Search

Summary: In this study a rigorous evaluation and comparison of the difference in water balance simulations resulted from using different downscaling techniques, GCMs and hydrological models is performed in upper Hanjiang basin in China. The study consists of the following steps: (1) the NCEP/NCAR reanalysis data for the period 1961–2000 are used to calibrate and validate the statistical downscaling techniques, i.e. SSVM (Smooth Support Vector Machine) and SDSM (Statistical Downscaling Model); (2) the A2 emission scenarios from CGCM3 and HadCM3 for the same period are used as input to the statistical downscaling models; and (3) the downscaled local scale climate scenarios are then used as the input to the Xin-anjiang and HBV hydrological models. The results show that: (1) for the same GCM, the simulated runoffs vary greatly when using rainfall provided by different statistical downscaling techniques as the input to the hydrological models; (2) although most widely used statistics in the literature for evaluation of statistical downscaling methods show SDSM has better performance than SSVM in downscaling rainfall except the Nash–Sutcliffe efficiency (NSC) and root mean square error-observations standard deviation ratio (RSR), the runoff simulation efficiency driven by SDSM rainfall is far lower than by SSVM; and (3) by comparing different statistics in rainfall and runoff simulation, it can be concluded that NSC and RSR between simulated and observed rainfall can be used as key statistics to evaluate the statistical downscaling models’ performance when downscaled precipitation scenarios are used as input for hydrological models. [Copyright &y& Elsevier]

Summary: Sensitivity analysis is important in understanding the relative importance of climatic variables to the variation of reference evapotranspiration (ETref). In this study, a non-dimensional relative sensitivity coefficient was employed to predict responses of ETref to perturbations of four climatic variables in the Changjiang (Yangtze River) basin. ETref was estimated with the FAO-56 Penman–Monteith equation. A 41-year historical dataset of daily air temperature, wind speed, relative humidity and daily sunshine duration at 150 national meteorological observatory stations was used in the analysis. Results show that the response of ETref can be precisely predicted under perturbation of relative humidity or shortwave radiation by their sensitivity coefficients; the predictive power under perturbations of air temperature and wind speed depended on the magnitude of the perturbation, season and region. The prediction errors were much smaller than the seasonal and regional variation of their sensitivity coefficients. The sensitivity coefficient could also be used to predict the response of ETref to co-perturbation of several variables. The accuracy of the prediction increases from the lower to the upper region. Spatial variations of long-term average monthly and yearly sensitivity coefficients were obtained by interpolation of station estimates. In general, relative humidity was the most sensitive variable, followed by shortwave radiation, air temperature and wind speed. The actual rank of the four climatic variables in terms of their sensitivity varied with season and region. The large spatial variability of the sensitivity coefficients of all the climatic variables in the middle and lower regions of the basin was to a large extent determined by the distinct wind-speed patterns in those two regions. [Copyright &y& Elsevier]

Summary: In this study the Penman–Monteith reference evapotranspiration, pan evaporation measured by a 20cm pan, and pan coefficient, i.e., the ratio of Penman–Monteith evapotranspiration to pan evaporation, at 150 meteorological stations during 1960–2000 in the Changjiang (Yangtze River) catchment in China are calculated, compared and regionally mapped. Their spatial distributions and temporal variations are examined and the causes for the variations are discussed. The spatial distributions of temporal trends in the reference evapotranspiration as well as in the meteorological variables that determine evapotranspiration are analyzed. The contributions of various meteorological variables to the temporal trend detected in the reference evapotranspiration and pan evaporation are then determined. The results show that: (1) the spatial distributions of reference evapotranspiration and pan evaporation are roughly similar. Spatial correlation coefficients between the reference evapotranspiration and the pan evaporation are high for both the seasonal and annual values. The temporal correlation between the two estimates is higher in the lower (humid) region than in the upper (semi-arid) region. The spatial distribution pattern of the pan coefficient is significantly influenced by wind speed and relative humidity in the region. Higher values of the pan coefficient were found in the central area of the catchment with a relatively high humidity (as compared with the upper area) and a very low wind speed (as compared with other areas); (2) for the whole catchment, there is a significant decreasing trend in both the reference evapotranspiration and the pan evaporation, which is mainly caused by a significant decrease in the net total radiation and to a lesser extent by a significant decrease in the wind speed over the catchment. No temporal trend is detected for the pan coefficient; (3) sensitivity analysis shows that the reference evapotranspiration is most sensitive to the net total radiation, followed by relative humidity, air temperature and wind speed. [Copyright &y& Elsevier]

The assessment of climate change impacts is usually done by calculating the change in drought conditions between future and historical periods by using multiple climate model simulations. However, this approach usually focuses on anthropogenic climate changes (ACCs) while ignoring the internal climate variability (ICV) caused by the chaotic nature of the climate system. Recent studies have shown that ICV plays an important role in the projected future climate change. To evaluate that role, this study quantifies the contribution of ICV to climate change impacts on regional droughts by using the signal-to-noise ratio (SNR) and the fraction of standard deviation (FOSD) as metrics for China. The internal climate variability or noise (i.e. ICV) is estimated as the inter-member variability of two climate models' large-member ensembles; the signal (i.e. ACC) and the climate model uncertainty (or inter-model uncertainty, IMU) are estimated as the ensemble mean and inter-model variability of 29 global climate models, respectively. The drought conditions are characterized by drought frequency, duration and severity, which are quantified by using the theory of run based on the standardized precipitation evapotranspiration index (SPEI). The results show that deteriorated drought conditions induced by ACCs are projected to occur over China. From the perspective of the SNR, the ICV impacts are less significant compared to the ACC impacts for drought metrics. Remarkable spatial variations of SNRs for future drought metrics are found, with values varying from 0.001 to exceeding 10. In terms of the FOSD, ICV contributions relative to the IMU are large, as FOSDs are >1 for around 22% grids. These results imply the significance of taking into account the impacts of ICV in drought assessment, any study ignores the influence of ICV may be biased. Unlabelled Image • This study quantifies the contribution of internal climate variability to climate change impacts on drought. • The internal climate variability is less influential compared to anthropogenic climate change on future droughts. • The internal climate variability contributes significantly to the uncertainty envelope of drought estimations. [ABSTRACT FROM AUTHOR]

Drought is a natural disaster and its occurrence and development can be attributed to a range of driving factors, such as deficient precipitation, wind, temperature, land use/land cover changes, and so on. Therefore, attribution and monitoring of droughts are complicated and challenging. Standardized Precipitation Evapotranspiration Index (SPEI) has been widely used in meteorological drought monitoring. Nonstationarity has not been considered in SPEI-based drought monitoring. Here we propose a modified SPEI considering the nonstationarity of hydrometeorological processes, i.e. NSPEI hereafter. We observe nonstationarity SPEI in the Northeast, the Huang-Huai-Hai Plain, the Yangtze River Delta, and the Qinghai-Tibet Plateau. The goodness-of-fit can be determined for 76% of the total stations. Under the assumption of nonstationarity, the drought monitoring performance of the NSPEI is more robust than the traditional SPEI. Besides, we evaluate drought conditions across China under four shared socioeconomic pathways (SSPs), i.e. SSP126, SSP245, SSP370 and SSP585. We identify intensifying droughts in southwest China but a wetting tendency in northern China in winter under the SSP126 scenario; but a significant wetting tendency in southwest China and a drying tendency in northeast China under SSP245, SSP370 and SSP585 scenarios. This study provides a novel meteorological drought index and highlights a fresh picture of drought conditions across China under different SSPs. • A non-stationary SPEI index is developed. • The drought monitoring performance of the NSPEI is robust than the traditional SPEI. • We reevaluated drought conditions across China in future. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the trends of the annual streamflow and precipitation and cross correlations between them were analyzed in nine large river basins of China during 1956–2005. The results indicate that: (1) the annual mean streamflow decreases in arid and semi-arid regions of north China; however, increasing trends occur in south and Southwest China; (2) the annual streamflow and precipitation exhibit reasonable correlation in nine large river basins except those located in inland areas. The annual streamflow over most areas of China is fed by precipitation; however, the decline in streamflow is faster than the decreases of precipitation since 1970s in the arid and semi-arid regions of north China. The relationship between the annual precipitation and streamflow presents a non-stationary state in north China. This non-stationary relationship is strongly influenced by both human activities and precipitation changes; (3) a significant increase of water use might be the major factor responsible for the steeper decline in streamflow than in precipitation in Haihe River, Yellow River and Songliao River basins in north China. In inland river areas, increase of water use and actual evapotranspiration might result in decline in streamflow although precipitation has an increase tendency. This paper sheds light on the non-stationary relationship between annual precipitation and streamflow and possible underlying causes, which will be helpful for a better understanding of the changes of precipitation and streamflow in China at large scale and in other regions of the world. [Copyright &y& Elsevier]

Summary: In this study, a GIS based simple and easily performed runoff routing approach based on travel time was developed to simulate storm runoff response process with consideration of spatial and temporal variability of runoff generation and flow routing through hillslope and river network. The watershed was discretized into grid cells, which were then classified into overland cells and channel cells through river network delineation from the DEM by use of GIS. The overland flow travel time of each overland cell was estimated by combining a steady state kinematic wave approximation with Manning’s equation, the channel flow travel time of each channel cell was estimated using Manning’s equation and the steady state continuity equation. The travel time from each grid cell to the watershed outlet is the sum of travel times of cells along the flow path. The direct runoff flow was determined by the sum of the volumetric flow rates from all contributing cells at each respective travel time for all time intervals. The approach was calibrated and verified to simulate eight storm runoff processes of Jiaokou Reservoir watershed, a sub-catchment of the Yongjiang River basin in southeast China using available topography, soil and land use data for the catchment. An average efficiency of 0.88 was obtained for the verification storms. Sensitivity analysis was conducted to investigate the effect of the area threshold of delineating river networks and parameter K relating channel velocity calculation on the predicted hydrograph at the basin outlet. The effects of different levels of grid size on the results were also studied, which showed that good results could be attained with a grid size of less than 200m in this study. [Copyright &y& Elsevier]

• A dynamic three-stage blending scheme is developed to simultaneously correct precipitation occurrence and intensity. • A new daily 0.25° precipitation dataset over mainland China is produced by merging four multi-satellite/reanalysis products. • Different data merging methods are systematically evaluated in 238 catchments over China. • Our developed approach can facilitate hydrological modelling by substantially improving the KGE of simulated streamflow. Satellite-retrieved and atmospheric reanalysis precipitation can bridge the spatiotemporal gaps of in-situ gauging networks, but estimation biases can limit their reliable applications in hydrological monitoring and modelling. To correct precipitation occurrence and intensity simultaneously, this study develops a three-stage blending approach to integrate three multi-satellite precipitation datasets (IMERG Final, TMPA 3B42V7 and PERSIANN-CDR), the ERA5 atmospheric reanalysis product and a gauge dataset within a dynamic framework. Firstly, the systematic biases of the four members were individually corrected by combining the local intensity scaling and ratio bias correction methods. Then, the "state weights" used for determining wet/dry events were optimized by evaluating a score function of the four bias-corrected members. Thirdly, the "intensity weights" were optimized using the cuckoo search (CS) algorithm and the Bayesian Model Averaging (BMA) method, respectively. The three-stage blending approach produced dynamic weights varying both spatially and temporally, and the performance was thoroughly evaluated over mainland China. Results show that the three-stage dynamic scheme performs better than individual datasets and two-stage blending methods in terms of all eight statistical metrics, and the CS algorithm outperforms the BMA method in the third stage. By randomly sampling validation sites using K-fold experiments, the developed algorithm also demonstrates a superior performance in ungauged regions. After interpolating and normalizing blending parameters of all gauges to entire domain using ordinary kriging, a new blended precipitation dataset with a daily 0.25° scale was produced. Four hydrological models are forced by blended and primary precipitations in 238 catchments over China, further confirming that the developed approach can facilitate hydrological modelling demonstrated by improving the Kling-Gupta efficiency of simulated streamflow by 12–35%. [ABSTRACT FROM AUTHOR]