Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield(SLY)in ungauged watersheds. However, using all the variables might reduce the applic...Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield(SLY)in ungauged watersheds. However, using all the variables might reduce the applicability of these models. Therefore, data reduction techniques(DRTs), e.g., principal component analysis(PCA), Gamma test(GT), and stepwise regression(SR), have been used to select the most effective variables. The artificial neural network(ANN) and multiple linear regression(MLR) are also common tools for SLY modeling. We conducted this study(1) to obtain the most effective variables influencing SLY through DRTs including PCA, GT, and SR, and then, to use them as input data for ANN and MLR; and(2) to provide the best SLY models. Accordingly, we used 14 physiographic, climatic, and hydrologic parameters from 42 watersheds in the Hyrcanian forest region(in northern Iran). The most effective variables as determined through DRTs as well as the original data sets were used as the input data for ANN and MLR in order to provide an SLY model. The results indicated that the SLY models provided by ANN performed much better than the MLR models, and the GT-ANN model was the best. The determination of coefficient,relative error, root mean square error, and bias were 99.9%, 26%, 323 t/year, and 6 t/year in the calibration period, and 70%, 43%, 456 t/year, and 407 t/year in the validation period, respectively. Overall, selecting the main factors that influence SLY and using artificial intelligence tools can be useful for water resources managers to quickly determine the behavior of SLY in ungauged watersheds.展开更多
Long-term probabilistic prediction of extreme rainfall at the regional scale is a significant tool in the mitigation of hydro-geological disasters: it actually provides the starting point in the design of strategic hy...Long-term probabilistic prediction of extreme rainfall at the regional scale is a significant tool in the mitigation of hydro-geological disasters: it actually provides the starting point in the design of strategic hydraulic infrastructures and emergency plans. A crucial task of regional estimation of extreme rainfall is how to include the complex effects of orographic barriers in a mathematical model for Intensity-Duration-Frequency (IDF) curves. Here, an analysis of how orography can affect extreme rainfall at different durations is presented for three orographic systems that are very relevant for hydrological risk assessment in the Campania Region in Southern Italy. Then, we introduce a power law model to link the amplification factor to the duration, thus allowing a simple and effective enhancement of the IDF model in mountainous areas.展开更多
A new generation of numerical prediction system GRAPES (a short form of Global/Regional Assimilation and PrEdiction System) was set up in China Meteorological Administration (CMA). This paper focuses on the scientific...A new generation of numerical prediction system GRAPES (a short form of Global/Regional Assimilation and PrEdiction System) was set up in China Meteorological Administration (CMA). This paper focuses on the scientific design and preliminary results of the numerical prediction model in GRAPES, including basic idea and strategy of the general scientific design, multi-scale dynamic core, physical package configuration, architecture and parallelization of the codes. A series of numerical experiments using the real data with horizontal resolutions from 10 to 280 km and idealized experiments with very high resolution up to 100 m are conducted, giving encouraging results supporting the multi-scale application of GRAPES. The results of operational implementation of GRAPES model in some NWP centers are also presented with stress at evaluations of the capability to predict the main features of precipitation in China. Finally the issues to be dealt with for further development are discussed.展开更多
在地基GPS气象学中,湿延迟(zenith wet delay,ZWD)与可降水量(precipitable water vapor,PWV)的转换系数K是一个重要的参数。本文利用中国低纬度地区(20.03°N^28.2°N)20个探空站2008~2011年的探空数据,分析了K值随测站纬度和...在地基GPS气象学中,湿延迟(zenith wet delay,ZWD)与可降水量(precipitable water vapor,PWV)的转换系数K是一个重要的参数。本文利用中国低纬度地区(20.03°N^28.2°N)20个探空站2008~2011年的探空数据,分析了K值随测站纬度和海拔的变化特征,并建立了一种无需站点气象数据,仅与站点纬度、年积日和海拔相关的区域转换系数K值模型。结果表明,各站平均K值分别随纬度、海拔的增大而减小,减小速率分别为0.0002/(°)和0.002/km;本文建立的区域模型的精度与单站模型或者通过加权平均温度(Tm)获得的转换系数K值的精度相当,可用于该区域无气象数据条件下GPS反演PWV。展开更多
基金supported by the Department of Environmental Science,Urmia Lake Research Institute,Urmia University
文摘Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield(SLY)in ungauged watersheds. However, using all the variables might reduce the applicability of these models. Therefore, data reduction techniques(DRTs), e.g., principal component analysis(PCA), Gamma test(GT), and stepwise regression(SR), have been used to select the most effective variables. The artificial neural network(ANN) and multiple linear regression(MLR) are also common tools for SLY modeling. We conducted this study(1) to obtain the most effective variables influencing SLY through DRTs including PCA, GT, and SR, and then, to use them as input data for ANN and MLR; and(2) to provide the best SLY models. Accordingly, we used 14 physiographic, climatic, and hydrologic parameters from 42 watersheds in the Hyrcanian forest region(in northern Iran). The most effective variables as determined through DRTs as well as the original data sets were used as the input data for ANN and MLR in order to provide an SLY model. The results indicated that the SLY models provided by ANN performed much better than the MLR models, and the GT-ANN model was the best. The determination of coefficient,relative error, root mean square error, and bias were 99.9%, 26%, 323 t/year, and 6 t/year in the calibration period, and 70%, 43%, 456 t/year, and 407 t/year in the validation period, respectively. Overall, selecting the main factors that influence SLY and using artificial intelligence tools can be useful for water resources managers to quickly determine the behavior of SLY in ungauged watersheds.
文摘Long-term probabilistic prediction of extreme rainfall at the regional scale is a significant tool in the mitigation of hydro-geological disasters: it actually provides the starting point in the design of strategic hydraulic infrastructures and emergency plans. A crucial task of regional estimation of extreme rainfall is how to include the complex effects of orographic barriers in a mathematical model for Intensity-Duration-Frequency (IDF) curves. Here, an analysis of how orography can affect extreme rainfall at different durations is presented for three orographic systems that are very relevant for hydrological risk assessment in the Campania Region in Southern Italy. Then, we introduce a power law model to link the amplification factor to the duration, thus allowing a simple and effective enhancement of the IDF model in mountainous areas.
基金Key Technologies Research and Development Program (Grant No. 2001BA607B02)National Key Technology Research and Development Program (Grant No. 2006BAC02B03)National Natural Science Foundation of China (Grant No. 40575050)
文摘A new generation of numerical prediction system GRAPES (a short form of Global/Regional Assimilation and PrEdiction System) was set up in China Meteorological Administration (CMA). This paper focuses on the scientific design and preliminary results of the numerical prediction model in GRAPES, including basic idea and strategy of the general scientific design, multi-scale dynamic core, physical package configuration, architecture and parallelization of the codes. A series of numerical experiments using the real data with horizontal resolutions from 10 to 280 km and idealized experiments with very high resolution up to 100 m are conducted, giving encouraging results supporting the multi-scale application of GRAPES. The results of operational implementation of GRAPES model in some NWP centers are also presented with stress at evaluations of the capability to predict the main features of precipitation in China. Finally the issues to be dealt with for further development are discussed.
文摘在地基GPS气象学中,湿延迟(zenith wet delay,ZWD)与可降水量(precipitable water vapor,PWV)的转换系数K是一个重要的参数。本文利用中国低纬度地区(20.03°N^28.2°N)20个探空站2008~2011年的探空数据,分析了K值随测站纬度和海拔的变化特征,并建立了一种无需站点气象数据,仅与站点纬度、年积日和海拔相关的区域转换系数K值模型。结果表明,各站平均K值分别随纬度、海拔的增大而减小,减小速率分别为0.0002/(°)和0.002/km;本文建立的区域模型的精度与单站模型或者通过加权平均温度(Tm)获得的转换系数K值的精度相当,可用于该区域无气象数据条件下GPS反演PWV。
