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

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.

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

10.22541/au.163257216.63565600/v1 ◽

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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Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

10.5194/hess-2016-282 ◽

2016 ◽

Cited By ~ 3

Author(s):

Xiaomang Liu ◽

Tiantian Yang ◽

Koulin Hsu ◽

Changming Liu ◽

Soroosh Sorooshian

Keyword(s):

Tibetan Plateau ◽

River Basin ◽

Yangtze River ◽

Yellow River ◽

Yangtze River Basin ◽

Information Source ◽

Hydrologic Model ◽

The Tibetan Plateau ◽

Climate Data ◽

Streamflow Simulation

Abstract. On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable observation alternatives for investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), is used as input of a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River Basin on the Tibetan Plateau. The results show that the simulated streamflow using PERSIANN-CDR precipitation is closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River Basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River Basin, PERSIANN-CDR precipitation and gauge-based precipitation have similar good performance in simulating streamflow. The evaluation of streamflow simulation capability in this study partly indicates that PERSIANN-CDR rainfall product has good potentials to be a reliable dataset and an alternative information source besides the sparse gauge network for conducting long term hydrological and climate studies on the Tibetan Plateau.

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Comparing bias correction methods in downscaling meteorological variables for hydrologic impact study in an arid area in China

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-12659-2014 ◽

2014 ◽

Vol 11 (11) ◽

pp. 12659-12696 ◽

Cited By ~ 4

Author(s):

G. H. Fang ◽

J. Yang ◽

Y. N. Chen ◽

C. Zammit

Keyword(s):

Water Resources ◽

River Basin ◽

Bias Correction ◽

Meteorological Data ◽

Hydrologic Model ◽

Temperature Correction ◽

Arid Area ◽

Tarim River ◽

Tarim River Basin ◽

Impact Study

Abstract. Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River Basin, Northwest China, and expected to be vulnerable to climate change. Regional Climate Models (RCM) have been proved to provide more reliable results for regional impact study of climate change (e.g. on water resources) than GCM models. However, it is still necessary to apply bias correction before they are used for water resources research due to often considerable biases. In this paper, after a sensitivity analysis on input meteorological variables based on Sobol' method, we compared five precipitation correction methods and three temperature correction methods to the output of a RCM model with its application to the Kaidu River Basin, one of the headwaters of the Tarim River Basin. Precipitation correction methods include Linear Scaling (LS), LOCal Intensity scaling (LOCI), Power Transformation (PT), Distribution Mapping (DM) and Quantile Mapping (QM); and temperature correction methods include LS, VARIance scaling (VARI) and DM. These corrected precipitation and temperature were compared to the observed meteorological data, and then their impacts on streamflow were also compared by driving a distributed hydrologic model. The results show: (1) precipitation, temperature, solar radiation are sensitivity to streamflow while relative humidity and wind speed are not, (2) raw RCM simulations are heavily biased from observed meteorological data, which results in biases in the simulated streamflows, and all bias correction methods effectively improved theses simulations, (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g. SD, percentile values) while LOCI method performed best in terms of the time series based indices (e.g. Nash–Sutcliffe coefficient, R2), (4) for temperature, all bias correction methods performed equally well in correcting raw temperature. (5) For simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with these of corrected precipitation, i.e. PT and QM methods performed equally best in correcting flow duration curve and peak flow while LOCI method performed best in terms of the time series based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other area and other models.

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Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

Hydrology and Earth System Sciences ◽

10.5194/hess-11-1373-2007 ◽

2007 ◽

Vol 11 (4) ◽

pp. 1373-1390 ◽

Cited By ~ 131

Author(s):

D. Sharma ◽

A. Das Gupta ◽

M. S. Babel

Keyword(s):

River Basin ◽

Bias Correction ◽

Hydrological Modeling ◽

Correction Method ◽

Hydrologic Model ◽

Global Climate Models ◽

Model Parameters ◽

Bias Correction Method ◽

Spatial Disaggregation ◽

Grid Nodes

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

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Integrated numerical model for irrigated area water resources management

Journal of Water and Climate Change ◽

10.2166/wcc.2019.042 ◽

2019 ◽

Vol 11 (4) ◽

pp. 980-991 ◽

Cited By ~ 4

Author(s):

Aidi Huo ◽

Xiaofan Wang ◽

Yan Liang ◽

Cheng Jiang ◽

Xiaolu Zheng

Keyword(s):

River Basin ◽

Water Resource Management ◽

Assessment Tool ◽

Swat Model ◽

Hydrologic Model ◽

Heihe River Basin ◽

Irrigation District ◽

Heihe River ◽

Model Parameters ◽

Long Term Effects

Abstract The likelihood of future global water shortages is increasing and further development of existing operational hydrologic models is needed to maintain sustainable development of the ecological environment and human health. In order to quantitatively describe the water balance factors and transformation relations, the objective of this article is to develop a distributed hydrologic model that is capable of simulating the surface water (SW) and groundwater (GW) in irrigation areas. The model can be used as a tool for evaluating the long-term effects of water resource management. By coupling the Soil and Water Assessment Tool (SWAT) and MODFLOW models, a comprehensive hydrological model integrating SW and GW is constructed. The hydrologic response units for the SWAT model are exchanged with cells in the MODFLOW model. Taking the Heihe River Basin as the study area, 10 years of historical data are used to conduct an extensive sensitivity analysis on model parameters. The developed model is run for a 40-year prediction period. The application of the developed coupling model shows that since the construction of the Heihe reservoir, the average GW level in the study area has declined by 6.05 m. The model can accurately simulate and predict the dynamic changes in SW and GW in the downstream irrigation area of Heihe River Basin and provide a scientific basis for water management in an irrigation district.

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Assessing the impacts of hydrologic and land use alterations on water temperature in the Farmington River basin in Connecticut

Hydrology and Earth System Sciences ◽

10.5194/hess-23-4491-2019 ◽

2019 ◽

Vol 23 (11) ◽

pp. 4491-4508 ◽

Cited By ~ 5

Author(s):

John R. Yearsley ◽

Ning Sun ◽

Marisa Baptiste ◽

Bart Nijssen

Keyword(s):

Land Use ◽

Water Temperature ◽

River Basin ◽

Land Surface ◽

Canopy Cover ◽

River System ◽

Stream Temperature ◽

Hydrologic Model ◽

Riparian Buffers ◽

Connecticut River

Abstract. Aquatic ecosystems can be significantly altered by the construction of dams and modification of riparian buffers, and the effects are often reflected in spatial and temporal changes to water temperature. To investigate the implications for water temperature of spatially and temporally varying riparian buffers and dam-induced hydrologic alterations, we have implemented a modeling system (DHSVM-RBM) within the framework of the state-space paradigm that couples a spatially distributed land surface hydrologic model, DHSVM, with the distributed stream temperature model, RBM. The basic modeling system has been applied previously to several similar-sized watersheds. However, we have made enhancements to DHSVM-RBM that simulate spatial heterogeneity and temporal variation (i.e., seasonal changes in canopy cover) in riparian vegetation, and we included additional features in DHSVM-RBM that provide the capability for simulating the impacts of reservoirs that may develop thermal stratification. We have tested the modeling system in the Farmington River basin in the Connecticut River system, which includes varying types of watershed development (e.g., deforestation and reservoirs) that can alter the streams' hydrologic regime and thermal energy budget. We evaluated streamflow and stream temperature simulations against all available observations distributed along the Farmington River basin. Results based on metrics recommended for model evaluation compare well to those obtained in similar studies. We demonstrate the way in which the model system can provide decision support for watershed planning by simulating a limited number of scenarios associated with hydrologic and land use alterations.

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