Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

Capability of TMPA products to simulate streamflow in upper Yellow and Yangtze River basins on Tibetan Plateau

Due to the high elevation, complex terrain, severe weather, and inaccessibility, direct meteorological observations do not exist over large portions of the Tibetan Plateau, especially the western part of it. Satellite rainfall estimates have been very important sources for precipitation information, particularly in rain gauge-sparse regions. In this study, Tropical Rainfall Measuring Mission （TRMM） Multi-satellite Precipitation Analysis （TMPA） products 3B42, RTV5V6, and RTV7 were evaluated for their applicability to the upper Yellow and Yangtze River basins on the Tibetan Plateau. Moreover, the capability of the TMPA products to simulate streamflow was also investigated using the Variable Infiltration Capacity （VIC） semi-distributed hydrological model. Results show that 3B42 performs better than RTVSV6 and RTV7, based on verification of the China Meteorological Administration （CMA） observational precipitation data. RTVSV6 can roughly capture the spatial precipitation pattern but overestimation exists throughout the entire study region. The anticipated improvements of RTV7 relative to RTVSV6 have not been realized in this study. Our results suggest that RTV7 significantly overestimates the precipitation over the two river basins, though it can capture the seasonal cycle features of precipitation. 3B42 shows the best performance in streamflow simulation of the abovementioned satellite products. Although involved in gauge adjustment at a monthly scale, 3B42 is capable of daily streamflow simulation. RTV5V6 and RTV7 have no capability to simulate streamflow in the upper Yellow and Yangtze River basins.

Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.