scholarly journals Hydrological modeling of Upper Ribb watershed, Abbay Basin, Ethiopia.

2020 ◽  
Keyword(s):  
Surface Runoff ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Simulated Data ◽  
Coefficient Of Determination ◽  
Climate Data ◽  
Water Balance Components ◽  
Discharge Data ◽  
Forested Areas

<p>Hydrological modeling of a watershed is necessary for water resources planning and management. The hydrology of upper Ribb watershed has been analyzed using spatially semi-distributed Soil and water assessment tool (SWAT) model. This study aimed to determine the water balance components and its relation with the rainfall which reaches to the surface of the earth. Different spatio-temporal (land use, soil, digital elevation model, climate data, river discharge) data were used for hydrological modelling of Upper Ribb watershed. The applicability of SWAT model in Upper Ribb watershed has been evaluated using coefficient of determination (R2) and Nash Sutcliff efficiency (NSE) parameters. The calibration results revealed the observed data showed a very good agreement with the simulated data with the R2 and NSE values of 0.90 and 0.84 respectively. Similarly, the validation results of streamflow were acceptable with the R2 and NSE values of 0.80 and 0.82 respectively. The monthly average streamflow from Upper Ribb watershed were found 13.39 m3/s. The major portion of the rainfall contributes to the surface runoff due to the major percentage of the watershed is covered with agricultural lands. The groundwater flow was high in forested areas, while evapotranspiration was found very high in water bodies (Ribb reservoir). In this study area the rainfall showed a direct relationship with the streamflow. The ratio of streamflow and evapotranspiration with rainfall was 0.61 and 0.36 respectively. Due to the presence of high amount of surface runoff and evapotranspiration the deep recharge which contributes to the ground water is not that much significant.</p>

scholarly journals Hydrological modeling using the SWAT Model in urban and peri-urban environments: The case of Kifissos experimental sub-basin (Athens, Greece)

2021 ◽  
Author(s):  
Evgenia Koltsida ◽  
Nikos Mamassis ◽  
Andreas Kallioras
Keyword(s):  
Surface Runoff ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Model Performance ◽  
Curve Number ◽  
Urban Environments ◽  
Time Intervals ◽  
Curve Number Method ◽  
Hourly Rainfall

Abstract. SWAT (Soil and Water Assessment Tool) is a continuous time, semi-distributed river basin model that has been widely used to evaluate the effects of alternative management decisions on water resources. This study, demonstrates the application of SWAT model for streamflow simulation in an experimental basin with daily and hourly rainfall observations to investigate the influence of rainfall resolution on model performance. The model was calibrated for 2018 and validated for 2019 using the SUFI-2 algorithm in the SWAT-CUP program. Daily surface runoff was estimated using the Curve Number method and hourly surface runoff was estimated using the Green and Ampt Mein Larson method. A sensitivity analysis conducted in this study showed that the parameters related to groundwater flow were more sensitive for daily time intervals and channel routing parameters were more influential for hourly time intervals. Model performance statistics and graphical techniques indicated that the daily model performed better than the sub-daily model. The Curve Number method produced higher discharge peaks than the Green and Ampt Mein Larson method and estimated better the observed values. Overall, the general agreement between observations and simulations in both models suggests that the SWAT model appears to be a reliable tool to predict discharge over long periods of time.

scholarly journals A Catchment Scale Assessment of Water Balance Components: A Case Study of Chittar Catchment in South India

2021 ◽  
Author(s):  
Dinagarapandi Pandi ◽  
Saravanan Kothandaraman ◽  
K S Kasiviswanathan ◽  
Mohan Kuppusamy
Keyword(s):  
Water Balance ◽  
Assessment Tool ◽  
Moving Average ◽  
Swat Model ◽  
Meteorological Variables ◽  
Coefficient Of Determination ◽  
Time Interval ◽  
Catchment Scale ◽  
Water Balance Components ◽  
Management And Development

Abstract Analyzing the Water Balance Components (WBCs) of catchment help in assessing the water resources for their sustainable management and development. This paper used Soil and Water Assessment Tool (SWAT) model mainly to analyze the variation in the WBCs through the change in the Land Use and Land Cover (LULC) and meteorological variables. For this purpose, the model used the inputs of LULC and meteorological variables between the year 2001-2020 at five year and daily time interval respectively from the Chittar river catchment. The developed models were evaluated using SWAT-CUP split-up procedure (pre-calibration and post-calibration). The model was found to be good in calibration and validation, yielding the coefficient of determination (R2) of 0.94 and 0.81 respectively. Furthermore, WBCs of the catchment were estimated for the near future (2021 - 2030) at monthly and annual scale. For this endeavour, LULC was forecasted for the year 2021 and 2026 using Celluar Automata (CA)-ANN and for the same period meteorological variables were also forecasted using the smoothing moving average method from the historical data.

scholarly journals Modeling future changes in the North-Estonian hydropower production by using SWAT

2015 ◽  
Vol 47 (4) ◽  
pp. 835-846 ◽  
Author(s):  
Ottar Tamm ◽  
Andres Luhamaa ◽  
Toomas Tamm
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Coefficient Of Determination ◽  
Water Volume ◽  
Discharge Data ◽  
Calibration And Validation ◽  
Hydropower Potential ◽  
Hydropower Production

Climate change is altering temperature, precipitation, and other climatic parameters, affecting sectors dependent on water resources, e.g. energy production. The purpose of this study is to analyze the possible influences of climate change on hydropower potential in North Estonia. In Estonian run-of-river hydropower plants, energy comes mainly from water volume. Thus, changes in hydropower production are related to changes in river runoff. The Soil and Water Assessment Tool (SWAT) model is used to study runoff responses to climate change in Kunda, Keila and Valgejõe river basins. A sequential uncertainty fitting algorithm is used for calibration and validation of hydrological models. Two modeling studies from EURO-CORDEX high-resolution simulations are used: RACMO regional climate model (RCM) from the Netherlands (KNMI) and HIRHAM5 RCM from Denmark (DMI). Hydrological model efficiency is evaluated with coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) and percent bias (PBIAS). The NSE values range from 0.71 to 0.77 during calibration and validation. The PBIAS reveals no significant bias. Daily discharge data of the baseline period (1971–2000) and the future period (2071–2100) for KNMI and DMI scenarios reveal an overall increase in hydropower potential. Larger changes are predicted by the DMI model, while KNMI prediction is lower, 25% and 45% respectively.

scholarly journals Satellite-based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia

2021 ◽  
Vol 13 (2) ◽  
pp. 221
Author(s):  
Jiabin Peng ◽  
Tie Liu ◽  
Yue Huang ◽  
Yunan Ling ◽  
Zhengyang Li ◽  
...  
Keyword(s):  
Central Asia ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Tropical Rainfall Measuring Mission ◽  
Northwest China ◽  
Lake Basin ◽  
Climate Data ◽  
Bosten Lake ◽  
Satellite Precipitation

Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: (1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. (2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. (3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. (4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. (5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone.

scholarly journals The China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) Application in China: A Case Study in Heihe River Basin

2017 ◽  
Author(s):  
Xian-yong Meng ◽  
Hao Wang ◽  
Si-yu Cai ◽  
Xue-song Zhang ◽  
Guo-yong Leng ◽  
...  
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Hydrologic Modeling ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Heihe River Basin ◽  
Heihe River ◽  
Climate Data ◽  
Weather Stations

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data.Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluatedtheir applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 &ndash; 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

scholarly journals Evaluation of the CRU TS3.1, APHRODITE_V1101, and CFSR Datasets in Assessing Water Balance Components in the Upper Vakhsh River Basin in Central Asia

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1334
Author(s):  
Aminjon Gulakhmadov ◽  
Xi Chen ◽  
Manuchekhr Gulakhmadov ◽  
Zainalobudin Kobuliev ◽  
Nekruz Gulahmadov ◽  
...  
Keyword(s):  
Water Balance ◽  
Central Asia ◽  
Observational Data ◽  
River Basin ◽  
Hydrological Modeling ◽  
Swat Model ◽  
Coefficient Of Determination ◽  
Climatic Research Unit ◽  
Water Balance Components ◽  
Monthly Streamflow

In this study, the applicability of three gridded datasets was evaluated (Climatic Research Unit (CRU) Time Series (TS) 3.1, “Asian Precipitation—Highly Resolved Observational Data Integration Toward the Evaluation of Water Resources” (APHRODITE)_V1101, and the climate forecast system reanalysis dataset (CFSR)) in different combinations against observational data for predicting the hydrology of the Upper Vakhsh River Basin (UVRB) in Central Asia. Water balance components were computed, the results calibrated with the SUFI-2 approach using the calibration of soil and water assessment tool models (SWAT–CUP) program, and the performance of the model was evaluated. Streamflow simulation using the SWAT model in the UVRB was more sensitive to five parameters (ALPHA_BF, SOL_BD, CN2, CH_K2, and RCHRG_DP). The simulation for calibration, validation, and overall scales showed an acceptable correlation between the observed and simulated monthly streamflow for all combination datasets. The coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) showed “excellent” and “good” values for all datasets. Based on the R2 and NSE from the “excellent” down to “good” datasets, the values were 0.91 and 0.92 using the observational datasets, CRU TS3.1 (0.90 and 0.90), APHRODITE_V1101+CRU TS3.1 (0.74 and 0.76), APHRODITE_V1101+CFSR (0.72 and 0.78), and CFSR (0.67 and 0.74) for the overall scale (1982–2006). The mean annual evapotranspiration values from the UVRB were about 9.93% (APHRODITE_V1101+CFSR), 25.52% (APHRODITE_V1101+CRU TS3.1), 2.9% (CFSR), 21.08% (CRU TS3.1), and 27.28% (observational datasets) of annual precipitation (186.3 mm, 315.7 mm, 72.1 mm, 256.4 mm, and 299.7 mm, out of 1875.9 mm, 1236.9 mm, 2479 mm, 1215.9 mm, and 1098.5 mm). The contributions of the snowmelt to annual runoff were about 81.06% (APHRODITE_V1101+CFSR), 63.12% (APHRODITE_V1101+CRU TS3.1), 82.79% (CFSR), 81.66% (CRU TS3.1), and 67.67% (observational datasets), and the contributions of rain to the annual flow were about 18.94%, 36.88%, 17.21%, 18.34%, and 32.33%, respectively, for the overall scale. We found that gridded climate datasets can be used as an alternative source for hydrological modeling in the Upper Vakhsh River Basin in Central Asia, especially in scarce-observation regions. Water balance components, simulated by the SWAT model, provided a baseline understanding of the hydrological processes through which water management issues can be dealt with in the basin.

scholarly journals Application of SWAT Model for Assessing Water Availability in Surma River Basin

2019 ◽  
Vol 5 (1) ◽  
pp. 29
Author(s):  
Syeda Zehan Farzana ◽  
Md. Abu Zafor ◽  
Jabed Al Shahariar
Keyword(s):  
Model Simulation ◽  
Water Discharge ◽  
Swat Model ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Water Balance Components ◽  
Time Step ◽  
Discharge Data ◽  
Space Agency ◽  
Physically Based

Water discharge is a significant hydrological parameter because it defines the shape, size and course of the stream. This study was initiated to evaluate the performance and applicability of the physically based SWAT model in analyzing the influence of hydrologic parameters on the streamflow variability and estimation of water balance components at the outlet of Kanaighat streamflow station (SW266) of Surma basin. A 30-m resolution digital elevation model (DEM) has been used to delineate catchment boundary. Land use map obtained from global source GLOBCOVER (Europe Space Agency) has been reclassified to match the SWAT land classes. The model was first calibrated for the period from 2003 to 2008 and then validated for the period from 2009 to 2013 using the observed monthly discharge data. Statistical model performance measures, coefficient of determination (R2) of 0.780, the Nash–Sutcliffe Index (NSI) of 0.47 and Percent bias (PBIAS) of -53.5%, for calibration and 0.878, 0.74 and -31.7%, respectively for validation, indicated good performance of the model simulation on monthly time step. The results showed that SWAT can simulate the hydrologic characteristics of the watershed very well.

scholarly journals Can We Calibrate a Daily Time-Step Hydrological Model Using Monthly Time-Step Discharge Data?

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1750 ◽  
Author(s):  
Soham Adla ◽  
Shivam Tripathi ◽  
Markus Disse
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Swat Model ◽  
Computation Time ◽  
Agricultural Watershed ◽  
Coefficient Of Determination ◽  
Computational Time ◽  
Time Step ◽  
Discharge Data ◽  
Monthly Data

Hydrological models are generally calibrated at longer time-steps (monthly, seasonal, or annual) than their computational time-step (daily), because of better calibration performance, lower computational requirements, and the lack of reliable temporally-fine observed discharge data (particularly in developing countries). The consequences of having different calibration and computation time-steps on model performance have not been extensively investigated. This study uses the Soil and Water Assessment Tool (SWAT) model to explore the correctness of calibrating a hydrological model at the monthly time-step even if the problem statement is suited to monthly modeling. Multiple SWAT models were set up for an agricultural watershed in the Indo-Gangetic basin. The models were calibrated with observed discharge data of different time-steps (daily and monthly) and were validated on data with the same or different time-steps. Intra- and inter-decadal comparisons were conducted to reinforce the results. The models calibrated on monthly data marginally outperformed the models calibrated on daily data when validated on monthly data, in terms of P- f a c t o r , R- f a c t o r , the coefficient of determination ( R 2 ), and Nash–Sutcliffe Efficiency ( N S E ). However, the monthly-calibrated models performed poorly as compared to daily-calibrated models when validated on daily discharge data. Moreover, the daily simulations from the monthly-calibrated models were unrealistic. Analysis of the calibrated parameters revealed that the daily- and monthly-calibrated models differed significantly in terms of parameters governing channel and groundwater processes. Thus, though the monthly-calibrated model captures the patterns in monthly discharge data fairly well, it fails to characterize daily rainfall-runoff processes. The results challenge the existing practice of using different calibration and computation time-steps in hydrological modeling, and suggest that the two time-steps should be the same, irrespective of the time-step required for modeling.

scholarly journals Analysis of Hydrological Characteristics of Blue Nile Basin, Nashe Watershed

2021 ◽  
Vol 11 (24) ◽  
pp. 11791
Author(s):  
Megersa Kebede Leta ◽  
Tamene Adugna Demissie ◽  
Muhammad Waseem
Keyword(s):  
Water Balance ◽  
Spatial Data ◽  
Stream Flow ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Water Yield ◽  
Budget Analysis

Hydrological modeling is a technique for understanding hydrologic characteristics and estimation of the water balance of watersheds for integrated water resources development and management. The Soil and Water Assessment Tool (SWAT) model was used for modeling the hydrological behavior of the Nashe watershed in the north-western part of Ethiopia. The spatial data, daily climate, and stream flow were the required input data for the model. The observed monthly stream flow data at the outlet and selected sub-watersheds in the catchment were used to calibrate and validate the model. The model performance was assessed between the simulated and observed streamflow by using sequential uncertainty fitting-2 (SUFI-2), generalized likelihood uncertainty estimation, parameter solution (Parasol) and particle swarm optimization. The sensitivity of 18 parameters was tested, and the most sensitive parameters were identified. The model performance was evaluated using p and r- factor, coefficient of determination, Nash Sutcliffe coefficient efficiency, percent bias during uncertainty analysis, calibration and validation. Therefore, based on the set of proposed evaluation criteria, the SUFI-2 algorithm has been able to provide slightly more reasonable outcomes and Parasol is the worst compared to the other algorithms. An analysis of monthly and seasonal water balance has been also accomplished for the Nashe catchment. The water balance parameters were distinct for the three seasonal periods in the catchment. The seasonal water budget analysis reveals that the watershed receives around 19%, 69%, and 12% of rainfall through the short rain, long rain and dry seasons, respectively. The received precipitation was lost due to evapotranspiration by 29%, 34% and 37% for each season respectively. The surface runoff contributes to the catchment by 5%, 86% and 9% of the water yield.

scholarly journals Evaluation and Hydrological Application of a Data Fusing Method of Multi-Source Precipitation Products-A Case Study Over Tuojiang River Basin

2021 ◽  
Vol 13 (13) ◽  
pp. 2630
Author(s):  
Yao Li ◽  
Wensheng Wang ◽  
Guoqing Wang ◽  
Siyi Yu
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Weighted Average ◽  
Probability Of Detection ◽  
Coefficient Of Determination ◽  
Maximum Probability ◽  
Satellite Precipitation

Precipitation is an essential driving factor of hydrological models. Its temporal and spatial resolution and reliability directly affect the accuracy of hydrological modeling. Acquiring accurate areal precipitation needs substantial ground rainfall stations in space. In many basins, ground rainfall stations are sparse and uneven, so real-time satellite precipitation products (SPPs) have become an important supplement to ground-gauged precipitation (GGP). A multi-source precipitation fusion method suitable for the Soil and Water Assessment Tool (SWAT) model has been proposed in this paper. First, the multivariate inverse distance similarity method (MIDSM) was proposed to search for the optimal representative precipitation points of GGP and SPPs in sub-basins. Subsequently, the correlation-coefficient-based weighted average method (CCBWA) was presented and applied to calculate the fused multi-source precipitation product (FMSPP), which combined GGP and multiple satellite precipitation products. The effectiveness of the FMSPP was proven over the Tuojiang River Basin. In the case study, three SPPs were chosen as the satellite precipitation sources, namely the Climate Forecast System Reanalysis (CFSR), Tropical Rainfall Measuring Mission Project (TRMM), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network Climate Data Record (PERSIANN-CDR). The evaluation indicators illustrated that FMSPP could capture the occurrence of rainfall events very well, with a maximum Probability of Detection (POD) and Critical Success Index (CSI) of 0.92 and 0.83, respectively. Furthermore, its correlation with GGP, changing in the range of 0.84–0.96, was higher in most sub-basins on the monthly scale than the other three SPPs. These results demonstrated that the performance of FMSPP was the best compared with the original SPPs. Finally, FMSPP was applied in the SWAT model and was found to effectively drive the SWAT model in contrast with a single precipitation source. The FMSPP manifested the highest accuracy in hydrological modeling, with the Coefficient of Determination (R2) of 0.84, Nash Sutcliff (NS) of 0.83, and Percent Bias (PBIAS) of only −1.9%.

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