Hydrologic Model for Runoff Simulation of the Kyzyl-Suu River

2021 ◽  
pp. 19-24
Author(s):  
Zheenbek E. Kulenbekov ◽  
Sagynbek Zh. Orunbaev ◽  
A. Zh. Zhumabaev
Keyword(s):  
Hydrologic Model ◽  
Runoff Simulation

scholarly journals Investigating the Applicability of Error Correction Ensembles of Satellite Rainfall Products in River Flow Simulations

2013 ◽  
Vol 14 (4) ◽  
pp. 1194-1211 ◽  
Author(s):  
Viviana Maggioni ◽  
Humberto J. Vergara ◽  
Emmanouil N. Anagnostou ◽  
Jonathan J. Gourley ◽  
Yang Hong ◽  
...  
Keyword(s):  
Error Propagation ◽  
River Flow ◽  
Tropical Rainfall Measuring Mission ◽  
Global Scale ◽  
Hydrologic Model ◽  
Runoff Simulation ◽  
Stochastic Error ◽  
Precipitation Estimation ◽  
Satellite Rainfall ◽  
The Impact

Abstract This study uses a stochastic ensemble-based representation of satellite rainfall error to predict the propagation in flood simulation of three quasi-global-scale satellite rainfall products across a range of basin scales. The study is conducted on the Tar-Pamlico River basin in the southeastern United States based on 2 years of data (2004 and 2006). The NWS Multisensor Precipitation Estimator (MPE) dataset is used as the reference for evaluating three satellite rainfall products: the Tropical Rainfall Measuring Mission (TRMM) real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). Both ground-measured runoff and streamflow simulations, derived from the NWS Research Distributed Hydrologic Model forced with the MPE dataset, are used as benchmarks to evaluate ensemble streamflow simulations obtained by forcing the model with satellite rainfall corrected using stochastic error simulations from a two-dimensional satellite rainfall error model (SREM2D). The ability of the SREM2D ensemble error corrections to improve satellite rainfall-driven runoff simulations and to characterize the error variability of those simulations is evaluated. It is shown that by applying the SREM2D error ensemble to satellite rainfall, the simulated runoff ensemble is able to envelope both the reference runoff simulation and observed streamflow. The best (uncorrected) product is 3B42RT, but after applying SREM2D, CMORPH becomes the most accurate of the three products in the prediction of runoff variability. The impact of spatial resolution on the rainfall-to-runoff error propagation is also evaluated for a cascade of basin scales (500–5000 km2). Results show a doubling in the bias from rainfall to runoff at all basin scales. Significant dependency to catchment area is exhibited for the random error propagation component.

Performance of SWAT Hydrologic Model for Runoff Simulation in Wang River Basin

2018 ◽  
Vol 28 (4) ◽  
Author(s):  
Pongwatana Sangkatananon ◽  
Chakrit Chotamonsak ◽  
Puangpetch Dhanasin
Keyword(s):  
River Basin ◽  
Hydrologic Model ◽  
Runoff Simulation

scholarly journals IMPACT OF LAND USE AND LAND COVER CHANGES ON RUNOFF PREDICTION IN OGBESE RIVER WATERSHED

2021 ◽  
Vol 15 (2) ◽  
pp. 297-308
Author(s):  
Obinna Obiora-Okeke
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Land Cover ◽  
Markov Chain Model ◽  
Hydrologic Model ◽  
Landsat 8 ◽  
Chain Model ◽  
Runoff Simulation ◽  
Flow Rates ◽  
Lulc Changes

Land use and land cover (LULC) changes in Ogbese watershed due to urbanization implies increased areas of low infiltration. This results to higher flow rates downstream the watershed. This study estimates the changes in peak flow rates at the watershed’s outlet for present and future LULC. Rainfall-runoff simulation was achieved with Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) version 4.2 while future LULC was projected with Markov Chain model. Rainfall inputs to the hydrologic model were obtained from intensity-duration-frequency curves for Ondo state. Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 were used to generate LULC images for the years 2002, 2015 and 2019. Six LULC classes were extracted as follows: built up area, bare surface, vegetation, wetland, rock outcrop and waterbody.  Future LULC in year 2025 and 2029 were projected with Markov Chain model. The model prediction was verified with Nash Sutcliffe Efficiency index (NSE). NSE value of 0.79 was calculated indicating LULC changes in the watershed was Markovian. Results show that built up area cover in 2019 is projected to increase by 26.1% in 2024 and 39.9% in 2029 and wetland is projected to decreased by 1.2% in 2024 and 2.3% by 2029. Runoff peaks for these LULC projections indicate increase by 0.24% in 2024 and 1.19% in 2029 at the watershed’s outlets for 100-year return period rainfall.

scholarly journals Integration of SRTM and TRMM date into the GIS-based hydrological model for the purpose of flood modelling

2012 ◽  
Vol 9 (4) ◽  
pp. 4747-4775 ◽  
Author(s):  
A. Akbari ◽  
A. Abu Samah ◽  
F. Othman
Keyword(s):  
Cell Size ◽  
Relevant Literature ◽  
Hydrologic Model ◽  
Flood Modeling ◽  
Runoff Simulation ◽  
M Cell ◽  
Modeling Tools ◽  
Watershed Delineation ◽  
Statistical Measures ◽  
Elevation Data

Abstract. Due to land use and climate changes, more severe and frequent floods occur worldwide. Flood simulation as the first step in flood risk management can be robustly conducted with integration of GIS, RS and flood modeling tools. The primary goal of this research is to examine the practical use of public domain satellite data and GIS-based hydrologic model. Firstly, database development process is described. GIS tools and techniques were used in the light of relevant literature to achieve the appropriate database. Watershed delineation and parameterizations were carried out using cartographic DEM derived from digital topography at a scale of 1:25 000 with 30 m cell size and SRTM elevation data at 30 m cell size. The SRTM elevation dataset is evaluated and compared with cartographic DEM. With the assistance of statistical measures such as Correlation coefficient (r), Nash-Sutcliffe efficiency (NSE), Percent Bias (PBias) or Percent of Error (PE). According to NSE index, SRTM-DEM can be used for watershed delineation and parameterization with 87% similarity with Topo-DEM in a complex and underdeveloped terrains. Primary TRMM (V6) data was used as satellite based hytograph for rainfall-runoff simulation. The SCS-CN approach was used for losses and kinematic routing method employed for hydrograph transformation through the reaches. It is concluded that TRMM estimates do not give adequate information about the storms as it can be drawn from the rain gauges. Event-based flood modeling using HEC-HMS proved that SRTM elevation dataset has the ability to obviate the lack of terrain data for hydrologic modeling where appropriate data for terrain modeling and simulation of hydrological processes is unavailable. However, TRMM precipitation estimates failed to explain the behavior of rainfall events and its resultant peak discharge and time of peak.

scholarly journals Investigating the Dynamic Influence of Hydrological Model Parameters on Runoff Simulation Using Sequential Uncertainty Fitting-2-Based Multilevel-Factorial-Analysis Method

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1177 ◽  
Author(s):  
Shuai Zhou ◽  
Yimin Wang ◽  
Jianxia Chang ◽  
Aijun Guo ◽  
Ziyan Li
Keyword(s):  
Bulk Density ◽  
Hydrological Model ◽  
Latin Hypercube Sampling ◽  
Soil Bulk Density ◽  
Hydrologic Model ◽  
Factorial Analysis ◽  
Interactive Effects ◽  
Model Parameters ◽  
Analysis Method ◽  
Runoff Simulation

Hydrological model parameters are generally considered to be simplified representations that characterize hydrologic processes. Therefore, their influence on runoff simulations varies with climate and catchment conditions. To investigate the influence, a three-step framework is proposed, i.e., a Latin hypercube sampling (LHS-OAT) method multivariate regression model is used to conduct parametric sensitivity analysis; then, the multilevel-factorial-analysis method is used to quantitatively evaluate the individual and interactive effects of parameters on the hydrologic model output. Finally, analysis of the reasons for dynamic parameter changes is performed. Results suggest that the difference in parameter sensitivity for different periods is significant. The soil bulk density (SOL_BD) is significant at all times, and the parameter Soil Convention Service (SCS) runoff curve number (CN2) is the strongest during the flood period, and the other parameters are weaker in different periods. The interaction effects of CN2 and SOL_BD, as well as effective hydraulic channel conditions (CH_K2) and SOL_BD, are obvious, indicating that soil bulk density can impact the amount of loss generated by surface runoff and river recharge to groundwater. These findings help produce the best parameter inputs and improve the applicability of the model.

scholarly journals Stepwise prediction of runoff using proxy data in a small agricultural catchment

2021 ◽  
Vol 69 (1) ◽  
pp. 65-75
Author(s):  
Borbála Széles ◽  
Juraj Parajka ◽  
Patrick Hogan ◽  
Rasmiaditya Silasari ◽  
Lovrenc Pavlin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Model Calibration ◽  
Agricultural Land ◽  
Pearson Correlation ◽  
Hydrologic Model ◽  
State Variables ◽  
Runoff Generation ◽  
Runoff Simulation ◽  
Proxy Data ◽  
Simulation Performance

AbstractIn this study, the value of proxy data was explored for calibrating a conceptual hydrologic model for small ungauged basins, i.e. ungauged in terms of runoff. The study site was a 66 ha Austrian experimental catchment dominated by agricultural land use, the Hydrological Open Air Laboratory (HOAL). The three modules of a conceptual, lumped hydrologic model (snow, soil moisture accounting and runoff generation) were calibrated step-by-step using only proxy data, and no runoff observations. Using this stepwise approach, the relative runoff volume errors in the calibration and first and second validation periods were –0.04, 0.19 and 0.17, and the monthly Pearson correlation coefficients were 0.88, 0.71 and 0.64, respectively. By using proxy data, the simulation of state variables improved compared to model calibration in one step using only runoff data. Using snow and soil moisture information for model calibration, the runoff model performance was comparable to the scenario when the model was calibrated using only runoff data. While the runoff simulation performance using only proxy data did not considerably improve compared to a scenario when the model was calibrated on runoff data, the more accurately simulated state variables imply that the process consistency improved.

scholarly journals Comparison of Rainfall-Runoff Simulation between Support Vector Regression and HEC-HMS for a Rural Watershed in Taiwan

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 191
Author(s):  
Shen Chiang ◽  
Chih-Hsin Chang ◽  
Wei-Bo Chen
Keyword(s):  
Support Vector Regression ◽  
Hydrologic Model ◽  
Data Driven ◽  
Support Vector ◽  
Features Selection ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Physically Based ◽  
Model Training ◽  
One Year

To better understand the effect and constraint of different data lengths on the data-driven model training for the rainfall-runoff simulation, the support vector regression (SVR) approach was applied to the data-driven model as the core algorithm in the present study. Various features selection strategies and different data lengths were employed in the training phase of the model. The validated results of the SVR were compared with the rainfall-runoff simulation derived from a physically based hydrologic model, the Hydrologic Modeling System (HEC-HMS). The HEC-HMS was considered a conventional approach and was also calibrated with a dataset period identical to the SVR. Our results showed that the SVR and HEC-HMS models could be adopted for short and long periods of rainfall-runoff simulation. However, the SVR model estimated the rainfall-runoff relationship reasonably well even if the observational data of one year or one typhoon event was used. In contrast, the HEC-HMS model needed more parameter optimization and inference processes to achieve the same performance level as the SVR model. Overall, the SVR model was superior to the HEC-HMS model in the performance of the rainfall-runoff simulation.

scholarly journals Effects of soil depth spatial variation on runoff simulation, using the Limburg Soil Erosion Model (LISEM), a case study in Faucon catchment, France

2012 ◽  
Vol 7 (No. 2) ◽  
pp. 52-63 ◽  
Author(s):  
P. Rahimy
Keyword(s):  
Overland Flow ◽  
Soil Depth ◽  
Spatial Prediction ◽  
Hydrologic Model ◽  
Runoff Simulation ◽  
Channel Network ◽  
Depth Maps ◽  
Cover Class ◽  
Runoff Hydrographs

Soil depth is an important parameter for models of surface runoff. Commonly used models require not only accurate estimates of the parameter but also its realistic spatial distribution. The objective of this study was to use terrain and environmental variables to map soil depth, comparing different spatial prediction methods by their effect on simulated runoff hydrographs. The study area is called Faucon, and it is located in the southeast of the French Alps. An additive linear model of “land cover class” and “overland flow distance to channel network” predicted the soil depth in the best way. Regression kriging (RK) used in this model gave better accuracy than ordinary kriging (OK). The soil depth maps, including conditional simulations, were exported to the hydrologic model of LISEM, where three synthetic rainfall scenarios were used. The hydrographs produced by RK and OK were significantly different only at rainfalls of low intensity or short duration.

scholarly journals Is the Deep-Learning Technique a Completely Alternative for the Hydrological Model?: A Case Study on Hyeongsan River Basin, Korea

2021 ◽  
Author(s):  
Jaewon Kwak ◽  
Heechan Han ◽  
Soojun Kim ◽  
Hung Soo Kim
Keyword(s):  
Deep Learning ◽  
River Basin ◽  
Hydrological Model ◽  
Short Term Memory ◽  
Hydrologic Model ◽  
Hydrologic Models ◽  
Runoff Simulation ◽  
Runoff Modeling ◽  
Learning Technique ◽  
Relationship Of

Abstract It is no doubt that the reliable runoff simulation for proper water resources management is essential. In the past, the runoff was generally modeled from hydrologic models that analyze the rainfall-runoff relationship of the basin. However, since techniques have developed rapidly, it has been attempted to apply especially deep-learning technique for hydrological studies as an alternative to the hydrologic model. The objective of the study is to examine whether the deep-learning technique can completely replace the hydrologic model and show how to improve the performance of runoff simulation using deep-learning technique. The runoff in the Hyeongsan River basin, South Korea from 2013 to 2020 were simulated using two models, 1) Long Short-Term Memory model that is a deep learning technique widely used in the hydrological study and 2) TANK model, and then we compared the runoff modeling results from both models. The results suggested that it is hard to completely replace the hydrological model with the deep-learning technique due to its simulating behavior and discussed how to improve the reliability of runoff simulation results. Also, a method to improve the efficiency of runoff simulation through a hybrid model which is a combination of two approaches, deep-learning technique and hydrologic model was presented.

scholarly journals Application and comparative study of SWAT and LSTNet models on runoff simulation in the Atsuma River basin

2021 ◽  
Author(s):  
Yuechao Chen ◽  
Yue Zhang ◽  
Qing Zhang ◽  
Xue Song ◽  
Jiajia Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
River Basin ◽  
Assessment Tool ◽  
Swat Model ◽  
Hydrologic Model ◽  
Quality Of Data ◽  
Runoff Simulation ◽  
Simulation Research ◽  
Learning Framework ◽  
Special Machine

Accurate runoff simulation is of great importance to understand watershed hydrologic cycle process, effective utilize water resources and respond flood disaster. Hydrologic model is one of the main tools for runoff simulation research and the continuous improvement in Machine Learning offers powerful tools for modeling of hydrologic process. This research took the runoff process of the Atsuma River basin in Hokkaido from 2015 to 2019 as object, proposed a special machine learning framework: Long-and Short-term Time-series Network (LSTNet) for runoff simulation, discussed the accuracy for runoff simulation of LSTNet model with (multivariate LSTNet Model) or without (univariate LSTNet Model) meteorological factors and Soil and Water Assessment Tool (SWAT) model respectively, analyzed the model selection for runoff simulation under different data conditions in the basin. The Nash-Sutcliffe efficiency coefficients (NSE) of the runoff simulation results in the validation (test) period were 0.633 (SWAT model), 0.643 (multivariate LSTNet model), and 0.716 (univariate LSTNet model) respectively. The results show that the accuracies of the two models for runoff simulation in the Atsuma River basin are all very high. SWAT model has prominent advantages in runoff simulation and shortcomings. LSTNet model shows great advantages and potential in runoff simulation. In summary, when target basin’ s data is accurate and complete, the accuracy of SWAT model in runoff simulation is high and stable. When the target basin lacks data or the quality of data is poor, LSTNet model can realize high-precision runoff simulation only based on the measured runoff data, which has a strong application.

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