scholarly journals Assessment of Long-term Groundwater Use Increase and Forest Growth Impact on Watershed Hydrology

2021 ◽  
Author(s):  
Wonjin Kim ◽  
Seongjoon Kim ◽  
Jinuk Kim ◽  
Jiwan Lee ◽  
Soyoung Woo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Assessment Tool ◽  
Model Performance ◽  
Forest Growth ◽  
Coefficient Of Determination ◽  
Observation Data ◽  
Watershed Hydrology ◽  
Groundwater Use ◽  
Two Factors ◽  
The Impact

Abstract This study used Soil and Water Assessment Tool (SWAT) to investigate the impacts of groundwater use increase and forest growth on the watershed hydrology of Geum River basin (9,645.5 km2), South Korea. Groundwater use increase and forest growth data from 1976 to 2015 were prepared in 10-year interval and were reflected to SWAT corresponding to each decade. SWAT was calibrated in the aspect of evapotranspiration, soil moisture, and streamflow using the observation data. The model performance for streamflow was evaluated by coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS). The calibration achieved the average R2 value of 0.73 ~ 0.82, NSE value of 0.75 ~ 0.81, RMSE value of 0.53 ~ 2.35 mm/day, and PBIAS value of -2.51 ~ + 11.74%, respectively. The model performance for evapotranspiration and soil moisture was evaluated by R2. The calibration result of evapotranspiration and soil moisture achieved average R2 value of 0.45 and 0.44, respectively. The calibrated model evaluated the impact of two factors on watershed hydrology. Decadal increase of groundwater use has decreased groundwater flow and increased groundwater recharge while decadal forest growth has mainly increased evapotranspiration that led to the decrease of other hydrological components. Resultingly, the change of two factors have imposed temporal decrease of total runoff on the watershed while the influence of two factors on annual streamflow loss was bigger in lower flow rate.

scholarly journals Assessment of Long-term Groundwater Abstraction and Forest Growth Impacts on Watershed Hydrology Using SWAT

2021 ◽  
Author(s):  
Wonjin Kim ◽  
Jinuk Kim ◽  
Soyoung Woo ◽  
Jiwan Lee ◽  
Sehoon Kim ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Weather Condition ◽  
Forest Growth ◽  
Influential Factor ◽  
Coefficient Of Determination ◽  
Observation Data ◽  
Watershed Hydrology ◽  
Groundwater Abstraction ◽  
Total Runoff ◽  
The Impact

This study used Soil and Water Assessment Tool (SWAT) to investigate the impacts of groundwater abstraction and forest growth on the watershed hydrology of Geum River basin (9,645.5 km2), South Korea. Groundwater abstraction (GA) and forest growth (FG) data from 1976 to 2015 (1980s;1976~1985, 1990s; 1986~1995, 2000s; 1996~2005, 2010s;2006~2015) were prepared with 10-year interval as SWAT input data, respectively. SWAT was calibrated (2006~2015) using daily observation data from two multipurpose dams and three multifunction weirs. The dam and weir calibration result showed coefficient of determination (R2) of 0.78, 0.81, Nash-Sutcliffe efficiency (NSE) of 0.79, 0.76, root mean square error (RMSE) of 1.96 mm/day, 0.55 mm/day, and PBIAS of -5.48%, 8.56%, respectively. The SWAT ran at each decade using corresponding GA and FG information under the same weather condition of the 2010s to evaluate the impact of GA and FG on hydrologic cycle. Influenced by both GA and FG, the streamflow at the watershed outlet showed the decrease of 1.3% (10.1 mm/year), 4.4% (34.2 mm/year), and 7.8% (60.3 mm/year) in the 1990s, the 2000s, and the 2010s, respectively. The hydrologic response of surface runoff, lateral flow, groundwater flow, and soil moisture showed decreasing trend while evapotranspiration and groundwater recharge showed increasing trend. GA imposed bigger influence on the spatial and temporal loss of streamflow than FG. Especially, it was discovered that the agricultural water use from groundwater was the most influential factor that has decreased total runoff in the target watershed for the last four decades.

scholarly journals Evaluating the impact of spatial variability of precipitation on streamflow simulation using a SWAT model

Water Policy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 178-196 ◽  
Author(s):  
Feng Xue ◽  
Peng Shi ◽  
Simin Qu ◽  
Jianjin Wang ◽  
Yanming Zhou
Keyword(s):  
Spatial Variability ◽  
Assessment Tool ◽  
Swat Model ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Interpolation Methods ◽  
Monthly Streamflow ◽  
Areal Precipitation ◽  
The Difference ◽  
The Impact

Abstract The spatial variability of precipitation is often considered to be a major source of uncertainty for hydrological models. The widely used Soil and Water Assessment Tool (SWAT) is insufficient to calculate a sub-basin's mean areal precipitation (MAP) since it only uses data from the rainfall station nearest to the centroid of each sub-basin. Therefore, Inverse Distance Weighting (IDW), Thiessen Polygons (TP) and Ordinary Kriging (OK) were applied as alternative interpolation methods in this study to calculate sub-basin MAP. The MAP results from the four methods used for the Xixian Basin were quite different in terms of amount and spatial distribution. The SWAT model performance was then assessed at monthly and daily timescales, based on Nash–Sutcliffe efficiency (NSE), the Coefficient of Determination (R2) as well as Percentage Bias (PBIAS) at the basin outlet. The results under different network densities and spatial distributions of gauge stations indicated that the modified MAP models did not have an advantage over the default Nearest Neighbour (NN) method in simulating monthly streamflow. However, the modified areal precipitation obtained through IDW and TP showed relatively high accuracy in simulating daily flows as the applied rainfall stations changed. The difference in terms of estimated rainfall and streamflow in this study confirmed that evaluation of interpolation methods is necessary before building a SWAT model.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

scholarly journals Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 594 ◽  
Author(s):  
Majid Fereidoon ◽  
Manfred Koch ◽  
Luca Brocca
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Hydrological Models ◽  
Streamflow Prediction ◽  
Soil Moisture Data ◽  
Monthly Runoff

Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

scholarly journals Assessing the Impact of Best Management Practices in a Highly Anthropogenic and Ungauged Watershed Using the SWAT Model: A Case Study in the El Beal Watershed (Southeast Spain)

Agronomy ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 576 ◽  
Author(s):  
Adrián López-Ballesteros ◽  
Javier Senent-Aparicio ◽  
Raghavan Srinivasan ◽  
Julio Pérez-Sánchez
Keyword(s):  
Coastal Lagoon ◽  
Best Management Practices ◽  
Management Practices ◽  
Assessment Tool ◽  
Swat Model ◽  
Fertilizer Application ◽  
Coefficient Of Determination ◽  
Check Dam ◽  
Best Management ◽  
The Impact

Best management practices (BMPs) provide a feasible solution for non-point source pollution problems. High sediment and nutrient yields without retention control result in environmental deterioration of surrounding areas. In the present study, the soil and water assessment tool (SWAT) model was developed for El Beal watershed, an anthropogenic and ungauged basin located in the southeast of Spain that drains into a coastal lagoon of high environmental value. The effectiveness of five BMPs (contour planting, filter strips, reforestation, fertilizer application and check dam restoration) was quantified, both individually and in combination, to test their impact on sediment and nutrient reduction. For calibration and validation processes, actual evapotranspiration (AET) data obtained from a remote sensing dataset called Global Land Evaporation Amsterdam Model (GLEAM) were used. The SWAT model achieved good performance in the calibration period, with statistical values of 0.78 for Kling–Gupta efficiency (KGE), 0.81 for coefficient of determination (R2), 0.58 for Nash–Sutcliffe efficiency (NSE) and 3.9% for percent bias (PBIAS), as well as in the validation period (KGE = 0.67, R2 = 0.83, NS = 0.53 and PBIAS = −25.3%). The results show that check dam restoration is the most effective BMP with a reduction of 90% in sediment yield (S), 15% in total nitrogen (TN) and 22% in total phosphorus (TP) at the watershed scale, followed by reforestation (S = 27%, TN = 16% and TP = 20%). All effectiveness values improved when BMPs were assessed in combination. The outcome of this study could provide guidance for decision makers in developing possible solutions for environmental problems in a coastal lagoon.

scholarly journals Using the Soil and Water Assessment Tool to Simulate the Pesticide Dynamics in the Data Scarce Guayas River Basin, Ecuador

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 696 ◽  
Author(s):  
Naomi Cambien ◽  
Sacha Gobeyn ◽  
Indira Nolivos ◽  
Marie Anne Eurie Forio ◽  
Mijail Arias-Hidalgo ◽  
...  
Keyword(s):  
River Basin ◽  
Assessment Tool ◽  
Model Development ◽  
Model Performance ◽  
Water Quality Monitoring ◽  
Freshwater Ecosystem ◽  
Set Up ◽  
The Impact ◽  
Water Assessment ◽  
Soil And Water

Agricultural intensification has stimulated the economy in the Guayas River basin in Ecuador, but also affected several ecosystems. The increased use of pesticides poses a serious threat to the freshwater ecosystem, which urgently calls for an improved knowledge about the impact of pesticide practices in this study area. Several studies have shown that models can be appropriate tools to simulate pesticide dynamics in order to obtain this knowledge. This study tested the suitability of the Soil and Water Assessment Tool (SWAT) to simulate the dynamics of two different pesticides in the data scarce Guayas River basin. First, we set up, calibrated and validated the model using the streamflow data. Subsequently, we set up the model for the simulation of the selected pesticides (i.e., pendimethalin and fenpropimorph). While the hydrology was represented soundly by the model considering the data scare conditions, the simulation of the pesticides should be taken with care due to uncertainties behind essential drivers, e.g., application rates. Among the insights obtained from the pesticide simulations are the identification of critical zones for prioritisation, the dominant areas of pesticide sources and the impact of the different land uses. SWAT has been evaluated to be a suitable tool to investigate the impact of pesticide use under data scarcity in the Guayas River basin. The strengths of SWAT are its semi-distributed structure, availability of extensive online documentation, internal pesticide databases and user support while the limitations are high data requirements, time-intensive model development and challenging streamflow calibration. The results can also be helpful to design future water quality monitoring strategies. However, for future studies, we highly recommend extended monitoring of pesticide concentrations and sediment loads. Moreover, to substantially improve the model performance, the availability of better input data is needed such as higher resolution soil maps, more accurate pesticide application rate and actual land management programs. Provided that key suggestions for further improvement are considered, the model is valuable for applications in river ecosystem management of the Guayas River basin.

scholarly journals Performance of HEC-HMS and SWAT to simulate streamflow in the sub-humid tropical Hemavathi catchment

2021 ◽  
Author(s):  
N. C. Sanjay Shekar ◽  
D. C. Vinay
Keyword(s):  
Model Calibration ◽  
Assessment Tool ◽  
Swat Model ◽  
Model Performance ◽  
Curve Number ◽  
Coefficient Of Determination ◽  
Hydrological Models ◽  
Daily Streamflow ◽  
Compensation Factor ◽  
Elevation Model

Abstract The present study was conducted to examine the accuracy and applicability of the hydrological models Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center (HEC)- Hydrologic Modeling System (HMS) to simulate streamflows. Models combined with the ArcGIS interface have been used for hydrological study in the humid tropical Hemavathi catchment (5,427 square kilometer). The critical focus of the streamflow analysis was to determine the efficiency of the models when the models were calibrated and optimized using observed flows in the simulation of streamflows. Daily weather gauge stations data were used as inputs for the models from 2014–2020 period. Other data inputs required to run the models included land use/land cover (LU/LC) classes resulting from remote sensing satellite imagery, soil map and digital elevation model (DEM). For evaluating the model performance and calibration, daily stream discharge from the catchment outlet data were used. For the SWAT model calibration, available water holding capacity by soil (SOL_AWC), curve number (CN) and soil evaporation compensation factor (ESCO) are identified as the sensitive parameters. Initial abstraction (Ia) and lag time (Tlag) are the significant parameters identified for the HEC-HMS model calibration. The models were subsequently adjusted by autocalibration for 2014–2017 to minimize the variations in simulated and observed streamflow values at the catchment outlet (Akkihebbal). The hydrological models were validated for the 2018–2020 period by using the calibrated models. For evaluating the simulating daily streamflows during calibration and validation phases, performances of the models were conducted by using the Nash-Sutcliffe model efficiency (NSE) and coefficient of determination (R2). The SWAT model yielded high R2 and NSE values of 0.85 and 0.82 for daily streamflow comparisons for the catchment outlet at the validation time, suggesting that the SWAT model showed relatively good results than the HEC-HMS model. Also, under modified LU/LC and ungauged streamflow conditions, the calibrated models can be later used to simulate streamflows for future predictions. Overall, the SWAT model seems to have done well in streamflow analysis capably for hydrological studies.

scholarly journals Assessment of the Impact of Land Cover Type on the Water Quality in Lake Tondano Using a SWAT Model

2021 ◽  
Vol 56 (1) ◽  
Author(s):  
Moh Sholichin ◽  
Tri Budi Prayogo
Keyword(s):  
Water Quality ◽  
Land Cover ◽  
Agricultural Land ◽  
Assessment Tool ◽  
Swat Model ◽  
Coefficient Of Determination ◽  
Organic N ◽  
Lake Water Quality ◽  
Total N ◽  
The Impact

Lake Tondano is the largest natural lake in North Sulawesi, Indonesia, which functions as a provider of clean water, hydroelectric power, rice field irrigation, inland fisheries, and tourism. This research aims to determine the effect of land cover types from the Tondano watershed on the lake water quality. The Soil and Water Assessment Tool (SWAT) model was used to evaluate the rate of soil erosion and the pollutant load of various land types in the watershed during the last ten years. Rainfall data is obtained from two rainfall stations, namely Paleloan Station and Noonan Station. The model is calibrated and validated before being used for analysis. We use climatological data from 2014 to 2019. The process of the SWAT model calibration and validation was carried out with the statistical formulas of the coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE). The results show that the potential for pollution load from the Tondao watershed is organic N of 0.039 kg/ha and organic P of 0.006 kg/ha coming from the agricultural land. The results of this study conclude that the fertility conditions of Lake Tondano are at the eutrophic level, where the pollutant inflow is collected in the lake waters, especially for the parameters of total N (1503697.44kg/year) and total P (144831.36kg/year). The SWAT simulation results show deviation between the modeling and field data collected with the value of R2 = 0.9303, and the significant level ≤ 10.

scholarly journals Hydrological model assessment for flood early warning in a tropical high mountain basin

2016 ◽  
Author(s):  
María Carolina Rogelis ◽  
Micha Werner ◽  
Nelson Obregón ◽  
Nigel Wright
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Model Performance ◽  
Distributed Model ◽  
High Mountain ◽  
Model Assessment ◽  
Lumped Model ◽  
Water Storage Capacity ◽  
Soil Moisture Accounting ◽  
The Impact

Abstract. A distributed model (TETIS), a semi-distributed model (TOPMODEL) and a lumped model (HEC HMS soil moisture accounting) were used to simulate the discharge response of a tropical high mountain basin characterized by soils with high water storage capacity and high conductivity. The models were calibrated with the Shuffle Complex Evolution algorithm, using the Kling and Gupta efficiency as objective function. Performance analysis and diagnostics were carried out using the signatures of the flow duration curve and through analysis of the model fluxes in order to identify the most appropriate model for the study area for flood early warning. The impact of varying grid sizes was assessed in the TETIS model and the TOPMODEL in order to chose a model with balanced model performance and computational efficiency. The sensitivity of the models to variation in the precipitation input was analysed by forcing the models with a rainfall ensemble obtained from Gaussian simulation. The resulting discharge ensembles of each model were compared in order to identify differences among models structures. The results show that TOPMODEL is the most realistic model of the three tested, albeit showing the largest discharge ensemble spread. The main differences among models occur between HEC HMS soil moisture accounting and TETIS, and HEC HMS soil moisture accounting and TOPMODEL, with HEC HMS soil moisture accounting producing ensembles in a range lower than the other two models. The ensembles of TETIS and TOPMODEL are more similar.

scholarly journals Assessment and Mitigation of Streamflow and Sediment Yield under Climate Change Conditions in Diyala River Basin, Iraq

Hydrology ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 63 ◽  
Author(s):  
Mahmoud S. Al-Khafaji ◽  
Rana D. Al-Chalabi
Keyword(s):  
Climate Change ◽  
Sediment Yield ◽  
Climate Models ◽  
Assessment Tool ◽  
Coefficient Of Determination ◽  
Base Period ◽  
Important Challenge ◽  
Diyala River ◽  
The Impact ◽  
Water Assessment

The impact of climate change on the streamflow and sediment yield in the Derbendkhan and Hemrin Watersheds is an important challenge facing the water resources of the Diyala River in Iraq. The Soil and Water Assessment Tool (SWAT) was used to project this impact on streamflow and sediment yield until year 2050 by applying five climate models for scenario A1B involving medium emissions. The models were calibrated and validated based on daily observed streamflow and sediment recorded for the periods from 1984 to 2013 and 1984 to 1985, respectively. The Nash–Sutcliffe efficiency and coefficient of determination values for the calibration (validation) were 0.61 (0.53) and 0.6 (0.62) for Derbendkhan and Hemrin, respectively. In addition, the average of the future predictions for the five climate models indicated that the streamflow (sediment yield) for the Derbendkhan and Hemrin Watersheds would decrease to 49% (43.7%) and 20% (30%), respectively, until 2050, compared with the observed flow of the base period from 1984 to 2013. The spatial analysis showed that 10.4% and 68% of the streamflow comes from Iraqi parts of the Derbendkhan and Hemrin Watersheds, respectively, while 10% and 60% of the sediment comes from the Iraqi parts of the Derbendkhan and Hemrin Watersheds, respectively. Deforestation of the northern part of the Hemrin Watershed is the best method to decrease the amount of sediment entering the Hemrin Reservoir.

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