SWAT modelling and MCDM for spatial valuation in small hydropower planning

Evaluation of Soil and Water Assessment Tool and Artificial Neural Network models for hydrologic simulation in different climatic regions of Asia

The Soil and Water Assessment Tool (SWAT) model has been successfully used to predict alterations in streamflow, evapotranspiration and soil water; however, it is not clear how effective or accurate SWAT is at predicting crop growth. Previous research suggests that while the hydrologic balance in each watershed is accurately simulated with SWAT, the SWAT model over or under predicts crop yield relative to fertilizer inputs. The SWAT model now has three alternative N simulation options: 1) SWAT model with an added flush of N (SWAT-flush); 2) N routines derived from the CENTURY model (SWAT-C); and 3) a one-pool C and N model (SWAT-One). The objective of this study was to evaluate the performance of SWAT-flush, SWAT-C, and SWAT-One as they affect wheat yield prediction. Simulated yields were compared to wheat yields in a 28-year fertilizer/wheat yield study in Lahoma, OK. Simulated yields were correlated with actual 28-year mean yield; however, none of the available N cycling models predicted yearly yields. SWAT-C simulated average yields were closer than other N sub-models to average actual yield. Annually there was a stronger correlation between SWAT-flush and actual yields than the other submodels. However, none of the N-cycling routines were able to accurately predict annual variability in yield at any fertilizer rate. We found that SWAT-C or SWAT-flush are the most viable choices for accurately simulating long-term average wheat yields although annual variations in yield prediction should be taken into consideration. Further research is needed to determine the effectiveness of SWAT-C and SWAT-flush in determining average and annual yield in various farming regions and with numerous agronomic crops.

Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

This study evaluated the performance of two water quality models in accordance to specific tasks designated in the USDA Agricultural Research Service Conservation Effects Assessment Project. The Soil and Water Assessment Tool (SWAT) and the Annualized Agricultural Non-Point Source (AnnAGNPS) models were applied uncalibrated to the Cedar Creek watershed within the St. Joseph River watershed in northeastern Indiana to predict streamflow and atrazine losses. In order to ultimately assess the benefits of conservation practices in agricultural watersheds (which is one of the major goals of the Conservation Effects Assessment Project), proper application of the SWAT and AnnAGNPS models is essential including baseline comparisons made in an uncalibrated mode aimed at eliminating bias due to parameter optimization. Streamflow prediction results show that SWAT model performance was superior to AnnAGNPS, with SWAT model efficiency values ranging from 0.66 to 0.25 and AnnAGNPS model efficiency values ranging from 0.13 to -2.06 for monthly and annual streamflow, respectively. For uncalibrated conditions, neither model was able to adequately simulate atrazine loss concentrations. Overall results suggest that for Conservation Effects Assessment Project modeling applications at the Cedar Creek watershed scale in this study, the use of the SWAT model would be preferable to AnnAGNPS in terms of overall model performance and model support technology (e.g., model interface and documentation).

The Citanduy Watershed is one of the largest watersheds on Java Island and is in critical condition. The Upper Citanduy Sub-watershed is the main sub-watershed in the Citanduy Watershed, which needs attention because it greatly affects the quantity and quality of its water. The Soil and Water Assessment Tool (SWAT) model, which is a hydrological model developed in the United States, can be used to predict the quantity of water. However, the water balance in the SWAT model uses the water balance for plants planted on dry land (up land) and is less suitable for watersheds that have rice fields (low land). For watersheds that have rice fields, pot hole and modified SWAT features have been tried and developed. This study aims to analyze the performance of the SWAT model with original features, pot hole features, and modified features in the QSWAT program to predict water discharge in the Upper Citanduy Sub-watershed. The results of the study indicate that the modified SWAT has the best performance compared to the original SWAT and pot-hole SWAT, as indicated by the NSE and R2 values. The NSE values of the original SWAT, pot hole SWAT, and modified SWAT were 0.70, 0.73, and 0.75, respectively. The R2 values of the modified SWAT, Original SWAT, and pot hole SWAT were 0.94, 0.93, and 0.94, respectively.

Impacts of manure application on SWAT model outputs in the Xiangxi River watershed

Riparian buffers are important features that help to mitigate suspended sediment loads within rivers running through agricultural landscapes. Evaluating their effectiveness for sediment control by different modelling approaches can help direct beneficial management practices. The Soil and Water Assessment Tool (SWAT) model and the Adapted Neuro-Fuzzy Inference System (ANFIS) based model were used for prediction of suspended sediment concentrations (SSC) and sediment loads in the Mill River watershed (PEI, Canada). Those models were then used to assess the impact of riparian buffer widths in reducing sediment loads. The ANFIS model predicted measured SSC more accurately than the SWAT model. The relationship between buffer width and sediment reduction was asymptotic, and the relationship begins to plateau when the width reaches 50 m. Increasing the buffer width from 15 to 100 m led to an increase in sediment loads retention of 30.5% and 36.2% of the total stream sediment load for the SWAT and ANFIS models, respectively. This study highlighted that a data-driven ANFIS based model can be used to simulate the impact of land use changes on the sediment delivery in a river.

The Soil and Water Assessment Tool (SWAT) model was used to assess the impacts of different land use scenarios on hydrological processes in the Fuhe watershed in Poyang Lake Basin, East China. A total of 12 model parameters were calibrated with observed monthly runoff data for 1982-1988 and validated for 1991-1998 for baseline conditions. The baseline test results of R2 and Nash-Sutcliffe model efficiency (NSE) values ranged between 0.88 and 0.94 across the calibration and validation periods, indicating that SWAT accurately replicated the Fuhe watershed streamflow. Several different land use scenarios were then simulated with the model, focusing on the impacts of land use change on the hydrology of the watershed. The results of hypothetical scenario simulations revealed that surface runoff declined while groundwater recharge and evapotranspiration (ET) increased, as forest land, agriculture land and/or grassland areas increased, as well as when paddy field and urban areas decreased. These results further showed that forest land has a higher capacity to conserve the water as compared to pasture land. The results of the real scenario simulations revealed that urbanization is the strongest contributor to changes in surface runoff, water yield, and ET. Urbanization can be considered as a potential major environmental stressor controlling hydrological components. Keywords: SWAT model, land use, streamflow, water budget, scenario simulation, Poyang Lake, Fuhe watershed DOI: 10.3965/j.ijabe.20150803.1132 Online first on [2015-03-17] Citation: Tao C, Chen X L, Lu J Z, Gassman P W, Sabine S, Jose-Miguel S P. Assessing impacts of different land use scenarios on water budget of Fuhe River, China using SWAT model. Int J Agric & Biol Eng, 2015; 8(3): 95－109.

<abstract><title><italic>Abstract. </italic></title> The land use and land cover (LULC) map of a watershed is a critical input to the Soil and Water Assessment Tool (SWAT) model. LULC is a categorical geospatial data layer that is typically developed based on models that establish relationships between pixel-based spectral reflectance and corresponding ground-truth information. Hence, LULC maps, like other classified remote-sensing datasets, are subject to error, which varies for each LULC category. The purpose of this study was to evaluate the effect of published LULC categorical errors on SWAT model uncertainty. A new algorithm was developed within the SWAT2009_LUC tool framework [see Transactions of the ASABE 54(5): 1649-1658] to produce multiple realizations of the LULC layer based on LULC categorical errors and integrate them dynamically within SWAT simulations. The enhanced SWAT2009_LUC tool and algorithm were tested for the SWAT model of the Illinois River Drainage Area in Arkansas (IRDAA) watershed. Tool evaluation results showed that the algorithm successfully integrated the LULC realizations within SWAT runs. Uncertainty evaluation showed that LULC categorical errors produced deviations in water yield output ranging from 0% to 8% at an annual scale and 0% to 19.9% at a monthly scale for the IRDAA subwatersheds. Results from this research highlight the importance of LULC categorical accuracy in the SWAT model and provide a generic tool for examining this uncertainty in any other watershed.

SWAT (Soil and Water Assessment Tool) model was calibrated for the flow rate of the Deachung lake with a large area of 3108.29 . Application of SWAT model requires significant number of input data and is prone to result in uncertainties due to errors in input data, model structure and model parameters. The SUFI-2 (Sequential Uncertainty Fitting Ver. 2) program and GLUE (Generalized Likelihood Uncertainty Estimation) program in SWAT-CUP (SWAT-Calibration and Uncertainty Program) are used to select the best parameters for SWAT model. Optimal combination of parameter values was determined through 2,000 iterative SWAT model runs. The Nash-Sutcliffe values and values were 0.87 and 0.89 respectively indicating both methods show good agreements with observed data successfully. RMSE and MSE values also showed similar results for both programs. It seems the SWAT-CUP has a great practical appeal for parameter optimization especially for large basin area and it also can be used for less experienced SWAT model users.

The impact of climate change on hydrological processes and nutrient input is one of the major uncertainties in projecting future global warming. The Soil and Water Assessment Tool (SWAT) model is applied to simulate the effects of temperature on the hydrology and sediment and nutrient load in the lower Pearl River Basin, South China. Calibration and validation results for SWAT showed that the Yamen estuary is appropriate for simulating the impacts of temperature change on both hydrological processes and nutrient input. The possible temperature change scenarios were set at T–2, T–1.5, T–1, T–0.5, T+0.5, T+1, T+1.5, T+2, T+2.5, T+3°C compared to the current temperature, T°C. The results showed that the relative variation of discharge with temperature was slight. The sediment load at T+3°C is increased by 13.58% compared to that at T°C. Inorganic N and P inputs to the Yamen estuary showed an increasing trend with the temperature increase from T–2°C to T+3°C. Global warming can reduce the vegetation land cover by slowing plant growth, which will aggravate the water and soil loss. Temperature changes would have an effect on mineralization and nitrification by changing soil temperature and temperatures. The warming is projected to cause an increase of sediment and nutrient load into the estuary and accelerate eutrophication in the coastal area. Citation Li, Y., Chen, B.-M., Wang, Z.-G. & Peng, S.-L. (2011) Effects of temperature change on water discharge, and sediment and nutrient loading in the lower Pearl River basin based on SWAT modelling. Hydrol. Sci. J. 56(1), 68–83.

Modelling the hydrology of North American Prairie watersheds is complicated because of the existence of numerous landscape depressions that vary in storage capacity. The Soil and Water Assessment Tool (SWAT) is a widely applied model for long‐term hydrological simulations in watersheds dominated by agricultural land uses. However, several studies show that the SWAT model has had limited success in handling prairie watersheds. In past works using SWAT, landscape depression storage heterogeneity has largely been neglected or lumped. In this study, a probability distributed model of depression storage is introduced into the SWAT model to better handle landscape storage heterogeneity. The work utilizes a probability density function to describe the spatial heterogeneity of the landscape depression storages that was developed from topographic characteristics. The integrated SWAT–PDLD model is tested using datasets for two prairie depression dominated watersheds in Canada: the Moose Jaw River watershed, Saskatchewan; and the Assiniboine River watershed, Saskatchewan. Simulation results were compared to observed streamflow using graphical and multiple statistical criterions. Representation of landscape depressions within SWAT using a probability distribution (SWAT–PDLD) provides improved estimations of streamflow for large prairie watersheds in comparison to results using a lumped, single storage approach. Copyright © 2016 John Wiley &amp; Sons, Ltd.

A large international watershed, the St. Clair‐Detroit River System, containing both extensive urban and agricultural areas, was modeled using the Soil and Water Assessment Tool (SWAT) model. The watershed, located in southeastern Michigan, United States, and southwestern Ontario, Canada, encompasses the St. Clair, Clinton, Detroit (DT), Sydenham (SY), Upper, and Lower Thames subwatersheds. The SWAT input data and model resolution (i.e., hydrologic response units, HRUs), were established to mimic farm boundaries, the first time this has been done for a watershed of this size. The model was calibrated (2007–2015) and validated (2001–2006) with a mix of manual and automatic methods at six locations for flow and water quality at various time scales. The model was evaluated using Nash–Sutcliffe efficiency and percent bias and was used to explore major water quality issues. We showed the importance of allowing key parameters to vary among subwatersheds to improve goodness of fit, and the resulting parameters were consistent with subwatershed characteristics. Agricultural sources in the Thames and SY subwatersheds and point sources from DT subwatershed were major contributors of phosphorus. Spatial distribution of phosphorus yields at HRU and subbasin levels identified locations for potential management targeting for both point and nonpoint sources and revealed that in some subwatersheds nonpoint sources are dominated by urban sources.

Groundwater drought risk assessment in the semi-arid Kansai river basin, West Bengal, India using SWAT and machine learning models

Response of benthic macroinvertebrate communities to climate change