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[Display omitted] • Complex network reveals the spatiotemporal propagations of extreme precipitation events (EPEs) in China. • EPEs over 218 river basins are classified into six communities with different seasonality from May to October. • There exist propagations of EPEs along the Yellow, Haihe, Liaohe and Songhua River Basins within one to eight days. Extreme precipitation events (EPEs) cause catastrophic losses to human lives and social economy. Focusing on the wet season from May to October, this paper formulates the complex network to characterize the spatiotemporal propagations of EPEs over 218 river basins in China. Specifically, based on the daily Multi-Source Weighted-Ensemble Precipitation version 2, the event synchronization along with significance test facilitates the complex network of EPEs from 1979 to 2020. By characterizing the topology of EPEs, the results show that synchronous EPEs generally occur over the Hetao Area of Yellow River Basin, Haihe River Basin and Northeast China. The complex network of EPEs classifies the 218 river basins into six communities. In May and June, more than 50 % of EPEs generally occur in South China. From July to September, over 60 % of EPEs tend to be observed in the Huang-Huai-Hai Plain, upper-middle reaches of Yellow River Basin, Northeast China and Southwest China. In October, about 65 % of EPEs happen in South and Southwest China. In the meantime, less than 10 % of EPEs occur in Northwest China. Among the synchronous EPEs starting in late June or early July and peaking in late July to early August, there exist propagations along the Yellow River, Haihe, Liaohe and Songhua River Basin within one to eight days. Eastward moisture transport along this pathway within four to six days drives the propagation of synchronous EPEs. The complex network analysis of this paper provides useful information for early warning and forecasting of EPEs and flood hazards. [ABSTRACT FROM AUTHOR]

[Display omitted] • A model for multi-objective optimization operation of multiple water sources under uncertainties was proposed. • Uncertainties in inflow and water demand forecasts can be considered simultaneously, and the correlation between uncertainties can be considered quantitatively. • The impact of inflow-water demand forecast uncertainties and their correlations on water resource operations are explored for water resources managers. Uncertainties in inflow and water demand forecasts bring risks to water resources management. Therefore, the research on multi-objective optimization operation of multiple water sources under uncertainties holds critical research significance and application value. This paper proposes a model for the multi-objective optimization operation of multiple water sources that can consider the inflow and water demand forecast dual uncertainties. In handling uncertainties, this paper adopts a scenario generation method based on joint distribution-Monte Carlo (JDMC), which allows for a quantitative consideration of the correlation between uncertainties. Moreover, the variable weight method and LINGO 20.0 are employed for solving multi-objective optimization issues to obtain stable Pareto frontiers. The proposed model is applied to the water receiving area inside Jiangsu Province of the South-to-North Water Transfer East Route Project (SNWTERP) in China. The main findings are as follows: 1) The impact of the inflow and water demand forecast uncertainties on the operation results is synergistic rather than antagonistic. Considering only a single uncertainty may result in water receiving area facing more severe water shortages or purchasing spot water at high prices. 2) There is a strong spatial correlation between uncertainties. If we ignore or only qualitatively consider the correlation between uncertainties, the impact of uncertainties on operation results will be underestimated or overestimated. 3) The Pareto frontiers are convex functions, indicating that decision-makers should choose a solution in the middle part. [ABSTRACT FROM AUTHOR]

• A new method was proposed to merge multiple satellite-based precipitation products (SPPs) • The method estimates grid scale errors of SPPs based on water budget closure. • The method was verified by comparing with original SPPs and three merging methods. • The performance of SPPs was slightly reduced when enforcing water budget closure. Merging multiple satellite/reanalysis-based precipitation products (SPPs) is a critical way to improve the accuracy of spatial precipitation (P) estimation. In this paper, a new method was proposed to merge multiple SPPs by considering rain gauge-based observations and water budget closure. Based on the errors of SPPs at gauged regions, the proposed method first estimates the mesh-based errors of SPPs as the intermediate variable using the error in water budget closure and then calculates the respective merging weights of SPPs in data merging. The performance of this proposed method was verified by comparing its results with the input of original SPPs and the data merged by five existing merging methods in mainland China at mesh scale and in nine major basins. Two scenarios were designed for the period from 2003.02 to 2016.12 at a monthly temporal resolution − with and without water budget closure constraints. Seven popular SPPs were considered, including GPM IMERG, PERSIANN, GSMaP, CHIRPS, ERA5, CFSV2, and TerraClimate. The results showed that the proposed method improved the P estimates in terms of CC, RMSE, MAE, PBIAS and KGE in both scenarios, and that closing the terrestrial water budget would slightly reduce the accuracy of SPPs against observations. The reduction percentage is about 5% in term of CC and KGE, and 12% in term of RMSE. This study is the first attempt to merge SPPs and gauge-based rain observation with consideration of water budget closure. It provides not only an effective and robust tool for the merging of multiple SPPs, but also important insights into the further development of SPP merging methods by considering water budget closure. [ABSTRACT FROM AUTHOR]

• Transition zones are the hotspots of bioclimatic changes under global warming. • Global warming extends growing season lengths but increases heat and water stresses. • Sustainable grasslands in China shrink and shift northwestward with global warming. Bioclimatic response to global warming is a key linkage between anthropogenic climate change and ecosystem sustainability, particularly for the impact-prone climate transition zones. This paper examines changes in heat-water conditions from 2006 to 2100 across China, using the fifth generation climate reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) and bias-adjusted climate projections by the Coupled Model Intercomparison Project Phase 5 (CMIP5). We then assess the effects of these changes on habitat suitability for responsive ecosystems using a species distribution model. The results reveal significant changes in bioclimatic environments across dry–wet climate transition zones under the present-day climate and the RCP8.5 scenario. By the end of the 21st century, local growing season length (GSL) could extend by 60 days and 30 % of the GSL could be under heat stress. Water conditions worsen overall in both the air and the soil, despite some improvements in the middle parts of transition zones. With altered heat-water conditions, sustainable grasslands shift northwestward, with a reduction of approximately 131 million km2 (46.6 % relative to 2006–2010) in total area. These changes primarily arise from alterations in winter and spring heat-water conditions. Our findings highlight the climate transition zones as hotspots of ecosystem vulnerability demanding particular attention in impact assessment and adaptation studies. [ABSTRACT FROM AUTHOR]

• A spatial random forest-based downscaling and merging method (SRF-DM) is proposed. • SRF-DM performs better than the original satellite-based precipitation products. • SRF-DM is more accurate than the classical machine learning-based methods. • Spatial autocorrelation is the most important variable. • SRF-DM has a good performance even when the gauge density is low. Publicly available satellite precipitation products (SPPs) are essential inputs for hydrological models. However, the existing SPPs suffer from coarse resolutions and large uncertainties, which greatly hinder their widespread applications. Thus, to fully utilize the unique characteristics of each individual SPP, a spatial random forest (SRF)-based downscaling and merging (SRF-DM) method is proposed to fuse multiple SPPs and gauge observations in this paper. The proposed method incorporates spatial autocorrelation information between precipitation observations through a covariate. This covariate is estimated using ordinary kriging interpolation on spatial neighbors that have a high correlation with the target location. The proposed method was used to generate 1-km daily precipitation data using the datasets from five satellite precipitation products (IMERG, GSMaP, CHIRPS, MORPH, and PERSIANN) and gauge observations in the period from January 1, 2015 to December 31, 2019 over Sichuan province, China. The performance of SRF-DM was compared with the original SPPs, XGBoost-based downscaling and merging (XGB-DM), two double-layer machine learning (ML) methods, namely RF-RF and RF-artificial neural network (RF-ANN), and three single ML methods, namely RF, ANN, and RF-based Merging Procedure (RF-MEP). The experimental results demonstrate the following findings: (i) SRF-DM is more accurate than the original SPPs across different temporal scales (i.e., daily, monthly, and seasonal) in terms of statistical and categorical accuracy measures, (ii) SRF-DM outperforms the six ML-based methods in estimating high precipitation and capturing spatial precipitation patterns, (iii) the spatial autocorrelation between neighboring precipitation data is the most significant factor in both downscaling and merging, and (iv) the performance of SRF-DM is influenced by gauge density, but it still yields good results even with a limited number of gauges. On the whole, SRF-DM is considered an effective tool for generating daily precipitation products with high accuracy and resolution, which is primarily attributed to its satisfactory performance in regions with diverse precipitation patterns and topography. [ABSTRACT FROM AUTHOR]

• The counter-demand method effectively simulates water diversion and distribution. • Successfully resolve the simulation of water extraction-conveyance-use in the Karez. • The average annual groundwater overexploitation was 173 million m3 in the ALB. • Excessive extraction of groundwater poses a threat to nature vegetation survival. The long-standing, irrational development and exploitation of water resources in the Aiding Lake Basin (ALB), coupled with excessive groundwater extraction, have led to the gradual depletion of Aiding Lake, the disappearance of Karez wells, the depletion of spring water, and the deterioration of the ecological environment, all of which are serious threats to the economy and survival and welfare of inhabitants. To address the complexities associated with the staggered distribution of natural rivers and artificial canals within the agricultural irrigated region of the ALB, an inverse computation method to gauge canal water requirements was used in building an intelligent simulation of water diversion and conveyance, incorporating different water use methodologies for complex canal systems within the irrigated region. Additionally, this paper characterizes the interaction between the catchment and conveyance section of the Karez wells and the underground aquifer based on the seasonal river module to simulate the entire flow process of the Karez well system, from water extraction and conveyance to groundwater recharge by infiltration. This study presents a coupled simulation model of seasonal river–lake-groundwater interactions in the ALB, where groundwater is simulated under both steady and unsteady flows. The findings indicate that from 2013 to 2022, the annual average groundwater recharge within the ALB was approximately 1.048 billion m3, while the discharge was 1.221 billion m3, with an annual average groundwater overexploitation of 173 million m3. Furthermore, the long-term overexploitation of groundwater has had a direct impact on the ecological environment, resulting in a survival crisis for natural vegetation except in the upper vegetative zone of Aiding Lake, which has good groundwater recharge conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • The drought pattern in China is closely related to the basin distribution and multiple teleconnection factors. • The intensity and frequency of drought in China exhibited an increasing trend. • The ENSO-AO had important positive effects in the major basins in China. • Multiple wavelet coherence helps identify the processes controlling drought changes. Due to increasing global warming and the frequent occurrence of severe drought risk in recent decades, many studies have investigated drought in China. However, the spatiotemporal heterogeneities of droughts are complicated, and a comprehensive analysis of the driving forces has not been thoroughly explored. In this paper, we studied the temporal and spatial patterns of drought in China from 1960 to 2018 and disclosed its relationship with multiple teleconnection factors on different time scales. The results show that (1) Drought in China has exhibited a fluctuating increasing trend over the past 59 years, with a standardized precipitation–evapotranspiration index (SPEI) rate of change of –0.12/10 a, and the most severe droughts occurred during 2000–2010. (2) There was a significant abrupt change in China's meteorological droughts induced by climate change around 1997. (3) The trend in China was characterized by drought in spring and autumn and humidification in winter, while the trend in the Southwest River Basin was characterized by drought. (4) A combination of factors can best explain the drought variations from basin to basin, and the El Niño-Southern Oscillation (ENSO)-Arctic Oscillation (AO) was most commonly the best predictor. The use of multiple wavelet coherence helps to identify the dominant drought change processes. [ABSTRACT FROM AUTHOR]

• A framework is proposed to analyze compound floods with multiple driving factors. • Local rainfall has a larger impact on the flooding levels of the GBA, China. • Flooding area is about 50 km2 and 1.3 m flooded depth for the worst scenario. • Findings have important implications to GBA's flooding control and management. Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods for complex urban river networks remain relatively scarce. This paper proposes a framework to simulate the coastal compound flooding process, with a series of scenarios designed to explore the main flooding drivers and their worst possible combination. The approach is used to quantify the induced flooding consequences, in which a two-dimensional model is employed to conduct a case study for a city with complex river networks. The Qianshan River basin in Zhuhai City, located in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China is used as the case study. Application results show that: (i) the occurrence and properties of flooding in this coastal area are jointly affected by upstream river flows, downstream tidal levels and local rainfall intensity with different contribution levels, (ii) compared to tidal levels, local rainfall intensity has a larger impact on the flooding levels of the study region, and (iii) the worst scenario where a 50-year return period upstream river flow co-occurring with high tide and a 100-year return period rainfall can produce a flooding area up to 50 km2 for the study region, with an average of 1.3 m flooded depth. The proposed framework with its findings offers an in-depth understanding and quantification of coastal urban flood characteristics within the GBA of China. [ABSTRACT FROM AUTHOR]

• Shale artificial fracture core models were developed with rough fracture surfaces. • Mineral hydration impairment on artificial fractures conductivity was revealed. • Mechanism of conductivity impairment were investigated from various aspects. • Hydration inhibitor increase fracture conductivity by 3.4 and 2.6 times, respectively. • Other factors impair fracture conductivity and implications were discussed. The shale gas fields are mainly in mountainous areas in China. Surface water is the major source of hydraulic fracturing fluid to develop shale gas. The clay mineral is generally rich in the shale gas reservoir. When encountered with low salinity surface water, the clay minerals are easy to hydrate in the process of fracturing and well shut-in. The hydration of clay minerals would result in the expansion, dispersion, and migration of mineral particles from the fracture surface, changing the structure and space of fractures, thereby affecting fracture conductivity. In this paper, shale artificial fracture core models were developed with rough fracture surfaces. With proppant-supported and self-supported fracture core models, the impact of hydration time, net pressure, and dislocation distance of fracture surfaces on the artificial conductivity were evaluated systematically. The results show that the conductivity of proppant-supported and self-supported fractures decreases by 85.3–95.1% under hydration. The turning point of conductivity is 8.0 hrs. and 4.3 hrs., respectively. At the initial stage of production, the artificial fracture could be protected better if the net pressure on the fracture is maintained from small to large. At the same time, the initial net pressure on fracture should be less than the proppant crushing pressure. Certain dislocation distances of self-supported fractures would augment the conductivity by 6.4–12.8 times. After hydration, illite and montmorillonite in the rock surface expanded and dispersed when encountered with water. The fracture surface strength decreases by 29.8%, while the surface roughness decreases by 46.1–51.6%, and some proppants were crushed. Then the fracture conductivity space was reduced. By adding KCl into the fracturing fluid, hydration can be effectively inhibited, which can increase the proppant-supported and self-supported fracture conductivity by 2.5–3.4 times, compared with fracturing fluid prepared with surface water. This study provides a theoretical basis for the optimization of shale gas reservoir fracturing technology and production strategy. [ABSTRACT FROM AUTHOR]