Projecting Hydrological Responses to Climate Change Using CMIP6 Climate Scenarios for the Upper Huai River Basin, China

The influence of climate change on the regional hydrological cycle has been an international scientific issue that has attracted more attention in recent decades due to its huge effects on drought and flood. It is essential to investigate the change of regional hydrological characteristics in the context of global warming for developing flood mitigation and water utilization strategies in the future. The purpose of this study is to carry out a comprehensive analysis of changes in future runoff and flood for the upper Huai River basin by combining future climate scenarios, hydrological model, and flood frequency analysis. The daily bias correction (DBC) statistical downscaling method is used to downscale the global climate model (GCM) outputs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and to generate future daily temperature and precipitation series. The Xinanjiang (XAJ) hydrological model is driven to project changes in future seasonal runoff under SSP245 and SSP585 scenarios for two future periods: 2050s (2031–2060) and 2080s (2071–2100) based on model calibration and validation. Finally, the peaks over threshold (POT) method and generalized Pareto (GP) distribution are combined to evaluate the changes of flood frequency for the upper Huai River basin. The results show that 1) GCMs project that there has been an insignificant increasing trend in future precipitation series, while an obvious increasing trend is detected in future temperature series; 2) average monthly runoffs in low-flow season have seen decreasing trends under SSP245 and SSP585 scenarios during the 2050s, while there has been an obvious increasing trend of average monthly runoff in high-flow season during the 2080s; 3) there is a decreasing trend in design floods below the 50-year return period under two future scenarios during the 2050s, while there has been an significant increasing trend in design flood during the 2080s in most cases and the amplitude of increase becomes larger for a larger return period. The study suggests that future flood will probably occur more frequently and an urgent need to develop appropriate adaptation measures to increase social resilience to warming climate over the upper Huai River basin.

Inconsistent variation of return periods of temperature extremum in China and its projection based on CMIP6 results

Increasingly extreme temperature events under global warming can have considerable impacts on sectors such as industrial activities, health, and transportation, suggesting that risk for these kinds of events under climate change and its regional sensitivity should be reassessed. In this study, the observation and multi-model simulations from CMIP6 are comprehensively used to explore the regional differences of the extreme temperature response to climate change from the perspective of return period (RP). The Gumbel model of generalized extremum distribution is applied to estimate the RP for the annual extremum of temperature based on Gaussian distribution of daily temperature. The analysis on the observation in selected three sites indicates that the regional inconsistency of RP variation is not only existed in extreme high temperature (HTx) but also in low temperature (LTn) during the past several decades. The annual amplitude of temperature extremum in the Northeast China is enlarged with summer becoming hotter and winter becoming colder while the opposite situation is detected in Huang-Huai River Basin with cooler summer and relatively stable winter, and South China is characterized by hotter summer and slight warmer winter. From the spatial distribution of the HTx and LTn variations of fix RP, it is found that the Northeast China and Jiang-Huai River Basin is the most sensitive areas, respectively, in the response of extreme low temperature and high temperature to global warming. However, the regional inconsistency of the extreme temperature change is only observed under SSP1-2.6 scenario in the CMIP6 simulation but gradually disappeared from SSP2-4.5 to SSP5-8.5.

Lag impacts of the anomalous July soil moisture over Southern China on the August rainfall over the Huang–Huai River Basin

The effect of soil moisture (SM) on precipitation is an important issue in the land–atmosphere interaction and shows largely regional differences. In this study, the SM of the ERA-Interim reanalysis and precipitation data of the weather stations were used to investigate their relationship over eastern China during July and August. Moreover, the WRF model was applied to further validate the effect of SM on rainfall. In the observations, a significantly negative relationship was found that, when the soil over southern China is wet (dry) in July, the rainfall decreases (increases) over the Huang–Huai–River basin (hereafter HHR) in August. In the model results, the soil can “memorize” its wet anomaly over southern China from July to August. In August, the wet soil increases the latent heat flux at surface and the air moisture at lower levels of the atmosphere, which is generally unstable due to the summer monsoon. Thus, upward motion is prevailing over southern China in August, and the increased surface air moisture is transported upwards. After that, the condensation of water vapor is enhanced at the middle and upper levels, increasing the release of latent heat in the atmosphere. The heat release forms a cyclonic circulation at the lower levels over eastern China, and induces the transport and convergence of water vapor increased over southern China in August. This further strengthens the upward motion over southern China and the cyclonic circulation at the lower levels. Therefore, positive feedback appears between water vapor transport and atmospheric circulation. Meanwhile, the cyclonic circulation over southern China results in a response of water vapor divergence and a downward motion over HHR. Consequently, the negative anomalies of precipitation occur over HHR in August. When the July soil is dry over southern China, the opposite results can be found through the similar mechanism.

Compound droughts and heatwaves over the Huai River Basin of China: From a perspective of the magnitude index

Compound droughts and heatwaves have garnered increasing research attentions due to their disastrous impacts on the structure and function of ecosystems and societies. A drought is generally characterized by precipitation deficit, and its negative impact can be amplified by the simultaneous occurrence of a heatwave. More recent studies have highlighted the multi characteristics of compound droughts and heatwaves, which may call for improved efforts on assessing the impact of compound extremes. In this study, a compound drought and heatwave magnitude index (CDHMI) is built to characterize the severity of compound extremes in the Huai River Basin (HRB) during 1961-2017. The CDHMI considers the impact of both drought/extreme heat conditions and the duration of extreme heat. In addition, the magnitude index has been graded according to the degree of severity to detect the most drastic extreme compound events. The results show that from 1961 to 2017, mild and moderate events occurred more often than severe events. A significant increase in all compound events was observed from 2003 to 2017. Compound droughts and heatwaves events, especially in drought status, have increased significantly with the global climate warming in recent decades. The assessment of the impact for compound droughts and heatwaves events over the HRB needs to be improved in the context of global climate changing. Therefore, the CDHMI can be used to accurately assess the risk of compound droughts and heatwaves.

Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China

Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations. In this study, we used daily gauge observations across the Huai River Basin (HRB) during 1983–2012 and four validation metrics to evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) capacity for detecting extreme precipitation and linear trends. The PERSIANN-CDR well captured climatologic characteristics of the precipitation amount- (PRCPTOT, R85p, R95p, and R99p), duration- (CDD and CWD), and frequency-based indices (R10mm, R20mm, and Rnnmm), followed by moderate performance for the intensity-based indices (Rx1day, R5xday, and SDII). Based on different validation metrics, the PERSIANN-CDR capacity to detect extreme precipitation varied spatially, and meanwhile the validation metric-based performance differed among these indices. Furthermore, evaluation of the PERSIANN-CDR linear trends indicated that this product had a much limited and even no capacity to represent extreme precipitation changes across the HRB. Briefly, this study provides a significant reference for PERSIANN-CDR developers to use to improve product accuracy from the perspective of extreme precipitation, and for potential users in the HRB.