Sequential Uncertainty Fitting Research Articles

Farmer adoption-based prompt networking and modeling for targeting optimal agro-conservation practices

Despite the availability of robust agricultural models, the successful on-ground implementation of all Best Management Practices (BMPs) remains elusive due to the lack of consideration for farmers' preferences. This study develops a unique network-based optimization technique coupled with Soil and Water Assessment Tool (SWAT) which delineates combined set of cost-effective BMPs at field-scale based on farmer's conservation identity in Ganges River Basin, India. SWAT was calibrated and validated using 10-year monthly discharge and nitrate data, and the selected BMPs were simulated using sequential uncertainty fitting framework. Farmers belonging to the Shahpur subdistrict were found to have the maximum normalized conservation identity of 90%, whereas the northeastern portion of the watershed contributed a maximum nitrate load of 7.5–9.92 kg/acre to the watershed outlet. An optimized combination of BMPs (riparian buffer-conservation tillage) with an efficiency of 0.28 was obtained through the modified network technique that targeted subbasins of the Morna subdistrict using farmer conservation identities. The results provide valuable guidance for watershed modelers, conservation planners, and researchers in pinpointing regions for targeted farmer training, subsidies, and other initiatives aimed at facilitating prompt field-scale BMP adoption. A flexible feature of the approach is its ability to modify the BMP action plans by replacing even technically feasible, cost-effective combinations based on farmers' preferences.

Applicability of seasonal forecasts from dynamical models for reservoir management practices

Despite the availability of reliable seasonal forecasts of Indian Summer Monsoon Rainfall (ISMR), the use of dynamical models driven by these forecasts for reservoir level management is limited. Reservoir water management can specially be useful if it can be done several months in advance, in view of an impending drought/flood scenario. The applicability of seasonal forecasts from the Monsoon Mission (MM) seasonal forecast model for seasonal and monthly inflow forecasts for tropical Indian reservoirs (Mula and Kangsabati) is studied using the Soil and Water Assessment Tool (SWAT) hydrological model, at a lead time of 3 months. Long-term observed inflow datasets are used for calibration and validation of SWAT-Calibration and Uncertainty Procedure (CUP) with Sequential Uncertainty Fitting (SUFI)-2 algorithm using insitumeteorological data. Observed inflows and inflow simulations are compared with simulated inflow using SWAT with same calibrated parameters, but with forcing derived from reforecasts from the MM model. The SWAT-CUP calibrated well with reasonable Nash Sutcliffe Efficiency (NSE) (Mula = 0.75, Kangsabati = 0.79) and Percentage Bias (PBIAS) (Mula = -28%, Kangsabati = 17%) for both reservoirs. The skill scores for streamflow predictions vary from 0.6-0.70 during the monsoon season, indicating reasonable accuracy for these predictions. The SWAT-MM model has a reasonable skill with 0.52-0.53 NSE and 26%-40% PBIAS. Therefore, SWAT-MM-based model has a good potential to forecast monthly and seasonal reservoir inflow for various agro-climatic zones of India. These forecasts when used in real-time, can serve as a guideline for managing the reservoir storage and release, and hence proving to be of great socio-economic importance.

Vulnerability assessment of hydrological ecosystem services under future climate and land use change dynamics

Hydrological Ecosystem Services (HESs), as an important part of ecosystem services, are those services that depend on hydrological processes. HESs vulnerability assessment at the watershed scale is used to explore highly vulnerable areas for management and protection measures. This study aimed at developing a method for HESs vulnerability assessment and achieve a quantitative integrated index under future climate and land use change scenarios. We focused on three regulating HESs, including water flow regulation, erosion regulation, and local climate regulation (LC). An integrated model of Markov chain and automatic cells was used to predict land use changes. Future climate data were estimated using the Climate Change Toolkit (CCT). The Soil and Water Assessment Tool (SWAT) model was developed for HESs vulnerability assessment in the Taleghan Watershed in Iran. The model was calibrated by river discharge using the Sequential Uncertainty Fitting (SUFI-2) and the Parallel Processing option. The results show that sub-watersheds with higher precipitation (PCP) and lower evapotranspiration (ET) are more efficient in water flow and local climate regulation. However, sub-watersheds with lower PCP and higher ET are more vulnerable to erosion. Changes in water flow regulation services and local climate regulation were more consistent with climate changes, and conversely, changes in erosion and sediment regulation services had a very high level of compliance with land use changes. The method applied in this study can easily be adapted to other regions to identify areas with more vulnerability and less resilience in improving the water and soil structure and function in the watershed.

Groundwater Recharge and Surface Runoff Modeling Response to Land Use and Land Cover Dynamics in a Mae Wong Watershed of Thailand

This study aims to develop efficient management strategies and assess the spatiotemporal dynamics of land use and land cover (LULC) on surface runoff modeling response dynamics for the long-term sustainability of watersheds. The soil and water assessment tool (SWAT) model was used to evaluate the LULC dynamics on GRSR in the Mae Wong Watershed (MWW) of Thailand. Using Landsat images, three different LULC maps (2011, 2021, and 2031) were created using the cellular automata markov chain (CA-Markov) model, and TerrSet 2020 geospatial monitoring and modeling software. In the overall MWW, the forestland has undergone deforestation and decreased by 2.10% of the total area and 2.72% of the total area has been transformed into agricultural lands due to human activity and population growth. The soil, LULC, weather, and the digital elevation model (DEM) were all used in the SWAT simulation procedure. To understand the groundwater recharge and surface runoff (GRSR) responses of each hydrologic response units (HRUs), the SWAT model was calibrated and verified using streamflow and the sequential uncertainty fitting (SUFI-2) technique from the SWAT calibration and uncertainty program (SWAT-CUP). The results indicate that there is a good agreement for both the calibration and validation phases of all LULC simulations. The study indicated that groundwater recharge has decreased over the last two decades while surface runoff has increased due to the forest area being converted to agricultural land. Thus, the study can support maximizing water management and strategies for systematically attaining sustainability.

Parameter Uncertainty and Sensitivity Analysis for Nutrient Modelling in a Forested Catchment Using the Sequential Uncertainty Fitting (SUFI-2) Algorithm in SWAT-CUP

Parameter Uncertainty and Sensitivity Analysis for Nutrient Modelling in a Forested Catchment Using the Sequential Uncertainty Fitting (SUFI-2) Algorithm in SWAT-CUP

Impacts of hydrometeorological factors on discharge simulation in the North West Himalayas: a SUFI-2 algorithm-driven investigation using the SWAT model.

The Soil and Water Assessment Tool (SWAT) is a computational hydrological model extensively utilised for developing sustainable strategies and viable approaches for prudent management of water resources. The central emphasis of this study is on the utilisation of SWAT model along with SWAT-CUP (SWAT calibration toolbox) to simulate streamflow in the upper Jhelum basin, the North West Himalayas, for a period of 20years from 2000 to 2019. The global sensitivity analysis algorithm, Sequential Uncertainty Fitting 2 (SUFI-2) of SWAT-CUP, is used for sensitivity and uncertainty analysis. The optimised parameter set estimated by SUFI-2 constitutes 11 parameters that are found to be sensitive with soil conservation service (SCS) curve number (CN) being the most influential parameter followed by snowmelt base temperature. Autocorrelation analysis using the autocorrelation function was conducted on the temperature and precipitation time series data, followed by a pre-whitening procedure to remove any autocorrelation effects. Subsequently, the modified Mann-Kendall (MMK) test was applied to examine trends in the annual temperature and precipitation data. The results indicated statistically significant positive trends in both datasets on an annual scale. The results for the calibration period (2003-2014) for monthly simulation displayed good model performance at three gauging stations, Rambiara, Sangam and Ram Munshi Bagh with R2 values of 0.83, 0.847, 0.829, P factor values of 0.73, 0.76, 0.75 and R factor values of 0.61, 0.58, 0.63, respectively. The validation results for monthly simulation for the 2015-2019 period showed good model agreement with R2 values of 0.817, 0.853, and 0.836, P factor values of 0.76, 0.8, and 0.75 and R factor values of 0.62, 0.53, and 0.65, respectively. The study concludes that the SWAT hydrological model can perform satisfactorily in high mountainous catchments and can be employed to analyse the impact of land use-land cover changes and the effect of climate variation on streamflow dynamics.

Multi-objective synchronous calibration and Pareto optimality of runoff and sediment parameters in an arid and semi-arid watershed.

Calibration methodologies must extract as much information as possible from available data, but it is not well understood in investigating the multi-objective synchronous calibration strategy by using multiple sources of information and by exploiting the data in better ways. The non-dominated sorting genetic algorithm II (NSGA-II) is introduced to study the calibration performance of runoff and sediment parameters under nine targeted scenarios, which considers the best choice to obtain high-cost performance results for decision makers through multi-objective optimization and the calculation of Pareto-optimal front with a high precision. (i) SWAT model has good adaptability in the runoff simulation of the Yanhe River watershed. Both the calibration results of NSGA-II and sequential uncertainty fitting approach-version 2 (SUFI-2) can meet the accuracy requirements of runoff simulation. Particularly, the NSGA-II based on multiple objective functions not only has strong applicability but also can better constrain the parameter process, making the calibrated model more in line with the physical conditions of the watershed. (ii) The two-site synchronous calibration of runoff or sediment can make full use of data information of different sites, reduce the impact of spatial heterogeneity on model parameters, and improve the calibration efficiency of the model. The single-site synchronous calibration of runoff and sediment based on NSGA-II not only has high calibration efficiency but also can avoid the tedious steps of calibrating runoff and sediment separately. (iii) The two-site synchronous calibration of runoff and sediment based on NSGA-II combines the advantages of the above synchronous calibration strategies, which can get Pareto-optimal front and represent the best trade-offs among different objectives, and its applicability is stronger than the traditional single-site or single-element calibration strategy. This study provides new and competing ways to evaluate hydrological models and their performance, and the multiple criteria approach for watershed modeling is one of the focuses in future research extensions.

Hydrological modeling using the Soil and Water Assessment Tool in urban and peri-urban environments: the case of Kifisos experimental subbasin (Athens, Greece)

Abstract. SWAT (Soil and Water Assessment Tool) is a continuous-time, semi-distributed, river basin model widely used to evaluate the effects of alternative management decisions on water resources. This study examines the application of the SWAT model for streamflow simulation in an experimental basin with mixed-land-use characteristics (i.e., urban/peri-urban) using daily and hourly rainfall observations. The main objective of the present study was to investigate the influence of rainfall resolution on model performance to analyze the mechanisms governing surface runoff at the catchment scale. The model was calibrated for 2018 and validated for 2019 using the Sequential Uncertainty Fitting (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–Ampt and 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 subdaily model (daily model, with NSE = 0.86, R2 = 0.87, and PBIAS = 4.2 %; subdaily model with NSE = 0.6, R2 = 0.63, and PBIAS = 11.7 %). The Curve Number method produced higher discharge peaks than the Green–Ampt and Mein–Larson method and better estimated 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 in a mixed-land-use basin with high complexity and spatial distribution of input data.

Assimilation versus optimization for SWAT calibration: accuracy, uncertainty, and computational burden analysis

Abstract The accurate estimation of runoff by hydrological models depends on proper model calibration. Sequential Data Assimilation (DA), as an online method, is used to estimate complex hydrological models' states and parameters simultaneously. Although DA was applied for estimating the Soil and Water Assessment Tool (SWAT) model's state and/or parameter, the previous research did not pay attention to the model calibration by DA or the comparison between DA and popular SWAT calibration methods. This paper compares Ensemble Kalman Filter (EnKF), a well-known DA method, with Sequential Uncertainty Fitting (SUFI2), a popular SWAT calibration method, to calibrate the model. We test the impact of the selected objective function in the SUFI2 application. We evaluate the results based on the multiple deterministic and uncertainty-based Goodness of Fit (GOF) measures and compare all scenarios based on the simulation accuracy, computational burden, uncertainty assessment, and parameter ranges. Results show that under the SUFI2 application, some GOFs might be located in unsatisfactory ranges while the algorithm obtains (very) good results concerning the objective functions. On the other hand, EnKF simultaneously locates most GOFs in (very) good ratings. Moreover, we found that the selection of SUFI2's objective function and the specification of uncertainty's error in EnKF have significant effects on the results.

Preliminary assessment of small hydropower potential using the Soil and Water Assessment Tool model: A case study of Central Equatoria State, South Sudan

South Sudan is focusing on the development of large hydropower (LHP) to urgently improve access to electricity in the country. However, LHP investments are expensive and take many years to construct. SHP can be deployed as an alternative to LHP developments as they can be constructed in a shorter time and urgently deliver electricity to locations in need. This study aims to assess run-of-river (ROR) SHP potential in Central Equatoria State (CES) in South Sudan. This is the first study to assess the SHP potential in South Sudan. The main contribution of this research is to produce a dataset showing the geographical locations and generation capacities of the potential SHP sites. The objective of this study is to identify candidate SHP sites in the study area, investigate their suitability for ROR plants and estimate their electricity generation potential. A digital elevation model and a stream network were used to locate sites with a minimum elevation drop of 5 meters, along third and fourth order streams. The Soil and Water Assessment Tool (SWAT) model was used to estimate river discharges in the study area. Model calibration and uncertainty analysis were performed using the Sequential Uncertainty Fitting (SUFI-2) algorithm and daily flow duration curves at 95% dependability were developed from the simulated discharge data. The results showed the availability of eighty-two (82) potential SHP sites along the Bahr el Jebel and the Kaya rivers in CES with a total output power of 1651.69 MW and average annual output energy of 8.68 TWh. Around 60% of the sites had a minimum output power of 10 MW. The highest potential was found in sites between Rejaf and Mongalla, whereas the lowest was along the Kaya river. The study recommended economic analysis and environmental impact assessments before any SHP installation in the area.

Simulation of Rainfall-Runoff process using SWAT model in Bouhamdane watershed, Algeria

The current research examines the runoff response in the Bouhamdane watershed in Algeria using the soil and water assessment tool (SWAT). The SWAT model is applied for the Bouhamane watershed, which includes three sub-watersheds and 45 Hydraulic Response Units (HRUs). To assess the ability and effectiveness of the model, one-gauge station in the basin (sabat) was chosen. Monthly discharge flow data are sourced from Algeria's National Water Resources Agency (NWRA). The soil and water assessment tool calibration uncertainty programs (SWAT-CUPs) with the sequential uncertainty fitting (SUFI 2) algorithm were used to calibrate and validate the model. The model was run from 1985 to 2004, with a calibration period between 1985 and 1994 and a validation period between 1995 and 2005. The model's runoff simulation efficiency has been improved by adjusting watershed input parameters. The SWAT model's performance was assessed statistically (coefficient of determination [R2], Nash-Sutcliffe Efficiency Coefficient [NSE], and Percent BIAS [PBIAS]). The monthly calibration R2, NSE, and PBIAS were 0.89, 0.68, and 43, respectively, and the monthly validation R2, NSE, and PBIAS were 0.78, 0.76, and 10.4, respectively. These results support that the SWAT model is an effective tool for simulating the surface runoff of the Bouhamdane watershed.

Predicting phosphorus and nitrate loads by using SWAT model in Vamanapuram River Basin, Kerala, India

Evaluations of probable environmental impacts of point and diffuse source pollution at regional sizes are essential to achieve sustainable development of natural resources such as land and water. This research focused on how nitrate and phosphorus load varied over time and space in the Vamanapuram River Basin (VRB). Phosphorus and nitrate loads have been evaluated in the VRB using the semi-distributed Soil and Water Assessment Tool (SWAT) hydrological model. SWAT Calibration and Uncertainty Programs (SWAT-CUP) have simulated the developed model using the Sequential Uncertainty Fitting, version 2(SUFI-2). The developed model was simulated for 2001 to 2008, and it was split into two-phase calibration and validation phases. Model performance was evaluated by the percentage of bias (PBAIS) and Nash-Sutcliffe efficiency coefficient (NSE). The simulated performance of nitrate was indicated as NSE = 0.22-0.59 and PBIAS = 51.86-65.88. The simulated performance of phosphorus showed NSE = 0.06-0.33 and PBIAS = 15.14-33.97. Total Phosphorus load was most sensitive to the organic Phosphorus enrichment ratio (ERORGP) and CH_N2 for streamflow simulation. This study concluded that the South-western region was a high potential for nutrient loads. This study will explain the nutrient load and guidelines for land management practice in the study area.

Understanding Hydrological Processes under Land Use Land Cover Change in the Upper Genale River Basin, Ethiopia

The expansion of cultivated land in place of natural vegetation has a substantial influence on hydrologic characteristics of a watershed. However, due to basin characteristics and the nature and intensity of landscape modification, the response varies across basins. This study aims to evaluate the performance of a soil and water assessment tool (SWAT) model and its applicability in assessing the effects of land use land cover (LULC) changes on the hydrological processes of the upper Genale River basin. The results of satellite change detection over the past 30 years (between 1986 and 2016) revealed that the landscape of the basin has changed considerably. They showed that settlement, cultivated, and bare land areas had increased from 0.16% to 0.28%, 24.4% to 47.1%, and 0.16% to 0.62%, respectively. On the contrary, land cover units such as forest, shrubland, and grassland reduced from 29.6% to 13.5%, 23.9% to 19.5%, and 21.8% to 18.9%, respectively. Based on monthly measured flow data, the model was calibrated and validated in SWAT-CUP using the sequential uncertainty fitting (SUFI-2) algorithm. The result showed that the model performed well with coefficient of determination (R2) ≥ 0.74, Nash–Sutcliffe efficiency (NSE) ≥ 0.72, and percent bias (PBIAS) between −5% and 5% for the calibration and validation periods. The hydrological responses of LULC change for the 1986, 2001, and 2016 models showed that the average annual runoff increased by 13.7% and 7.9% and groundwater flow decreased by 2.85% and 2.1% between 1986 and 2001 and 2001 and 2016, respectively. Similarly, the total water yields increased from 324.42 mm to 339.63 mm and from 339.63 mm to 347.32 mm between 1986 and 2001 and 2001 and 2016, respectively. The change in hydrological processes, mainly the rise in runoff and total water yield as well as the reduction in lateral and groundwater flow in the watershed, resulted from LULC changes. This change has broader implications for the planning and management of the land use and water resource development.

Calibration, validation and application of the SWAT model to determine the hydrological benefit of wetland rehabilitation in KwaZulu-Natal, South Africa

In South Africa, with highly variable and intense land-use practices, coupled with limited soil fertility and water resources, there has been a long history of encroachment of arable lands (sugarcane and timber plantations) into surrounding wetlands. Although wetland delineation within the timber and sugar sectors is well-defined in policy, and existing and proposed legislation, there are significant areas of non-compliance. The spatially-explicit Soil Water Assessment Tool (SWAT) was adopted to investigate the interactions of climate, land-use and soil on the water-use of natural and encroached wetlands. This paper documents the calibration, validation and application of the SWAT model on Quaternary Catchment (QC) U20G, which is a 498 km2 catchment that forms part of the uMngeni River basin. The SWAT-CUP parameter sensitivity and optimization model was tested with daily observed streamflow data for this catchment. Parameters were modified using the sequential uncertainty fitting (SUFI-2) analysis routine to calibrate the model. The simulated flow had a close fit to the observed flow with a regression coefficient (r2) of 0.87 and a Nash-Sutcliffe (NS) coefficient of 0.8. Through the buffer scenario analysis, the model showed that if the wetland and a 20-m buffer were to be returned to a natural state, there could be a 16% increase in the annual streamflow contribution, with an upper limit of a 60% increase in some hydrologic response units (HRUs). Thus there would be a hydrological gain if wetlands and sensitive buffer areas were to be cleared of commercial timber species and sugarcane.

Parameter Sensitivity Analysis of SWAT Modeling in the Upper Heihe River Basin Using Four Typical Approaches

Parameter sensitivity analysis is a significant part of quantifying model uncertainty, effectively identifying key parameters, and improving the efficiency of parameter optimization. The Soil and Water Assessment Tool (SWAT) model was applied to the upper Heihe River basin (UHRB) in China to simulate the monthly runoff for 11 years (1990–2000). Four typical sensitivity analyses, namely, the Morris screening, Sobol analysis, Fourier amplitude sensitivity test (FAST), and extended Fourier amplitude sensitivity test (EFAST), were used to determine the critical parameters affecting hydrological processes. The results show that the sensitivity parameters defined by the four methods were significantly different, resulting in a specific difference in the simulation effect of the SWAT model. The reason may be the different sampling process, sensitivity index, and calculation principle of each method. The snow-melt base temperature (SMTMP) and snowfall temperature (SFTMP) related to the snow-melt process, the available water capacity of the soil layer (SOL_AWC), saturated hydraulic conductivity (SOL_K), depth from the soil surface to the bottom of the layer (SOL_Z), moist bulk density (SOL_BD), deep aquifer percolation fraction (RCHRG_DP), and threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN) related to the soil water and groundwater movement, baseflow alpha factor for bank storage (ALPHA_BNK) related to the base flow regression, and average slope steepness (HRU_SLP) are all very sensitive parameters. The 10 key parameters were optimized 100 times with the sequential uncertainty fitting procedure version 2 (SUFI-2). The Nash–Sutcliffe efficiency coefficient (NSE), Kling–Gupta efficiency coefficient (KGE), mean square error (MSE), and percentage bias (PBIAS) were 0.89, 200, 8.60, and 0.90, respectively. The simulation results are better than optimizing the sensitive parameters defined by the single method and all the selected parameters. The differences illustrate the rationality and importance of parameter sensitivity analysis for hydrological models and the synthesis of multiple approaches to define sensitive parameters. These conclusions have reference significance in the parameter optimization of the SWAT model when studying alpine rivers by constructing the SWAT model.

Evaluation of Gridded Precipitation Data for Hydrologic Modeling in North-Central Texas

Over the past few decades, several high-resolution gridded precipitation products have been developed using multiple data sources and techniques, including measured precipitation, numerical modeling, and remote sensing. Each has its own sets of uncertainties and limitations. Therefore, evaluating these datasets is critical in assessing their applicability in various climatic regions. We used ten precipitation datasets, including measured (in situ), gauge-based, and satellite-based products, to assess their relevance for hydrologic modeling at the Bosque River Basin in North-Central Texas. Evaluated datasets include: (1) in situ station data from the Global Historical Climate Network (GHCN); (2) gauge-based dataset Daymet and the Parameter-elevation Regression on Independent Slope Model (PRISM); (3) satellite-based dataset Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Early and Late, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and PERSIANN-CCS (Cloud Classification System); (4) satellite-based gauge-corrected dataset IMERG-Final, PERSIANN-CDR (Climate Data Record), and CHIRPS (Climate Hazards Group Infrared Precipitation with Station data). Daily precipitation data (2000–2019) were used in the Soil and Water Assessment Tool (SWAT) for hydrologic simulations. Each precipitation dataset was used with measured monthly United States Geological Survey (USGS) streamflow data at three locations in the watershed for model calibration and validation. The SUFI-2 (Sequential Uncertainty Fitting) method on the SWAT-CUP (Calibration and Uncertainty Program) was used to quantify and compare the uncertainty in streamflow simulations from all precipitation datasets. The study has also analyzed the uncertainties in SWAT model parameter values under different gridded precipitation datasets. The results showed similar or better model calibration/validation statistics from gauge-based (Daymet and PRISM) and satellite-based gauge-corrected products (CHIRPS) compared with the GHCN data. However, satellite-based precipitation products such as PERSIANN-CCS and PERSIANN-CDR unveil comparatively inferior to capture in situ precipitation and simulate streamflow. The results showed that gauge-based datasets had comparable and even superior performances in some metrics compared with the GHCN data.

Parameter Uncertainty Analysis for Streamflow Simulation Using SWAT Model in Nashe Watershed, Blue Nile River Basin, Ethiopia

The hydrological model is an important tool in water resource management, allocation, and prediction. However, the hydrological models are subject to the uncertainty resulting from different sources of errors involved in the large number of parameters. The hydrological models in the management of water resources play a very significant role in quantifying uncertainty. Therefore, uncertainty analysis implementation is essential to advance confidence in modeling before performing the hydrological simulation. The purpose of the study was to assess the uncertainty parameters for the streamflow using the Soil and Water Assessment Tool (SWAT) hydrological model integrated sequential uncertainty fitting (SUFI-2) algorithm to Nashe watershed located in the Northwestern, Upper Blue Nile River Basin. The required input data for this study were digital elevation model, land use, soil map and data, meteorological data (precipitation, minimum and maximum temperature, wind speed, solar radiation, and relative humidity), and streamflow data. The calibration and validation model was computed to simulate the observed streamflow data from 1985 to 2008 including two years of warm-up periods. Model calibration, validation, and analysis of parameter uncertainty were conducted for both daily and monthly observed streamflows at the gauging stations through SUFI-2, which is one of the algorithms of the SWAT-Calibration and Uncertainty Program (SWAT_CUP). The results show that CN_2, GW_DELAY, ALPHA_BNK, CH_N2, and SOL_AWC were the most sensitive parameter for the monthly period and had a great impact on the streamflow simulation. Modeling results indicated that the method provides better results for the monthly time period than the daily time period for both calibration and validation. The result indicated that R2 and NSE were 0.89 and 0.85 and 0.82 and 0.79, respectively, monthly and daily during the calibration. The validation likewise demonstrated a good performance with R2 and NSE results of 0.88 and 0.78 and 0.85 and 0.76, respectively, for monthly and daily time periods. The results of this study provide a scientific reference based on uncertainty analysis to decision-makers to improve the decision support process in river basin management.

Impacts and Implications of Land Use Land Cover Dynamics on Groundwater Recharge and Surface Runoff in East African Watershed

Assessing the spatiotemporal dynamics of land use land cover (LULC) change on water resources is vital for watershed sustainability and developing proper management strategies. Evaluating LULC scenarios synergistically with hydrologic modeling affords substantial evidence of factors that govern hydrologic processes. Hence, this study assessed the spatiotemporal effects and implications of LULC dynamics on groundwater recharge and surface runoff in Gilgel Gibe, an East African watershed, using the Soil and Water Assessment Tool (SWAT) model. Three different LULC maps (2000, 2010, and 2020) were derived from Landsat images, and the comparisons pointed out that the land-use pattern had changed significantly. The agricultural land and grassland cover increased by 3.76% and 1.36%, respectively, from 2000 to 2020. The implications acquired for 2000 show that forested land covers decreased by 5.49% in 2020. The SWAT simulation process was executed using a digital elevation model, soil, LULC, and weather data. The model was calibrated and validated using streamflow data to understand the surface runoff and groundwater recharge responses of each Hydrologic Response Units on reference simulation periods using the Calibration and Uncertainty Program (SWAT-CUP), Sequential Uncertainty Fitting (SUFI-2) algorithm. The observed and simulated streamflows were checked for performance indices of coefficient of determination (R2), Nash–Sutcliffe model efficiency (NSE), and percent bias (PBIAS) on monthly time steps. The results show that there is good agreement for all LULC simulations, both calibration and validation periods (R2 & NSE ≥ 0.84, −15 < PBIAS < +15). This reveals that for the LULC assessment of any hydrological modeling, the simulation of each reference period should be calibrated to have reasonable outputs. The study indicated that surface runoff has increased while groundwater decreased over the last two decades. The temporal variation revealed that the highest recharge and runoff occurred during the wet seasons. Thus, the study can support maximizing water management strategies and reducing adverse driving environmental forces.

Prioritization of Susceptible Watershed to Sediment Yield and Evaluation of Best Management Practice: A Case Study of Awata River, Southern Ethiopia

Soil erosion is currently a global problem that causes land degradation and long-lasting challenges in Ethiopia. Sediment yield is influenced by the watershed characteristics such as land cover, soil class, and slope, which are considered the drivers of soil erosion in the basin. The middle Awata watershed is highly susceptible to soil erosion due to its topographical features. This study is therefore aimed at estimating sediment yield, examining its spatial distribution, and evaluating the selected Best Management Practices (BMPs) to reduce soil erosion-prone areas at downstream. The model simulation was done by dividing the total watershed area of 1912 km2 into 37 subbasins and 294 hydrologic response units (HRUs) for 31 years (1988–2018). The model’s uncertainty evaluation was carried out on monthly basis using the Sequential Uncertainty Fitting (SUFI-2) algorithm. The performance of the model was evaluated by statistical parameters that gave R2 = 0.76, NSE = 0.75, RSR = 0.51, and PBIAS = 5.6% for calibration and R2 = 0.75, NSE = 0.74, RSR = 0.51, and PBIAS = 2.7% for validation of streamflow. Meanwhile, sediment yield in the watershed was also simulated with R2 = 0.69, NES = 0.66, RSR = 0.58, and PBIAS = 3.7% for calibration and R2 = 0.67, NES = 0.65, RSR = 0.61, and PBIAS = 5.6% for validation of sediment distribution. The simulated annual average sediment yield was 34.543 × 103 ton/year at the outlet of the middle Awata watershed. The developed spatial distribution of sediment yield identified the first twelve upstream subwatersheds as being soil erosion-prone areas. Following an evaluation of the four selected BMPs, it was determined that parallel terracing is the most recommended method for soil erosion reduction option in all critical subbasins found in the watershed.

Assessment of soil erosion risk in a semi-arid climate watershed using SWAT model: case of Tata basin, South-East of Morocco

Increased sedimentation is the main problem that affects dam efficiency by reducing storage capacity. Planning for dam construction and maintenance requires design strategies that heavily depend on integrated basin models, properly identifying principal sediment origins within the watershed and qualifying the sediment production rate. In this research work, the physically-based watershed SWAT model, defined as the Soil and Water Assessment Tool, was used to estimate the rate of sediment production for future dams in the Tata basin, located in southeast Morocco. The model was calibrated and tested for uncertainty by the employment of the algorithm Sequential Uncertainty Fitting-2. The outputs were used for assessing critical sediment source areas. Calibration and validation of the model were performed by monthly data. The values for Nash–Sutcliffe efficiency coefficient, Percent bias coefficient and determination coefficient (R2), respectively, during the calibration period 1990–1998 (0.96, −13% and 0.96) and the validation period 1999–2006 (0.77, + 11% and 0.93) indicate the accordance with the results obtained for the measured flow and the simulated flow values. The annual sediment yield of the Tata basin extends from 0 to 11 t/ha/y with a mean of 2.3 t/ha/y. The spatial distribution of these sediments varies from upstream to downstream. The downstream basin generated more sediment to the river per unit area, though it was less than the total amount of the basin for the upstream area. This variation is influenced by the increased downstream surface runoff and also by other characteristics of the basin such as slope and lithology. The low erosion places correspond to areas with lithological formations that are more resistant to erosion.