Sequential Uncertainty Fitting Research Articles

A Comparison of SWAT Model Calibration Techniques for Hydrological Modeling in the Ganga River Watershed

Abstract The Ganga River, the longest river in India, is stressed by extreme anthropogenic activity and climate change, particularly in the Varanasi region. Anticipated climate changes and an expanding populace are expected to further impede the efficient use of water. In this study, hydrological modeling was applied to Soil and Water Assessment Tool (SWAT) modeling in the Ganga catchment, over a region of 15 621.612 km2 in the southern part of Uttar Pradesh. The primary goals of this study are: ① To test the execution and applicability of the SWAT model in anticipating runoff and sediment yield; and ② to compare and determine the best calibration algorithm among three popular algorithms—sequential uncertainty fitting version 2 (SUFI-2), the generalized likelihood uncertainty estimation (GLUE), and parallel solution (ParaSol). The input data used in the SWAT were the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Landsat-8 satellite imagery, soil data, and daily meteorological data. The watershed of the study area was delineated into 46 sub-watersheds, and a land use/land cover (LULC) map and soil map were used to create hydrological response units (HRUs). Models utilizing SUFI-2, GLUE, and ParaSol methods were constructed, and these algorithms were compared based on five categories: their objective functions, the concepts used, their performances, the values of P-factors, and the values of R-factors. As a result, it was observed that SUFI-2 is a better performer than the other two algorithms for use in calibrating Indian watersheds, as this method requires fewer runs for a computational model and yields the best results among the three algorithms. ParaSol is the worst performer among the three algorithms. After calibrating using SUFI-2, five parameters including the effective channel hydraulic conductivity (CH_K2), the universal soil-loss equation (USLE) support parameter (USLE_P), Manning’s n value for the main channel (CH_N2), the surface runoff lag time (SURLAG), and the available water capacity of the soil layer (SOL_AWC) were observed to be the most sensitive parameters for modeling the present watershed. It was also found that the maximum runoff occurred in sub-watershed number 40 (SW#40), while the maximum sediment yield was 50 t·a−1 for SW#36, which comprised barren land. The average evapotranspiration for the basin was 411.55 mm·a−1. The calibrated model can be utilized in future to facilitate investigation of the impacts of LULC, climate change, and soil erosion.

Assessing model parameters sensitivity and uncertainty of streamflow, sediment, and nutrient transport using SWAT

The objectives of this study were to (1) simulate streamflow, total sediment (TS), total phosphorus (TP), and total nitrogen (TN) loads; and (2) use the sequential uncertainty fitting (SUFI-2) algorithm to quantify the model parameter sensitivity and uncertainty in simulating streamflow, TS, TP, and TN loads using Soil and Water Assessment (SWAT) model in Big Sunflower River watershed (BSRW). The model was calibrated from 1996 to 2003 and validated from 2004 to 2010 for daily streamflow, TS load, TP load, and TN load. The model performed well simulating daily streamflow (R2 = 0.58–0.75, NSE = 0.47–0.75), TS load (R2 = 0.50–0.72, NSE = 0.47–0.66), and TP load (R2 = 0.79–0.82, NSE = 0.73–0.77), and the model performance was slightly low for TN load (R2 = 0.13–0.31, NSE = −0.09 to 0.07). This study determined that parameter uncertainty was greatest for simulating TN load (p-factor = 0.48, r-factor = 1.25) and that parameter uncertainty was lowest for simulating streamflow (p-factor = 0.70–0.78, r-factor = 1.18–1.19). Output uncertainty was much greater during peak streamflow and peak pollutant loads compared to periods of low streamflow and low pollutant loads. The sensitivity analyses found that streamflow was most sensitive to Manning’s roughness coefficient for the main channel (CH_N2), TS load was most sensitive the peak rate adjustment factor for sediment routing in the tributary channels (ADJ_PKR), TP load was most sensitive to the Phosphorus enrichment ratio for loading with sediments (ERORGP), and TN load was most sensitive to the denitrification exponential rate coefficient (CDN). Uncertainty was found to be much greater during peak streamflow and peak pollutant loads compared to periods of low streamflow and low pollutant loads.

Prediction of sedimentation in reservoirs by combining catchment based model and stream based model with limited data

Estimation of sedimentation in reservoirs helps in the management and design of the reservoir׳s useful capacity. This research was done on the Awash River basin at the Koka Dam Reservoir in Ethiopia. The method applied was the loose integration of the Soil and Water Assessment Tool (SWAT) model and Hydrologic Engineering Center-River Analysis System (HEC-RAS) model for the estimation of the sediment load reaching the reservoir. The SWAT model was used for the estimation of erosion at the catchment level, and the HEC-RAS model was applied to estimate the sediment transport in the river channel. The implemented method allows sedimentation in the floodplains and bed shear stress to be considered in the sediment modeling, which cannot be considered in the SWAT model. In addition, the river cross sectional properties and the hydrodynamic processes in the rivers were considered in the modeling process. The data used in this study are a combination of i) observed data collected by government agencies, ii) data available online in data repositories, and iii) data extracted from remote sensing in the Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM). The calibration and validation of the SWAT model was done by using Sequential Uncertainty Fitting (SUIF-2) calibration and validation tools. The HEC-RAS model was calibrated by adjusting the roughness factor. The output from the integrated approaches gives better estimates of flow and sediment near the inlet to the reservoir, with coefficients of determination of 0.85 and 0.67, respectively, and Nash Sutcliffe coefficients of model fit efficiency of 0.90 and 0.62, respectively, for daily simulations.

Sensitivity and uncertainty analysis for streamflow prediction using multiple optimization algorithms and objective functions: San Joaquin Watershed, California

Uncertainty analysis prior to the model calibration is key to the effective implementation of the hydrologic models. The major application of sensitivity analysis is to indicate the uncertainties in the input parameters of the model, which affects the model performance. There are different optimization algorithms developed and applied in the hydrologic model, which can be performed with different objective functions to calibrate and quantify the uncertainties in the system. The purpose of this study was to evaluate the model calibration performance and sensitivity of parameters using three optimization algorithms and five objective functions for predicting monthly streamflow. Sequential Uncertainty Fitting (SUFI-2), Generalized Likelihood Uncertainty Estimation (GLUE), and Parameter Solution (ParaSol) were used to calibrate the monthly streamflow for the semi-arid San Joaquin Watershed in California by using Soil and Water Assessment Tool (SWAT) model. The best performance metrics (R2, NSE, PBIAS, P-factor, and R-factor) were obtained by SUFI-2 while using NSE as the objective function. The coefficient of determination (R2), Nash–Sutcliffe Efficiency (NSE), the percentage of bias (PBIAS), Kling-Gupta efficiency (KGE) and Ratio of the standard deviation of observations to root mean square error (RSR) were used as an objective function to assess the monthly calibration performance. KGE was found to be a suitable objective function to calibrate this complex and snowmelt-dominated watershed. The findings from this study will serve as a guideline for hydro-ecological researchers to achieve further watershed management goals.

Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China

The China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool model (CMADS) have been widely applied in recent years because of their accuracy. An evaluation of the accuracy and efficiency of the Soil and Water Assessment Tool (SWAT) model and CMADS for simulating hydrological processes in the fan-shaped Lijiang River Basin, China, was carried out. The Sequential Uncertainty Fitting (SUFI-2) algorithm was used for parameter sensitivity and uncertainty analysis at the daily scale. The pair-wise correlation between parameters and the uncertainties associated with equifinality in model parameter estimation were investigated. The results showed that the SWAT model performed well in predicting daily streamflow for the calibration period (2009–2010). The correlation coefficient (R2) was 0.92, and the Nash-Sutcliffe model efficiency coefficient (NSE) was 0.89. For the validation period (2011–2018), R2 = 0.89, NSE = 0.88, and reasonable values for the P-factor, R-factor, and percent bias (PBIAS) were obtained. In addition, the spatial and temporal variation of evapotranspiration (ET), surface runoff, and groundwater discharge were analyzed. The results clearly showed that spatial variation in surface runoff and groundwater discharge are strongly related to precipitation, while ET is largely controlled by land use types. The contributions to the water budget by surface runoff, groundwater discharge, and lateral flow were very different in flood years and dry years.

Evaluation of the Climate Forecast System Reanalysis Weather Data for Watershed Modeling in Upper Awash Basin, Ethiopia

Availability of reliable meteorological data for watershed modeling is one of the considerable challenges in the Awash River Basin in Ethiopia. To overcome this challenge, the Climate Forecast System Reanalysis (CFSR) global weather data was evaluated and compared with the limited conventional weather data available in the Upper Awash Basin. The main objective of this study was to search for an optional data source for hydrological modeling, instead of using the limited available data, and for data-scarce areas of the basin. The Soil and Water Assessment Tool model was used to compare the performance of the two weather datasets at simulating monthly streamflow. For calibration, validation, and uncertainty analysis, the sequential uncertainty fitting algorithm was used. The model evaluation statistics showed that the CFSR global weather data performed similarly to the conventional weather data for simulating the observed streamflow at Melka Kunture. At Keleta, where the conventional data is scarce, the CFSR performed better. The CFSR performance at the two sub-basins indicated that it performed better for the large sub-basin, Melka Kunture. Generally, the CFSR weather data are a good addition to the dataset for areas where no reliable weather data exists for hydrological modeling in the basin. The precipitation data of the CFSR are slightly higher than that of the conventional data, which also resulted in a relatively higher water balance components.

Simulation of surface runoff using semi distributed hydrological model for a part of Satluj Basin: parameterization and global sensitivity analysis using SWAT CUP

The ideal allotment of controlled water assets for diversified schemes is needed for continuous and controlled advancement. In present study SWAT (Soil and Water Assessment Tool), a physically based semi-distributed hydrological model was applied in the Satluj Basin (Suni to Kasol) for hydrologic modeling. 23 sub-basins were demarcated from the study area, which consists of 325 Hydrological Response Units (HRUs) with regard to specific slope, soil and land use combination. SWAT-CUP (SWAT-Calibration and Uncertainty Programs) is one of the new developments for calibration/sensitivity analysis of watershed models that incorporate a semi-automated approach SUFI-2 (Sequential Uncertainty Fitting) which includes both manual and automatic calibration and incorporating global sensitivity analysis. Parameters for Sensitivity Analysis used to measure statistics for goodness-of-fit. For the study area, CN2, SOIL_K, RCHRG_DP, CH_K2 were considered as the most sensitive parameters. The model Calibration (1986–1998) and Validation (1999–2011) were executed with initial 3 years considered as a warm-up period (1983–1985) at Kasol site which is considered as anoutlet of the study area. The statistical performance of the model was assessed using the coefficient of variation (R2), and Nash–Sutcliffe efficiency (NSE), during model Calibration, R² for the daily runoff was obtained as 0.98 with Nash-Sutcliff of 0.96 respectively. The validation also signified satisfactory results with R2 of 0.96 and NSE of 0.94 respectively. The results have shown excellent correlation between observed and simulated discharge on daily time steps. The results and ensuing inferences would be of immense help to the hydrological fraternity and the water resource managers.

Parameter Uncertainty Analysis of the SWAT Model in a Mountain-Loess Transitional Watershed on the Chinese Loess Plateau

Hydrological models play an important role in water resource management, but they always suffer from various sources of uncertainties. Therefore, it is necessary to implement uncertainty analysis to gain more confidence in numerical modeling. The study employed three methods (i.e., Parameter Solution (ParaSol), Sequential Uncertainty Fitting (SUFI2), and Generalized Likelihood Uncertainty Estimation (GLUE)) to quantify the parameter sensitivity and uncertainty of the SWAT (Soil and Water Assessment Tool) model in a mountain-loess transitional watershed—Jingchuan River Basin (JCRB) on the Loess Plateau, China. The model was calibrated and validated using monthly observed streamflow at the Jingchuan gaging station and the modeling results showed that SWAT performed well in the study period in the JCRB. The parameter sensitivity results demonstrated that any of the three methods were capable for the parameter sensitivity analysis in this area. Among the parameters, CN2, SOL_K, and ALPHA_BF were more sensitive to the simulation of peak flow, average flow, and low flow, respectively, compared to others (e.g., ESCO, CH_K2, and SOL_AWC) in this basin. Although the ParaSol method was more efficient in capturing the most optimal parameter set, it showed limited ability in uncertainty analysis due to the narrower 95CI and poor P-factor and R-factor in this area. In contrast, the 95CIs in SUFI2 and GLUE were wider than ParaSol, indicating that these two methods can be promising in analyzing the model parameter uncertainty. However, for the model prediction uncertainty within the same parameter range, SUFI2 was proven to be slightly more superior to GLUE. Overall, through the comparisons of the proposed evaluation criteria for uncertainty analysis (e.g., P-factor, R-factor, NSE, and R2) and the computational efficiencies, SUFI2 can be a potentially efficient tool for the parameter optimization and uncertainty analysis. This study provides an insight into selecting uncertainty analysis method in the modeling field, especially for the hydrological modeling community.

Hydrological Modeling in Data-Scarce Catchments: The Kilombero Floodplain in Tanzania

Deterioration of upland soils, demographic growth, and climate change all lead to an increased utilization of wetlands in East Africa. This considerable pressure on wetland resources results in trade-offs between those resources and their related ecosystem services. Furthermore, relationships between catchment attributes and available wetland water resources are one of the key drivers that might lead to wetland degradation. To investigate the impacts of these developments on catchment-wetland water resources, the Soil and Water Assessment Tool (SWAT) was applied to the Kilombero Catchment in Tanzania, which is like many other East African catchments, as it is characterized by overall data scarcity. Due to the lack of recent discharge data, the model was calibrated for the period from 1958–1965 (R2 = 0.86, NSE = 0.85, KGE = 0.93) and validated from 1966–1970 (R2 = 0.80, NSE = 0.80, KGE = 0.89) with the sequential uncertainty fitting algorithm (SUFI-2) on a daily resolution. Results show the dependency of the wetland on baseflow contribution from the enclosing catchment, especially in dry season. Main contributions with regard to overall water yield arise from the northern mountains and the southeastern highlands, which are characterized by steep slopes and a high share of forest and savanna vegetation, respectively. Simulations of land use change effects, generated with Landsat images from the 1970s up to 2014, show severe shifts in the water balance components on the subcatchment scale due to anthropogenic activities. Sustainable management of the investigated catchment should therefore account for the catchment–wetland interaction concerning water resources, with a special emphasis on groundwater fluxes to ensure future food production as well as the preservation of the wetland ecosystem.

Simulating streamflow in an ungauged catchment of Tonlesap Lake Basin in Cambodia using Soil and Water Assessment Tool (SWAT) model

The study objective of this work is to test the applicability of the Soil and Water Assessment Tool (SWAT) model to simulate the streamflow through calibration and validation for both daily and monthly basis in the Stung Pursat River catchment, an ungauged sub-catchment of Tonle Sap Basin in Cambodia. In order to achieve the objective of the study, SWAT model was set up with study period of 10 years (2001–2010). Additionally, ArcSWAT2012 and ArcGIS10.2.2 combined with Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were used to conduct uncertainty analysis, calibration and validation of the SWAT model using daily observed streamflow data at the catchment outlet. Daily simulation produced the results with Nash-Sutcliffe Simulation Efficiency (NSE) of 0.38, Percent Bias (PBIAS) of +5.1% and Root Mean square error-observation standard deviation Ratio (RSR) of 0.79 in calibration period, and with NSE of −6.61, PBIAS of −78.38% and RSR of 2.67 in validation period. The streamflow results of monthly time series were improved and acceptable with NSE of 0.60 while decrease the value of PBIAS and RSR to 1.14, and 0.63 respectively. The SWAT model should be used to simulate monthly streamflow in such an ungauged catchment like the Stung Pursat catchment due to data scarcity and uncertainty.

SWAT model uncertainty analysis, calibration and validation for runoff simulation in the Luvuvhu River catchment, South Africa

The soil and water assessment tool (SWAT) was calibrated for the Luvuvhu River catchment, South Africa in order to simulate runoff. The model was executed through QSWAT which is an interface between SWAT and QGIS. Data from four weather stations and four weir stations evenly distributed over the catchment were used. The model was run for a 33-year period of 1983–2015. Sensitivity analysis, calibration and validation were conducted using the sequential uncertainty fitting (SUFI-2) algorithm through its interface with SWAT calibration and uncertainty procedure (SWAT-CUP). The calibration process was conducted for the period 1986 to 2005 while the validation process was from 2006 to 2015. Six model efficiency measures were used, namely: coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) index, root mean square error (RMSE)-observations standard deviation ratio (RSR), percent bias (PBIAS), probability (P)-factor and correlation coefficient (R)-factor were used. Initial results indicated an over-estimation of low flows with regression slope of less than 0.7. Twelve model parameters were applied for sensitivity analysis with four (ALPHA_BF, CN2, GW_DELAY and SOL_K) found to be more distinguishable and sensitive to streamflow (p < 0.05). The SUFI-2 algorithm through the interface with the SWAT-CUP was capable of capturing the model's behaviour, with calibration results showing an R2 of 0.63, NSE index of 0.66, RSR of 0.56 and a positive PBIAS of 16.3 while validation results revealed an R2 of 0.52, NSE of 0.48, RSR of 0.72 and PBIAS of 19.90. The model produced P-factor of 0.67 and R-factor of 0.68 during calibration and during validation, 0.69 and 0.53 respectively. Although performance indicators yielded fair and acceptable results, the P-factor was still below the recommended model performance of 70%. Apart from the unacceptable P-factor values, the results obtained in this study demonstrate acceptable model performance during calibration while validation results were still inconclusive. It can be concluded that calibration of the SWAT model yielded acceptable results with exception to validation results. Having said this, the model can be a useful tool for general water resources assessment and not for analysing hydrological extremes in the Luvuvhu River catchment.

Modelling hydrological response under climate change scenarios using SWAT model: the case of Ilala watershed, Northern Ethiopia

This study evaluates surface runoff generation under climate change scenarios for Ilala watershed in Northern highlands of Ethiopia. The climate change scenarios were analyzed using delta based statistical downscaling approach of RCPs 4.5 and 8.5 in R software packages. Hydrological response to climate changes were evaluated using the Soil and Water Assessment Tool model. The Soil Water Analysis Calibration and Uncertainty Program of Sequential Uncertainty fitting version 2 algorithm was also used to compute the uncertainty analysis, calibration and validation process. The results show that the minimum and maximum temperature increases for the future of 1.7 and 4.7 °C respectively. However, the rainfall doesn’t show any significant increase or decrease trend in the study area. The 95% prediction uncertainty brackets the average values of observation by 71 and 74% during the calibration and validation processes, respectively. Similarly, R-factor equals to 0.5 and 0.6 during calibration and validation periods. The simulated and observed hydrographs of the total river yield showed a good agreement during calibration (NSE = 0.51, R2 = 0.54) and validation (NSE = 0.54, R2 = 0.63). From the total rainfall received only 6.2% portion of the rainfall was changed into surface runoff. The rainfall-runoff relationship was strongly correlated with R2 = 0.97. Moreover, there had been also high evapotranspiration (ET) loss in the watershed; almost 75% of the total rainfall was lost as ET and 7.8% as ground water recharge. Due to an increase trend in temperature and evaporation loss for the future, the surface runoff also declined from 1.74% in RCP4.5 near-term to 0.36% in RCP8.5 end-term periods. This implies, proper planning and implementation of appropriate water management strategies is needed for sustainable water resources management in the region.

Assessment of Dam Overtopping Reliability using SUFI Based Overtopping Threshold Curve

Estimation of earth-fill dam height and dimension of spillway are strongly depend on accurate evaluation of dam overtopping reliability. Ideally, whole random and effective variables on overtopping should be considered for dam overtopping reliability assessment. However, taking into account all affecting random variables as well as overtopping reliability assessment using algorithms such as Monte Carlo (MC) could be excessively time consuming and impossible in some cases. In this study, new approach has been introduced as conditional reliability method and it has been tried to reduce the simulations time significantly by using combination of rainfall threshold theory, SUFI (Sequential Uncertainty Fitting) and MC methods. According two defined indexes, the obtain results shown, the relative error of new approach in computation of Jamishan dam overtopping reliability is less than 0.23%. Also, time saving in new method against to regular MC method is more than 87%. So, the new introduced method has more efficiency with acceptable accuracy for assessment of dam overtopping reliability. In addition, among hydrological and hydraulic uncertainties, hydrological uncertainties have more effect on overtopping reliability.

Calibration of Spatially Distributed Hydrological Processes and Model Parameters in SWAT Using Remote Sensing Data and an Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin

In this paper, evapotranspiration (ET) and leaf area index (LAI) were used to calibrate the SWAT model, whereas remotely sensed precipitation and other climatic parameters were used as forcing data for the 6300 km2 Day Basin, a tributary of the Red River in Vietnam. The efficacy of the Sequential Uncertainty Fitting (SUFI-2) parameter sensitivity and optimization model was tested with area specific remote sensing input parameters for every Hydrological Response Units (HRU), rather than with measurements of river flow representing a large set of HRUs, i.e., a bulk calibration. Simulated monthly ET correlations with remote sensing estimates showed an R2 = 0.71, Nash–Sutcliffe Efficiency NSE = 0.65, and Kling Gupta Efficiency KGE = 0.80 while monthly LAI showed correlations of R2 = 0.59, NSE = 0.57 and KGE = 0.83 over a five-year validation period. Accumulated modelled ET over the 5-year calibration period amounted to 5713 mm compared to 6015 mm of remotely sensed ET, yielding a difference of 302 mm (5.3%). The monthly flow at two flow measurement stations were adequately estimated (R2 = 0.78 and 0.55, NSE = 0.71 and 0.63, KGE = 0.59 and 0.75 for Phu Ly and Ninh Binh, respectively). This outcome demonstrates the capability of SWAT model to obtain spatial and accurate simulation of eco-hydrological processes, also when rivers are ungauged and the water withdrawal system is complex.

Uncertainty analysis of hydrological modeling in a tropical area using different algorithms

Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and Rfactor, coefficient of determination (R2), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor>0.83, R-factor 0.91, NSE>0.89, and 0.18<PBIAS<0.32. Hence, we would suggest to use SUFI-2 initially to set the parameter ranges, and further use PSO for final analysis. Indeed, the uncertainty analysis must be accounted when the outcomes of the model use for policy or management decisions.

Rainfall–runoff simulations of extreme monsoon rainfall events in a tropical river basin of India

The present study analyzes the runoff response during extreme rain events over the basin of Subarnarekha River in India using soil and water assessment tool (SWAT). The SWAT model is configured for the Subarnarekha River basin with 32 sub-basins. Three gauging stations in the basin (viz., Adityapur, Jamshedpur and Ghatshila) were selected to assess the model performance. Daily stream flow data are taken from Central Water Commission, India—Water Resources Information System. Calibration and validation of the model were performed using the soil and water assessment tool-calibration uncertainty programs (SWAT-CUPs) with sequential uncertainty fitting (SUFI-2) algorithm. The model was run for the period from 1982 to 2011 with a calibration period from 1982 to 1997 and a validation period from 1998 to 2011. The sensitivity of basin parameters has been analyzed in order to improve the runoff simulation efficiency of the model. The study concluded that the model performed well in Ghatshila gauging station with a Nash–Sutcliffe efficiency (NSE) of 0.68 during calibration and 0.62 during validation at daily scale. The model, thus calibrated and validated, was then applied to evaluate the extreme monsoon rain events in recent years. Five extreme events were identified in Jamshedpur and Ghatshila sub-basins of Subarnarekha River basin. The simulation results were found to be good for the extreme events with the NSE of 0.89 at Jamshedpur and 0.96 at Ghatshila gauging stations. The findings of this study can be useful in runoff simulation and flood forecasting for extreme rainfall events in Subarnarekha River basin.

Assessing Parameter Uncertainty of a Semi‐Distributed Hydrology Model for a Shallow Aquifer Dominated Environmental System

AbstractThis paper examines the performance of a semi‐distributed hydrology model (i.e., Soil and Water Assessment Tool [SWAT]) using Sequential Uncertainty FItting (SUFI‐2), generalized likelihood uncertainty estimation (GLUE), parameter solution (ParaSol), and particle swarm optimization (PSO). We applied SWAT to the Waccamaw watershed, a shallow aquifer dominated Coastal Plain watershed in the Southeastern United States (U.S.). The model was calibrated (2003‐2005) and validated (2006‐2007) at two U.S. Geological Survey gaging stations, using significant parameters related to surface hydrology, hydrogeology, hydraulics, and physical properties. SWAT performed best during intervals with wet and normal antecedent conditions with varying sensitivity to effluent channel shape and characteristics. In addition, the calibration of all algorithms depended mostly on Manning's n‐value for the tributary channels as the surface friction resistance factor to generate runoff. SUFI‐2 and PSO simulated the same relative probability distribution tails to those observed at an upstream outlet, while all methods (except ParaSol) exhibited longer tails at a downstream outlet. The ParaSol model exhibited large skewness suggesting a global search algorithm was less capable of characterizing parameter uncertainty. Our findings provide insights regarding parameter sensitivity and uncertainty as well as modeling diagnostic analysis that can improve hydrologic theory and prediction in complex watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.

SWAT modeling of hydrology, sediment and nutrients from the Grand River, Ontario

Abstract The Grand River watershed (GRW) is an important agricultural area in Southern Ontario. Land use has been modified by various human endeavors, altering hydrology and increasing export of sediment and nutrients. The objective of this study was to predict spatial and temporal patterns of hydrology, and export of sediment and nutrients from the GRW to Lake Erie using the Soil and Water Assessment Tool (SWAT) model. The Sequential Uncertainty FItting (SUFI2) program was used to calibrate and validate stream flow for years 2001–2010. Calibration and validation of the SWAT model for monthly stream flow at York indicated good model performance (R2, NSE, and PBIAS = 0.64, 0.63 and 7.1 for calibration (2001–2005); = 0.82, 0.74 and 0.2, for validation (2006–2010)). The model was applied to predict sediment and nutrient export from the GRW into Lake Erie. Predicted loading at Dunnville (near the mouth) was 2.3 × 105 tonnes y−1 total suspended sediment, 7.9 × 103 tonnes y−1 TN, and 2.3 × 102 tonnes y−1 TP. This SWAT model can now be used to investigate the relative effects of best management practices, and to forecast effects of climate change, on sustainable water management, hydrology, and sediment and nutrient export to Lake Erie.

Streamflow estimation in ungauged catchments using regionalization techniques

Continuous streamflow regionalization in ungauged catchments is contemplated as a challenging task. In a developing country like India where the subject of prediction in ungauged basins (PUB) is not prevalent, thirty-two catchments were specified in order to analyze continuous streamflow estimation. Spatial proximity (Inverse Distance Weighted, Kriging, and global mean), regression and physical similarity were the regionalization approaches implemented in conjunction with SWAT (Soil and Water Assessment Tool) for streamflow estimation in each catchment treated as ungauged in turn. Kriging and IDW were the two methods that produced superior results than other applied techniques in terms of Nash-Sutcliff Efficiency (NSE), RMSE-observations standard deviation ratio (RSR) Percentage bias (PBIAS) and Peak percent threshold statistics (PPTS). Physical similarity and regression approaches, which were based on catchment attributes exhibited better results than global mean approach. Sequential Uncertainty Fitting (SUFI-2) tool analyzed the uncertainty associated with regionalization techniques in terms of 95% prediction uncertainty (95PPU). The comparative assessment proposes that presence of well-gauged catchments in proximity with the ungauged catchment is more beneficial than catchments having resemblance between them in terms of physiographic attributes for continuous streamflow prediction.

Hydrological modeling of the Mikkés watershed (Morocco) using ARCSWAT model

This study aims to contribute to water resource management in Mikkes watershed using the SWAT (Soil and Water Assessment Tool) model. This model requires several input data, such as rainfall, temperature, digital elevation model, soil characteristics, and land use. The outputs of the model are water balance and sediment yield at sub-basin. The SWAT model was run and calibrated using Sequential Uncertainty Fitting (SUFI-2) method in SWAT–CUP software for the period 1979–2007 with an efficiency of adjusted model rated as good (NSE = 0.67, PBIAIS = 6.78, RSR = 0.56). The balance of water generated by the model shows a dominance of evapotranspiration which represents 65% of rainfall, while surface runoff is 15% and total aquifer recharge is 20%. The model indicates that the sediment yields are largest in the southern area of the basin resulting in high slope, high rainfall, land use and erodible soils. These results revealed that hydrologic model can be used efficiently to support integrated watershed management.