Based on the developed distributed model for calculating astronomical solar radiation (ASR), monthly ASR with a resolution of 1 km×1 km for the rugged terrains of Yellow River Basin was calculated, with DEM data ...Based on the developed distributed model for calculating astronomical solar radiation (ASR), monthly ASR with a resolution of 1 km×1 km for the rugged terrains of Yellow River Basin was calculated, with DEM data as the general characterization of terrain. This model gives an all-sided consideration on factors that influence the ASR. Results suggest that (1) Annual ASR has a progressive decrease trend from south to north; (2) the magnitude order of seasonal ASR is: summer>spring>autumn>winter; (3) topographical factors have robust effect on the spatial distribution of ASR, particularly in winter when a lower sun elevation angle exists; (4) the ASR of slopes with a sunny exposure is generally 2 or 3 times that of slopes with a shading exposure and the extreme difference of ASR for different terrains is over 10 times in January; (5) the spatial differences of ASR are relatively small in summer when a higher sun elevation angle exists and the extreme difference of ASR for different terrains is only 16% in July; and (6) the sequence of topographical influence strength is: winter>autumn>spring>summer.展开更多
The study investigated the trend of extreme flood events in the Pearl River basin during 1951-2010. Stream flow data at 23 gauging stations were used for the study. The Pearson type III distribution was selected for t...The study investigated the trend of extreme flood events in the Pearl River basin during 1951-2010. Stream flow data at 23 gauging stations were used for the study. The Pearson type III distribution was selected for the flood frequency analysis. Results indicate that extreme flood events increase significantly in the Pearl River Basin since 1980. At the 23 gauging stations, there are 16 (70%) stations show positive (increasing) trends in 1981-2010. Most of the 16 stations are located along the West River and North River. While 7 (30%) stations show negative (decreasing) trends, and are found in the East River and the southeast region of the West River Basin.展开更多
For the Z-R relationship in radar-based rainfall estimation, the distribution of corresponding R values for a given Z value (or the corresponding Z value for a given R value) may be highly skewed. However, the traditi...For the Z-R relationship in radar-based rainfall estimation, the distribution of corresponding R values for a given Z value (or the corresponding Z value for a given R value) may be highly skewed. However, the traditional power-law model is physically deduced and fitted under the normal-distribution presumption of radar wave echoes associated with a rain rate value, and it may not be very appropriate. Considering this problem, the authors devised several generalized linear models with different forms and distribution presumptions to represent the Z-R relationship. Radar-reflectivity scans observed by a CINRAD/SC Doppler radar and 5-minute rainfall accumulation recorded by 10 ground gauges were used to fit these models. All data used in this study were collected during some large rainfalls of the period from 2005 to 2007. The radar and all gauges were installed in the catchment of the Yishu River, a branch of the Huaihe River in China. Three models based on normal distribution and a dBZ presumption of gamma distribution were fitted using maximum-likelihood techniques, which were resolved by genetic algorithms. Comparisons of estimated maximized likelihoods based on assumptions of gamma and normal distribution showed that all generalized linear models (GLMs) of presumed gamma distribution were better fitted than GLMs based on normal distribution. In a comparison of maximum-likelihood, the differences between these three models were small. Three error statistics were used to assess the agreement between radar estimated rainfall and gauge rainfall: relative bias (B), root mean square error (RMSE), and correlation coefficient (r). The results showed that no one model was excellent in all criteria. On the whole, the GLM-based models gave smaller relative bias than the traditional power-law model. It is suggested that validations conducted in many previous works should have been made against a specific criterion but overlooked others.展开更多
A grid and Green-Ampt based （Grid-GA）distributed hydrologic physical model was developed for flood simulation and forecasting in semi-humid and semi-arid basin. Based on topographical information of each grid cell e...A grid and Green-Ampt based （Grid-GA）distributed hydrologic physical model was developed for flood simulation and forecasting in semi-humid and semi-arid basin. Based on topographical information of each grid cell extracted fi＇om the digital elevation model （DEM） and Green-Ampt infiltration method, the Grid-GA model takes into consideration the redistribution of water content, and consists of vegetation and root interception, evapotranspiration, runoff generation via the excess infiltration mechanism, runoff concentration, and flow routing. The downslope redis- tribution of soil moisture is explicitly calculated on a grid basis, and water exchange among grids within runoff routing along the river drainage networks is taken into consideration. The proposed model and Xin＇anjiang model were ap- plied to the upper Lushi basin in the Luohe River, a tributary of the Yellow River, with an area of 4 716 km2 for flood simulation. Results show that both models perform well in flood simulation and can be used for flood forecasting in semi-humid and semi-arid region.展开更多
Modeling the hydrological processes at catchment scale requires a flexible distributed scheme to represent the catchment to- pography, river network and vegetation pattern. This study has developed a distributed schem...Modeling the hydrological processes at catchment scale requires a flexible distributed scheme to represent the catchment to- pography, river network and vegetation pattern. This study has developed a distributed scheme for eco-hydrological simulation in the upper Heihe River. Based on a 1 km x 1 km grid system, the study catchment is divided into 461 sub-catchments, whose main streams form the streamflow pathway. Furthermore, a 1 km grid is represented by a number of topographically similar ＂hillslope-valley＂ systems, and the hillslope is the basic unit of the eco-hydrological simulation. This model is tested with a simplified hydrological simulation focusing on soil-water dynamics and streamflow routing. Based on a 12-year simulation from 2001 to 2012, it is found that variability in hydrological behavior is closely associated with climatic and landscape condi- tions especially vegetation types. The subsurface and groundwater flows dominate the total river runoff. This implies that the soil freezing and thawing process would significantly influence the runoff generation in the upper Heihe basin. Furthermore, the runoff components and water balance characteristics vary among different vegetation types, showing the importance of coupling the vegetation pattern into catchment hydrological simulation. This paper also discusses the model improvement to be done in future study.展开更多
基金Major State Basic Research Development Program of ChinaNo.G19990436-01 No.G20000779
文摘Based on the developed distributed model for calculating astronomical solar radiation (ASR), monthly ASR with a resolution of 1 km×1 km for the rugged terrains of Yellow River Basin was calculated, with DEM data as the general characterization of terrain. This model gives an all-sided consideration on factors that influence the ASR. Results suggest that (1) Annual ASR has a progressive decrease trend from south to north; (2) the magnitude order of seasonal ASR is: summer>spring>autumn>winter; (3) topographical factors have robust effect on the spatial distribution of ASR, particularly in winter when a lower sun elevation angle exists; (4) the ASR of slopes with a sunny exposure is generally 2 or 3 times that of slopes with a shading exposure and the extreme difference of ASR for different terrains is over 10 times in January; (5) the spatial differences of ASR are relatively small in summer when a higher sun elevation angle exists and the extreme difference of ASR for different terrains is only 16% in July; and (6) the sequence of topographical influence strength is: winter>autumn>spring>summer.
基金supported by National Basic Research Program of China (No. 2010CB428405)Special Public Sector Research Program of Ministry of Water Resources(No.201301040 and 201301070)+2 种基金National Natural Science Foundation of China (No. 41001012)Foundation forthe Author of National Excellent Doctoral Dissertation of China (No. 201161)Qing Lan Project and Program for New Century Excellent Talents in University
文摘The study investigated the trend of extreme flood events in the Pearl River basin during 1951-2010. Stream flow data at 23 gauging stations were used for the study. The Pearson type III distribution was selected for the flood frequency analysis. Results indicate that extreme flood events increase significantly in the Pearl River Basin since 1980. At the 23 gauging stations, there are 16 (70%) stations show positive (increasing) trends in 1981-2010. Most of the 16 stations are located along the West River and North River. While 7 (30%) stations show negative (decreasing) trends, and are found in the East River and the southeast region of the West River Basin.
基金financially supported by the National Natural Science Foundation of China (Grant No. 40971024)the National Basic Research Program of China (Grant No. 2006CB400502)the Special Meteorology Project (GYHY(QX)2007-6-1)
文摘For the Z-R relationship in radar-based rainfall estimation, the distribution of corresponding R values for a given Z value (or the corresponding Z value for a given R value) may be highly skewed. However, the traditional power-law model is physically deduced and fitted under the normal-distribution presumption of radar wave echoes associated with a rain rate value, and it may not be very appropriate. Considering this problem, the authors devised several generalized linear models with different forms and distribution presumptions to represent the Z-R relationship. Radar-reflectivity scans observed by a CINRAD/SC Doppler radar and 5-minute rainfall accumulation recorded by 10 ground gauges were used to fit these models. All data used in this study were collected during some large rainfalls of the period from 2005 to 2007. The radar and all gauges were installed in the catchment of the Yishu River, a branch of the Huaihe River in China. Three models based on normal distribution and a dBZ presumption of gamma distribution were fitted using maximum-likelihood techniques, which were resolved by genetic algorithms. Comparisons of estimated maximized likelihoods based on assumptions of gamma and normal distribution showed that all generalized linear models (GLMs) of presumed gamma distribution were better fitted than GLMs based on normal distribution. In a comparison of maximum-likelihood, the differences between these three models were small. Three error statistics were used to assess the agreement between radar estimated rainfall and gauge rainfall: relative bias (B), root mean square error (RMSE), and correlation coefficient (r). The results showed that no one model was excellent in all criteria. On the whole, the GLM-based models gave smaller relative bias than the traditional power-law model. It is suggested that validations conducted in many previous works should have been made against a specific criterion but overlooked others.
基金Supported by National Natural Science Foundation of China (No.50479017)Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) (No. IRT0717)
文摘A grid and Green-Ampt based （Grid-GA）distributed hydrologic physical model was developed for flood simulation and forecasting in semi-humid and semi-arid basin. Based on topographical information of each grid cell extracted fi＇om the digital elevation model （DEM） and Green-Ampt infiltration method, the Grid-GA model takes into consideration the redistribution of water content, and consists of vegetation and root interception, evapotranspiration, runoff generation via the excess infiltration mechanism, runoff concentration, and flow routing. The downslope redis- tribution of soil moisture is explicitly calculated on a grid basis, and water exchange among grids within runoff routing along the river drainage networks is taken into consideration. The proposed model and Xin＇anjiang model were ap- plied to the upper Lushi basin in the Luohe River, a tributary of the Yellow River, with an area of 4 716 km2 for flood simulation. Results show that both models perform well in flood simulation and can be used for flood forecasting in semi-humid and semi-arid region.
基金supported by the National Natural Science Foundation of China(Grant No.91225302)
文摘Modeling the hydrological processes at catchment scale requires a flexible distributed scheme to represent the catchment to- pography, river network and vegetation pattern. This study has developed a distributed scheme for eco-hydrological simulation in the upper Heihe River. Based on a 1 km x 1 km grid system, the study catchment is divided into 461 sub-catchments, whose main streams form the streamflow pathway. Furthermore, a 1 km grid is represented by a number of topographically similar ＂hillslope-valley＂ systems, and the hillslope is the basic unit of the eco-hydrological simulation. This model is tested with a simplified hydrological simulation focusing on soil-water dynamics and streamflow routing. Based on a 12-year simulation from 2001 to 2012, it is found that variability in hydrological behavior is closely associated with climatic and landscape condi- tions especially vegetation types. The subsurface and groundwater flows dominate the total river runoff. This implies that the soil freezing and thawing process would significantly influence the runoff generation in the upper Heihe basin. Furthermore, the runoff components and water balance characteristics vary among different vegetation types, showing the importance of coupling the vegetation pattern into catchment hydrological simulation. This paper also discusses the model improvement to be done in future study.
