Identification of Suitable Hydrologic Response Unit Thresholds for Soil and Water Assessment Tool Streamflow Modelling

Use of a non-zero hydrologic response unit(HRU) threshold is an effective way of reducing unmanageable HRU numbers and simplifying computational cost in the Soil and Water Assessment Tool(SWAT) hydrologic modelling. However, being less representative of watershed heterogeneity and increasing the level of model output uncertainty are inevitable when minor HRU combinations are disproportionately eliminated. This study examined 20 scenarios by running the model with various HRU threshold settings to understand the mechanism of HRU threshold effects on watershed representation as well as streamflow predictions and identify the appropriate HRU thresholds. Findings show that HRU numbers decrease sharply with increasing HRU thresholds. Among different HRU threshold scenarios, the composition of land-use, soil, and slope all contribute to notable variations which are directly related to the model input parameters and consequently affect the streamflow predictions. Results indicate that saturated hydraulic conductivity, average slope of the HRU, and curve number are the three key factors affecting stream discharge when changing the HRU thresholds. It is also found that HRU thresholds have little effect on monthly model performance, while evaluation statistics for daily discharges are more sensitive than monthly results. For daily streamflow predictions, thresholds of 5%/5%/5%(land-use/soil/slope) are the optimum HRU threshold level for the watershed to allow full consideration of model accuracy and efficiency in the present work. Besides, the results provide strategies for selecting appropriate HRU thresholds based on the modelling goal.

Defining Soil and Water Assessment Tool(SWAT)hydrologic responseunits(HRUs)by field boundaries

The Soil and Water Assessment Tool(SWAT)is widely used to relate farm management practices to their impacts on surface waters at the watershed scale,yet its smallest spatial unit is not generally defined by physically meaningful boundaries.The hydrologic response unit(HRU)is the smallest spatial unit of the model,and the standard HRU definition approach lumps all similar land uses,soils,and slopes within a subbasin based upon user-defined thresholds.This standard method provides an efficient way to discretize large watersheds where simulation at the field scale may not be computationally feasible.In relatively smaller watersheds,however,defining HRUs to specific spatial locations bounded by property lines or field borders would often be advantageous,yet this is not currently possible within the ArcSWAT interface.In this study,a simple approach is demonstrated that defines HRUs by field boundaries through addition of uniquely named soils to the SWAT user soil database and creation of a field boundary layer with majority land use and soil attributes.Predictions of nitrogen,phosphorus,and sediment losses were compared in a case study watershed where SWAT was set up using both the standard HRU definition and field boundary approach.Watershed-scale results were reasonable and similar for both methods,but aggregating fields by majority soil type masked extremely high soil erosion predicted for a few soils.Results from field-based HRU delineation may be quite different from the standard approach due to choosing a majority soil type in each farm field.This approach is flexible such that any land use and soil data prepared for SWAT can be used and any shapefile boundary can divide HRUs.

Geo-cognitive computing method for identifying“source-sink”landscape patterns of river basin non-point source pollution

The aim of this study was to quantitatively evaluate the influences of landscape composition and spatial structure on the transmission process of non-point source pollutants in different regions.The location-weighted landscape contrast index,using the hydrological response unit(HRULCI)as the minimum research unit,was proposed in this paper.Through the description of the endemic landscape types and various geographical factors in the basin,the index calculation can reflect the impact of the“source-sink”landscape structure on the non-point source pollution in different regions and quantitatively evaluate the contribution of different landscape types and geographical factors to non-point source pollution.This study constructed a method of geo-cognitive computing for identifying“source-sink”landscape patterns of river basin non-point source pollution at two levels.1)The basin level:the spatial distribution and landscape combination of the entire basin are identified,and the crucial“source”and“sink”landscape types are obtained to measure the differences in the non-point source pollutant transmission processes between the“source”and“sink”landscapes in the different watersheds.2)The landscape level:HRULCI is calculated based on multiple geographical correction weighting factors.By using the idea of intersecting geographic information system(GIS)and landscape ecology,the landscape spatial pattern and ecological processes are linked.Compared with the traditional method for studying landscape patterns,the calculation of HRULCI makes the proposed method more ecologically significant.Lastly,a case study was evaluated to verify the significance of the proposed research method by taking the Yanshi River basin,a sub-basin belonging to the Jiulong River basin located in Fujian Province,China,as the experimental study zone.The results showed that this method can reflect the spatial distribution characteristics of the“source-sink”types and their relationship with non-point source pollution.By comparing the resulting calculation based on HRULCI,the risk of nutrient loss and the influence of landscape patterns and ecological processes on non-point pollution in different catchments can be obtained.