Precise runoff forecasting is important for regional water resources planning. Reliable prediction remains difficult in the middle and lower Yangtze River because traditional hydrological models often underperform, while many machine learning methods act as black boxes. We propose a two-stage long short-term memory (LSTM) approach for multi-step runoff forecasting. The framework integrates the Xinanjiang (XAJ) model, LSTM, ensemble empirical mode decomposition (EEMD), and error correction (EC). This design addresses compatibility issues between hydrological processes and signal decomposition, improving predictive accuracy while retaining physical interpretability. For a 7-day lead time, EC-X-LSTM improves correlation coefficient (r, + 4.63

Enhancing the scientific foundation and sustainability of managing water resources in river systems requires an understanding of the spatiotemporal effects of climate change and different human activities (e.g., reservoir operations and changes in land use/cover) on blue water (BW) and green water (GW) resources. Nonetheless, little research has simultaneously considered the interactive effects of various human activities (including reservoir operations) and climate factors on how the BW and GW varies over time and disperses over different regions. Using the tropical Nandu River Basin in Hainan Province, China as an example, this study proposes an integrated assessment framework combining the Soil and Water Assessment Tool (SWAT), the full factorial experimental design, temporal trend analysis, and spatial autocorrelation analysis to examine the influences of reservoir operation, land use/cover and climate on BW and GW resources across multiple temporal and spatial dimensions. The results indicate that 1) climate change is the dominant driver of blue and green water variations, contributing over 50% to green water changes, while land use/cover and reservoir regulation exert stronger influences on blue water, especially in middle-upper reaches and downstream of the main channel; 2) in the temporal dimension, under the climate change scenarios S3, S5, S6, and S8, blue water increased at rates >5 mm a(-1), and green water growth was mainly driven by enhanced actual evapotranspiration; and 3) in the spatial dimension, multiple drivers jointly reshaped clustering patterns, with blue water exhibiting expanded low-low clusters in the middle-upper reaches and green water showing strengthened low-low clusters in downstream areas. Overall, the proposed framework effectively disentangles the mechanisms by which multiple factors shape blue and green water dynamics and provides scientific guidance for optimizing water regulation in tropical basins.

Understanding runoff changes under climate change and human activities is critical for adaptive basin management. However, many Budyko equations overlook key factors such as snowmelt and soil water storage. To address this gap, we developed a Budyko-constrained machine learning framework (Budyko-ML), which integrates physical consistency with data-driven flexibility. The framework extends the Budyko equation to account for changes in snow water equivalent (Delta SWE) and soil water storage (Delta S). To reduce spatial heterogeneity, K-means clustering was used to group similar basins. The framework was then applied to attribute runoff changes, with SHapley Additive exPlanations (SHAP) quantifying the contributions of climate and human drivers. Generalized Additive Models (GAM) revealed nonlinear thresholds of key variables. Findings show that the Budyko constraints improved attribution stability, with Nash-Sutcliffe Efficiency (NSE) increasing by 30% compared to purely data-driven models. Most basins exhibited declining runoff, primarily driven by climate change, with potential evapotranspiration (PET) identified as the dominant factor. GAM analysis revealed that as mean temperature and aridity increased, the thresholds for PET and effective precipitation's influence on runoff changes decreased. This framework offers a physically interpretable, spatially adaptive method for diagnosing runoff changes under non-stationary conditions.

Emergent constraints reduce uncertainties in future climate projections by the comparison with current climate and observations. However, previous methods for emergent constraints are limited to variables following normal or multivariate normal distributions. Here, we devise a copula-based emergent constraint (CEC) framework that enables the flexible selection of marginal distribution functions and the combination of multiple constraints. The Markov chain Monte Carlo (MCMC) algorithm is applied to numerically estimate the posterior distribution derived from Bayes’ theorem. This new framework achieves narrower uncertainties in the projections of future global warming than previous approaches that assume normal distributions. Combining two constraints in the Northern and Southern Hemispheres further reduces uncertainties after the integration of different information. Due to the flexibility in distribution functions and constraint size, the CEC framework is applicable to more variables and interactions across various spheres of Earth’s system.

Understanding the interactions between water security, carbon storage, and economic and social development (WCE) is critical for achieving sustainable urbanization. This study establishes a coupled WCE system model for Wuhan, China, by integrating System Dynamics (SD) and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to quantify dynamic socioeconomic processes and spatial ecological patterns. A mechanical model was further employed to quantify system coordination and assess urban sustainability. Future scenarios were simulated to identify optimal development pathways. The results showed: (1) During the historical period (2000-2020), green economic and social development enhanced resource utilization efficiency (+289%) but reduced ecological security levels (-41%). (2) WCE system coordination improved (0.45 -> 0.68), while the primary constraint shifted from water scarcity to carbon storage deficit. (3) Future scenario simulations (2021-2030) indicate declining coordination under baseline scenario, whereas the low-carbon green strategy achieves optimal coordination (>0.8), significantly outperforming ecological protection and high-intensity urbanization strategies. This work provides the first quantitative assessment of WCE interactions and coordination status in megalopolises along the Yangtze River Basin, demonstrating the advantage of SD-InVEST coupling in integrating socioeconomic dynamics with ecosystem spatial processes. The proposed framework offers a scientific tool for comprehensive urban planning incorporating water-carbon-economy nexus, with the low-carbon green pathway identified as essential for enhancing sustainability.

Global climate change and resource depletion increase the risks of water and land resource scarcity, yet our understanding of how to optimize the matching of these resources and their economic implications remains limited. This study proposes a novel framework based on matching degree analysis to evaluate the matching status of water-land resources and its impact on economic development. Applying a multi-dimensional matching approach to the lower Yellow River floodplain, we explore the spatial and temporal dynamics of water-land matching across different economic development type, along with the impact on the economy. By integrating the Cellular Automata Markov (CA-Markov) model with CMIP6 climate scenarios, we analyze future land-use changes and resource dynamics under various climate conditions. The findings reveal significant regional heterogeneity in the role of water and land resources in economic development. Production-intensive areas exhibit stable resource support, while agriculture-dependent areas show more variability, overall, the degree of water-land matching positively influences economic performance in most regions (66%). As urbanization progresses, gross domestic product (GDP) and urbanization rates emerge as dominant factors shaping resource-economic dynamics. Future projections indicate that water and land resources will stably support economic production in both industrial and agricultural regions. However, regions such as Taiqian and Dong'e, which focus on modern agriculture and ecological protection, are expected to experience more significant fluctuations in resource availability and economic outcomes. This study provides a holistic framework for optimizing resource management and informs policies to enhance regional resilience amid climate change and resource constraints.

The El Niño-Southern Oscillation (ENSO) is a key ocean-atmospheric mode that affects global weather and climate through atmospheric teleconnections. It remains unclear how robust and stable ENSO teleconnection patterns are under external perturbations. Here, we use observations to demonstrate that volcanic eruptions disrupt ENSO teleconnections with land surface air temperature in boreal summer across the globe. During eruption years, correlations are found to shift in 62.27% of regions where temperature is significantly connected to ENSO during non-eruption years. State-of-the-art Earth System Models fail to capture this disruptive effect, highlighting challenges in simulating the patterns of climate anomalies and extremes under external perturbations. The disruptive effect identified here raises questions about the stationarity assumption in ENSO reconstructions based upon temperature-sensitive proxy records. Our findings call for an improved characterization of regional climate impacts following volcanic eruptions.

Drought propagation from meteorological drought (MD) to hydrological drought (HD) involves a temporal delay driven by multiple determinants and complex response dynamics and poses substantial challenges to river basin drought management. This study investigates the MD-HD drought progression in the Upper and Middle Han River Basin (UMHRB). We quantify the duration and intensity of drought propagation and establish the MD propagation thresholds triggering HDs under different drought categories. Furthermore, this study analyzes how meteorological variables and land surface conditions modulate drought progression and employs the coupled water-energy balance equation to reveal the underlying mechanisms influencing drought propagation. HD events generally followed MD episodes, with propagation thresholds for the UMHRB were determined via Bayes’ theorem. The results indicate spatially decreasing sensitivity of HD to MD from upstream to downstream in the UMHRB. Temperature emerged as the predominant factor influencing drought progression, whereas precipitation and potential evapotranspiration (PET) showed secondary effects. Additionally, watershed’s physical characteristics and anthropogenic activities, which significantly correlated with the parameters ω of the coupled water-energy balance equations, also modulated drought propagation. However, the parameter ω failed to fully represent the influence of human activities in the Huangzhuang section, suggesting a limitation that could be addressed by integrating factors pertinent to drought progression.

Baseflow is a critical hydrological variable linked to a variety of environmental factors. The construction of a dam may modify baseflow generation and adjust its environmental dependence via the regulation of discharge. However, its effects on the relationships among air temperature, water temperature, and baseflow remain unclear. Here, we use copula functions to explore the relationships among these variables in the Yangtze River and show how they have changed in response to the Three Gorges Dam (TGD). Because of the buffering effects of the baseflow, baseflow is negatively correlated to water temperature, and the relationship between baseflow and water temperature lessens as air temperature rises. Construction of the TGD attenuates the relationship between baseflow and water temperature when air temperature is low, and this attenuation is especially prominent in winter and spring. Our findings will enhance environmental protection in the Yangtze River and improve the ecological operations of the TGD.

Although the Coupled Model Intercomparison Project 6 (CMIP6) can well predict large-scale climatic factors,its effect on projecting watershed scales is still different from the measured data.The error of climate models is even bigger over the Tibetan Plateau,which is a high-altitude region with complicated terrain.Based on the historical scenario of the latest generation of high-resolution CMIP6 model and a variety of future climate emission scenarios such as SSP126,SSP245,SSP370,and SSP585,this paper conducts downscaling analysis and evaluates the projection performance of various statistical downscaling methods such as bias correction,KNN,and SDSM.On this basis,the best statistical downscaling method is used to project future precipitation over the Tibetan Plateau, and the spatial-temporal evolution characteristics of the projected precipitation are analyzed and compared with the historical precipitation over the Tibetan Plateau.The results reveal that the applicability amongst the three statistical downscaling methods in the Tibetan Plateau is large,with the linear regression downscaling method performing the best,followed by the bias correction method and the KNN analogy method.According to the analysis of future precipitation projections,the average precipitation and extreme precipitation over the Tibetan Plateau in the next 80 years will exhibit an overall upward trend,although the rise will be slight,and the spatial distribution will not change much.The results can provide a scientific foundation for the evaluation,planning,and management of water resources on the Tibetan Plateau.

Abstract Over the past two decades, more frequent and intense climate events have seriously threatened the operation of water transfer projects in the Pacific Rim region. However, the role of climatic change in driving runoff variations in the water source areas of these projects is unclear. We used tree-ring data to reconstruct changes in the runoff of the Hanjiang River since 1580 CE representing an important water source area for China’s south-north water transfer project. Comparisons with hydroclimatic reconstructions for the southwestern United States and central Chile indicated that the Pacific Rim region has experienced multiple coinciding droughts related to ENSO activity. Climate simulations indicate an increased likelihood of drought occurrence in the Pacific Rim region in the coming decades. The combination of warming-induced drought stresses with dynamic El Niño (warming ENSO) patterns is a thread to urban agglomerations and agricultural regions that rely on water transfer projects along the Pacific Rim.

Study region: The Lhasa River Basin (LRB), Qinghai-Tibet Plateau (QTP), China Study focus: This study investigates the application of three Budyko-type models with timevarying parameter and further explore the attribution of runoff change in the QTP. Three Budyko models with time-varying parameters estimated by linear, quadratic, sine and linear stepwise regression equations have been applied and compared their adaptability. According to the comparison results, the sensitivity decomposition analysis has been utilized based on the bestfitted Budyko model to separate the impacts of climate change and human activities on runoff during the 1988-2015 period. This study is aiming at exploring the pattern of the regional hydrological cycle and improving our understanding of the driver mechanism of runoff change in the alpine mountain area. New hydrological insights for the region: The time-varying Budyko-type models significantly improve the performance of runoff simulation compared to constant Budyko-type models, while the Choudhury-Yang model performs best, and the sine equations of simulated parameter & omega; best reflects the time variation pattern of parameters, which declares that & omega; have certain periodic characteristics. The result of runoff attribution shows that precipitation is the main factor increasing runoff before 2003 and human activity variables mainly lead to the decreasing trend of runoff after 2003, while PDO and PNA affect the runoff-generated process through the relationships with the environmental variables in the LRB.

干旱指数对干旱的研究至关重要.随着气候变化和人类活动的影响,水文序列的频率分布随时间变化,因此需考虑干旱指数的非一致性.标准化降水指数SSPI认为降水服从Gamma分布,对应的分布函数参数通常稳定不变,而实际的分布函数参数与时间序列具有一定相关性,因此利用GAMLSS模型以时间序列为协变量构建了降水序列的非一致性Gamma分布模型,在此基础上计算出非一致性标准化降水指数NNSPI.以云南省为例,分别计算了1960～2019年的逐月NNSPI、SSPI,将两者进行对比分析,发现两者对干旱的识别结果总体趋势相近,但在1960～1985年NNSPI识别的干旱数量和等级强于SSPI,1986～2005年两者相近,而在2006～2019年,SSPI识别的干旱数量和等级逐渐超过NNSPI,进一步分析表明,考虑了分布函数参数随时间变化的NNSPI能更精确反映实际干旱情况.

Daily inflow forecasting is of vital importance in reservoir economic operation. In the context of hydrometeorological forecasting, the effectiveness of the data-driven models has been demonstrated as bias correctors for physically-based models or direct forecasting models. However, existing studies only highlight the performance improvements provided by the data-driven model, lacking a comprehensive investigation on whether the data-driven model should be used as bias correctors or direct forecasting models. This study constructs long short-term memory (LSTM)-based preprocessing and postprocessing techniques for a hydrological model, which are tested by linear scaling preprocessing and autoregressive (AR) postprocessing models. The integrated model is compared with the LSTM-only model. The Shuibuya and Zuojiang reservoirs in China are selected as case studies. Results indicate that: (1) LSTM-based bias correctors are effective in both preprocessing and postprocessing and (2) the integrated model is comparable to the LSTM-only model when trained with four or more years of data, while it is better than the LSTM-only model when trained with less data. These findings demonstrate that data-driven methods can effectively correct the bias in physically-based model output, and integrating the physical and data-driven models is useful in improving multi-step ahead reservoir inflow forecasting if limited data can be obtained.

气候变化与人类活动是流域径流变化的重要原因.随着人类活动逐渐频繁,不同地区的水文情势不同程度地发生着变化,因此对径流变化机理的分析一直是水文学界的重要议题之一.Budyko水热耦合平衡公式是常用的径流变化机理分析工具,因其一方面考虑了一定的物理成因,另一方面公式参数较为简单,由此在径流变化归因较长时间尺度分析上得到了广泛应用,但当前对时变形式的Budyko公式研究及应用仍有待加强.以贵州省六硐河流域为例,通过Mann-Kendall检验和突变检验方法对研究区年径流进行趋势和突变分析,基于水热耦合平衡原理构造了包括线性、二次多项式以及具有物理机制等形式的Budyko参数时变辨识公式,以Nash效率系数为评价指标,验证最适应研究区的水热耦合公式,最后计算降水量、潜在蒸散发量和人类活动等因素对径流变化的贡献率,进行六硐河流域径流变化归因分析.研究结果表明,近年来随着气候变化影响加剧,六硐河流域径流呈明显的下降趋势,在1997年发生显著性突变.比较降水量、潜在蒸散发量和人类活动因素对径流的敏感性,发现降水减少是流域径流减少的主导因素,其贡献率为49%.同时,随着人类经济社会的发展,大型水利设施的兴建,人类活动对流域径流变化的影响在逐步显现,其贡献率约为26%.

The Gravity Recovery and Climate Experiment (GRACE) mission provides an unprecedented way to assess terrestrial water storage changes (TWSC) worldwide. However, hydroclimatic drivers of GRACE-derived TWSC in different climates have not been systematically examined, which hinders its applications in water resources management and hydrological model development. In this study, we derived the monthly TWSC using the GRACE dataset, and analyzed its partial correlations with precipitation (P), evapotranspiration (ET), runoff (R), and temperature (T) from 2003 to 2019 under the Ko center dot ppen-Geiger world climate classification system. Relative contributions of the four hydroclimatic elements (P, ET, R, and T) to TWSC were quantified using the hierarchical partitioning method. The results indicate that P mainly controls TWSC in tropical climates, hot semi-arid climate, and temperate and continental climates with dry winter. ET is the primary driver of TWSC in mid-and high -latitude regions that feature temperate and continental climates with no dry season, subarctic climates, and polar climates. T mainly influences TWSC in cold arid climates, temperate and continental climates with dry summer or no dry season, and ice cap climate. However, TWSC in many arid climates near the Tropic of Cancer and the Tropic of Capricorn are not well explained by all four hydroclimatic variables according to their non-significant partial correlations. Large trends in annual TWSC are found in South America, western Canada, eastern contiguous United States, and Africa, which are likely due to marked changes in P based on their similar spatial pattern. In particular, decreasing TWSC are found in most continental climates, which manifests a deterioration of water resources availability in these regions. Overall, our results clarify the major hydroclimatic drivers of TWSC in different climates at a global scale, which may help improve TWSC modeling and prediction through a judicious selection of explanatory hydroclimatic variables in different climates.

水量平衡是水文水资源领域最基本的原理,通过研究区域水量平衡,可以确定区域各水文要素之间关系、合理认识和评价区域水资源、指导水资源优化配置.基于水量平衡原理,分别采用平衡差和区间降雨径流系数对郁江流域各水电站、水电站区间进行计算分析,发现在实际运行中存在明显水量不平衡现象.该现象的存在,对梯级水电站的防洪度汛、来水预测预报、联合优化调度等造成一系列的影响,并影响电站的经济效益和安全生产.通过对水量平衡各要素及其计算过程的分析结果看,郁江流域水电站水库之间水量不平衡的主要原因是运行多年的特性曲线发生了改变.

The Budyko framework is an effective and widely used method for describing long-term water balance in large catchments. However, it only considers the limits of water and energy in evaporation (E), and ignores the impacts of climate seasonality and water storage capacity (Sc), resulting in errors for Mediterranean climate and catchments with small Sc. Here we combined the Ponce-Shetty model with Budyko hypothesis, and analytically generalized Budyko framework with physical accounts of climate seasonality and Sc. Precipitation (P), potential evaporation (PE), and Sc are used to represent the limits of water, energy, and space for E, respectively. Our results show that previous Budyko-type equations can be treated as special cases of generalized Budyko-type equations with uniform P and PE and infinite Sc. The new generalized equations capture the observed decrease in E due to asynchronous P and PE and small Sc, and perform better than the Budyko-type equations with varying parameters in the contiguous United States with fewer parameters. Overall, our generalization of Budyko framework improves the robustness and accuracy for estimating mean annual E with the aid of physical interpretation, and will facilitate water balance assessment at regional to global scales.

Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), two successive satellite-based missions starting in 2002, have provided an unprecedented way of measuring global terrestrial water storage anomalies (TWSA). However, a temporal gap exists between GRACE and GRACE-FO products from July 2017 to May 2018, which introduces bias and uncertainties in TWSA calculations and modeling. Previous studies have incorporated hydroclimatic factors as predictors for filling the gap, but most of them utilized artificial intelligence or pure statistical models that generally de-trended TWSA and had no physical foundation. Thus, a physically-based reconstruction is required for increasing robustness. In this study, we bridge the temporal gap by developing an empirical hydrological model. The "abcd" model, a T-based snow component, and linear correction are utilized to represent runoff generation, snow dynamics, and long-term trends. The testing results indicate that our hydrological model can successfully reconstruct TWSA in tropical, temperature, and continental climates, although further improvement is needed for arid climates. Our reconstruction for the gap achieves high accuracy and robustness as shown by the evaluations against sea-level budget and GLDAS-derived TWSA. Compared to previous studies using artificial intelligence or statistical techniques, our hydrological model performs similarly in the gap filling but does not involve de-trended or de-seasonalized transformations, which will facilitate the combination of GRACE and GRACE-FO products and improve the physical understanding of global TWSA.