A new algorithm to automatically extract drainage networks and catchments based on triangulation irregular networks(TINs) digital elevation model(DEM) was developed. The flow direction in this approach is determined b...A new algorithm to automatically extract drainage networks and catchments based on triangulation irregular networks(TINs) digital elevation model(DEM) was developed. The flow direction in this approach is determined by computing the spatial gradient of triangle and triangle edges. Outflow edge was defined by comparing the contribution area that is separated by the steepest descent of the triangle. Local channels were then tracked to build drainage networks. Both triangle edges and facets were considered to construct flow path. The algorithm has been tested in the site for Hawaiian Island of Kaho'olawe, and the results were compared with those calculated by ARCGIS as well as terrain map. The reported algorithm has been proved to be a reliable approach with high efficiency to generate well-connected and coherent drainage networks.展开更多
Concern on alteration of sediment natural flow caused by developments of water resources system, has been addressed in many river basins around the world especially in developing and remote regions where sediment data...Concern on alteration of sediment natural flow caused by developments of water resources system, has been addressed in many river basins around the world especially in developing and remote regions where sediment data are poorly gauged or ungauged. Since suspended sediment load (SSL) is predominant, the objectives of this research are to: 1) simulate monthly average SSL (SSLm) of four catchments using artificial neural network (ANN);2) assess the application of the calibrated ANN (Cal-ANN) models in three ungauged catchment representatives (UCR) before using them to predict SSLm of three actual ungauged catchments (AUC) in the Tonle Sap River Basin;and 3) estimate annual SSL (SSLA) of each AUC for the case of with and without dam-reservoirs. The model performance for total load (SSLT) prediction was also investigated because it is important for dam-reservoir management. For model simulation, ANN yielded very satisfactory results with determination coefficient (R2) ranging from 0.81 to 0.94 in calibration stage and 0.63 to 0.87 in validation stage. The Cal-ANN models also performed well in UCRs with R2 ranging from 0.59 to 0.64. From the result of this study, one can estimate SSLm and SSLT of ungauged catchments with an accuracy of 0.61 in term of R2 and 34.06% in term of absolute percentage bias, respectively. SSLA of the AUCs was found between 159,281 and 723,580 t/year. In combination with Brune’s method, the impact of dam-reservoirs could reduce SSLA between 47% and 68%. This result is key information for sustainable development of such infrastructures.展开更多
Simulation of runoff in ungauged catchments has always been a challenging issue, receiving significant attention more importantly in practical applications. This study aims at calibration of an Artificial Neural Netwo...Simulation of runoff in ungauged catchments has always been a challenging issue, receiving significant attention more importantly in practical applications. This study aims at calibration of an Artificial Neural Network (ANN) model which is capable to apply in an ungauged basin. The methodology is applied to two sub-catchments located in the Northern East of Iran. To examine the effect of physical characteristics of the catchment on the capability of the model generalization, it is attempted to synthesize effective parameters using empirical methods of runoff estimation. Firstly, the model was designed for a pilot sub-catchment and the statistical comparison between simulated runoff, and target depicted the capability of ANN to accurately estimate runoff over a catchment. Then, the calibrated model was generalized to another sub-catchment assumed as an ungauged basin while there are runoff data to compare the result. The result showed that the designed model is relatively capable to estimate monthly runoff for a homogenous ungauged catchment. The method presented in this study in addition to adding effective spatial parameters in simulation runoff and calibration of model by using empirical methods and the integration of any useful accessible data, examines the adaptability of model to an ungauged catchment.展开更多
介绍了自动提取数字河网的常用方法和不足,阐述了利用DEM和DRLN(digital river and lake network)的改进算法的基本思路。基于全球陆地一公里基础高程GLOBE数据,利用遥感影像获得的自然流域水系矢量数据对DEM进行重新处理,自动提取了汉...介绍了自动提取数字河网的常用方法和不足,阐述了利用DEM和DRLN(digital river and lake network)的改进算法的基本思路。基于全球陆地一公里基础高程GLOBE数据,利用遥感影像获得的自然流域水系矢量数据对DEM进行重新处理,自动提取了汉江流域的数字河网,能够有效避免了原始DEM可能造成的错误。最后,利用ArcHydro工具构建了具有拓扑关系的水文网络,从而为进一步开展汉江流域分布式水文模拟和计算以及水资源优化配置等分析提供了充足的空间信息。展开更多
基金the National Basic Research Program(973)of China(No.2007CB714103)
文摘A new algorithm to automatically extract drainage networks and catchments based on triangulation irregular networks(TINs) digital elevation model(DEM) was developed. The flow direction in this approach is determined by computing the spatial gradient of triangle and triangle edges. Outflow edge was defined by comparing the contribution area that is separated by the steepest descent of the triangle. Local channels were then tracked to build drainage networks. Both triangle edges and facets were considered to construct flow path. The algorithm has been tested in the site for Hawaiian Island of Kaho'olawe, and the results were compared with those calculated by ARCGIS as well as terrain map. The reported algorithm has been proved to be a reliable approach with high efficiency to generate well-connected and coherent drainage networks.
文摘Concern on alteration of sediment natural flow caused by developments of water resources system, has been addressed in many river basins around the world especially in developing and remote regions where sediment data are poorly gauged or ungauged. Since suspended sediment load (SSL) is predominant, the objectives of this research are to: 1) simulate monthly average SSL (SSLm) of four catchments using artificial neural network (ANN);2) assess the application of the calibrated ANN (Cal-ANN) models in three ungauged catchment representatives (UCR) before using them to predict SSLm of three actual ungauged catchments (AUC) in the Tonle Sap River Basin;and 3) estimate annual SSL (SSLA) of each AUC for the case of with and without dam-reservoirs. The model performance for total load (SSLT) prediction was also investigated because it is important for dam-reservoir management. For model simulation, ANN yielded very satisfactory results with determination coefficient (R2) ranging from 0.81 to 0.94 in calibration stage and 0.63 to 0.87 in validation stage. The Cal-ANN models also performed well in UCRs with R2 ranging from 0.59 to 0.64. From the result of this study, one can estimate SSLm and SSLT of ungauged catchments with an accuracy of 0.61 in term of R2 and 34.06% in term of absolute percentage bias, respectively. SSLA of the AUCs was found between 159,281 and 723,580 t/year. In combination with Brune’s method, the impact of dam-reservoirs could reduce SSLA between 47% and 68%. This result is key information for sustainable development of such infrastructures.
文摘Simulation of runoff in ungauged catchments has always been a challenging issue, receiving significant attention more importantly in practical applications. This study aims at calibration of an Artificial Neural Network (ANN) model which is capable to apply in an ungauged basin. The methodology is applied to two sub-catchments located in the Northern East of Iran. To examine the effect of physical characteristics of the catchment on the capability of the model generalization, it is attempted to synthesize effective parameters using empirical methods of runoff estimation. Firstly, the model was designed for a pilot sub-catchment and the statistical comparison between simulated runoff, and target depicted the capability of ANN to accurately estimate runoff over a catchment. Then, the calibrated model was generalized to another sub-catchment assumed as an ungauged basin while there are runoff data to compare the result. The result showed that the designed model is relatively capable to estimate monthly runoff for a homogenous ungauged catchment. The method presented in this study in addition to adding effective spatial parameters in simulation runoff and calibration of model by using empirical methods and the integration of any useful accessible data, examines the adaptability of model to an ungauged catchment.
文摘介绍了自动提取数字河网的常用方法和不足,阐述了利用DEM和DRLN(digital river and lake network)的改进算法的基本思路。基于全球陆地一公里基础高程GLOBE数据,利用遥感影像获得的自然流域水系矢量数据对DEM进行重新处理,自动提取了汉江流域的数字河网,能够有效避免了原始DEM可能造成的错误。最后,利用ArcHydro工具构建了具有拓扑关系的水文网络,从而为进一步开展汉江流域分布式水文模拟和计算以及水资源优化配置等分析提供了充足的空间信息。
