Study region: Catchment area above the Huaxian station along the Wei River Basin, China. Study focus: This study attempts to construct a new Non-stationary Standardized Streamflow Index (NSSI) applicable to the variable streamflow sequence of the Wei River Basin based on the climate index and the optimal anthropogenic index, and analyse the drought characteristics of the basin. The climate index is used to quantify climate change factors and three anthropogenic indices are used to quantify the factor of human activities, including the reservoir index, the human-induced index calculated based on the Variable Infiltration Capacity (VIC) hydrological model and the Long Short-Term Memory (LSTM) model machine learning approach, respectively. New hydrological insights for the region: The human-induced index based on the LSTM model is more suitable for quantifying anthropogenic factors in the Wei River Basin. The NSSI performs better than the SSI in drought identification. The NSSI based on the LSTM model can capture more frequent severe drought and extreme drought events. The frequency of severe drought and extreme drought is higher in summer and autumn than in the others. The NSSI can better characterize the hydrological drought processes under a non-stationary condition, thus it can provide a more effective reference for regional drought assessment and related policy-making from the perspective of a changing environment.

骤发干旱(简称骤旱)是一种以速度快、强度高为特征的极端事件.然而,对热浪影响下的骤旱过程研究相对有限.文章选取渭河流域作为研究区,根据日最高气温和表层土壤含水量识别热浪和骤旱事件;将骤旱事件分为与热浪相遇和未与热浪相遇的骤旱两种类型,通过比较两类骤旱的历时和发展速度等特征,揭示热浪如何影响骤旱过程.分析结果表明,1981-2020年渭河流域的骤旱和热浪事件发生频次显著增加(p＜0.1),在与(未与)热浪相遇的骤旱事件的历时和发展速度平均值分别为8.03(9.66)个候(5 d为1候)和25.44(23.78)个分位变化/候,表明在热浪影响下骤旱事件通常会历时缩短,发展速度加快.研究结果可为热浪影响下的骤旱评估和监测提供理论依据.

Global climate change and reservoir regulations can alter the natural flow of rivers. Influenced by these two drivers, flood sequences may no longer satisfy the assumption of stationary, thereby making it difficult to accurately analysis flood frequency and to design water conservancy projects. Therefore, it is of great signifi-cance to analyse the non-stationary frequency of flood sequences in a changing environment. In this study, we proposed a method for conducting nonstationary flood frequency analysis caused by cascade reservoirs as well as the low-frequency climate indices. The proposed non-stationary model 2, with the explanatory variables of climate indices and modified reservoir index (MRI), was compared with the traditional stationary model and the widely used non-stationary model 1 with time as the explanatory variable. The study was conducted at six hydrological stations in the main stream and tributaries of the upper reaches of the Yangtze River in China (considered as the Three Gorges Reservoir Area). The results of the generalized additive model for location, scale and shape (GAMLSS) showed that the Akaike information criterion and Bayesian information criterion values of the proposed non-stationary model method 2 are smaller than those of the two comparison models. When the low-frequency South Oscillation Index is high or the Arctic Oscillation and North Pacific Oscillation are low, the stationary model underestimates the design value of flood quantiles compared with the non-stationary model 2. Compared with the non-stationary model 1, the MRI and low-frequency climate indices as the explanatory variables in model 2 can better describe the non-stationary characteristics of flood frequency and amplitude. addition, the non-stationary model considering external physical factors can provide better prediction of future design flood compared with two traditional models.

Study region: The source area of the Yellow River (SAYR) located in the northeastern part of the Qinghai-Tibet Plateau, China Study focus: This study attempts to produce the quantity-duration-frequency (QDF) relationships of lowflow through a nonstationary approach of the generalized extreme value (GEV) models. Detailed derivation of theoretical model of different structures and parameter estimation is presented as the central part, followed by model evaluation and recommendation. Time and two climate indices, i.e., Arctic Oscillation (AO) and Eastern Asia (EA), are selected as covariates. New hydrological insights for the region: For the SAYR, nonstationary GEV models incorporating additional climate-informed covariates show apparent strengths over stationary GEV model. Using AO and EA as covariates, dual-covariate model has not shown apparent advantages over single-covariate model. The introduction of climate indices as critical covariates, in addition to time, is found to be a good choice in the construction of nonstationary GEV models for obtaining the QDF relationships and analyzing multivariate properties of lowflow. The results could help to guide the emergency management and regulation of water resources during the lowflow season, and to assistant the design of water engineering for the future period.

The non-stationary characteristics of the average runoff in driest days are tested in this paper based on the measured runoff data of Tangnaihai Hydrological Station from 1957 to 2018 using the 5-year moving average and Mann-Kendall abrupt change test to analyze the effect of climate change on the non-stationary of hydrological extreme value. According to the Kendall rank correlation analysis method, the climate index was selected, and the non-stationary generalized extreme value distribution model（generalized extreme value, GEV） was constructed with time and climate index as covariates. Parameter estimation and model selection were carried out, and application effects of the stationary and non-stationary GEV models in the runoff simulation of dry season were compared. The results indicated that the dry season runoff shows obvious non-stationary characteristics. Meanwhile, the simulated value of the stationary GEV model is higher than the measured value, and the GEV model with the western Pacific index as covariate produces better fit for the extreme value, which better explained the volatility of extreme dry events.

The availability of the new generation Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06 products facilitates the utility of long-term higher spatial and temporal resolution precipitation data (0.1° × 0.1° and half-hourly) for monitoring and modeling extreme hydrological events in data-sparse watersheds. This study aims to evaluate the utility of IMERG Final run (IMERG-F), Late run (IMERG-L) and Early run (IMERG-E) products, in flood simulations and frequency analyses over the Mishui basin in Southern China during 2000–2017, in comparison with their predecessors, the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products (3B42RT and 3B42V7). First, the accuracy of the five satellite precipitation products (SPPs) for daily precipitation and extreme precipitation events estimation was systematically compared by using high-density gauge station observations. Once completed, the modeling capability of the SPPs in daily streamflow simulations and flood event simulations, using a grid-based Xinanjiang model, was assessed. Finally, the flood frequency analysis utility of the SPPs was evaluated. The assessment of the daily precipitation accuracy shows that IMERG-F has the optimum statistical performance, with the highest CC (0.71) and the lowest RMSE (8.7 mm), respectively. In evaluating extreme precipitation events, among the IMERG series, IMERG-E exhibits the most noticeable variation while IMERG-L and IMERG-F display a relatively low variation. The 3B42RT exhibits a severe inaccuracy and the improvement of 3B42V7 over 3B42RT is comparatively limited. Concerning the daily streamflow simulations, IMERG-F demonstrates a superior performance while 3B42V7 tends to seriously underestimate the streamflow. With regards to the simulations of flood events, IMERG-F has performed optimally, with an average DC of 0.83. Among the near-real-time SPPs, IMERG-L outperforms IMERG-E and 3B42RT over most floods, attaining a mean DC of 0.81. Furthermore, IMERG-L performs the best in the flood frequency analyses, where bias is within 15% for return periods ranging from 2–100 years. This study is expected to contribute practical guidance to the new generation of SPPs for extreme precipitation monitoring and flood simulations as well as promoting the hydro-meteorological applications.