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 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
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 ...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 展开更多
The Xinanjiang(XAJ)model has been successfully applied in humid and semi-humid regions.Considering the geomorphologic factors to accurately estimate floods,this study adopted the geomorphologic instantaneous unit hydr...The Xinanjiang(XAJ)model has been successfully applied in humid and semi-humid regions.Considering the geomorphologic factors to accurately estimate floods,this study adopted the geomorphologic instantaneous unit hydrograph(GIUH)method to calculate the surface runoff instead of the experience unit hydrograph(EUH)in the original model.The geomorphologic factors of the case study basin were obtained by using a digital elevation model(DEM)and the Terrain analysis using Digital Elevation Models(TauDEM).Furthermore,the dynamic Muskingum model was used for the channel flood routing.This study focused on the simulation of heavy precipitation and floods over the Chong River,which is a tributary river to the Songhua River on the right bank in northeast China.The detailed steps of the method were shown,up to the estimated value of flood runoff discharges and flood peaks and their comparison with observed values.The average deterministic coefficients(DCs)of model calibration and validation were 0.89 and 0.83,respectively.The results show that the model precision is high and the model is feasible for flood forecasting.Lastly,some methodological perspectives to enhance the method are presented.展开更多
文摘碳储量(Carbon storage,CS)影响陆地生态系统碳循环过程的稳定和平衡,定量评价CS时空变化与未来预测是实现区域可持续发展的关键环节,对区域生态环境管理具有重要意义。以黑土区典型农业小流域—阿什河流域为研究区,探讨以小流域为研究尺度的CS“历史-未来”时空变化,整合不同尺度下CS进而制定碳管理目标。结果表明,利用生态系统服务和权衡的综合评估模型(Integrated valuation of ecosystem services and trade-offs,InVEST)量化1995—2020年CS,总体减少171.42×104 t,年均损失达到6.87×104 t,以2010—2015年CS下降最显著。CS空间分布具有异质性,其高值区分布在流域上游区域。通过整合行政单元和水文单元尺度下的CS,以管理职责为导向,采用聚类分析方法划分该流域碳管理单元(3个单元和5个亚单元)。对各单元CS制定管理目标,利用CA-Markov模型预测自然情景和政策情景下2025年CS,表明碳管理措施的制定可有效减缓区域CS损失,减缓效率达47.52%。该研究可为明确黑土区农业小流域CS发展模式和可持续发展策略提供科学参考。
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funded by the National Key R&D Programs of China(Grant No.2017YFB0504201,2015BAJ02B02)the Natural Science Foundation of China(Grant No.61473286,61375002)the Natural Science Foundation of Hainan Province(Grant No.20164178).
文摘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
文摘The Xinanjiang(XAJ)model has been successfully applied in humid and semi-humid regions.Considering the geomorphologic factors to accurately estimate floods,this study adopted the geomorphologic instantaneous unit hydrograph(GIUH)method to calculate the surface runoff instead of the experience unit hydrograph(EUH)in the original model.The geomorphologic factors of the case study basin were obtained by using a digital elevation model(DEM)and the Terrain analysis using Digital Elevation Models(TauDEM).Furthermore,the dynamic Muskingum model was used for the channel flood routing.This study focused on the simulation of heavy precipitation and floods over the Chong River,which is a tributary river to the Songhua River on the right bank in northeast China.The detailed steps of the method were shown,up to the estimated value of flood runoff discharges and flood peaks and their comparison with observed values.The average deterministic coefficients(DCs)of model calibration and validation were 0.89 and 0.83,respectively.The results show that the model precision is high and the model is feasible for flood forecasting.Lastly,some methodological perspectives to enhance the method are presented.
