Evaluating SWAT model performance, considering different soils data input, to quantify actual and future runoff susceptibility in a highly urbanized basin

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method

Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

Identifying the impacts of land use landscape pattern and climate changes on streamflow from past to future

Identifying the individual and combined hydrological response of land use landscape pattern and climate changes is key to effectively managing the ecohydrological balance of regions. However, their nonlinearity, effect size, and multiple causalities limit causal investigations. Therefore, this study aimed to establish a comprehensive methodological framework to quantify changes in the landscape pattern and climate, evaluate trends in streamflow response, and analyze the attribution of streamflow events in five basins in Beijing from the past to the future. Future climate projections were based on three general circulation models (GCMs) under two shared socioeconomic pathways (SSPs). Additionally, the landscape pattern in 2035 under a natural development scenario was simulated by the patch-generating land use simulation (PLUS). The Soil and Water Assessment Tool (SWAT) was applied to evaluate the streamflow spatial and temporal dynamics over the period 2005-2035 with multiple scenarios. A bootstrapping nonlinear regression analysis and boosted regression tree (BRT) model were used to analyze the individual and combined attribution of landscape pattern and climate changes on streamflow, respectively. The results indicated that in the future, the overall streamflow in the Beijing basin would decrease, with a slightly reduced peak streamflow in most basins in the summer and a significant increase in the autumn and winter. The nonlinear quadratic regression more effectively explained the impact of landscape pattern and climate changes on streamflow. The trends in the streamflow change depended on where the relationship curve was in relation to the threshold. In addition, the impacts of landscape pattern and climate changes on streamflow were not isolated but were joint. They presented a nonlinear, non-uniform, and coupled relationship. Except for the YongDing River Basin, the annual streamflow change was influenced more by the landscape pattern. The dominant factors and the critical pair interactions varied from basin to basin. Our findings have implications for city planners and managers for optimizing ecohydrological functions and promoting sustainable development.

Coupling SWAT and DPSIR models for groundwater management in Mediterranean catchments

Groundwater is an essential natural resource and has a significant role in human and environmental health as well as in the economy. Management of subsurface storage remains an important option to meet the combined demands of humans and ecosystems. The increasing need to find multi-purpose solutions to address water scarcity is a global challenge. Thus, the interactions leading to surface runoff and groundwater recharge have received particular attention over the last decades. Additionally, new methods are developed to incorporate the spatial-temporal variation of recharge in groundwater modeling. In this study, groundwater recharge was spatiotemporally quantified using the Soil and Water Assessment Tool (SWAT) in the Upper Volturno-Calore hydrological basin in Italy and the results were compared with other two basins in Greece (Anthemountas and Mouriki). SWAT model was applied in actual and future projections (2022-2040) using the Representative Concentration Pathway (RCP) 4.5 emissions scenario to evaluate changes in precipitation and assess the future hydrologic conditions, along with, the Driving Force-Pressure-State-Impact-Response (DPSIR) framework that was applied in all the basins as a low-cost analysis of integrated physical, social, natural, and economic factors. According to the results, no significant variations in runoff are predicted in the Upper Volturno-Calore basin for the period 2020-2040 while the potential evapotranspiration percentage varies from 50.1% to 74.3% and infiltration around 5%. The limited primary data constitutes the main pressure in all sites and exaggerates the uncertainty of future projections.

Study on the planning and influential factors of the safe width of riparian buffer zones in the upper and middle reaches of the Ziwu River, China

In this study, we employed the random forest model to identify the riparian buffer zone in the upper and middle reaches of the Ziwu River, used the Soil and Water Assessment Tool (SWAT) to simulate and calculate the nonpoint source pollution load in the riparian buffer zone, and used empirical formulas to estimate the pollutant concentration when surface runoff passes the edge of the riparian buffer zone. Moreover, through correlation analysis, we identified the main factors that affect the safe width of the riparian buffer zone. By combining these factors with the characteristic parameters of the riparian buffer zone and the water quality demand, we analyzed and calculated the safe width of the riparian buffer zone. Our findings are as follows: ① the simulated values of the SWAT model were highly consistent with the measured values. Specifically, the calibration and verification results of the hydrological station achieved Ens ≥ 0.65, RE <  ± 15%, and R2 ≥ 0.85, while the overall total nitrogen and total phosphorus loads achieved Ens ≥ 0.65, RE <  ± 15%, and R2 > 0.65. ② We found that the total nitrogen (TN) and total phosphorus (TP) loads in the riparian buffer zone gradually increased from upstream to downstream. Among these loads, the normal season had the largest TN and TP concentrations reaching the edge of the riparian buffer zone, while the dry season had the minimum concentrations. ③ The factors affecting the safe width of the riparian buffer zone included the connectivity, slope of the buffer zone, cultivated land area, and regional population density. For the effective protection of water quality, it is recommended that the upstream, midstream, and downstream buffer zones be at least 77.9 m, 33.37 m, and 60.25 m wide, respectively.

Examining the open-source datasets for water quantity and quality using the soil and water assessment tool (SWAT)

Water quality modeling is very important for the management of water resources. In this study, the upper part of the Porsuk Basin in Türkiye is analyzed using SWAT. In the analysis, in addition to the data provided by the General Directorate of State Hydraulic Works (DSI), the freely available flow and water quality data from the GEMStat data portal were used. This study presents a discussion of the practicality of GEMStat data for a water quality model. For this purpose, firstly, the SWAT model was constructed with freely available global data sources on elevation, land use/land cover, and soil type. Then, the model flow outputs were calibrated and validated for both DSI and GEMStat data in three different time periods. As a result, the flow calibration and validation success in the daily time step is 0.64 and 0.44 according to the Nash-Sutcliffe efficiency (NSE). The model was also validated using GEMStat flow data and calibrated using GEMStat water quality data such as nitrate (NO3), total suspended solids (TSS), and dissolved oxygen (DO) with a reasonable value. Hence, the results showed that GEMStat flow and water quality data can be used as auxiliary open-source data in the modeling process.

A review of Soil and Water Assessment Tool (SWAT) studies of Mediterranean catchments: Applications, feasibility, and future directions

The Soil and Water Assessment Tool (SWAT) is a well-established eco-hydrological model that has been extensively applied to watersheds across the globe. This work reviews over two decades (2002-2022) of SWAT studies conducted on Mediterranean watersheds. A total of 260 articles have been identified since the earliest documented use of the model in a Mediterranean catchment back in 2002; of which 62% were carried out in Greece, Italy, or Spain. SWAT applications increased significantly in recent years since 86% of the reviewed papers were published in the past decade. A major objective for most of the reviewed works was to check the applicability of SWAT to specific watersheds. A great number of publications included procedures of calibration and validation and reported performance results. SWAT applications in the Mediterranean region mainly cover water resources quantity and quality assessment and hydrologic and environmental impacts evaluation of land use and climate changes. Nevertheless, a tendency towards a multi-purpose use of SWAT is revealed. The numerous examples of SWAT combined with other tools and techniques outline the model's flexibility. Several studies performed constructive comparisons between Mediterranean watersheds' responses or compared SWAT to other models or methods. The effects of inputs on SWAT outputs and innovative model modifications and improvements were also the focus of some of the surveyed articles. However, a significant number of studies reported difficulties regarding data availability, as these are either scarce, have poor resolution or are not freely available. Therefore, it is highly recommended to identify and develop accurate model inputs and testing data to optimize the SWAT performance.

Multi-Variable SWAT Model Calibration Using Satellite-Based Evapotranspiration Data and Streamflow

In this study, monthly streamflow and satellite-based actual evapotranspiration data (AET) were used to evaluate the Soil and Water Assessment Tool (SWAT) model for the calibration of an experimental sub-basin with mixed land-use characteristics in Athens, Greece. Three calibration scenarios were performed using streamflow (i.e., single variable), AET (i.e., single variable), and streamflow–AET data together (i.e., multi-variable) to provide insights into how different calibration scenarios affect the hydrological processes of a catchment with complex land use characteristics. The actual evapotranspiration data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The calibration was achieved with the use of the SUFI-2 algorithm in the SWAT-CUP program. The results suggested that the single variable calibrations showed moderately better performance than the multi-variable calibration. However, the multi-variable calibration scenario displayed acceptable outcomes for both streamflow and actual evapotranspiration and indicated reasonably good streamflow estimations (NSE = 0.70; R2 = 0.86; PBIAS = 6.1%). The model under-predicted AET in all calibration scenarios during the dry season compared to MODIS satellite-based AET. Overall, this study demonstrated that satellite-based AET data, together with streamflow data, can enhance model performance and be a good choice for watersheds lacking sufficient spatial data and observations.

Three-Dimensional Hole Size (3DHS) Approach for Water Flow Turbulence Analysis over Emerging Sand Bars: Flume-Scale Experiments

The many hydrodynamic implications associated with the geomorphological evolution of braided rivers are still not profoundly examined in both experimental and numerical analyses, due to the generation of three-dimensional turbulence structures around sediment bars. In this experimental research, the 3D velocity fields were measured through an acoustic Doppler velocimeter during flume-scale laboratory experimental runs over an emerging sand bar model, to reproduce the hydrodynamic conditions of real braided rivers, and the 3D Turbulent Kinetic Energy (TKE) components were analyzed and discussed here in detail. Given the three-dimensionality of the examined water flow in the proximity of the experimental bar, the statistical analysis of the octagonal bursting events was applied to analyze and discuss the different flume-scale 3D turbulence structures. The main novelty of this study is the proposal of the 3D Hole Size (3DHS) analysis, used for separating the extreme events observed in the experimental runs from the low-intensity events.

Oil Palm Yield Estimation Based on Vegetation and Humidity Indices Generated from Satellite Images and Machine Learning Techniques

Palm oil has become one of the most consumed vegetable oils in the world, and it is a key element in profitable global value chains. In Costa Rica, oil palm cultivation is one of the three crops with the largest occupied agricultural area. The objective of this study was to explain and predict yield in safe time lags for production management by using free-access satellite images. To this end, machine learning methods were performed to a 20-year data set of an oil palm plantation located in the Central Pacific Region of Costa Rica and the corresponding vegetation indices obtained from LANDSAT satellite images. Since the best correlations corresponded to a one-year time lag, the predictive models Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Recursive Partitioning and Regression Trees (RPART), and Neural Network (NN) were built for a Time-lag 1. These models were applied to all genetic material and to the predominant variety (AVROS) separately. While NN showed the best performance for multispecies information (r2 = 0.8139, NSE = 0.8131, RMSE = 0.3437, MAE = 0.2605), RF showed a better fit for AVROS (r2 = 0.8214, NSE = 0.8020, RMSE = 0.3452, MAE = 0.2669). The most relevant vegetation indices (NDMI, MSI) are related to water in the plant. The study also determined that data distribution must be considered for the prediction and evaluation of the oil palm yield in the area under study. The estimation methods of this study provide information on the identification of important variables (NDMI) to characterize palm oil yield. Additionally, it generates a scenario with acceptable uncertainties on the yield forecast one year in advance. This information is of direct interest to the oil palm industry.

Multi-Step Calibration Approach for SWAT Model Using Soil Moisture and Crop Yields in a Small Agricultural Catchment

The quantitative prediction of hydrological components through hydrological models could serve as a basis for developing better land and water management policies. This study provides a comprehensive step by step modelling approach for a small agricultural watershed using the SWAT model. The watershed is situated in Petzenkirchen in the western part of Lower Austria and has total area of 66 hectares. At present, 87% of the catchment area is arable land, 5% is used as pasture, 6% is forested and 2% is paved. The calibration approach involves a sequential calibration of the model starting from surface runoff, and groundwater flow, followed by crop yields and then soil moisture, and finally total streamflow and sediment yields. Calibration and validation are carried out using the r-package SWATplusR. The impact of each calibration step on sediment yields and total streamflow is evaluated. The results of this approach are compared with those of the conventional model calibration approach, where all the parameters governing various hydrological processes are calibrated simultaneously. Results showed that the model was capable of successfully predicting surface runoff, groundwater flow, soil profile water content, total streamflow and sediment yields with Nash-Sutcliffe efficiency (NSE) of greater than 0.75. Crop yields were also well simulated with a percent bias (PBIAS) ranging from −17% to 14%. Surface runoff calibration had the highest impact on streamflow output, improving NSE from 0.39 to 0.77. The step-wise calibration approach performed better for streamflow prediction than the simultaneous calibration approach. The results of this study show that the step-wise calibration approach is more accurate, and provides a better representation of different hydrological components and processes than the simultaneous calibration approach.

SWAT Model Adaptability to a Small Mountainous Forested Watershed in Central Romania

This study aims to build and test the adaptability and reliability of the Soil and Water Assessment Tool hydrological model in a small mountain forested watershed. This ungauged watershed covers 184 km2 and supplies 90% of blue water for the Brașov metropolitan area, the second largest metropolitan area of Romania. After building a custom database at the forest management compartment level, the SWAT model was run. Further, using the SWAT-CUP software under the SUFI2 algorithm, we identified the most sensitive parameters required in the calibration and validation stage. Moreover, the sensitivity analysis revealed that the surface runoff is mainly influenced by soil, groundwater and vegetation condition parameters. The calibration was carried out for 2001–2010, while the 1996–1999 period was used for model validation. Both procedures have indicated satisfactory performance and a lower uncertainty of model results in replicating river discharge compared with observed discharge. This research demonstrates that the SWAT model can be applied in small ungauged watersheds after an appropriate parameterisation of its databases. Furthermore, this tool is appropriate to support decision-makers in conceiving sustainable watershed management. It also guides prioritising the most suitable measures to increase the river basin resilience and ensure the water demand under climate change.

Quantifying the Contribution of Agricultural and Urban Non-Point Source Pollutant Loads in Watershed with Urban Agglomeration

Urban agglomeration is a new characteristic of the Chinese urbanization process, and most of the urban agglomeration is located in the same watershed. Thus, urban non-point source (NPS) pollution, especially the characteristic pollutants in urban areas, aggravates NPS pollution at the watershed scale. Many agricultural studies have been performed at the watershed scale; however, few studies have provided a study framework for estimating the urban NPS pollution in an urban catchment. In this study, an integrated approach for estimating agricultural and urban NPS pollution in an urban agglomeration watershed was proposed by coupling the Soil and Water Assessment Tool (SWAT), the event mean concentration (EMC) method and the Storm Water Management Model (SWMM). The Hun-Taizi River watershed, which contains a typical urban agglomeration and is located in northeastern China, was chosen as the study case. The results indicated that the per unit areas of total nitrogen (TN) and total phosphorus (TP) in the built-up area simulated by the EMC method were 11.9% and 23 times higher than the values simulated by the SWAT. The SWAT greatly underestimated the nutrient yield in the built-up area. This integrated method could provide guidance for water environment management plans considering agricultural and urban NPS pollution in an urban catchment.

Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China

As a supplement to gauge observation data, many satellite observations have been used for hydrology and water resource research. This study aims to analyze the quality of the Integrated Multisatellite Retrieval for Global Precipitation Measurement (GPM IMERG) products and their hydrological utility in the Xinfengjiang River reservoir basin (XRRB), a mountainous region in southern China. The grid-based soil and water assessment tool (SWAT) model was used to construct a hydrological model of the XRRB based on two scenarios. The results showed that on a daily scale, the IMERG final run (FR) product was more accurate than the others, with Pearson’s correlation coefficients (CORR) of 0.61 and 0.71 on the grid accumulation scale and the average scale, respectively, and a relative bias (BIAS) of 0.01. In Scenario I (the SWAT model calibrated by rain gauge data), the IMERG-based simulation showed acceptable hydrologic prediction ability on the daily scale and satisfactory hydrological performance on the monthly scale. In Scenario II (the SWAT model calibrated by the FR), the hydrological performances of the FR on the daily and monthly scales were slightly better than those in Scenario I (the CORR was 0.64 and 0.85, the BIAS was 0.01 and −0.02, and the NSE was 0.43 and 0.84). These results showed the potential of the FR for hydrological modeling in tropical mountain watersheds in areas where information is scarce. This study is useful for hydrological, meteorological, and disaster studies in developing countries or remote areas with sparse or low-quality networks of ground-based observation stations.

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

Quantitative Analysis of the Influencing Factors and Their Interactions in Runoff Generation in a Karst Basin of Southwestern China

The unique geological conditions of karst regions create highly heterogeneous habitat characteristics, and the addition of human disturbance results in rocky desertification. Water and soil loss are the core questions, and moreover, runoff is the key factor in this process. To further investigate these problems, a typical karst peak cluster depression in southwestern China was selected for this study. Based on the optimal simulation of the runoff yield and flow in this area, the factor detectors and interaction detectors in the geographical detector method were used to quantitatively analyze the factors influencing runoff and their interactions for different geomorphic types. The results show that: (1) the three main factors influencing the total river runoff, surface runoff, and groundwater are landscape fragmentation, land use type, and precipitation, but the ranking of these main influencing factors in each geomorphic type region exists different; (2) the dominant factor in the relatively higher elevation regions is precipitation; (3) the interaction detector results reveal that the interactions between factors enhance the overall influence of a single factor on the runoff generation in all of the geomorphic type regions, including two interaction types of nonlinear enhancement and bifactor enhancement; and (4) the interactions between the factors in the middle elevation plain, middle elevation terrace, and middle relief mountain regions are stronger than those in the middle elevation hill and small relief mountain regions. Quantitative analysis of the factors influencing runoff in karst areas cannot only promote optimization of the water and soil services, but it also provides a scientific basis for improving the comprehensive treatment of rocky desertification.