The changing patterns of watersheds in a landscape, driven by human activities, play an important role in non-point source pollution processes. This paper aims to improve the location-weighted landscape contrast index...The changing patterns of watersheds in a landscape, driven by human activities, play an important role in non-point source pollution processes. This paper aims to improve the location-weighted landscape contrast index using remote sensing and GIS technology to account for the effects of scale and ecological processes. The hydrological response unit(HRU) with a single land use and soil type was used as the smallest unit. The relationship between the landscape index and typical ecological processes was established by describing the influence of the landscape pattern on non-point source pollution. To verify the research method, this paper used the Yanshi River basin as a study area. The results showed that the relative intensity of non-point source pollution in different regions of the watershed and the location-weighted landscape contrast index based on the minimum HRU can qualitatively reflect the risk of regional nutrient loss.展开更多
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. H...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.展开更多
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...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.展开更多
为合理确定不同精度土地利用数据下SWAT模型水文响应单元(Hydrologic research unit, HRU)划分阈值,提高模型模拟精度和效率,以漳河灌区杨树垱流域为研究区,设置土地利用、梯度和土壤类型的组合方案,探究5和10 m两种不同土地利用精度对...为合理确定不同精度土地利用数据下SWAT模型水文响应单元(Hydrologic research unit, HRU)划分阈值,提高模型模拟精度和效率,以漳河灌区杨树垱流域为研究区,设置土地利用、梯度和土壤类型的组合方案,探究5和10 m两种不同土地利用精度对HRU阈值划分和径流模拟效果的影响。结果表明,不同方案下R2和NSE>0.6,RSR<0.7,模型可较好模拟杨树垱流域径流变化过程。在HRU划分过程中,土地利用类型被合并比例随阈值增加而增加,阈值达30%时,农田和水面变化率均超过30%,在低精度土地利用数据下更明显。不同精度土地利用数据在同一阈值下模拟结果相差较小,3种评价指标相对误差均小于15%;在5 m精度土地利用数据下,HRU阈值设定为10%时模拟精度和运行效率最佳。展开更多
利用混合模型综合模拟不透水表面的时空演化规律及其水环境效应是定量探索湖滨型城市可持续发展模式的有效途径。以滇池流域为研究区域,借助遥感与GIS技术探索2000—2016年城市化过程中不透水表面的时空演变特征及其扩张规律,并采用分...利用混合模型综合模拟不透水表面的时空演化规律及其水环境效应是定量探索湖滨型城市可持续发展模式的有效途径。以滇池流域为研究区域,借助遥感与GIS技术探索2000—2016年城市化过程中不透水表面的时空演变特征及其扩张规律,并采用分区元胞自动机模型对2021年和2031年不透水表面的分布进行模拟与预测。进而在子流域和水文响应单元的尺度上计算2000—2031年不透水表面的覆盖率(ISC),对滇池流域历史和未来的水环境城市非点源污染风险进行评价。结果表明:(1)滇池流域不透水表面的扩张具有以滇池湖体为中心向外辐射的显著特征,不透水表面面积增加了286.28 km 2,增长速率为17.9 km 2/年。2006—2009年增长最快为38.8 km 2/年,其覆盖率从2000年的10.16%增加到2016年的20.64%;(2)相比较不分区元胞自动机模型,分区元胞自动机模型在模拟用地变化时的精度有显著提升(Kappa系数提高超过16%,总体精度提高超过26%),可以借以模拟未来不透水表面的扩张情景;(3)子流域和水文响应单元两种尺度下的不透水表面覆盖率都逐年升高,城市非点源污染风险也逐年增大,若不加以重视,风险将会进一步增加。研究结果可为调整土地利用结构、协调城镇建设与水环境保护提供科学依据。展开更多
基金Supported by the National Key R&D Programs of China(Nos.2017YFB0504201,2015BAJ02B)the National Natural Science Foundation of China(Nos.61473286,61375002)the Natural Science Foundation of Hainan Province(No.20164178)
文摘The changing patterns of watersheds in a landscape, driven by human activities, play an important role in non-point source pollution processes. This paper aims to improve the location-weighted landscape contrast index using remote sensing and GIS technology to account for the effects of scale and ecological processes. The hydrological response unit(HRU) with a single land use and soil type was used as the smallest unit. The relationship between the landscape index and typical ecological processes was established by describing the influence of the landscape pattern on non-point source pollution. To verify the research method, this paper used the Yanshi River basin as a study area. The results showed that the relative intensity of non-point source pollution in different regions of the watershed and the location-weighted landscape contrast index based on the minimum HRU can qualitatively reflect the risk of regional nutrient loss.
基金Under the auspices of National Natural Science Foundation of China(No.31901153)Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA23070103)。
文摘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.
基金Primary funding for this work came from a USDA NRCS Conservation Innovation GrantThis work was also partially funded by the University of Michigan Graham Sustainability Instituteby the Great Lakes Restoration Initiative(administered by USEPA)through a NOAA-GLERL SOAR project.
文摘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.
文摘为合理确定不同精度土地利用数据下SWAT模型水文响应单元(Hydrologic research unit, HRU)划分阈值,提高模型模拟精度和效率,以漳河灌区杨树垱流域为研究区,设置土地利用、梯度和土壤类型的组合方案,探究5和10 m两种不同土地利用精度对HRU阈值划分和径流模拟效果的影响。结果表明,不同方案下R2和NSE>0.6,RSR<0.7,模型可较好模拟杨树垱流域径流变化过程。在HRU划分过程中,土地利用类型被合并比例随阈值增加而增加,阈值达30%时,农田和水面变化率均超过30%,在低精度土地利用数据下更明显。不同精度土地利用数据在同一阈值下模拟结果相差较小,3种评价指标相对误差均小于15%;在5 m精度土地利用数据下,HRU阈值设定为10%时模拟精度和运行效率最佳。
文摘利用混合模型综合模拟不透水表面的时空演化规律及其水环境效应是定量探索湖滨型城市可持续发展模式的有效途径。以滇池流域为研究区域,借助遥感与GIS技术探索2000—2016年城市化过程中不透水表面的时空演变特征及其扩张规律,并采用分区元胞自动机模型对2021年和2031年不透水表面的分布进行模拟与预测。进而在子流域和水文响应单元的尺度上计算2000—2031年不透水表面的覆盖率(ISC),对滇池流域历史和未来的水环境城市非点源污染风险进行评价。结果表明:(1)滇池流域不透水表面的扩张具有以滇池湖体为中心向外辐射的显著特征,不透水表面面积增加了286.28 km 2,增长速率为17.9 km 2/年。2006—2009年增长最快为38.8 km 2/年,其覆盖率从2000年的10.16%增加到2016年的20.64%;(2)相比较不分区元胞自动机模型,分区元胞自动机模型在模拟用地变化时的精度有显著提升(Kappa系数提高超过16%,总体精度提高超过26%),可以借以模拟未来不透水表面的扩张情景;(3)子流域和水文响应单元两种尺度下的不透水表面覆盖率都逐年升高,城市非点源污染风险也逐年增大,若不加以重视,风险将会进一步增加。研究结果可为调整土地利用结构、协调城镇建设与水环境保护提供科学依据。
