson–Darling statistic was also calculated for the data series and can provide insight into which distribution may adequately model the low-flow characteristics. A regionalization study was also performed using the AMF–WEI3 and DBT–GP methods.
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Wambua, Raphael M. "Hydrological Drought Forecasting Using Modified Surface Water Supply Index (SWSI) and Streamflow Drought Index (SDI) in Conjunction with Artificial Neural Networks (ANNs)." International Journal of Service Science, Management, Engineering, and Technology 10, no. 4 (October 2019): 39–57. http://dx.doi.org/10.4018/ijssmet.2019100103.
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Hydrological drought in upper Tana River basin adversely affects water resources. In this study, a hydrological drought was forecasted using a Surface Water Supply Index (SWSI), a Streamflow Drought Index (SDI) and an Artificial Neural Networks (ANNs). The best SWSI involved combinations of rainfall and the index values integrated into ANNs. The best forecasts with SDI entailed composite functions of rainfall, stream flow and SDI. Different ANN models for both SWSI and SDI with lead times of 1 to 24 months were tested at hydrometric stations. Results show that the forecasting ability of all the networks decreased with the increase in lead-time. The best ANNs with specific architecture performed differently based on forecasting lead-time. SWSI drought forecasts were better than those of the SDI for all lead-times. The SWSI and SDI depicted R values of 0.752 and 0.732 for station 4AB05 for one-month lead-time. The findings are useful as an effective hydrological-drought early warning for viable mitigation and preparedness approaches to minimize the negative effects of drought.
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Rock, Luc, and Bernhard Mayer. "Identifying the influence of geology, land use, and anthropogenic activities on riverine sulfate on a watershed scale by combining hydrometric, chemical and isotopic approaches." Chemical Geology 262, no. 3-4 (May 2009): 121–30. http://dx.doi.org/10.1016/j.chemgeo.2009.01.002.
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Harvey, Catherine L., Harry Dixon, and Jamie Hannaford. "An appraisal of the performance of data-infilling methods for application to daily mean river flow records in the UK." Hydrology Research 43, no. 5 (April 12, 2012): 618–36. http://dx.doi.org/10.2166/nh.2012.110.
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River flow records are fundamental for the sustainable management of water resources and even very short gaps can severely compromise their utility. Suitably-flagged flow estimates, derived via judicious infilling, are potentially highly beneficial to data users. The UK National River Flow Archive provides stewardship of, and access to, UK river flow records. While many datasets held on the archive are complete, gaps remain across a wide range of flow records. A comprehensive assessment of existing techniques for infilling these gaps is currently lacking. This paper therefore assesses 15 simple infilling techniques (including regression, scaling and equipercentile approaches), each relying upon data transfer from hydrologically-similar donor stations, to generate estimates of flow at 26 representative gauging stations. Results reveal the overall superiority of equipercentile and multiple regression techniques compared to the poorer capability of catchment area scaling. Donor station choice has a strong influence on technique performance. Modifying datasets to improve homogeneity, by seasonally grouping flows or excluding certain periods, offers improved performance. These findings provide a foundation upon which guidance on infilling river flow records can be based in future, allowing hydrometric practitioners and data end-users alike to adopt a consistent and auditable approach towards infilling.
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Msilini, A., P. Masselot, and T. B. M. J. Ouarda. "Regional Frequency Analysis at Ungauged Sites with Multivariate Adaptive Regression Splines." Journal of Hydrometeorology 21, no. 12 (December 2020): 2777–92. http://dx.doi.org/10.1175/jhm-d-19-0213.1.
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AbstractHydrological systems are naturally complex and nonlinear. A large number of variables, many of which not yet well considered in regional frequency analysis (RFA), have a significant impact on hydrological dynamics and consequently on flood quantile estimates. Despite the increasing number of statistical tools used to estimate flood quantiles at ungauged sites, little attention has been dedicated to the development of new regional estimation (RE) models accounting for both nonlinear links and interactions between hydrological and physio-meteorological variables. The aim of this paper is to simultaneously take into account nonlinearity and interactions between variables by introducing the multivariate adaptive regression splines (MARS) approach in RFA. The predictive performances of MARS are compared with those obtained by one of the most robust RE models: the generalized additive model (GAM). Both approaches are applied to two datasets covering 151 hydrometric stations in the province of Quebec (Canada): a standard dataset (STA) containing commonly used variables and an extended dataset (EXTD) combining STA with additional variables dealing with drainage network characteristics. Results indicate that RE models using MARS with the EXTD outperform slightly RE models using GAM. Thus, MARS seems to allow for a better representation of the hydrological process and an increased predictive power in RFA.
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He, Qianwen, and Frank Molkenthin. "Improving the integrated hydrological simulation on a data-scarce catchment with multi-objective calibration." Journal of Hydroinformatics 23, no. 2 (January 29, 2021): 267–83. http://dx.doi.org/10.2166/hydro.2021.132.
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Abstract The process-based hydrological model Soil and Water Assessment Tool ensures the simulation's reliability by calibration. Compared to the commonly applied single-objective calibration, multi-objective calibration benefits the spatial parameterization and the simulation of specific processes. However, the requirements of additional observations and the practical procedure are among the reasons to prevent the wider application of the multi-objective calibration. This study proposes to consider three groups of objectives for the calibration: multisite, multi-objective function, and multi-metric. For the study catchment with limited observations like the Yuan River Catchment (YRC) in China, the three groups corresponded to discharge from three hydrometric stations, both Nash–Sutcliffe efficiency (NSE) and inversed NSE for discharge evaluation, and MODIS global terrestrial evapotranspiration product and baseflow filtered from discharge as metrics, respectively. The applicability of two multi-objective calibration approaches, the Euclidean distance and nondominated sorting genetic algorithm II, was analyzed to calibrate the above-mentioned objectives for the YRC. Results show that multi-objective calibration has simultaneously ensured the model's better performance in terms of the spatial parameterization, the magnitude of the output time series, and the water balance components, and it also reduces the parameter and prediction uncertainty. The study thus leads to a generalized, recommended procedure for catchments with data scarcity to perform the multi-objective calibration.
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Neri, Mattia, Juraj Parajka, and Elena Toth. "Importance of the informative content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density." Hydrology and Earth System Sciences 24, no. 11 (November 6, 2020): 5149–71. http://dx.doi.org/10.5194/hess-24-5149-2020.
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Abstract. The setup of a rainfall-runoff model in a river section where no streamflow measurements are available for its calibration is one of the key research activities for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to estimate the model parameters based on the hydrometric information available in the region. The informative content of the dataset (i.e. which and how many gauged river stations are available) plays an essential role in the assessment of the best regionalisation method. This study analyses how the performances of regionalisation approaches are influenced by the “information richness” of the available regional dataset, i.e. the availability of potential donors, and in particular by the gauging density and by the presence of nested donor catchments, which are expected to be hydrologically very similar to the target section. The research is carried out over a densely gauged dataset covering the Austrian country, applying two rainfall-runoff models and different regionalisation approaches. The regionalisation techniques are first implemented using all the gauged basins in the dataset as potential donors and then re-applied, decreasing the informative content of the dataset. The effect of excluding nested basins and the status of “nestedness” is identified based on the position of the closing section along the river or the percentage of shared drainage area. Moreover, the impact of reducing station density on regionalisation performance is analysed. The results show that the predictive accuracy of parameter regionalisation techniques strongly depends on the informative content of the dataset of available donor catchments. The “output-averaging” approaches, which exploit the information of more than one donor basin and preserve the correlation structure of the parameter, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions. Moreover, with the use of an optimised set of catchment descriptors as a similarity measure, rather than the simple geographical distance, results are more robust to the deterioration of the informative content of the set of donors.
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Castiglioni, S., A. Castellarin, A. Montanari, J. O. Skøien, G. Laaha, and G. Blöschl. "Geostatistical regionalization of low-flow indices: PSBI and Top-Kriging." Hydrology and Earth System Sciences Discussions 7, no. 5 (September 23, 2010): 7231–61. http://dx.doi.org/10.5194/hessd-7-7231-2010.
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Abstract. Recent studies highlight that geostatistical interpolation, which has been originally developed for the spatial interpolation of point data, can be effectively applied to the problem of regionalization of hydrometric information. This study compares two innovative geostatistical approaches for the prediction of low-flows in ungauged basins. The first one, named Physiographic-Space Based Interpolation (PSBI), performs the spatial interpolation of the desired streamflow index (e.g., annual streamflow, low-flow index, flood quantile, etc.) in the space of catchment descriptors. The second technique, named Topological kriging or Top-Kriging, predicts the variable of interest along river networks taking both the area and nested nature of catchments into account. PSBI and Top-Kriging are applied for the regionalization of Q355 (i.e., the streamflow that is equalled or exceeded 355 days in a year, on average) over a broad geographical region in central Italy, which contains 51 gauged catchments. Both techniques are cross-validated through a leave-one-out procedure at all available gauges and applied to a subregion to produce a continuous estimation of Q355 along the river network extracted from a 90 m DEM. The results of the study show that Top-Kriging and PSBI present complementary features and have comparable performances (Nash-Sutcliffe efficiencies in cross-validation of 0.89 and 0.83, respectively). Both techniques provide plausible and accurate predictions of Q355 in ungauged basins and represent promising opportunities for regionalization of low-flows.
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Bennis, Saad, Sylvain Côté, and Narut Kang. "Validation des données hydrométriques par des techniques multivariées de filtrage." Canadian Journal of Civil Engineering 23, no. 1 (February 1, 1996): 218–30. http://dx.doi.org/10.1139/l96-023.
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The purpose of this research project was to develop a method for automatic validation of historical daily natural runoff data. Reservoir level measurements, on which natural runoff calculations are directly based were validated. Depending on the number of limnimeters installed, two different approaches were used to validate and adjust reservoir level times series. The best conditions (those discussed here) are when a reservoir has several water-level stations. Under these conditions, multivariate filtering is used to validate time series of recorded levels at each station. This method, called the multifilter method consists of comparing deviations between the value predicted with an autoregressive model, the measured historical value, and an estimate obtained using a regression model at neighbouring stations. Among the measured value and the estimate derived from the linear regression model, the closest value to the forecast was retained. One advantage in validating historical hydrometric data is the availability of data before and after the date to be validated. In other words, to validate the value of level Nt not only are the values Nt−1Nt−2, … available, but also the values Nt+1, Nt+2 … at the station to be validated as well as at neighbouring stations. To take advantage of this, the multifilter validation process was performed twice: in the usual time direction and backwards. The historical value was considered faulty and discarded only if it was rejected in both the forward and backward validation processes. All techniques developed have been incorporated into the software called ValiDeb and successfully tested at the Gatineau River site in Quebec. Key words: validation, filtering, multivariate, equipment redundance, analysis, levels, runoff, Kalman. [Journal translation]
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Salis, Hugo Henrique Cardoso de, Adriana Monteiro da Costa, Annika Künne, Luís Filipe Sanches Fernandes, and Fernando António Leal Pacheco. "Conjunctive Water Resources Management in Densely Urbanized Karst Areas: A Study in the Sete Lagoas Region, State of Minas Gerais, Brazil." Sustainability 11, no. 14 (July 19, 2019): 3944. http://dx.doi.org/10.3390/su11143944.
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Headwater catchments store valuable resources of quality water, but their hydraulic response is difficult to assess (model) because they are usually deprived of monitoring stations, namely hydrometric stations. This issue becomes even more pertinent because headwater catchments are ideal for the practice of conjunctive water resources management involving the supply of towns with groundwater and surface water, a solution that can be used to mitigate overexploitation of groundwater resources in densely urbanized and populated areas. In this study, a stepwise approach is presented whereby, in a first stage, a gauged basin was modeled for stream flow using the JAMS J2000 framework, with the purpose to obtain calibrated hydraulic parameters and ecological simulated stream flow records. Having validated the model through a comparison of simulated and measured flows, the simulated record was adjusted to the scale of an ungauged sub-basin, based on a new run of JAMS J2000 using the same hydraulic parameters. At this stage, a second validation of modeled data was accomplished through comparison of the downscaled flow rates with discharge rates assessed by field measurements of flow velocity and water column height. The modeled basin was a portion of Jequitiba River basin, while the enclosed sub-basin was the Marinheiro catchment (state of Minas Gerais, Brazil). The latter is a peri-urban watershed located in the vicinity of Sete Lagoas town, a densely urbanized and populated area. This town uses 15.5 hm3 year−1 of karst groundwater for public water supply, but the renewable resources were estimated to be 6.3 hm3 year−1. The impairment between abstraction and renewable resources lasts for decades, and for that reason the town experiences systemic water table declines and sinkhole development. The present study claims that the storage of quality water in the Marinheiro catchment, in a dam reservoir, would help alleviate the depletion of groundwater resources in the karst aquifer because this catchment could deliver 4.73 hm3 year−1 of quality surface water to the municipality without endangering ecologic flows. The construction of a small dam at the outlet of Marinheiro catchment could also improve aquifer recharge. Presently, the annual recharge in this catchment approaches 1.47 hm3 but could be much larger if the small dam was installed in the water course and the captured stream water managed properly.
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Muste, Marian, Kyutae Lee, and Jean-Luc Bertrand-Krajewski. "Standardized uncertainty analysis for hydrometry: a review of relevant approaches and implementation examples." Hydrological Sciences Journal 57, no. 4 (April 11, 2012): 643–67. http://dx.doi.org/10.1080/02626667.2012.675064.
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LUMB, L. IAN, TOM B. LOW, and ARTHUR DI LEO. "QUANTITATIVE CLASSIFICATION OF CLOUD MICROPHYSICAL IMAGERY VIA FRACTAL-DIMENSION CALCULATIONS." Fractals 02, no. 03 (September 1994): 399–404. http://dx.doi.org/10.1142/s0218348x94000521.
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The use of fractal-dimension calculations, for quantitative classification of various objects, is well established in many areas of the physical and life sciences. Such fractal-dimension calculations are useful in that they furnish some measure of geometrical complexity that is not available through "traditional" approaches. In the present context, we focus attention on the application of such calculations to cloud microphysical data. In particular, we consider data from in-situ, two-dimensional particle imagery. Whereas previous efforts have typically characterized the imaged hydrometeoric fields by dimensional and statistical measures, distinguishing between the various hydrometeoric types should also be possible with fractal dimension based analyses. Although typical data sets may include thousands of individual images, each of these single images is typically quite small in size, and has intensity values that span only a few levels. Thus the need for computationally efficient algorithms, that can process these individual images, presents interesting challenges for fractal-dimension calculations. Here we report on our preliminary findings regarding the capacity and information dimension of various synthetic images.
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Perks, Matthew T., Silvano Fortunato Dal Sasso, Alexandre Hauet, Elizabeth Jamieson, Jérôme Le Coz, Sophie Pearce, Salvador Peña-Haro, et al. "Towards harmonisation of image velocimetry techniques for river surface velocity observations." Earth System Science Data 12, no. 3 (July 8, 2020): 1545–59. http://dx.doi.org/10.5194/essd-12-1545-2020.
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Abstract. Since the turn of the 21st century, image-based velocimetry techniques have become an increasingly popular approach for determining open-channel flow in a range of hydrological settings across Europe and beyond. Simultaneously, a range of large-scale image velocimetry algorithms have been developed that are equipped with differing image pre-processing and analytical capabilities. Yet in operational hydrometry, these techniques are utilised by few competent authorities. Therefore, imagery collected for image velocimetry analysis (along with reference data) is required both to enable inter-comparisons between these differing approaches and to test their overall efficacy. Through benchmarking exercises, it will be possible to assess which approaches are best suited for a range of fluvial settings, and to focus future software developments. Here we collate and describe datasets acquired from seven countries across Europe and North America, consisting of videos that have been subjected to a range of pre-processing and image velocimetry analyses (Perks et al., 2020, https://doi.org/10.4121/uuid:014d56f7-06dd-49ad-a48c-2282ab10428e). Reference data are available for 12 of the 13 case studies presented, enabling these data to be used for reference and accuracy assessment.
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McKergow, Lucy A., Ian P. Prosser, Rodger B. Grayson, and Dale Heiner. "Performance of grass and rainforest riparian buffers in the wet tropics, Far North Queensland. 1. Riparian hydrology." Soil Research 42, no. 4 (2004): 473. http://dx.doi.org/10.1071/sr02155.
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The long and intense storms of the wet tropics present extreme conditions for testing the effectiveness of riparian buffers. This study presents results of a hydrometric investigation of 4 riparian buffers on 2 commercial banana plantations in Far North Queensland, Australia. It investigates runoff generation and riparian hydrology on hillslopes with differing slopes, contributing areas, and topographic convergence. Both grass and rainforest buffers were examined. Surface and subsurface hydrology were measured for 4 wet seasons (December–April) using paired flumes, piezometers, and tensiometers. All buffers experienced large volumes of surface runoff, with peak discharges ranging from 30 L/s on planar hillslopes to 350 L/s on a highly convergent site. Event runoff : rainfall ratios ranged between 0.01 and 0.65. Grass buffers with smaller contributing areas (<0.3 ha) were able to dissipate the energy of surface runoff under all conditions. In a larger (5 ha), highly convergent hillslope, surface runoff became channelised upslope of the buffer and the vetiver hedges and grass were not able to prevent scouring of a channel through the buffer, reducing its performance. Infiltration occurred in all buffers during small events, and at the convergent buffer during large events, most likely due to the presence of deep soil fill. In contrast, exfiltration occurred in the grass buffers on planar and moderately converging slopes during large events. There, the riparian soil approached saturation and return flow and seepage were measured. Under exfiltration, soil strength may be decreased and riparian buffers are needed to decrease erosion hazard. Localised saturation was observed in the rainforest buffer beneath a planar hillslope during large events, where soils were deeper and dried out more quickly than in the adjacent grass buffer. This study documents the high runoff volumes and peak discharges on cropped slopes in the wet tropics, and evaluates riparian hydrological processes. Infiltration is unlikely to be an important buffer function in this environment, but an additional role of buffers is to reduce the erosion hazard presented by exfiltration.
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Dinata, Alharia. "Analisis Karakteristik Fisik Subdas Air Lematang dan Subdas Air Selangis Besar Terhadap Debit Banjir Puncak." JURNAL ILMIAH BERING'S 4, no. 02 (October 9, 2017): 44–50. http://dx.doi.org/10.36050/berings.v4i02.132.
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Watershed has distinctive physical characteristics between the and another watershed like a river basin shape, the length of the main river, watershed area, the order of the river, the river network density, and the slope of the river. From these physical characteristics, it can be used to analyze peak discharge in a watershed which has not yet had a river hydrometry station. Synthetic unit hydrograph (HSS) is one of the most widely used empirical approaches in estimating unit hydrograph in a watershed. HSS GAMA I is a method that has been developed in Indonesia with results close to the data in the field, this method uses physical data characteristic watershed in calculating Qp. The purpose of this research is to analyze the influence of physical characteristics of sub-watershed Air Lematang and Sub-watershed Air Selangis Besar to Qp and to analyze the flood discharge of the two sub-watershed designs. The analysis result shows that Qp sub-watershed Air Lematang is 9.8 m³ / d with subdistrict area 295.88 km² and TR 2.08 hours with main river length 49,93 km. Qp Sub-watershed Air Selangis Besar 11.13 m³ / d with an area of 288.02 km² and TR 1.61 hours with the main river length of 34.51 km. Q100Tr sub-watershed Air Lematang and sub-watershed of Air Selangis Besar were 873.44 m³ / d and 826,86 m³ / d respectively. The effect of physical characteristics of the two sub-basins on Qp is broadly understood and the shape of sub-watershed and TR is influenced by the source factor and the length of the main river.
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Ljubičić, Robert, Dariia Strelnikova, Matthew T. Perks, Anette Eltner, Salvador Peña-Haro, Alonso Pizarro, Silvano Fortunato Dal Sasso, Ulf Scherling, Pietro Vuono, and Salvatore Manfreda. "A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations." Hydrology and Earth System Sciences 25, no. 9 (September 22, 2021): 5105–32. http://dx.doi.org/10.5194/hess-25-5105-2021.
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Abstract. While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements must be quantified and mitigated. The physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements, such as velocimetry. A common practice in data preprocessing is compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of static features – to first estimate and then compensate for such motion. Most existing stabilisation approaches rely either on customised tools developed in-house, based on different algorithms, or on general purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for selecting a particular tool in given conditions has not been found in the literature. In this paper, we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In an attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root mean squared differences (RMSDs) of inter-frame pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying static features in videos, i.e. manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed light on details of implementation, which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can be used in order to measure the velocity estimation uncertainty induced by UAS motion.
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Ghasempour, Roghayeh, Kiyoumars Roushangar, and Parveen Sihag. "Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches." Water Supply, March 30, 2021. http://dx.doi.org/10.2166/ws.2021.094.
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Abstract Sediment transportation and accurate estimation of its rate is a significant issue for river engineers and researchers. In this study, the capability of kernel based approaches including Kernel Extreme Learning Machine (KELM) and Gaussian Process Regression (GPR) was assessed for predicting the river daily Suspended Sediment Discharge (SSD). For this aim, the Mississippi river, with three consecutive hydrometric stations, was selected as the case study. Based on the sediment and flow characteristics during the period of 2005–2008, several models were developed and tested under two scenarios (i.e. modeling based on each station's own data or the previous stations' data). Two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used for enhancing the SSD modeling capability. Also, data post-proceeding was done using Simple Linear Averaging (SLAM) and Nonlinear Kernel Extreme Learning Machine Ensemble (NKELME) methods. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the models' capability up to 35%. It was found that SSD modeling based on the station's own data led to better results; however, using the integrated approaches, the previous station's data could be applied successfully for the SSD modeling when a station's own data were not available.
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Lakshmi, Potharlanka Jhansi, Rubén Apaza Apaza, Ahmed Alkhayyat, Haydar Abdulameer Marhoon, and Ameer A. Alameri. "Hybrid wavelet-gene expression programming and wavelet-support vector machine models for rainfall-runoff modeling." Water Science and Technology, December 5, 2022. http://dx.doi.org/10.2166/wst.2022.400.
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Abstract It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an increase in the use of machine learning approaches to model rainfall-runoff. In this study, the accuracy of rainfall-runoff modeling approaches such as Support Vector Machine (SVM), Gene Expression Programming (GEP), Wavelet-SVM (WSVM), and Wavelet-GEP (WGEP) is evaluated. Python is used to run the simulation. The research area is the Yellow River basin in central China, and in the west of the region, the Tang-Nai-Hai hydrometric station has been selected. The train state data ranges from 1950 to 2000, while the test state data ranges from 2000 to 2020. The analysis looks at two different types of rainy and non-rainy days. The WGEP simulation performed best, with a Nash-Sutcliffe efficiency (NSE) of 0.98, while the WSVM, GEP, and SVM simulations performed poorly, with NSEs of 0.94, 0.89, and 0.77, respectively. As a result, combining hybrid methods with Wavelet improved simulation accuracy, which is now the highest for the WGEP method.
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"Determination of Estimated Hydrological Characteristics in the Absence of Hydrometric Observations: Potential for the Use of Regional Gridded River Runoff Reanalysis." Water sector of Russia: problems, technologies, management, no. 2, 2020 (2020): 83–101. http://dx.doi.org/10.35567/1999-4508-2020-2-6.
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Engineers in the field of hydrology often face the task of determining the hydrological characteristics for river gauges not covered by a regular network of hydrometric observations. This task is usually solved using classical methods and approaches, as described in SP 33-101-2003 «Determination of Basic Hydrological Characteristics», and is highly time-consuming, both in terms of search and acquisition of hydrological data and the time-consuming computational procedure. At the same time, modern progress in hydrological modeling allows us to look at the solution to this problem from a different angle. Thus, in this paper we propose to reduce the problem of determining the characteristics of river runoff in the absence of observation data to a simpler problem of determining the characteristics of river runoff in the presence of observation data. For this purpose, we propose to use regional gridded reanalysis of river runoff R5 as a source of model-based runoff approximation in the river gauges of interest. The results of the performed calculations show high potential of the proposed approach for determining the characteristics of river runoff. Thus, for the studied Upper Volga river basins the deviations of the calculated values from the reference data of the State Hydrological Institute are negligeable. R5 runoff reanalysis was developed using freely distributed software and open data sources – GR4J conceptual hydrological model and WFDEI meteorological reanalysis. The R5 database covers both a large geographical domain within the European territory of Russia and a long time period (1979–2016) and is freely available at: https://zenodo.org/record/2600695.
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Antonenkov, D. A. "Technical Means and Methods for Studying the Fine Structure of a Water Flow using Video Registration." Ekologicheskaya bezopasnost' pribrezhnoy i shel'fovoy zon morya, no. 3 (September 2021). http://dx.doi.org/10.22449/2413-5577-2021-3-123-131.
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The article discusses developed technical means and methods allowing to obtain in situ spatial distribution of velocity of suspended particle movement by a water flow. A brief description is given of the developed measuring complex based on video recording of the aquatic environment. The method of performing full-scale experiments by the example of study of sea estuaries is described. The article presents measurement results and examples of spatial distributions of flow velocities required for solving various problems related to mass transfer, coast erosion and silting of water areas. Analysis of expedition research results obtained using the developed complex allowed to create methodical approaches to assess the current state of a water body and informative parameters of its state using a visualization method. Based on the created methods, data on the fine structure of the flow in the Chernaya River estuary area were obtained. Comparison of the averaged data on the water flow velocity with the measurement results obtained through standard technical means, namely a hydrometric current meter, showed minimal discrepancies not exceeding 11 %. The developed method and technical means make it possible to study the water flow dynamics and to describe the flow fine structure, which is necessary for studying the mechanisms of erosion, sediment accumulation and transport.
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Birkhead, AL, CA Brown, AR Joubert, A. Singh, and T. Tlou. "The Pongola Floodplain, South Africa – Part 1: Two-dimensional hydrodynamic modelling in support of an environmental flows assessment." Water SA 44, no. 4 October (October 31, 2018). http://dx.doi.org/10.4314/wsa.v44i4.21.
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The Pongola Floodplain in the Makhathini Flats is an area of low topographic relief between the 1973-commissioned Jozini Dam, and the Usuthu River which borders Mozambique. The floodplain system is characterised by a complex mosaic of meandering river channels, levees, and floodplains interspersed with pans (or depressions) and wetlands. The landmark 1982 study of the floodplain, Man and the Pongolo Floodplain, suggested a pattern of flows to ‘maintain the floodplain’ based on socio-ecological criteria. Since 1998, however, annual releases have been primarily targeted at the needs of recession agriculture and inundation of the floodplain in the Ndumu Reserve. No releases have been specifcally aimed at maintaining the floodplain ecosystem and the services it delivers to support the livelihoods of local communities. In 2013, the Department of Water and Sanitation commissioned an Ecological Reserve study of the Usuthu/Mhlatuze Water Management Area, which incorporates the Pongola Floodplain. This paper describes two-dimensional hydrodynamic modelling using RMA2 to inform this flow assessment. Four computational Pongola Floodplain models have been developed since 1979, including cell-based, one- and two-dimensional approaches. The RMA2 model is based on existing topographical, hydrological and hydraulic information, and was calibrated and verifed for the period 2008 to 2010 using water-level data from the local hydrometric monitoring network. Generally, good replications have been achieved in terms of peaks, rising and recession limbs, recession of ponded pan water-levels, and low-flow river stages. The RMA2 modelling represents an advancement of previous hydrodynamic studies of the floodplain and contributes to an improved understanding of its hydraulic behaviour. Model application was for the 15-year period 1990 to 2004, and simulations included naturalised, present management (2014), and 7 potential dam operational scenarios. The results were post-processed for analyses in the DRIFT DSS, described in the companion paper.
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Soekarno, Indratmo. "CIRATA RESERVOIR LIFETIME PREDICTION USING NEW HYDROMETRICS AND SEDIMENT APPROACHES." International Journal of GEOMATE 18, no. 65 (January 1, 2020). http://dx.doi.org/10.21660/2019.64.18023.
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