Hydrological models are crucial for characterizing large-scale water quantity variations and correcting GNSS reference station vertical displacements.We evaluated the robustness of multiple models,such as the Global L...Hydrological models are crucial for characterizing large-scale water quantity variations and correcting GNSS reference station vertical displacements.We evaluated the robustness of multiple models,such as the Global Land Data Assimilation System (GLDAS),the Famine Early Warning System Network Land Data Assimilation System (FLDAS),the National Centers for Environmental Prediction (NCEP),and the WaterGAP Global Hydrology Model (WGHM).Inter-model and outer comparisons with Global Positioning System (GPS) coordinate time series,satellite gravity field Mascon solutions,and Global Precipitation Climatology Centre (GPCC) guide our assessment.Results confirm WGHM's 26% greater effectiveness in correcting nonlinear variations in GPS height time series compared to NCEP.In the Amazon River Basin,a 5-month lag between FLDAS,GLDAS,and satellite gravity results is observed.In eastern Asia and Australia,NCEP's Terrestrial Water Storage Changes (TWSC)-derived surface displacements correlate differently with precipitation compared to other models.Three combined hydrological models (H-VCE,H-EWM,and H-CVM) utilizing Variance Component Estimation (VCE),Entropy Weight Method (EWM),and Coefficient of Variation Method (CVM) are formulated.Correcting nonlinear variations with combined models enhances global GPS height scatter by 15%-17%.Correlation with precipitation increases by 25%-30%,and with satellite gravity,rises from 0.2 to 0.8 at maximum.The combined model eliminates time lag in the Amazon Basin TWSC analysis,exhibiting a four times higher signal-to-noise ratio than single models.H-VCE demonstrates the highest accuracy.In summary,the combined hydrological model minimizes discrepancies among individual models,significantly improving accuracy for monitoring large-scale TWSC.展开更多
Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relie...Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.展开更多
Precipitation is an important component of global water and energy transport and a major aspect of climate change. Due to the scarcity of meteorological observations, the precipitation climate over Tibet has been insu...Precipitation is an important component of global water and energy transport and a major aspect of climate change. Due to the scarcity of meteorological observations, the precipitation climate over Tibet has been insufficiently documented. In this study, the distribution of precipitation during the rainy season over Tibet from 1980 to 2013 is described on monthly to annual time scales with meteorological observations. Furthermore, four precipitation products are compared to observations over Tibet. These datasets include products derived from the Asian Precipitation-Highly-Resolved Observational Data(APHRO), the Global Precipitation Climatology Centre(GPCC), the University of Delaware(UDel), and the China Meteorological Administration(CMA). The error, relative error, standard deviation, root-mean-square error, correlations and trends between these products for the same period are analyzed with in situ precipitation during the rainy season from May to September. The results indicate that these datasets can broadly capture the temporal and spatial precipitation distribution over Tibet. The precipitation gradually increases from northwest to southeast. The spatial precipitation in GPCC and CMA are similar and positively correlated to observations. Areas with the largest deviations are located in southwestern Tibet along the Himalayas. The APHRO product underestimates, while the UDel, GPCC, and CMA datasets overestimates precipitation on the basis of monthly and inter-annual variation. The biases in GPCC and CMA are smaller than those in APHRO and UDel with a mean relative error lower than 10% during the same periods. The linear trend of precipitation indicates that the increase in precipitation has accelerated extensively during the last 30 years in most regions of Tibet. The CMA generally achieves the best performance of these four precipitation products. Data uncertainty in Tibet might be caused by the low density of stations, complex topography between the grid points and stations, and the interpolation methods, which can also produce an obvious difference between the gridded data and observations.展开更多
Rainfall data with an appropriate spatial resolution is a key input to hydrological models.However,networks of rain gauges are often sparsely and unevenly distributed in large catchments,especially in developing count...Rainfall data with an appropriate spatial resolution is a key input to hydrological models.However,networks of rain gauges are often sparsely and unevenly distributed in large catchments,especially in developing countries.High-resolution rainfall datasets,such as the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources(APHRODITE),the Climate Forecast System Reanalysis(CFSR),the Climatic Research Unit Time Series(CRUTS),the Global Precipitation Climatology Centre(GPCC)and the Tropical Rainfall Measuring Mission(TRMM),have become available to overcome such limitations.The objective of this study was to evaluate the impacts of four land-based rainfall products(APHRODITE,CFSR,CRU-TS,and GPCC)and a satellite-based rainfall product(TRMM)on streamflow of the upper catchment of Tri An reservoir in Vietnam using the Hydrological Modeling System(HEC-HMS).In addition,the available rain gauges data were used for comparison purpose.Result indicates that the TRMM and GPCC data show their best match to rain gauges data in simulating the streamflow in the period 1999–2007.Generally,the results indicate that the TRMM and GPCC data could be alternative solutions.展开更多
基金funded by the National Natural Science Foundation of China (42174030)Major Science and Technology Program for Hubei Province (Grant No.2022AAA002)+2 种基金Special fund of Hubei Luojia Loboratory (220100020)the National Natural Science Foundation of China under Grant 42304031the China Postdoctoral Science Foundation 2022M722441。
文摘Hydrological models are crucial for characterizing large-scale water quantity variations and correcting GNSS reference station vertical displacements.We evaluated the robustness of multiple models,such as the Global Land Data Assimilation System (GLDAS),the Famine Early Warning System Network Land Data Assimilation System (FLDAS),the National Centers for Environmental Prediction (NCEP),and the WaterGAP Global Hydrology Model (WGHM).Inter-model and outer comparisons with Global Positioning System (GPS) coordinate time series,satellite gravity field Mascon solutions,and Global Precipitation Climatology Centre (GPCC) guide our assessment.Results confirm WGHM's 26% greater effectiveness in correcting nonlinear variations in GPS height time series compared to NCEP.In the Amazon River Basin,a 5-month lag between FLDAS,GLDAS,and satellite gravity results is observed.In eastern Asia and Australia,NCEP's Terrestrial Water Storage Changes (TWSC)-derived surface displacements correlate differently with precipitation compared to other models.Three combined hydrological models (H-VCE,H-EWM,and H-CVM) utilizing Variance Component Estimation (VCE),Entropy Weight Method (EWM),and Coefficient of Variation Method (CVM) are formulated.Correcting nonlinear variations with combined models enhances global GPS height scatter by 15%-17%.Correlation with precipitation increases by 25%-30%,and with satellite gravity,rises from 0.2 to 0.8 at maximum.The combined model eliminates time lag in the Amazon Basin TWSC analysis,exhibiting a four times higher signal-to-noise ratio than single models.H-VCE demonstrates the highest accuracy.In summary,the combined hydrological model minimizes discrepancies among individual models,significantly improving accuracy for monitoring large-scale TWSC.
基金supported by the National Key Research and Development Program of China(2023YFC3206300)the National Natural Science Foundation of China(42477529,42371145,42261026)+2 种基金the China-Pakistan Joint Program of the Chinese Academy of Sciences(046GJHZ2023069MI)the Gansu Provincial Science and Technology Program(22ZD6FA005)the National Cryosphere Desert Data Center(E01Z790201).
文摘Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.
基金supported by the National Natural Science Foundation of China (Grant No. 41130960)Key Science and Technology Plan of Tibet Autonomous Region (Grant No. XZ201703-GA-01)
文摘Precipitation is an important component of global water and energy transport and a major aspect of climate change. Due to the scarcity of meteorological observations, the precipitation climate over Tibet has been insufficiently documented. In this study, the distribution of precipitation during the rainy season over Tibet from 1980 to 2013 is described on monthly to annual time scales with meteorological observations. Furthermore, four precipitation products are compared to observations over Tibet. These datasets include products derived from the Asian Precipitation-Highly-Resolved Observational Data(APHRO), the Global Precipitation Climatology Centre(GPCC), the University of Delaware(UDel), and the China Meteorological Administration(CMA). The error, relative error, standard deviation, root-mean-square error, correlations and trends between these products for the same period are analyzed with in situ precipitation during the rainy season from May to September. The results indicate that these datasets can broadly capture the temporal and spatial precipitation distribution over Tibet. The precipitation gradually increases from northwest to southeast. The spatial precipitation in GPCC and CMA are similar and positively correlated to observations. Areas with the largest deviations are located in southwestern Tibet along the Himalayas. The APHRO product underestimates, while the UDel, GPCC, and CMA datasets overestimates precipitation on the basis of monthly and inter-annual variation. The biases in GPCC and CMA are smaller than those in APHRO and UDel with a mean relative error lower than 10% during the same periods. The linear trend of precipitation indicates that the increase in precipitation has accelerated extensively during the last 30 years in most regions of Tibet. The CMA generally achieves the best performance of these four precipitation products. Data uncertainty in Tibet might be caused by the low density of stations, complex topography between the grid points and stations, and the interpolation methods, which can also produce an obvious difference between the gridded data and observations.
基金Ho Chi Minh City’s Department of Science and Technology(HCMC-DOST)and Institute for Computational Science and Technology(ICST)under grant number 15/2017/HĐ-KHCNTT.Additionally,this research is partly funded by Vietnam National University Ho Chi Minh City(VNU-HCM)under grant number C2017-48-02.
文摘Rainfall data with an appropriate spatial resolution is a key input to hydrological models.However,networks of rain gauges are often sparsely and unevenly distributed in large catchments,especially in developing countries.High-resolution rainfall datasets,such as the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources(APHRODITE),the Climate Forecast System Reanalysis(CFSR),the Climatic Research Unit Time Series(CRUTS),the Global Precipitation Climatology Centre(GPCC)and the Tropical Rainfall Measuring Mission(TRMM),have become available to overcome such limitations.The objective of this study was to evaluate the impacts of four land-based rainfall products(APHRODITE,CFSR,CRU-TS,and GPCC)and a satellite-based rainfall product(TRMM)on streamflow of the upper catchment of Tri An reservoir in Vietnam using the Hydrological Modeling System(HEC-HMS).In addition,the available rain gauges data were used for comparison purpose.Result indicates that the TRMM and GPCC data show their best match to rain gauges data in simulating the streamflow in the period 1999–2007.Generally,the results indicate that the TRMM and GPCC data could be alternative solutions.
