The International Symposium of Hydrological Sciences and High-efficiency Water Resources Utilization under the Changing Environment (ISHW 2019) was held in Wuhan, China, from October 24 to 26, 2019. It was co-organized by the School of Water Resources and Hydropower Engineering, and the State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, and sponsored by the 111 Project ‘Evolution Mechanism and Adaptive Strategies of Hydrological and Water Resources System under the Changing Environment’ of the Ministry of Education of China and the Research Council of Norway (FRINATEK Project 274310).
The ISHW 2019 brought together over 300 participants from more than 30 universities, institutions, and enterprises around the world to address the issues related to hydrological sciences and water resources management under the changing environment. The participants had access to a multitude of diverse and exciting scientific sessions, social and networking events, and opportunities to interact with professional fellow scholars. In this symposium, a wide variety of cutting-edge topics centered around (1) Sponge City; (2) Water Resources Management; (3) Frequency Analysis of Nonstationary Hydrological Extremes; (4) Ecohydrology; (5) Climate Change; and (6) Information Technology (IT) and Artificial Intelligence (AI) in Hydrology and Water Resources.
This special issue publishes selected papers related to climate change and hydrological extremes, i.e. droughts and floods, which are globally important natural hazards with associated costly impacts on society and the environment. Floods and droughts result from the superposition of different processes at various space and time scales: physical processes in the atmosphere, catchments, the river systems, and anthropogenic activities. However, the characteristics of hydrological extremes have been altered due to climate change and variability, such that approaches for their detection, attribution, and the frequency of occurrence need to be revisited as they are no longer stationary processes. For more accurate estimation of hydrological extremes under nonstationary and uncertain conditions, there is a need for holistic assessments. Time–frequency analysis, hydrological modeling, physical-cause analysis, multivariate statistical analysis, and uncertainty analysis are powerful tools for detecting, attributing, and making frequency analysis of nonstationary hydrological extremes in a changing climate. Both nonstationarity and uncertainty of frequency analysis of extreme hydrological events should be integrated to reveal the possible operational alternatives to the assumption of stationarity in hydrological extremes frequency analysis.
This special issue of Hydrological extremes in a changing environment: modeling and attribution analysis includes eight papers, which are mainly grouped into two sections.
The first group of papers concentrates on hydrological drought issues under the nonstationary condition including the prediction of the standardized precipitation index (SPI) time series (Li et al. 2022a), investigation for the influencing factors of hydrological drought (Zhou & Zhou 2021), demonstration of the characteristics of propagation from the meteorological to hydrological drought (Ma et al. 2022), and the hydro-environmental response to the interbasin water resources development (Zhang et al. 2022). Li et al. (2022a) proposed a hybrid model coupled with singular spectrum analysis (SSA) and backpropagation artificial neural network (BP-ANN) named the SSA–BP-ANN model to overcome the limitation of the conventional ANN on predicting the nonstationary SPI time series. The proposed SSA–BP-ANN model is tested in case studies at three meteorological stations in Northern Shannxi Province, China. The results show that this model can produce more accurate predictions than the BP-ANN model, which has great potential for promoting drought early warning in arid regions. Zhou & Zhou (2021) investigated drought characteristics of the Pearl River basin during 1960–2015 including the drought duration, severity, intensity, affected area, and centroids based on the Standardized Evapotranspiration Deficit Index (SEDI) and three-dimensional clustering algorithm. The results reveal how these characteristics have affected net primary productivity (NPP) and improve the insight into the relationship between NPP and drought, which helps decision-makers manage droughts and provides guidance for drought-related studies across other regions. Ma et al. (2022) evaluated the characteristics and dynamics of drought propagation in different seasons and their linkages with key forcing factors through the SPI and the Standardized Runoff Index (SRI). This study establishes the multiple linear regression model to quantitatively explore the influence of meteorological factors, underlying surface features, and teleconnection factors on the propagation time variations, and the Wei River Basin is selected as a study case. The results indicate that the differences of propagation time from meteorological to hydrological drought in different seasons and teleconnection factors including Arctic Oscillation, El Niño-Southern Oscillation, and Pacific Decadal Oscillation have strong influences on the propagation in autumn. Since interbasin water resource development has been exerting an increasingly evident impact on the hydro-environment of river basins, Zhang et al. (2022) selected the Han River as a study case to reveal the hydro-environmental response to China's interbasin water resources development. The results indicate that the runoff obviously decreases and low-flow duration significantly increases by 4–5 months after the operation of the South-to-North Water Transfer Project (SNWTP). Consequently, the flow decrease significantly contributed to the water quality deterioration in the middle and lower Han River.
The second group of papers concentrates on proposing novel approaches to the design flood analysis under the changing environment (Tan et al. 2021; Yan et al. 2021; Li et al. 2022b; Zhou et al. 2022). Yan et al. (2021) investigated the recommendation for the requirement on the length of data in nonstationary flood frequency analysis (NFFA). Twenty stations with record length longer than 100 years in Norway are selected and investigated by using both generalized extreme value (GEV)-ST and GEV-NS models with linearly varying location parameter (denoted by GEV-NS0). The results reveal that a minimum of 60 years of flood observations is recommended for NFFA for the selected basins in Norway. Zhou et al. (2022) proposed an integrated approach that combined the Generalized Additive Model for Location, Scale and Shape (GAMLSS) parametric method, the Copula function, and the Bayesian Uncertainty Processor (BUP) technique to make reliable probabilistic interval estimations of design floods, which is testified by the long-term (1954–2018) observed datasets of the Hanjiang River of China. The results demonstrate that the proposed approach can provide reliable probabilistic interval estimations of design floods, meanwhile reducing the uncertainty of NFFA. Tan et al. (2021) developed a nonlinear reservoir model in which the relationships of reservoir water level with reservoir volume and discharge are assumed to be nonlinear in order to accurately describe the routing process as it takes into consideration the interactions between hydrological loading and different discharge structures. The results show that the structure return period based on the linear model leads to an underestimation of the flood risk under the conditions of the high reservoir water level. Li et al. (2022b) presented the evaluation of a multisite statistical weather generator (MulGETS: Multisite weather Generator of École de Technologie Supérieure) based on its simulation effect of precipitation in the Yangtze River Basin, which effectively generates spatially correlated sequences of precipitation with maintaining the spatial and temporal distribution characteristics. The results verify the rationality of the MulGETS in downscaling precipitation in the Yangtze River Basin on the spatial and temporal scales.
All the papers collectively reflect hydrological extreme issues under the changing environment. The Guest Editors would like to acknowledge the invaluable contributions of all the anonymous reviewers, and the consistently helpful guidance and support of Emma Gulseven at IWA Publishing and the Emeritus Editor-in-Chief, Professor Chong-Yu Xu.
