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Study region: the Yellow River Basin(YRB) in China. Study focus: Ecological conservation and high-quality development of YRB has been declared as the national strategy of China. However, frequent flood hazards are a major challenge to this strategy. Further, flood behavior and impacts of the YRB are inadequately understood. We identified five floodplains using VIC-Cama model and evaluated flood risk during 1978–2015 for 50–1000-year return periods(RP) events in terms of the exposure of population (Ep), cropland (Ec), forest and grass (Efg), and settlements (Es), additionally, the contribution of climate and human behavior to flood magnitude and flood risk have been explored. New hydrological insights for the region: Results show increased Ep and Es but decreased Ec and Efg from 1978 to 2015. Exposure caused by 50–1000-year RP flood events were usually 1–2.47 times larger than those of 1978–2015. It was found that consecutive 144-day total precipitation was the prime driver of flood magnitude variation. Areas with highly-concentrated socioeconomic activities were increasingly susceptible to floods, and flood risks therefore increased during the 1978–2015 period across the YRB. Furthermore, changes in flood magnitude greatly influenced the susceptibility of population and settlements to flood risk, while spatial patterns of flood disaster-bearing bodies impacted the exposure of cropland and forest to flood risk. This study highlights YRB flood risks and relevant drivers, and helps understand flood risks of large rivers over the globe.

Study region: The Huai River Basin, China. Study focus: This study chooses the daily flow data of Huai River and its tributaries, Shi River and Hong River during 1959–2016. The co-occurrence of floods in different tributaries of the catchment is assessed by analyzing the flood peak and flood timing at three different gauging stations located in the river network. What’s more, the parameters(estimated from the 30-year scale time windows) of the marginal and joint distributions are assumed stationary and nonstationary respectively to explore how the trend of coincidence probability(CP) is effected by them. New hydrological insights for the region: The results show that, over the period analyzed, the most probable time of flood co-occurrence over the gauge stations in the study area tends to move backwards from 10 July to about 20 July. The probability of flood co-occurrence at the Bantai and Jiangjiaji gauging stations, which singularly experience an increase in flood peak, increases from 9.25×10−9(the 1st window) to 1.17×10−5(the 25th window) when the return period of flood is 20 year, while in the same condition, the CP of Bantai and Wangjiaba decreased from 6.16×10−6 to 2.48×10−7.

In the Horqin Sandy Land, the dynamics of the soil-plant-atmosphere-water system and changes in plant water availability significantly impact water resources management and vegetation restoration. This study determined the hydrogen and oxygen isotopic compositions (δD and δ18O) of precipitation, plant water, soil water, and groundwater as well as carbon isotopes of plant leaves during the growing season and different precipitation events (8.9 mm, 18.1 mm, and 28.3 mm) in 2020. Target areas included a semimobile dune with Artemisia halodendron communities (SMAH), a meadow corn field (MCF), and a meadow wetland dominated by Phragmites australis (MPA). The water uptake patterns of the three typical plants were determined using the Iso-Source model. The results showed significant differences in the seasonal variations of δD and δ18O in precipitation, plant water, soil water, and groundwater among SMAH, MCF, and MPA throughout the water cycle, which was attributed to the different origins, distribution, and evaporation mechanisms of water. The influence of precipitation on soil water transport processes varied with the change and transformation of rainfall intensity and ecosystem type. Water conditions and root distribution determined seasonal water use patterns of three plants. The water use efficiency (WUE) of the three typical plants followed the order: corn > Phragmites australis > Artemisia halodendron. And their seasonal WUE were not only consistent with the contribution rate of deep soil water, but also affected by the water use strategies adopted. However, the response of their WUE to rainfall pulses was not significant on the whole. Rainfall intensity, pre-rain mass soil moisture content characteristics and vegetation characteristics are important factors affecting the contribution distribution of different water sources to plants under rainfall events. Artemisia halodendron displays ecological plasticity against seasonal water deficit, facilitating its extensive colonization in dune ecosystem and further promoting the process of dune fixation. The distribution pattern of vegetation water use in the meadow ecosystem is not very ideal at present, and water-carbon processes would gradually become imbalanced in the long run. The research results will provide a reference for sustainable water resource utilization in semi-arid areas through ecohydrological processes.

Study region: The largest inland river basin in China (the Tarim River Basin) Study focus: This study introduces a novel classification scheme for dry and warm snow droughts with focus on their duration, severity, and intensity. The Nonparametric Standardized SWE Index (NSWEI) and the Standardized Precipitation Index (SPI) were employed to assess these drought types. The optimal parameters-based geographical detector (OPGD) model and machine learning (including random forest model and Shapley-XGBoost algorithm) were applied to reveal the spatial and time dynamic driving forces of droughts. New hydrological insights for the regions: Results indicated that warm-type droughts occurred more frequently and with greater intensity, exhibiting a larger spatial coverage compared to dry-type droughts, which can be attributed to warm-type droughts dominated by temperature and vapor pressure deficit, whereas dry-type are predominantly influenced by relative humidity and solar radiation. Moreover, the dynamics of snow droughts exhibited a contrasting pattern between the south (experiencing an upward trend) and the north (experiencing a descending trend). In the south, dry-type droughts were exacerbated by the strength of North Pacific Oscillation (PDO)-precipitation coupling, while warm-type droughts are influenced by the strength of North Atlantic Oscillation (NAO)-precipitation coupling and El Niño Southern Oscillation (ENSO)-precipitation.

Abstract Although urban irrigation can modulate local hydrothermal conditions and mitigate urban heat island effects, its impact on moist heat stress (MHS) is poorly understood. Employing the Weather Research and Forecasting Single-Layer Urban Canopy Model (WRF-SLUCM), we evaluated the effect of urban irrigation on the MHS in Beijing, China, and found that the updated initial soil moisture (SM) field improved the simulation of temperature, relative humidity, and wind speed. Besides, urban irrigation reduced urban and rural MHS, and particularly reduced afternoon and evening MHS by up to 1.2 °C but increased morning MHS by up to 0.4 °C. In addition, the effect of different irrigation times on MHS showed that irrigation at 02 and 20 h increased urban and rural MHS, with the best cooling effect at 00 and 13 h, which reduced the MHS by up to 2.65 °C in urban areas and 0.71 °C in rural areas. The findings highlighted mechanistically the effect of urban irrigation on MHS and shed light on how to mitigate urban heat island effects on urban sustainable development.

Integrated water and fertilizer management is important for promoting sustainable development of facility agriculture, and biochar plays an important role in guaranteeing food production, as well as alleviating water shortages and the overuse of fertilizers. The field experiment had twelve treatments and a control (CK) trial including two irrigation amounts (I1, 100% ETm; I2, 60% ETm; where ETm is the maximum evapotranspiration), two nitrogen applications (N1, 360 kg ha–1; N2, 120 kg ha–1) and three biochar application levels (B1, 60 t ha–1; B2, 30 t ha–1 and B3, 0 t ha–1). A multi-objective synergistic irrigation–nitrogen–biochar application system for improving tomato yield, quality, water and nitrogen use efficiency, and greenhouse emissions was developed by integrating the techniques of experimentation and optimization. First, a coupled irrigation–nitrogen–biochar plot experiment was arranged. Then, tomato yield and fruit quality parameters were determined experimentally to establish the response relationships between irrigation–nitrogen–biochar dosage and yield, comprehensive quality of tomatoes (TCQ), irrigation water use efficiency (IWUE), partial factor productivity of nitrogen (PFPN), and net greenhouse gas emissions (NGE). Finally, a multi-objective dynamic optimization regulation model of irrigation–nitrogen–biochar resource allocation at different growth stages of tomato was constructed which was solved by the fuzzy programming method. The results showed that the application of irrigation and nitrogen to biochar promoted increase in yield, IWUE and PFPN, while it had an inhibitory effect on NGE. In addition, the optimal allocation amounts of water and fertilizer were different under different scenarios. The yield of the S1 scenario increased by 8.31% compared to the B1I1N2 treatment; TCQ of the S2 scenario increased by 5.14% compared to the B2I2N1 treatment; IWUE of the S3 scenario increased by 10.01% compared to the B1I2N2 treatment; PFPN of the S4 scenario increased by 9.35% compared to the B1I1N2 treatment; and NGE of the S5 scenario decreased by 11.23% compared to the B2I1N1 treatment. The optimization model showed that the coordination of multiple objectives considering yield, TCQ, IWUE, PFPN, and NGE increased on average from 4.44 to 69.02% compared to each treatment when the irrigation–nitrogen–biochar dosage was 205.18 mm, 186 kg ha–1 and 43.31 t ha–1, respectively. This study provides a guiding basis for the sustainable management of water and fertilizer in greenhouse tomato production under drip irrigation fertilization conditions.

Abstract Intensifying droughts under climatic warming are of widespread concern owing to their devastating impacts on water resources, societies and ecosystems. However, the effects of exogeneous drivers on regional droughts remain poorly understood. Using the Lagrangian method, atmospheric reanalysis data and climate projections from the Coupled Model Inter-comparison Project phase 6 (CMIP6), we show how exogenous precipitation minus evaporation (PME) deficit drives droughts across China. More specifically, we demonstrate that four distinct trajectories of such exogenous PME deficit fuel regional droughts. Three of these trajectories relate to oceanic PME deficit originating from the North Atlantic, eastern Bering Sea and Indian Ocean, and one trajectory characterizes exogenous terrestrial PME deficit from the Siberian Plateau. We show that during 1980–2020, droughts induced by exogenous PME deficit account for 45% of all droughts that occurred in China’s coastal region, and for 7% of all droughts in the northwestern regions. Under climate scenario SSP245 (SSP585), limiting warming to 1.5 °C compared to 2 °C above pre-industrial levels could avoid 60% (84%) of exogenous drought exposure. This would in turn reduce population exposure by 40% (49%), and economic exposure by 73% (66%). Our study unravels how exogenous PME deficit drives droughts in China, underscoring the role that external drivers have on regional droughts and associated future prediction.

Study region: The Yellow River Basin (YRB) in China. Study focus: This study is devoted to simulating flood changes with return periods of 10, 20, 30, 50, 100 and 200 years under three Representative Concentration Pathways (RCPs) using CaMa-Flood models, and examining the drivers. New hydrological insights for the region: In the historical period (1961–2005), the middle and lower YRB and specifically the Wei River Basin (WRB) (a tributary of the middle YRB) are dominated by higher flood risks with once-in-200-year flood magnitude of 9989.63 m3/s. Under climate change scenarios, the relative change in the once-in-100-year flood magnitude increased from 125.0% (RCP2.6) to 204.9% (RCP8.5) during the historical period (1961–2005) to the long-term period (2076–2092). The historical flood-prone regions were more susceptible to increased flood magnitude. Meanwhile, the area with increased flood risks ranged from 73.6% to 95.2% in the YRB. Increased flood magnitude was typically related to lengthened return interval of floods under the RCP2.6 and RCP6.0 scenarios, while decreased flood magnitude was commonly linked to increased return intervals under the RCP8.5 scenarios. Notably, we observed higher correlations between 7-day maximum precipitation and floods compared to those between daily maximum precipitation, soil moisture, and floods. Co-occurrences between flood events and hydrometeorological extremes were higher in the upper and middle YRB.

Velocity distribution plays a fundamental role in understanding the hydrodynamics of open-channel flow. Among a multitude of approaches, the entropy-based approach holds great promise in achieving a reasonable characterisation of the velocity distribution. In entropy-based methods, the distribution depends on a key parameter, known as the entropy parameter (a function of the time-averaged mean velocity and maximum velocity), that relates to channel characteristics, such as channel roughness and channel bed slopes. The entropy parameter was regarded as constant for lack of experimental evidence, which would otherwise demonstrate if it had any correlation with channel properties. A series of experiments were conducted to collect velocity data in the laboratory flume for seven different values of the channel bed slope. The experimental data analysis revealed dissimilar fluctuations in entropy parameter values with varying bed slopes, with the lowest coefficient of variation in Renyi's (∼0.5%) and the highest in Shannon's case (∼10%). Performance evaluation of the predicted results substantiated good accuracy for all three entropies with the best results of Renyi entropy and lent strong support for using a constant (overall average) value of the entropy parameter for a specific channel cross-section rather than separate values for each channel bed slope. HIGHLIGHTS Verification of the influence of the channel bed slope on entropy parameters.; Velocity observations for mild, horizontal, and adverse channel bed slopes.; Shannon, Tsallis, and Renyi entropy-based velocity distributions.; Statistical and experimental evidence supporting the constant nature of all the entropy parameters.; Modified equation to estimate mean and maximum velocity ratio in terms of the Renyi entropy parameter.;

Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many statistical downscaling models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD model. Thus, it is desirable to compare the performance of SD models to ensure their adaptability in future climate studies. In this study, a statistical downscaling model (SDSM) or multi-linear regression and the Least Square Support Vector Machine (LS-SVM) were used to do downscaling and compare the results with those obtained from general circulation model (GCM) for identifying the best SD model for the Indira Sagar Canal Command area located in Madhya Pradesh, India. The GCM, Hadley Centre Coupled Model version 3 (HadCM3), was utilized to extract and downscale precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) for 1961–2001 and then for 2001–2099. Before future projections, both SD models were initially calibrated (1961–1990) and validated (1991–2001) to evaluate their performance for precipitation and temperature variables at all gauge stations, namely Barwani, East Nimar, and West Nimar. Results showed that the precipitation trend was under-predicted owing to large errors in downscaling, while temperature was over-predicted by SD models. HIGHLIGHTS Precipitation values are under-predicted, while temperature values are over-predicted by statistical downscaling models.; Large errors in downscaling precipitation are observed, since downscaling of precipitation is more problematic than temperature.; Statistical measures (R2, RMSE, SSE, NSE, and MAE) showed good agreement between observed and downscaled climate variables for SDSM and LS-SVM.;

The spatiotemporal evolution of vegetation and its influencing factors is crucial for understanding the relationship between vegetation and climate change, which helps guide the management of regional ecosystems effectively. Utilizing the Fractional Vegetation Cover (FVC) data and various meteorological elements from 1982 to 2021, this research employed methodologies, such as grey relational analysis, path analysis, and the time-lag effect, to examine the impact of climate change on FVC fluctuations. It introduced a comprehensive qualitative and quantitative analysis of the multi-factor climate–vegetation relationship, enhancing the understanding of the interaction between the climate and vegetation growth. The findings indicate that 77.41% of the wetland vegetation cover in the Yellow River Basin (YRB) has significantly decreased. Precipitation and evapotranspiration emerged as the primary factors affecting FVC, with soil moisture and temperature having a lesser impact. Given the crucial influence of climate factors’ time lag on vegetation dynamics, especially the notable cumulative lag effects observed in arid regions, such as precipitation accumulating over approximately 1.963 months (on average) and evapotranspiration lagging by about 1.727 months (on average), this study offers valuable theoretical insights on vegetation restoration efforts amidst the challenges posed by climate change.

The water diversion project in Central Yunnan Province (WDP-YN) is the largest water diversion project under construction in China. However, the ecological effects of this water diversion project are still unclear. This study utilized Sentinel-2 remote sensing data to estimate fractional vegetation cover (FVC), maps spatiotemporal variations of FVC in construction areas from 2017 to 2022, and evaluates the impact of the WDP-YN on regional vegetation coverage using buffer analysis and vegetation type transition matrix methods. The study led to the following findings: (1) From 2017 to 2022, FVC within 10 km of the tunnel construction route showed a slightly downward trend or remained relatively stable with no significant changes in the spatial pattern of FVC. (2) Before and after the construction of WDP-YN, over 60% of the area within 10 km of the tunnel construction route showed no change in FVC. On Construction Route Section I (CRS-I), vegetation improved and/or degraded within 12.90% (14.10%) of the area and the regions with degraded FVC concentrated in the northern CRS-I. For Construction Route Section II (CRS-II), 11.96% and 27.51% of the regions were dominated by improved and/or degraded FVC. Vegetation changes near Groundwater Monitoring Point a (GMPa) were relatively stable. (3) The WDP-YN degraded vegetation within 2 km of both sides of CRS-I, slowing down the increase in FVC, while the WDP-YN improved vegetation within 2–6 km of both sides of CRS-II, the closer the distance to CRS-II, the faster the increase in FVC and the decrease in FVC slowed down within 0–2 km of both sides of CRS-II. This study sheds light on the impacts of water diversion infrastructure on vegetation coverage and provides practical guidance and reference for eco-environment protection and ecological restoration given water conservancy projects in China and other regions of the world.

Abstract Although reservoirs serve as a lifeline for drought mitigation and adaptation, reservoir‐based droughts (RBD) have received limited attention. Especially RBD that incorporate reservoir evaporation have remained overlooked. Here, we develop an Integrated Reservoir Drought Index (IRDI) using a Frank copula by combining the effects of reservoir storage and evaporation rate for 164 global reservoirs. Results show that Africa, South America, and Australia have experienced relatively longer RBD, whereas Asia and Africa have experienced more intense RBD. Furthermore, when significant (p‐value

Prediction of the longitudinal dispersion coefficient (LDC) is essential for the river and water resources engineering and environmental management. This study proposes ensemble models for predicting LDC based on multilayer perceptron (MULP) methods and optimization algorithms. The honey badger optimization algorithm (HBOA), salp swarm algorithm (SASA), firefly algorithm (FIFA), and particle swarm optimization algorithm (PASOA) are used to adjust the MULP parameters. Then, the outputs of the MULP-HBOA, MULP-SASA, MULP-PASOA, MULP-FIFA, and MULP models were incorporated into an inclusive multiple model (IMM). For IMM at the testing level, the mean absolute error (MEAE) was 15, whereas it was 17, 18, 23, 24, and 25 for the MULP-HBOA, MULP-SASA, MULP-FIFA, MULP-PASOA, and MULP models. The study also modified the structure of MULP models using a goodness factor which decreased the CPU time. Removing redundant neurons reduces CPU time. Thus, the modified ANN model and the suggested IMM model can decrease the computational time and further improve the performance of models.

Study region: The Yellow River Basin (YRB) in China. Study focus: Ecological conservation and high-quality development of the YRB have been incorporated into the national strategy of China. Here we investigated into the impacts of El Nino-Southern Oscillation (ENSO) on rainstorm and flood risk in the YRB, paid special attention to disaster-induced losses and explained the mechanisms of ENSO affecting the risk. New hydrological insights for the region: ENSO tends to amplify rainstorm and flood risk in the YRB due to the increase in rainstorm days (rainstorm amount) by 33.6 % (34.1 %) in the next year with the previous summertime El Nino, by 20.3 % (19.3 %) in the current year with summertime La Nina, and by 70.7 % (72.5 %) in the ENSO phase shift year, and leads to a marked increase in disaster-induced losses. The summertime La Nina (El Nino) can increase the annual extreme runoff amount by 25.8 % (decrease by 8.4 %) in the lower YRB in the current year. At 500hpa, the higher air pressure over Eastern Europe and Okhotsk Sea in July, and the higher air pressure over the northern Eurasian continent and the lower air pressure over Lake Baikal and Balkhash in August tend to amplify rainstorm and flood risk. At 850hpa, the increase of water vapor transport from the south and cold air activities in high latitudes tend to increase the risk.

In this paper, field spectroradiometer and aboveground biomass (AGB) data were acquired at the harvest stage at two sites in semiarid grasslands in Inner Mongolia, China. Four forms of commonly used vegetation indices (VIs) using all possible combinations of narrow-band first derivative (FDR) and raw reflectance (RR) were calculated, and the best FDR-VIs and RR-VIs were chosen by a linear regression analysis against AGB. The stepwise multiple linear regression (SMLR) models using the optimal FDR-VIs, RR-VIs, and both FDR-VIs and RR-VIs as input variables were developed for estimating the AGB. Results demonstrated that the estimation performance using the best FDR-VIs were comparable with the best RR-VIs, while the accuracy has been further improved by combining the best FDR-VIs and RR-VIs (maximum decrease in RMSE of 44% and minimum RMAE of 4.7%). The approach was found to be an important step for more accurate and effective grassland AGB estimation.

Abstract Food security is a key target in the UN Sustainable Development Goals (SDGs) and is also one of the biggest challenges for China, the largest developing country in population in the world. Massive attention has been directed to the future impacts of hydrometeorological extremes on crop yield. However, knowledge gaps still stand concerning the effectiveness of irrigation, as the largest water consumption sector, on agricultural production under different climate scenarios. Here we showed the drought based on the modified Palmer Drought Severity Index and analyzed the drought-induced wheat yield losses in irrigated and non-irrigated agriculture across China according to three methods including the Multiple Linear Regression method, Deep Learning algorithm, and Erosion-Productivity Impact Calculator model. We found that the droughts become more intensive in the future, and drought-induced wheat yield loss under RCP8.5 scenario was expected to reach 32–49%. Intercomparison of drought-induced wheat yield changes between irrigated and non-irrigated agriculture indicated significantly less drought-induced crop-yield losses given sufficient irrigation under the RCP2.6 and RCP6.0 scenarios. However, our results under RCP8.5 suggested that the effectiveness of irrigation in enhancing food security is minor under this high-emission future climate scenario. These findings allowed us to revisit the effectiveness of irrigation in a warming climate and highlighted the importance of climate change mitigation in food security.

Aerial surveying with unmanned aerial vehicles (UAVs) has been popularly employed in river management and flood monitoring. One of the major processes in UAV aerial surveying for river applications is to demarcate the cross-section of a river. From the photo images of aerial surveying, a point cloud dataset can be abstracted with the structure from the motion technique. To accurately demarcate the cross-section from the cloud points, an appropriate delineation technique is required to reproduce the characteristics of natural and manmade channels, including abrupt changes, bumps and lined shapes. Therefore, a nonparametric estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was tested in the current study to demarcate the cross-section of a river with a point cloud dataset from aerial surveying. The proposed technique was tested with synthetically simulated trapezoidal, U-shape and V-shape channels. In addition, the proposed KLR model was compared with the traditional polynomial regression model and another nonparametric technique, locally weighted scatterplot smoothing (LOWESS). The experimental study was performed with the river experiment center in Andong, South Korea. Furthermore, the KLR model was applied to two real case studies in the Migok-cheon stream on Hapcheon-gun and Pori-cheon stream on Yecheon-gun and compared to the other models. With the extensive applications to the feasible river channels, the results indicated that the proposed KLR model can be a suitable alternative for demarcating the cross-section of a river with point cloud data from UAV aerial surveying by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps as well as different shapes. Finally, the limitation of the UAV-driven demarcation approach was also discussed due to the penetrability of RGB sensors to water.

Soil moisture plays a crucial role in determining the yield of winter wheat. The Huang-Huai-Hai (HHH) Plain is the main growing area of winter wheat in China, and frequent occurrence of drought seriously restricts regional agricultural development. Hence, a daily-scale Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI), based on winter wheat crop coefficient (Kc), was developed in the present study to evaluate the impact of drought characteristics on winter wheat in different growth stages. Results showed that the water demand for winter wheat decreased with the increase in latitude, and the water shortage was affected by effective precipitation, showing a decreasing trend from the middle to both sides in the HHH Plain. Water demand and water shortage showed an increasing trend at the jointing stage and heading stage, while other growth stages showed a decreasing trend. The spatial distributions of drought duration and intensity were consistent, which were higher in the northern region than in the southern region. Moreover, the water shortage and drought intensity at the jointing stage and heading stage showed an increasing trend. The drought had the greatest impact on winter wheat yield at the tillering stage, jointing stage, and heading stage, and the proportions of drought risk vulnerability in these three stages accounted for 0.25, 0.21, and 0.19, respectively. The high-value areas of winter wheat loss due to drought were mainly distributed in the northeast and south-central regions.

The growing increase in demand for water and the lack of balance between water supply and demand have led to conflicts among the downstream stakeholders of the international Hirmand River. This river is shared between Iran and Afghanistan and is located in the southeast of Iran, in the Sistan region. The Vardkhaneh is divided into two branches, Parian and Sistan, and it is the only main source of water in the Sistan region of Iran. The inner part of Hirmand catchment is considered bankrupt concerning its water resources, so there is a need to take the current status and resulting issues into account in order to resolve conflicts. In Hirmand catchment, four different games of bankruptcy theory, namely proportional (Pr), adjusted proportional (AP), constrained equal award (CEA), and constrained equal losses (CEL), were developed as optimization models based on genetic algorithms. For this purpose, the catchment was simulated with 26 scenarios using the WEAP 2022 version software for an average time period. The results of the bankruptcy game modeling showed that water could be effectively allocated to resolve conflicts among stakeholders. It is therefore recommended to use such a model to resolve fights and optimally allocate resources, even in bankrupt catchments.

Understanding the relationship between the plant-soil carbon dynamics and environment is important for the management of arid and semi-arid ecosystems. Under long-term overgrazing and reclamation, the landscape of the Horqin Sandy Land has transformed into a dune-meadow ecotone. However, the effects of landscape evolution on the carbon dynamics in this region remain poorly investigated. This study evaluated changes in plant-soil organic carbon content and stable carbon isotope ratios (δ13C) attributable to vegetation restoration in dune ecosystems and wetland degradation in meadow ecosystems, and evaluate the indicative implications and driving mechanisms of the 13C enrichment in the plant-soil system. The dune ecosystems comprised semimobile dune with 25- and 15-year-old Artemisia Halodendron communities (SMAH1, SMAH2), fixed dune with 30- and 40-year-old Caragana microphylla communities (FCM1, FCM2), semimobile dune with 40-year-old Salix gordejevii community (SMSG), and transitional zone with a 30-year-old artificial Populus forest (TPF) in the gentle lower part. The meadow ecosystems comprised a corn field that transformed from natural meadow over 40 years (MCF) and a natural meadow with Phragmites australis community (MPA). The results show that plant leaves in different landscapes in the cascade ecosystems had various δ13C. And the mechanism of the leaves carbon isotope fractionation with increasing leaf age was significant and consistent, but different vegetation communities had different nutrient utilization strategies during ontogeny. Landscape types and soil depths are the key factors affecting SOC content, its δ13C (δ13Cs) and SOC stock changes. And the overall soil carbon turnover was accelerated with vegetation restoration. Especially in TPF, vegetation restoration is particularly significant for SOC, which is mainly reflected in the upper 20 cm of soil profile. The degradation of MPA to MCF led to loss of SOC content and storage and accelerated the soil carbon turnover rate. The δ13Cs of the dune and meadow ecosystems were positively correlated with organic-rich litter nitrogen content and were significantly negatively correlated with SOC, soil moisture, clay content, and total organic carbon to total nitrogen ratio of organic-rich litter. The revegetation using native and introduced tree and shrub species increased SOC content in dune ecosystems and significantly promoted the degree of its coupling with soil nitrogen and water.

The reference evapotranspiration (ETo) pertains to the evapotranspiration of cold-season grasses with an approximate height of 0.12 m or full-covered alfalfa with a height of 0.50 m. Accurate short-term ETo forecasts are indispensable for informed irrigation decisions by relevant departments and individuals. Four deep learning (DL) models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (Bi-LSTM), and Bidirectional GRU (Bi-GRU), as well as two calibrated empirical models (Hargreaves-Samani (HS) and reduced-set Penman–Monteith (RPM)), were used to evaluate the performance of the ETo forecast with a lead time of 1–7 d using temperature forecasts in different climates. The results reveal that the DL models and calibrated HS and RPM models exhibited comparable trends in the ETo forecasts for lead times of 1–7 d. Nonetheless, the DL models consistently outperformed the HS and RPM models across the diverse climatic regions in China. The DL models displayed an average root mean square error (RMSE) and mean absolute error (MAE) of less than 0.887 and 0.633 mm/d, respectively. Moreover, the mean correlation coefficient (R) and accuracy (ACC) exceeded 0.807% and 89.701%, respectively. Among the DL models, the LSTM model demonstrated slightly superior performance in short-term daily ETo forecasts in diverse climates. The LSTM model exhibited RMSE and MAE ranges of 0.563–0.875 mm/d and 0.418–0.626 mm/d, respectively, along with R and ACC ranges of 0.81–0.90 and 89.94–98.11%, respectively. Furthermore, even with an increase in lead time, the DL models continued to exhibit strong predictive capabilities, consistently surpassing the performance of the HS and RPM models. Overall, the trained DL models presented an exceptional ability to forecast the short-term daily ETo in various climatic regions of China. These models require only a few input variables and readily available data, making them highly advantageous for practical applications in ETo forecasting. Such models hold promise for significantly enhancing regional agricultural water-resource planning and management.

The ecological conservation and high-quality development of the Yellow River Basin (YRB) has been the prime strategy of China. Wherein, the Inner Mongolia reach of the Yellow River Basin (IM-YRB) is a critical ecological barrier in northern China, playing an unshakable position in ecological and food security in the YRB. However, evaluation of ecological conditions and relevant driving factors have not aroused considerable concerns. Here we propose an ecological degradation index system based on the Pressure-State-Response (PSR) multi-scale comprehensive assessment framework and evaluate the ecological environment degradation intensity in the IM-YRB during 2000 to 2018. We find continual degradation of the ecological environment with increasing degradation intensity in the IM-YRB during 2000 to 2018. Moreover, we quantify that 24.16% of the IM-YRB had highly significant degradation (99% confidence level) of the ecological environment. The areas with significant degradation (95% confidence level) of the ecological environment accounted for 57.19% of the IM-YRB. The population density, land use, coal mining and fires are the main influencing factors behind ecological environment degradation in the IM-YRB. In addition, there occurred widespread land use conversion (>50% of the study region in area) amongst grassland, arable land, sands, construction land and unused land and these land conversions can be found mainly in the Yinshan Mountains (from grassland to abandoned land, the Hetao Irrigation District (from grassland to arable land) and the Kubuqi Desert (from sand, arable land and unused land to grassland). This study acts as the theoretical ground for sustainability and conservation of the regional ecological environment.

Abstract Compound climate extremes (here referred to compound dry–hot events and compound pluvial–hot events) result in devastating disasters which threaten water‐food‐energy security. However, in a warming scenario, the risk of occurrence, the quantification of uncertainty, and associated drivers of compound climate extremes—particularly compound pluvial–hot events—have not been fully explored. By leveraging climate model large ensembles, it is revealed that the risk of compound climate extremes is projected to increase 2–3 times over most global land masses in future Representative Concentration Pathway (RCP) 8.5 forcing compared with historical forcing. Increased risks of compound climate extremes are mainly attributed to the changes in temperature and changes in dependence between precipitation and temperature, while the change in precipitation contributing to risk of these two compound climate extremes exhibits approximately spatial complementary. In the warming world, the hot spots of compound dry–hot extremes mainly lie in Europe, South Africa, and the Amazon, while those of compound pluvial–hot extremes mostly lie in the eastern USA, eastern and southern Asia, Australia, and central Africa. These findings help stakeholders and decision makers develop a package of climate change adaptation strategies to manage and mitigate the risk of compound climate extremes.

Total phosphorus (T-P) refers to the concentration of phosphorus in water and is one of the important parameters for eutrophication in lakes and rivers. In current research, neuroscience dependent (i.e., singular and double-platform synthetic) approaches were employed to predict the river T-P concentration. Singular techniques were developed utilizing machine learning based (support vector machines (SVMs), stochastic gradient boosting (SGB)), and deep learning based multilayer perceptron (DMLP) models. Besides, double-platform synthetic techniques were developed by integrating a prior data-processing (Discrete-wavelet) algorithm with the singular techniques. Six input scenarios conditional on different water quantity and quality parameters acquired from two stations (Hwangji and Toilchun), South Korea, were employed for appraising singular and double-platform synthetic techniques. The various promoted models were evaluated using four statistical standards viz., mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and Pearson correlation coefficient (CCp), and four visual references viz., scatter diagram, box-and-whisker plot, violin plot, and Taylor diagram. It can be indicated from the outcomes that the double-platform synthetic techniques did not always lead to more accurate predictions than the singular techniques. Further, results also supplied that the SGB with the 6th input (MAE = 0.012 mg/L, RMSE = 0.014 mg/L, and CCp = 0.650 for Hwangji; MAE = 0.011 mg/L, RMSE = 0.017 mg/L, and CCp = 0.963 for Toilchun) scenario model demonstrated the best accuracy for predicting river T-P concentration by the singular techniques for both stations, whereas the discrete-wavelet SVMs with the 4th input (MAE = 0.007 mg/L, RMSE = 0.011 mg/L, and CCp = 0.765 for Hwangji) and discrete-wavelet SGB with the 5th input (MAE = 0.012 mg/L, RMSE = 0.017 mg/L, and CCp = 0.953 for Toilchun) scenario models provided the best predictive accuracy among double-platform synthetic techniques, respectively.

Abstract Heatwaves have afflicted human health, ecosystem, and socioeconomy and are expected to intensify under warming climate. However, few efforts have been directed to moist heat stress (MHS) considering relative humidity and wind speed, and moist heat stress risk (MHSR) considering exposure and vulnerability. Here we showed MHS and MHSR variations across China during 1998–2100 using China Meteorological Administration Land Data Assimilation System datasets, the 6th Coupled Model Intercomparison Project (CMIP6) merged datasets, Gross Domestic Product, population and leaf area index. We detected increased MHS across China under different Shared Socioeconomic Pathways (SSPs). Specifically, the historical MHS occurred mostly during mid-July to mid-August. We found increasing trends of 0.08%/year, 0.249%/year, and 0.669%/year in the MHS-affected areas under SSP126, SSP245, and SSP585, respectively. Furthermore, we observed the highest increasing rate of MHSR in Northwest and Southwest China, while the MHSR across Northeast and North China under SSP126 shifted from increasing to decreasing trends. Noteworthy is that the increasing trend of MHSR under SSP585 is 1.5–2.6 times larger than that under SSP245, especially in North and South China. This study highlights spatiotemporal evolutions of MHS and MHSR and mitigation to moisture heat stress in a warming climate.

A temporal trend analysis of weather variables is needed for developing mitigation and adaptation strategies to climate change. The objective of this work was to study temporal trends of 17 extreme temperature indices in North-Central Mexico (NCM). We used daily observations of maximum and minimum temperatures from 127 weather stations for 34 years (1980–2013). The extreme temperature indices were calculated with the RClimDex v1.9 package. Analysis of trend indices was carried out with the Mann–Kendall technique and the rate of change with the Theil–Sen estimator. Except for the growing season length, all-temperature indices showed statistically significant positive trends (SSPT) (p≤0.05$p\leq\nobreak 0.05$) between 1 and 62; except for the cold spell duration indicator, all indices showed statistically significant negative trends (SSNT) between 3 and 62. The SSPT of all indices varied between 0.246 °C decade−1 (TMINMEAN) and 48 days decade−1 (SU25), while the SSNT varied between −55.238 days decade−1 (SU25) and −0.181 °C decade−1 (TMINMEAN). In NCM, the increase in some indices could be causing an increase in crop water requirements and poorly meeting some crop's chilling requirements. In crops, such as grapevine, peach, and apple, an excellent short-term strategy to adapt to the decrease in cold winter and to compensate for the satisfaction of poor chilling hours using biostimulant of sprouting should be considered. New varieties/hybrids of corn and beans for dealing with the increase in warm days (TX90p), summer days (SU25), warm nights (TN90p), and tropical nights must be available; also, these new varieties should have shorter cycle and should be cold resistant. The results of this work constitute a reliable tool for generating answers for measures of adaptation to the onslaught of climate change in NCM.

As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD5) concentration, which is a stream pollutant with an extreme circumstance of organic loading and controlling aquatic behavior in the eco-environment. Leading monitoring approaches including machine leaning and deep learning have been evolved for a correct, trustworthy, and low-cost prediction of BOD5 concentration. The addressed research investigated the efficiency of three standalone models including machine learning (extreme learning machine (ELM) and support vector regression (SVR)) and deep learning (deep echo state network (Deep ESN)). In addition, the novel double-stage synthesis models (wavelet-extreme learning machine (Wavelet-ELM), wavelet-support vector regression (Wavelet-SVR), and wavelet-deep echo state network (Wavelet-Deep ESN)) were developed by integrating wavelet transformation (WT) with the different standalone models. Five input associations were supplied for evaluating standalone and double-stage synthesis models by determining diverse water quantity and quality items. The proposed models were assessed using the coefficient of determination (R2), Nash-Sutcliffe (NS) efficiency, and root mean square error (RMSE). The significance of addressed research can be found from the overall outcomes that the predictive accuracy of double-stage synthesis models were not always superior to that of standalone models. Overall results showed that the SVR with 3th distribution (NS = 0.915) and the Wavelet-SVR with 4th distribution (NS = 0.915) demonstrated more correct outcomes for predicting BOD5 concentration compared to alternative models at Hwangji station, and the Wavelet-SVR with 4th distribution (NS = 0.917) was judged to be the most superior model at Toilchun station. In most cases for predicting BOD5 concentration, the novel double-stage synthesis models can be utilized for efficient and organized data administration and regulation of water pollutants on both stations, South Korea.

Study region: Lancang River Basin (LRB) Study focus: Previous studies provided different results in terms of the roles played by climate change and human activities, leaving a knowledge gap in the complex streamflow variability and its physical causes in LRB. In the study, a Budyko framework-aided analytical approach was applied to explore the streamflow decrease in LRB and its physical causes, by especially clarifying the different performances of eight Budyko equations and the influences of diverse baseline periods. New hydrological insights for the region: Results revealed that the four parametric Budyko equations can provide proper parameters to reflect changes in catchment characteristics (including human activities) and their influences, and thus, they performed better than the four non-parametric Budyko equations. It was further found that more reasonable attribution results of streamflow change were obtained when a longer baseline period (at least 20 years) was used. Results also indicated that human activities (accounting for −158.9%) dominated the streamflow decrease during the transition period (1986–2004), by offsetting the positive effects of climate change (accounting for 58.9%). During the impact period (2005–2015), human activities (accounting for −65.6%) still dominated the streamflow decrease, and climate change (accounting for −34.4%) further aggravated the streamflow decrease in LRB.

Given China’s status as one of the most water-scarce countries globally, its rapid development of urbanization and sustained economic growth have led to increasing pressure on the urban water supply. Water pricing is also receiving increasing attention as an important tool for water resource management. This study analyzes the spatial and temporal characteristics of domestic water prices in China and their drivers. To this end, domestic water price data from 285 cities in China were collected. Spatial statistical analysis and geodetector were used to examine the spatial distribution and temporal patterns of domestic water prices from 2007 to 2020, as well as to identify the primary factors influencing these prices. The following results are noted: (1) The national average domestic water price has increased from 2 RMB/m3 to 3.12 RMB/m3, where the northeast and eastern regions have higher prices than the national average, while the central and western regions have lower prices. (2) The spatial distribution of urban domestic water prices presents clear differences characteristic of north–south and spatial agglomeration effects; the high-value area of domestic water prices is mainly concentrated in Beijing–Tianjin–Hebei. (3) On a national and regional scale, the price of domestic water is closely related to economic development, water resources, and resident’s income level. Furthermore, this study revealed that the interaction between pairwise factors played a more significant role in influencing domestic water prices compared with the individual impact of each factor. This finding contributes to a deeper understanding of the spatiotemporal heterogeneity in domestic water pricing and offers valuable insights and guidance for water pricing reform in China.

Background Neuroinflammation causing disruption of the blood-brain barrier and immune cell extravasation into the brain parenchyma may cause delirium; however, knowledge of the exact pathophysiologic mechanism remains incomplete. The purpose of our study was to determine whether cytokine profiles differ depending on whether delirium occurs in the setting of sepsis, coronavirus disease 2019 (COVID-19), or recent surgery. Methods This prospective observational cohort study involved 119 critically ill patients admitted to a multidisciplinary intensive care unit (ICU) during 2019 and 2020. Delirium was identified using the validated confusion assessment method for the ICU. Multiple delirium risk factors were collected daily including clinical characteristics, hospital course, lab values, vital signs, surgical exposure, drug exposure, and COVID-19 characteristics. Serums samples were collected within 12 hours of ICU admission and cytokine levels were measured. Results: The following proinflammatory cytokines were elevated in our delirium population: tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-18, C-C motif ligand (CCL) 2, CCL3, C-X-C motif chemokine ligand (CXCL)1, CXCL10, IL-8, IL-1 receptor antagonist, and IL-10. Analysis of relative cytokine levels in those patients that developed delirium in the setting of sepsis, COVID-19, and recent surgery showed elevations of CCL2, CXCL10, and TNF-α in both the sepsis and COVID-19 group in comparison to the postsurgical population. In the postsurgical group, granulocyte colony-stimulating factor was elevated and CXCL10 was decreased relative to the opposing groups. Conclusions We identify several cytokines and precipitating factors known to be associated with delirium. However, our study suggests that the cytokine profile associated with delirium is variable and contingent upon delirium precipitating factors.

Suspended sediment (SS) is an essential indicator for assessing watershed health. However, the temporal variation of SS, called sediment graph (SG) using readily available data, is not always considered, particularly in un-gauged watersheds, which are many in developing countries. Since field measurements of SS are time-consuming and costly, the synthetic SG seems to be a promising alternative. Therefore, it is essential to have reliable SS data for watershed management. This study aimed at simulating SGs through conceptual analysis of soil erosion and sediment yield at the watershed scale. To that end, soil erosion, sediment yield, and sediment routing were modeled using 38 storm events collected during 2011 and 2019 at the Galazchai Watershed in West Azerbaijan Province, Iran. Initially, the Time-Area Method (TAM) was applied, and then two strategies were considered to improve the TAM performance, including RUSLE and sediment delivery ratio (SDR) using gradient ratio and WaTEM/SEDEM methods. Comparing simulated SGs with recorded ones showed that the SDR-based method had the lowest relative error in time to peak and base time, but the peak value had the highest relative error. Results also showed that TAM developed using the spatially distributed travel time method had a better performance than the channel longitudinal profile method. Overall, TAM could not simulate the temporal variation of sediment and needs further research.

In recent decades, water scarcity is a significant constraint for socioeconomic development and threatens livelihood in an agriculture-based developing country like Pakistan. The water crisis in the country is projected to exacerbate in the coming years, especially in the southern parts. This dire situation calls for an investigation of major droughts, associated water scarcity, and changes in teleconnection patterns over Southern Pakistan. Moderate to low Southeastern monsoon (SEM) precipitation triggered the extreme drought episode (2017–2020) over Southern Pakistan and intensified the water scarcity. This study explored the severity of the respective drought event, underlying mechanisms, and changes in water scarcity over Southern Pakistan. To investigate the future changes (1980–2050) in water scarcity, coupling models (global hydrological models (GHMs)-global climate models (GCMs)) have been used to achieve the interannual performance of water availability and total water consumption. Besides, future scenarios used in this study are the combinations of SSPs and RCPs, including middle-of-the-road (SSP1-RCP4.5) and fossil-fueled development (SSP2-RCP8.5). The findings indicated a precipitation deficit of 45% during the 4-year (2017–2020), depicting the worst events in the past 50 years. South Pakistan observed the worst SEM droughts over the last 50 years, as 2000–2005 was the worst drought (precipitation deficit of 75%), followed by 2017–2020 with a 49% of precipitation deficit. Water scarcity was exacerbated by the extreme dry spells that developed over most of southern Pakistan between 2017 and 2020 as a result of moderate-to-exceptionally low SEM precipitation. Furthermore, this drought episode was accompanied by the cool phase in the Pacific and equatorial Indian Oceans. The future changes in water scarcity over the southern regions of Pakistan present a sharp increase under the SSP2-RCP8.5 scenario and are anticipated to be intensified in already stressed regions. This research is essential for environmentalists, and water resources managers, and provided crucial information to identify the hot spot areas in the target region so that water scarcity problems could be reduced in the future.

Determination of the longitudinal dispersion coefficient (LDC) is fundamental to the development of strategies for environmental management of river systems. This paper presents an integrated model for an estimation of the longitudinal dispersion coefficient by a fusion of optimized intelligent models (optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)) via committee machine (CM), with optimization done by the Bat-inspired algorithm (BA). The optimization eliminates the associated loss of accuracy of the intelligent models, which is a direct consequence of an improper adjustment of parameters (weights and biases in the neural network, membership's functions in the fuzzy inference system, and user-defined parameters in support vector regression). Data gathered from literature are employed to validate the proposed integrated model. A comparison between the optimized models and a committee machine, based on statistical parameters, shows that the committee machine model can attain high accuracy. Sensitivity analysis (SA) shows the contribution of each optimized model to the committee machine and ranks the contribution of the optimized models in ascending order as optimized neural network, optimized fuzzy inference system, and optimized support vector regression, each significantly correlated with the accuracy of longitudinal dispersion coefficient prediction. HIGHLIGHTS In this study, the longitudinal dispersion coefficient (LDC) is modeled.; Optimized models (OMs; OSVR, ONN, and OFIS) are employed for modeling purposes.; The bat-inspired algorithm (BA) is utilized for improving intelligent models.; The OMs are integrated by a committee machine (CM) with a BA combiner.; The CM generates outstanding results.;

Abstract This study assesses the feedbacks between water, food, and energy nexus at the national level with a dynamic-system model, taking into account the qualitative and quantitative environmental water needs. Surface and groundwater resources are considered jointly in the water resources subsystem of this dynamic system. The developed model considers the effects of reducing the per capita use water and energy on its system’s components. Results indicate that due to feedbacks the changes in per capita uses of water and energy have indirect and direct effects. About 40% of the total water savings achieved by the per capita change policy was related to energy savings, in other words, it is an indirect saving. Implementation of per capita use reductions compensates for 9% of the decline of Iran's groundwater reservoirs (non-renewable resources in the short term) that occur during the five-year study period. The Manageable and Exploitable Renewable Water Stress Index (MRWI) corresponding to water and energy savings equals 214.5%, which is better than its value under the current situation (which is equal to 235.1%).

Abstract Lake Urmia, the twentieth largest lake in the world, is the most valuable aquatic ecosystem in Iran. The lake water level has decreased in recent years due to human activities and climate change. Several studies have highlighted the significant roles of climatic and anthropogenic factors on the shrinkage of the lake. Management policies for water resources harvesting must be adopted to adapt to climate change and avoid the consequent problems stemming from the drought affecting Lake Urmia, and rationing must be applied to the upstream water demands. This study analyzes strategies and evaluates their effectiveness in overcoming the Urmia Lake crisis. Specifically, system dynamics analysis was performed for simulating the water volume of Lake Urmia, and the Hadley Centre coupled model was applied to project surface temperature and precipitation for two future periods: 2021–2050 and 2051–2080. Six management scenarios were considered for decreasing the allocation of agricultural water demand corresponding to two options: (1) one-reservoir option (Bukan reservoir only), and (2) six-reservoir option. The net inflow of Urmia Lake was simulated for the two future periods with the IHACRES model and with artificial neural network models under the six management scenarios. The annual average volumes of Lake Urmia would be 30 × 109 and 12 × 109 m3 over the first and second future periods, respectively, without considering the management scenarios. The lake volumes would rise by about 50% and 75% for the first and second periods, respectively under the management scenarios that involve strict protective measures and elimination of the effect of all dams and their reservoirs. Implementing strict measures would increase the annual average lake volume to 21 × 109 m3 in the second period; yet, this volume would be less than the long-term average and strategic volume. The human water use would be completely eliminated under Scenario 6. Nevertheless, Lake Urmia would experience a considerable loss of storage because of drought.

Total water storage in Eurasia can be driven by both climate variability and human activities, with the latter suggested as the key factor for water loss. However, here the authors show that drying in the low-latitude Atlantic Ocean is the dominant force in storage depeletion during 2003-2017.

Different regions exhibit significant differences in the characteristics of extreme precipitation, and the differentiation of such characteristics is not reported for all regions. To distinguish the characteristics of extreme precipitation in different regions in China, we extracted the distribution characteristics of extreme precipitation from daily precipitation data at 649 stations in China, 1961–2017, spatially grouped them by cluster analysis, and, finally, evaluated the effectiveness of zoning by a homogeneity test. Results showed that China can be divided into 33–35 extreme precipitation zones. Our map exhibits better homogeneity than the conventional climate map and other methods reported in the literature and better characterizes the regional distribution characteristics of extreme precipitation. It is noted that long–duration extreme precipitation has a more significant regional distribution consistency than short–duration extreme precipitation. Further, the western and northern regions of China are more prone to sudden, high–intensity extreme precipitation events, whereas the southeastern region is more vulnerable to frequent, high–intensity extreme precipitation events.

Pomegranate (Punica granatum L.) is a fruit tree that is globally distributed, especially in warm areas with low annual rainfall and limited water availability. This species exemplifies the critical role of water in agriculture and the need for efficient irrigation practices due to its characteristics, cultivation requirements, and geographic diffusion. In this study, we investigated the effects of drip irrigation and mulching on the vegetative growth, yield, and fruit quality attributes of pomegranate. The experiment involved three irrigation regimes (100% of evapotranspiration, 80%, and 60%) and three mulching treatments (no mulch, plastic mulch, and organic mulch) in a factorial combination. Both irrigation and mulching had significant positive influences on the yield and fruit quality attributes. Specifically, deficit irrigation strategies showed a negative impact on the fruit yield per tree, with a greater effect observed as the severity of the irrigation deficit increased. Mulching, on the other hand, led to a significant increase in the fruit yield, primarily attributed to an increase in fruit size. Furthermore, the analysis indicated that irrigation and mulching treatments had distinct effects on fruit traits such as the fruit length, width, volume, and rind thickness. Interestingly, the study highlighted that the effects of irrigation and mulching on fruit quality attributes were mostly independent of each other, suggesting an additive influence rather than an interaction between the two factors. These findings underscore the importance of considering irrigation and mulching practices for optimizing fruit quality in pomegranate cultivation, particularly in semi-arid regions. The results contribute valuable insights for farmers and researchers seeking to enhance fruit production and quality.

Multispecies pollutant migration often occurs in polluted groundwater systems. Most of the multispecies problems that have been dealt in the literature assume constant transport parameters, primarily because analytical solutions for varying parameters become a challenge. The present study analytically solves a two-species convection-dispersion transport equation, considering spatially varying dispersion coefficient and seepage velocity, which corresponds to the steady migration in a steady flow domain. Indeed, the methodology can be adopted for other cases, such as the transient migration in a steady flow domain and transient migration in an unsteady flow domain, without any difficulty. Three kinds of homotopy-based methods, namely the homotopy perturbation method (HPM), homotopy analysis method (HAM), and optimal homotopy asymptotic method (OHAM), are used to derive approximate analytical solutions in the form of truncated series. In homotopy analysis method, the convergence-control parameter ℏ plays a key role in the convergence of the series solution. It is observed that for a specific case of this parameter, namely ℏ=−1, the HAM-based solution recovers the HPM-based solution. For the verification of homotopy-based solutions, we utilize the MATLAB routine pdepe, which efficiently solves a class of parabolic PDEs (single/system). An excellent agreement is found between the derived analytical solutions and the numerical solutions for all three methods. Further, a quantitative assessment is carried out for the derived solutions. Also, convergence theorems are proposed for the series solutions obtained using HAM and OHAM.

Water quality plays a crucial role in management of water resources. Water quality indexes (WQIs) are frequently used methods to assess water quality for drinking purposes. A WQI can be predicted using chemical analysis which might not, however, be viable for a longer period in all country-scale rivers. Thus, in this investigation, two neural-based soft computing techniques – an artificial neural network (ANN) and a generalized regression neural network (GRNN) – and one hybrid soft computing techniques – an adaptive neuro-fuzzy interference system (ANFIS) with four membership functions – were used to predict WQIs in Khorramabad, Biranshahr and Alashtar sub-watersheds in Iran. Ten distinct physiochemical parameters were used as input variables and WQI as output. Simultaneously, a correlation plot and pairs were used to ascertain the relation of input and output variables. The soft computing techniques were compared using six fitness criteria: Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), Legates-McCabe Index (LMI), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of correlation (CC). Results indicated that ANN better predicted WQI than did GRNN and ANFIS. Among the different membership functions of ANFIS, ANFIS_trimf was far better than were the others. Thus, it was concluded that ANN was a viable tool for the prediction of a WQI. HIGHLIGHTS Water quality index values were predicted by using three soft computing techniques.; Ten distinct physiochemical parameters were utilized in modelling.; The soft computing techniques were compared using six fitness criteria: NSE, MAE, LMI, RMSE, MAPE, CC.;

Study region: the Yellow River Basin (YRB) in China. Study focus: Global warming potentially accentuates risks of extreme precipitation and floods that afflict regional socioeconomy. However, spatiotemporally-coupled relations between flood risk and socioeconomic development in the YRB are not properly understood. Here we used the Coupled Coordination Degree Model (CCDM) to evaluate the coupled relationship between flood risk and socioeconomic development in the YRB, China. New hydrological insights for the region: The regions with flood hazard along the mainstream of the YRB and decreasing flood hazard in the direction from southeast to northwest of the YRB, high hazard and higher hazard areas accounted for 10.39%. Meanwhile, overall flood exposure and vulnerability are decreasing from southeast to northwest YRB. Higher socioeconomic restorability highly alleviates the risk of flood disasters in central and northern Shandong, northern Henan, central Shaanxi, southern Shanxi provinces, and parts of Inner Mongolia. Relations between socioeconomic development and flood disaster risk are varying across the YRB. The lower YRB is dominated by developed socioeconomy but higher flood disaster risk. The high socioeconomic exposure and vulnerability of the lower YRB will increase the risk of flood disasters. The “transitional” zone in the middle YRB needs to properly handle the contradiction between economy and flood risk to avoid increased risk. Overall, socioeconomic development has a weak relation with flood disaster risk in the upper YRB.

Abstract An increase in the frequency of extreme rainfall events has caused more severe floods than before under the influence of climate change, and is receiving a lot of attention in data‐scarce areas like the Loess Plateau, especially in some small basins without hydrometeorological data, and to avert the risk of disaster needs the estimation of extreme rainfall quantiles. In this study, generalized additive models (GAM) are combined with a nonlinear canonical correlation analysis (NLCCA) procedure to estimate extreme rainfall quantiles at ungauged sites in the Wei River basin on the Loess Plateau. Also, the single inverse distance weighting (IDW) interpolation, NLCCA‐backpropagation (BP), and NLCCA‐radial basis function (RBF) models were used as comparative methods for testing the efficiency of the NLCCA‐GAM model. Because meteorological data are generally lacking in rainfall‐ungauged basins, data at ungauged sites was interpolated by IDW interpolation when NLCCA delineated homogeneous regions. Results of validation indicated that maximum daily rainfall quantiles in the Loess Plateau were well estimated by NLCCA‐GAM‐based regional frequency analysis, implying the NLCCA‐GAM is a useful tool for estimating design extreme rainfall in ungauged basins. Results of comparison demonstrated that NLCCA‐GAM was more robust and better reflected the nonlinear relationship between explanatory and response variables in the region than did comparative approaches. In addition, it was found that the NLCCA‐GAM approach made full use of the data from sites in homogeneous regions to mine the valuable nonlinear correlation between ungauged and gauged sites and estimate extreme rainfall quantiles at ungauged sites.

Study region: Southeast Australia. Study focus: We investigated meteorological and hydrological drought characteristics using Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSFI) and Effective Drought Index (EDI). Drought Hazard Index (DHI) was derived based on the probability of drought occurrence and Thiessen polygons using SPI/EDI, whereas Drought Frequency Index (DFI) was derived based on number of drought events, and data length using SPI, EDI, and SSFI. The modified Mann-Kendall test was applied to detect trends in streamflow data and hydrological droughts. Furthermore, correlation between meteorological and hydrological drought indices for different timesteps was assessed through Pearson’s and Spearman’s rank correlation analysis. Finally, the drought propagation time (DPT) from meteorological to hydrological drought was estimated by ‘theory of run.’ New hydrological insights for the region: Our major findings include: (i) The spatial coverage of DHI and DFI, based on SPI/EDI, illustrate that mainly south and coastal regions of the study area are the most ‘drought-prone’ (ii) A considerable proportion of streamflow stations shows a significant trend of decrease in annual streamflow, with the most dominant year of abrupt change is 1996; (iii) Hydrological droughts are increasing in the study area; (iv) Performance of EDI with SSFI is found to be better than SPI at 3-month timestep; and (v) DPT can be found using ‘theory of run’ however, defined DPT cannot be directly applied to other regions.

Study region: China Study focus: This study focuses on the continental-scale spatiotemporal calibration of the Blaney–Criddle equation (BC) for different climate zones in China. Daily meteorological data from 82 weather stations in China were collected for the periods of 1970–2004 and 2005–2014, respectively, to calibrate and validate the BC model. The coefficients of the BC model were spatially and temporally calibrated in different climate types via a linear regression method. New hydrological insights for the region: (1) Performance of the quarterly calibrated BC model was the most satisfactory. Statistical indices were in the range of 0.449–0.797 mm d−1 for the mean absolute error, 0.567–0.969 mm d−1 for the root mean square error and 0.82–0.953 for the index of agreement. The accuracy ranges increased from 42.95–96.56 % to 87.18–98.74 %; (2) Correction coefficient b and a for every quarter was mapped. Value of b showed a bell-shaped consist with temperature, while a showed an opposite trend. The quarterly calibrated BC model provided satisfactory results in the temperate monsoon and temperate continental climate zones; and (3) results indicated maximum temperature was the most influential factor for reference evapotranspiration (ET0), followed by minimum temperature. This study provides a more accurate BC model to calculate ET0 for different climate types in China and a reference for agricultural decision-makers in irrigation areas that lack sufficient meteorological data.

Abstract Water use by the agricultural sector along with inefficient irrigation methods and climate change has led to the depletion and insecurity of water resources and consequent instability of the agricultural system. Defining benchmarks and comparing them is essential for sustainable system management performance. The sustainability performance of an agricultural system depends on various factors related to water, energy, and food. This study selects and ranks sustainability performance indicators (SPIs) of agricultural systems with the analytical hierarchy process (AHP). Expert opinions on agricultural sustainability were obtained from Iran’s Regional Water Organization. The factors and variables affecting the management of water resources in agricultural systems in a basin area are evaluated with 17 SPIs (10 indicators of water resources sustainability, 3 energy sustainability indicators, and 4 food sustainability indicators) that measure the sustainability of agricultural systems. The AHP reduced the number of indicators to a small number of effective indicators. Results of pairwise comparison and the subsequent determination of the weight of each indicator show that the indicators of water consumption, groundwater level stability, vulnerability of water resources, and water stress have the largest weights (i.e., importance) for agricultural system sustainability at the basin scale. These selected indicators can be applied to agricultural water systems (AWSs).

Abstract Transboundary river basins give rise to complex water-sharing decision making that can be analyzed as a game in the sense of dynamic game theory, as done in this work. The sharing of transboundary water resources depends on the long-term shifting interactions between upstream and downstream countries, which has received limited research attention in the past. The water-sharing strategy of a riparian country depends on the strategies of other countries over time. This paper presents an evolutionary game method to analyze the long-term water-sharing strategies of countries encompassing transboundary river basins over time. The method analyzes the evolutionary strategies of riparian countries and investigates evolutionary stable strategies (ESSs) considering the payoff matrix. The evolutionary game method is applied to a river basin shared by three countries assuming two types of benefits and one type of cost to countries as decision variables of a game that reflects water use, economic and political gains, and socio-economic losses of countries. Numerical examples illustrate the strategies resulting from the evolutionary game processes and the role of several parameters on the interaction between riparian countries. The countries’ strategies are analyzed for several levels of benefits and costs, and the convergence of the strategies to a stable point is assessed. Results demonstrate the role that the upstream country’s potential benefits and the cost of conflict (i.e., non-cooperation) to other countries has on reaching a stable point in the game. This work’s results show the potential benefit to the upstream country under cooperative strategy must exceed its benefits from water use under non-cooperative strategy to gain the full stable cooperation of downstream countries. This work provides a method to resolve water-sharing strategies by countries sharing transboundary river basins and to evaluate the implications of cooperation or non-cooperation.

Abstract The worsening water scarcity has imposed a significant stress on food production in many parts of the world. This stress becomes more critical when countries seek self-sufficiency. A literature review shows that food self-sufficiency has not been assessed as the main factor in determining the optimal cultivation patterns. However, food self-sufficiency is one of the main policies of these countries and requires the most attention and concentration. Previous works have focused on the virtual water trade to meet regional food demand and to calculate trade flows. The potential of the trade network can be exploited to improve the cropping pattern to ensure food and water security. To this end, and based on the research gaps mentioned, this study develops a method to link intra-country trade networks, food security, and total water footprints (WFs) to improve food security. The method is applied in Iran, a water-scarce country. The study shows that 781 × 106 m3 of water could be saved by creating a trade network. Results of the balanced trade network are input to a multi-objective optimization model to improve cropping patterns based on the objectives of achieving food security and preventing water crises. The method provides 400 management scenarios to improve cropping patterns considering 51 main crops in Iran. Results show a range of improvements in food security (19–45%) and a decrease in WFs (2–3%). The selected scenario for Iran would reduce the blue water footprint by 1207 × 106 m3, and reduce the cropland area by 19 × 103 ha. This methodology allows decision makers to develop policies that achieve food security under limited water resources in arid and semi-arid regions.

Natural streamflow reconstruction is highly significant to assess long-term trends, variability, and pattern of streamflow, and is critical for addressing implications of climate change for adaptive water resources management. This study proposed a simple statistical approach named NSR-SVI (natural streamflow reconstruction based on streamflow variation identification). As a hybrid model coupling Pettitt's test method with an iterative algorithm and iterative cumulative sum of squares algorithm, it can determine the reconstructed components and implement the recombination depending only on the information of change points in observed annual streamflow records. Results showed that NSR-SVI is suitable for reconstructing natural series and can provide the stable streamflow processes under different human influences to better serve the hydrologic design of water resource engineering. Also, the proposed approach combining the cumulative streamflow curve provides an innovative way to investigate the attributions of streamflow variation, and the performance has been verified by comparing with the relevant results in nearby basin. HIGHLIGHTS A statistical approach is proposed to improve the accuracy of hydrological variation detection, and further reconstruct natural streamflow only depending on the variation information of streamflow.; The hybrid method has better performance on detecting the multiple change points in mean and variance.; The proposed approach combining the cumulative curve provides a way to investigate the attribution of streamflow variation.;