Permafrost degradation on the Tibetan Plateau (TP) has intensified under recent climate warming, profoundly affecting hydrological processes and ecosystem stability. The root zone represents a key interface where ecological and hydrological processes interact, yet its water storage capacity remain poorly constrained due to the scarcity of direct observations in high-altitude environments. In this study, we employ an observation-based water balance approach to estimate root zone storage capacity (SR), defined as the maximum ecosystem-accessible volume that can be accessed by roots to allows plant water use during critical drought periods. Spatial patterns of SR across the TP display considerable heterogeneity (24-278 mm, 1st-99th percentiles; Mean f SD = 93 f 12 mm), governed jointly by hydroclimatic and biological factors. Permafrost regions show significantly lower SR (67 f 6 mm) than seasonal-frost regions (110 f 15 mm). Segmented regression combined with Davies' supremum test identifies an ecohydrological turning point at an active layer thickness (ALT) of 2.2-2.5 m, beyond which SR shifts from increasing to decreasing trends. This transition coincides with marked declines in the evaporation ratio, Budyko-Fu's omega parameter, and measured belowground phytomass, reflecting restricted access of roots to supra-permafrost water. The results highlight a critical threshold linking permafrost degradation to vegetation water use and provide a quantitative basis for understanding and predicting the coupled assessing ecohydrological vulnerability of alpine/high-plateau ecosystems under ongoing climatic warming.

The high mountainous regions of western Mongolia serve as vital “water towers” for Indigenous herders and fragile ecosystems. However, climate change presents serious threats to the high-altitude cryosphere—glaciers and permafrost—jeopardizing water security. Artificial ice reservoir technologies have emerged as adaptive solutions to address seasonal water shortages, especially in the spring. These systems involve freezing diverted melt water during winter and gradually releasing it as it melts in warmer months for drinking, irrigation, and livestock watering.This review systematically evaluates four major approaches—traditional ice harvesting, diversion-based artificial glaciers, in-stream glacier enhancement, and recent innovations such as Ice Walls and Ice Stupas. Beyond describing techniques, we critically analyze their efficiency, scalability, and applicability under different climatic and socio-economic conditions. The Ice Stupa, initially developed in Ladakh, shows promising potential for adaptation in the Mongolian Altai due to its low-cost construction, flexibility, and controlled seasonal release. However, its success still depends heavily on local hydrological conditions, community participation, and ongoing maintenance.To our knowledge, this is the first review to contextualize artificial ice reservoir technologies for Mongolia. By synthesizing global experiences with local environmental challenges, we highlight lessons learned, outline key design and implementation criteria, and suggest directions for future field testing. As climate pressures grow, these nature-based solutions could help enhance long-term water resilience strategies in high-altitude, arid regions.

High-mountain systems act as the planet's vital water towers, sustaining freshwater supplies for billions of people. Climate change is exacerbating hydrological imbalances in these regions, yet the moisture sources maintaining their precipitation-the primary water input-remain poorly quantified. Here, we combine two atmospheric moisture tracking methods to analyze 73 water tower units (WTUs), revealing that terrestrial evaporation contributes approximately half of total precipitation (52% UTrack, 50% WAM-2layers) over these regions, with inland WTUs relying more on land-sourced moisture than coastal systems. Transpiration from short vegetation dominates terrestrial contributions globally, while forest transpiration (e.g., Amazon) and bare-soil evaporation (e.g., interior Tibetan Plateau) are regionally significant. Importantly, oceanic moisture drives snowfall, whereas terrestrial transpiration sustains rainfall-except in the southern Tibetan Plateau. These findings refine the atmospheric water cycle's role in high-mountain hydrology, offering a mechanistic basis to project water tower resilience under global change.

Abstract Snowmelt is a critical component of the global water cycle and a vital freshwater source for both ecosystems and human societies. Yet the global partitioning of snowmelt into runoff and evaporation remains poorly quantified. Here, using a process‐based hydrological model (FLEX‐Global) forced by meteorological data from 1980 to 2014 and validated against observed streamflow and snow water equivalent, we present a comprehensive assessment of global snowmelt partitioning. The model results are independently supported by two additional approaches: an empirical partitioning equation and inverse estimations from three global hydrological models. We show that 53%–71% of snowmelt runs off globally (excluding Antarctica and Greenland), while 29%–47% contributes to evaporation. Snowmelt partitioning exhibits distinct latitudinal and climatic patterns: contributions of snowmelt to both runoff and evaporation increase with latitude. In cold–humid high‐latitude regions, more than 60% of snowmelt becomes runoff, whereas in mid‐latitude arid regions, 63%–91% is released from the terrestrial ecosystems as evaporation. Elevation further modulates snow hydrology in mid‐latitude mountains, where snowmelt generates 58%–74% of total runoff and 51%–66% of total evaporation—significantly higher than contributions at lower elevations. The traditional definition of snowmelt runoff (snowfall/total runoff) estimates that snowfall accounts for 38% of total runoff, whereas our snowmelt‐partitioning approach (snowmelt runoff/total runoff) yields a much lower contribution of 11%–18%. Our results underscore snowmelt's dual role in sustaining freshwater availability and supporting vegetation water demand, redefining its importance in the global hydrological cycle and associated ecosystem services.

Hydropower expansion creates tension between renewable energy goals and freshwater ecosystem health. However, the global impacts of dams across the full spectrum of freshwater biodiversity remain poorly quantified. Here, we combine remote sensing and global threatened species datasets to assess dam-related risk patterns for fish, mollusks, mammals, odonates, and amphibians. We show that threatened freshwater species are consistently more common near dams than elsewhere, with mammals showing the highest exposure. Most species whose risk status changed between 1996 and 2022 shifted toward higher threat levels in dam-influenced areas affected by habitat loss and reduced river connectivity. Planned future dams, concentrated in Global South countries, may further elevate extinction risk, particularly for critically endangered fish. These findings support sustainable planning that balances energy development with freshwater conservation.

Adaptation of ecosystems’ root zones to climate change critically affects drought resilience and vegetation productivity. However, a global quantitative assessment of this mechanism is missing. In this study, we applied the mass curve technique (MCT) based on water balance to estimate the global root zone water storage capacity (SR) using high-quality observation-based data. Our results show that the global average SR increased by 11%, from 182 to 202 mm in 1982–2020. The total increase of SR equals to 1652 billion m3 over the past four decades. SR increased in 9 out of 12 land cover types, while three relatively dry types experienced decreasing trends, potentially suggesting the crossing of ecosystems’ tipping points. Our results underscore the importance of accounting for root zone dynamics under climate change to assess drought impacts.

The freeze-thaw processes play a critical role in regulating water partitioning in cryospheric catchments, yet their underlying mechanisms remain insufficiently quantified or poorly understood. We extended the FLEX-Topo framework to an isotope-aided version (FLEX Topo -iso) and further to a freeze-thaw-coupled cryospheric model (FLEX Cryo -iso) to quantify runoff components and landscape contributions in a mountainous catchment of the upper Heihe River, China. FLEX Topo -iso shows that stable isotope constraints improve runoff component estimation, but streamflow simulation degrades without explicit frozen soil processes. FLEX Cryo -iso successfully reproduces the temporal dynamics of streamflow, delta 18 O, and soil freeze-thaw depth, showing markedly improved performance compared to the FLEX Topo -iso model. Source apportionment for 2013-2016 indicates groundwater dominance (69.74% f 2.19%), followed by snow and glacier melt (17.12% f 0.97%) and rainfall (13.14% f 1.90%), consistent with isotope end-member validation. Spatially, runoff is mainly generated from alpine desert (58.88% f 2.78%), supplemented by hillslope vegetation, glacier, and riparian area. During the frozen period, hydrological connectivity is minimal, and streamflow is dominated by groundwater, with hillslope vegetation in seasonally frozen soils contributing more to runoff than permafrost-limited alpine desert. During thawing, surface and subsurface flows progressively reconnect, and meltwater and rainfall begin to contribute to streamflow. During the thawed period, hydrological connectivity peaks, with groundwater remaining dominant while meltwater and rainfall jointly sustain peak flows. During refreezing, hydrological connectivity declines first at high elevations, and streamflow is sustained by delayed groundwater discharge through taliks. Runoff contributions from permafrost areas vary in parallel with seasonal changes in hydrological connectivity. By coupling isotopic constraints with explicit freeze-thaw representation, FLEX Cryo -iso provides a robust framework for runoff partitioning and for understanding freeze-thaw-driven runoff and hydrological connectivity.

Studying evolution characteristics and attribution analysis of hydrological drought in the Ganjiang River Basin in recent years can better prevent and control hydrological drought in Ganjiang River basin. Using monthly runoff data from Waizhou station in Ganjiang spanning from 1961 to 2020, this article first employs two mutation testing methods to comprehensively identify the year of runoff mutation. Afterward, we utilize the ABCD hydrological model, coupled with seven deep learning algorithms to simulate the streamflow change of Waizhou station in Ganjiang River basin. Finally, the standardized runoff index is applied to describe the hydrological drought, and we analyze the evolution characteristics of hydrological drought and quantitatively assess the effects of human interventions and climate change on hydrological drought in the Ganjiang River Basin. The insights drawn from this research can be summarized as follows: (1) The results of the mutation analysis method indicate that there was a significant mutation in runoff in 1991. (2) The ABCD model can perform well in simulating and predicting runoff, with accuracies reaching 0.82 and 0.88. (3) Combining the ABCD hydrological model with deep learning algorithms can improve the accuracy of simulating runoff changes in the Ganjiang River. Among them, the ABCD-random forest method has the highest accuracy, reaching 0.89 and 0.94. (4) Climate change has a stronger impact on monthly hydrological drought compared to human activities. (5) Climatic factors are the primary determinants of seasonal hydrological drought changes. The findings of this study could provide a valuable reference for the optimal use of water resources and the proactive management of hydrological disasters in the Ganjiang area.

For inland rivers with complex topographies, however, conventional nadir altimeters often fail to meet accuracy requirements and exhibit substantial spatial sampling limitations. Fortunately, the Surface Water and Ocean Topography (SWOT) Satellite, with its wide swath, high spatial resolution, and improved vertical precision, offers new opportunities for the dynamic monitoring of rivers and small water bodies. However, the short duration since SWOT data became available means that existing validations of SWOT over rivers are often limited by insufficient samples, coarse temporal alignment with in-situ data, and a lack of physical explanation for accuracy variations across diverse basins. Here, we used in-situ water levels from 52 monitoring stations located within the Yangtze River Basin and river width data extracted from Sentinel-2 imagery to evaluate the accuracy of the SWOT Water Surface Elevation (WSE) and river width (RW). The results showed that although the SWOT WSE achieved moderate accuracy across all 52 monitoring stations (MAE = 0.42 m), the 28 waterway survey stations located along the Yangtze River main stem exhibited higher accuracy (MAE = 0.36 m) than the 15 monitoring stations on natural tributaries (MAE = 0.50 m) among the 43 natural river-channel monitoring stations. In addition, the nine reservoir stations were reported separately because they were governed by fundamentally different physical error mechanisms. Regrettably, the SWOT RW showed relatively low accuracy with a mean normalized MAE/m of 0.32 (where MAE/m means MAE/RW), with a spatial pattern similar to WSE, with higher accuracy along the gently sloping and wide-channel sections of the Yangtze River main stem where channel gradients are gentler and river widths are larger (MAE/m = 0.21), but its accuracy decreased in the Jinsha River and other tributaries (MAE/m = 0.50). On this basis, we investigated the physical processes through which geomorphological conditions regulated the accuracy of SWOT WSE, focusing on two key aspects: observation geometry (e.g. incidence angle, the angle between the river channel and the SWOT track, and interferometric baseline stability) and phase noise (e.g. RW, sandbars, river channel meandering, steep riverbanks, and river channel slope). The analysis demonstrated that the accuracy of SWOT in complex basins is determined by the degree of alignment between the dynamic observation geometry and static geomorphic characteristics. In summary, the SWOT provides a high-accuracy WSE but a moderate RW dataset and enables the dynamic monitoring of inland rivers and small water bodies globally in near-real-time.

Under rapid warming, the impact of advancing seasonal permafrost thaw on vegetation dynamics remains debated, particularly regarding whether earlier spring phenology induces late-season productivity deficits. Integrating multi-source remote sensing data over the Altai Mountains, this study employs a double machine learning framework to quantify the causal effects of thaw timing on vegetation growth. The results indicate that mid-May acts as a critical phenological tipping point. Thaw onset prior to this date provides a net subsidy, significantly enhancing peak growing season leaf area index with a magnitude comparable to precipitation and solar radiation. Our causal inference and strict matching analysis confirm the positive stimulation effect during the peak growing season (July). It also confirms that the legacy effects of the spring thaw almost completely dissipate by the late growing season (October). These results suggest caution when applying rigid linear assumptions to complex multivariate systems as such assumptions may fail to capture the true underlying controls.

This study investigates climate- and human-induced hydrological changes in the Zavkhan River–Khyargas Lake Basin, a highly sensitive arid and semi-arid region of Central Asia. Using Mann-Kendall, innovative trend analysis, and Sen’s slope estimation methods, historical climate trends (1980–2100) were analyzed, while land cover changes represented human impacts. Future projections were simulated using the MIROC model with Shared Socioeconomic Pathways (SSPs) and the Tank model. Results show that during the past 40 years, air temperature significantly increased (Z=3.93***), while precipitation (Z=−1.54*) and river flow (Z=−1.73*) both declined. The Khyargas Lake water level dropped markedly (Z=−5.57***). Land cover analysis reveals expanded cropland and impervious areas due to human activity. Under the SSP1.26 scenario, which assumes minimal climate change, air temperature is projected to rise by 2.0°C, precipitation by 21.8 mm, and river discharge by 1.61 m3/s between 2000 and 2100. These findings indicate that both global warming and intensified land use have substantially altered hydrological and climatic processes in the basin, highlighting the vulnerability of western Mongolia’s water resources to combined climatic and anthropogenic influence.

Under the combined influence of climate change and human activities, extreme precipitation events (EPEs) occur frequently, posing a severe threat to ecological security. Revealing the spatio-temporal differentiation laws of EPEs in multiple climate zones and these driving mechanisms is one of the core scientific issues in responding to climate change. Based on Fifth generation of ECMWF atmospheric reanalyses for climate(ERA5)data and 88 kinds of atmospheric circulation indices, a multi-method coupling framework was constructed by integrating trend analysis, correlation test and geographic detector model, for analyzing the evolution characteristics of EPEs in 13 climate zones around the world from 1951 to 2020 and their driving mechanisms of atmospheric circulation. Results showed that: (1) Consecutive dry days (CDD), consecutive wet days (CWD), and moderate rainy days (R10) in more than 50% of the global land all presented a downward trend. Annual total precipitation (PRCPTOT), the number of heavy rain days (R20), the heavy precipitation(R95p), the extremely heavy precipitation(R99p), the maximum daily precipitation (RX1day), and the 5-day maximum precipitation (RX5day) in more than 50% of the global land all presented an upward trend. (2) Spatial heterogeneity characteristics were significant. CDD showed an upward trend in a few regions of Asia and Africa, and PRCPTOT, R10, R20, R95p, R99p, RX1day and RX5day showed an upward trend in low latitudes, especially in tropical rainforest climate regions.(3)Northern Hemisphere Subtropical High Area Index (NHSHAI), Atlantic-European Polar Vortex Intensity Index (APVII), Pacific Polar Vortex Area Index (PPVAI) and East Pacific 850mb Trade Wind Index (EPTWI) have a wide impact on EPE changes in the global climate region. (4) Interactions between atmospheric circulation factors can lead to nonlinear amplification or two-factor enhancement effects on changes in extreme precipitation events. The synergistic explanatory power of two factors (q = 0.30-0.80) is 3%-68% greater than that of any single factor (q = 0.03-0.60). This study deepens the understanding of EPE dynamics from the perspective of multi-scale mutual feedback mechanisms, providing a scientific basis for formulating regional differentiated climate adaptation strategies.

Abstract Northern high-latitude permafrost is facing unprecedented wildfire disturbances, driving an anomalous regional increase in annual carbon emissions (8.1 ± 2.9 TgC yr⁻¹ from 1997 to 2023) against a backdrop of declining global wildfire emissions. To elucidate these complex dynamics, this review conceptualizes the “Permafrost Critical Zone” (PCZ) and adopts a holistic Earth-system perspective to evaluate the cascading impacts of wildfires on vulnerable cryospheric landscapes. We synthesize how fire-induced organic layer combustion and surface albedo reduction destabilize the PCZ, drastically elevating ground surface temperatures by up to 7 °C and deepening the active layer by up to six times. These severe thermal shocks fundamentally rewire hydrological pathways, accelerating ground ice melt, altering supra-permafrost water storage, and amplifying surface runoff. Concurrently, wildfires abruptly reduce microbial diversity and restructure cold-adapted biological communities, initiating divergent post-fire vegetation succession trajectories. While ecological and hydrothermal recovery is essential for restoring carbon and water fluxes, the compounding effects of repeated fires under a warming climate threaten to irreversibly degrade these environments. We conclude by highlighting critical knowledge gaps and emphasizing the necessity of integrating PCZ dynamics into global models to predict impending climate tipping points and to inform long-term sustainable development strategies.

Under global warming, the frequent occurrence of extreme temperature events (ETE) poses a serious threat to ecological security and sustainable socio-economic development. Understanding the spatial and temporal variation of extreme temperatures and their driving factors across multiple climate regions is a core scientific question in climate research. Using ERA5 reanalysis data and 88 atmospheric circulation indices, this study applies the Köppen-Geiger climate classification system to examine the evolution of ETE across 13 global land climate zones from 1951 to 2024 and assesses their relationship with atmospheric circulation through a multi-method framework combining trend analysis, correlation testing, and the Geodetector model. The results showed: (1) extreme warm events increased in most regions, whereas extreme cold events declined. (2) Spatial heterogeneity in ETE was evident. The cold spell duration index (CSDI) increased across large areas of Eurasia. The decline rates of frost days (FD) and icing days (ID) in high-latitude climate zones exceeded those in other regions. The increasing rates of extreme temperature value indices (TNn, TNx, TXn, and TXx) were higher in mid-high-latitude climate zones than those in low-latitude region. (3) The Northern Hemisphere Subtropical High Area Index (NHSHA), North American Polar Vortex Area Index (NAPVA), Antarctic Oscillation Index (AAO), and Asian Polar Vortex Intensity Index (APVI) exerted significant effects on global extreme temperature changes. (4) Interactions among atmospheric circulation factors produced nonlinear enhancement or two-factor enhancement effects on ETE. The explanatory power of paired atmospheric circulation variables (q=0.26–0.84) was 3

Study region The Urumqi Glacier No.1 (UGN1) and Dongkemadi Glacier (DG) catchments in China, both highly glacierized alpine catchments, provide critical water resources for downstream rivers and are sensitive to climate change impacts. Study focus Accurate simulation of glacier runoff processes remains challenging due to limited observational data. Here, we introduce FLEXG-iso, an isotope-aided glacier hydrological model that couples stable water isotopes with glacier mass balance (GMB) to simulate runoff generation and partition runoff components in these catchments. We also assess the transferability of model structures and parameters between UGN1 and DG catchments. New hydrological insights for the region Results demonstrate that incorporating stable water isotopes and GMB data enhances the identification of parameters controlling snow and ice accumulation and melt. FLEXG-iso provides more robust partitioning of runoff components, distinguishing throughflow and groundwater, even with limited tracer availability, and improves runoff simulation accuracy (KGE > 0.8) when transferring optimal parameter sets. These findings highlight the potential of isotope-aided modeling to improve both calibration and transferability of glacier hydrological models, offering a robust framework for understanding runoff dynamics in high mountain regions under climate change.

Topography and vegetation are critical factors influencing catchment hydrology; however, their individual contributions are often underestimated in hydrological models. This limitation is particularly evident in cold, mountainous regions such as the Mongolian Plateau, where observational data are sparse. To address this, we employed a stepwise, top-down modelling strategy based on a flexible modelling framework to systematically assess the influence of topography and vegetation on hydrological processes in the Bogd Uliastai and Zavkhan Guulin river basins. Beginning with a lumped model (FLEXL), we successively integrated snow processes (FLEXL-S), topographic distribution (FLEXD), and finally, a landscape-based parameterization accounting for vegetation heterogeneity (FLEXT). Both FLEXD and FLEXT outperformed the lumped models in simulating runoff and snow water equivalent (SWE). Interestingly, FLEXT showed similar performance to FLEXD - likely due to limited vegetation heterogeneity - it offers more physically realistic parameterization by explicitly representing landscape units, suggesting its potential in more complex basins. The ratio of snowmelt runoff to streamflow was quantified as 23.6 % +/- 0.7 % and 15.9 % +/- 1.3 % in the Bogd Uliastai and Zavkhan Guulin river basins, respectively, with peaks in spring and a clear increase with elevation. At high elevations, runoff is primarily snowmelt-driven, resulting in delayed and gradual runoff, whereas lower elevations dominated by rainfall generate rapid runoff. Controlled by distinct dominant hydrological mechanisms, different landscape units contribute unequally to streamflow. This study underscores the pivotal roles of topography and vegetation in runoff generation and demonstrates the effectiveness of a stepwise modelling framework for improving hydrological understanding in cryospheric and data-scarce regions.

Human-engineered “gray” infrastructure (e.g., artificial reservoirs) and ecosystem-based “green” infrastructure (e.g., forests) both provide essential water storage capacities to buffer hydrological variability under rising climate uncertainty. However, a quantitative assessment of gray (Sgray) and green (Sgreen) water storage capacities across global major river basins remains lacking. In this study, we estimate ecosystems’ root zone storage capacity as a proxy for Sgreen using the mass curve technique. Combined with artificial reservoir storage capacities, our results showed that Sgray and Sgreen exhibit highly spatial heterogeneity, with the largest volumetric Sgray in Yenisei (459 km3) and Sgreen in Amazon (1,427 km3). Over the period 1959–2020, Sgray experienced a consistent increase, while Sgreen exhibited fluctuations. Notably, in 12 % of the global major river basin area (1.3 × 107 km2), including the Colorado, Yenisei, and Yangtze, indicating that human-engineered regulation of hydrology has become more influential than terrestrial ecosystem in these regions. These gray dominated basins are also projected to experience a decline in Sgreen, suggesting the potential need to protect and enhance green infrastructure. This study provides a scientific basis for the integrated management of gray and green infrastructures, offering insights to enhance the resilience and sustainability of water resources at the basin scale.

Root zone maximum water deficit(S Rmax ) refers to the maximum water consumption of the root zone during drought,which directly influences the partitioning of precipitation between infiltration and runoff. It is a key parameter in land surface hydrological modeling. Since the implementation of the Grain-for-Green Project（GFG） on the Loess Plateau（LP）, vegetation restoration has achieved significant success, resulting in the “greening” of LP while simultaneously reducing surface runoff.However, the lack of consideration for the root zone, a key link between terrestrial ecological and hydrological processes, has hindered understanding of ecohydrological mechanisms and limited comprehensive assessments of regional water resource management and ecological engineering outcomes. This study analyzes the spatiotemporal dynamic of S Rmax on the LP from 1982 to 2018 using multi-source datasets and the Mass Curve Technique. Additionally, we employ a hybrid machine learningstatistical attribution model to quantify the contributions of land use and climate change to the S Rmax dynamic. The results indicate an average S Rmax of 85.3 mm across the LP, with significant variations among land use types: natural forest（116.3 mm） >planted forest（104.6 mm） > grassland（87.0 mm） > cropland（78.8 mm）. Following the implementation of GFG, S Rmax increased by 37.7%, with an upward trend observed across all land use types, particularly in changed land type, which experienced the largest increases. The attribution model achieved a coefficient of determination（R 2 ） of 0.92. The key factors driving S Rmax variation varied by land use type: in unchanged land type, climate change accounted for 53.8% of the S Rmax increase, whereas land use change explained 71.3% of the increase in changed land type, with GFG contributing 52.1%. These findings provide a scientific basis for enhancing drought resilience and implementing the “Water-for-Greening” strategy on the LP and similar regions under changing environmental conditions.

The Xin'anjiang model has been widely applied in watershed rainfall-runoff simulation and hydrological forecasting，with significant international influence. In the model，the soil tension water storage capacity is the core parameter for runoff generation calculation，theoretically defined as the water retained by the soil between field capacity and wilting point. However，both hydrological research and practical applications have demonstrated that this theoretical physical interpretation is not strictly accurate. With the recent availability of massive global hydrological datasets and the deepening understanding of multi-scale hydrological mechanisms-especially advances in eco-hydrology-it is now possible to refine the concept and physical explanation of tension water storage capacity.Through theoretical analysis and validation with independent data sources，this study argues that soil tension water storage capacity should be redefined as the root zone storage capacity of terrestrial ecosystems. Clarifying this concept is of great theoretical significance for hydrology and provides a foundation for methodological innovations in determining this core parameter. Traditionally，root zone storage capacity is determined through parameter calibration based on watershed rainfall-runoff data，which is severely constrained in data-scarce regions. This study proposes a novel approach to infer ecosystem root zone storage capacity through surface fluxes: at the landscape scale，it can be retrieved from terrestrial ecosystem flux observations，while at larger scales-even globally-it can be accurately calculated using atmospheric-land surface reanalysis datasets or remotely sensed evaporation data.Numerous studies have shown that the root zone storage capacity obtained through this new approach significantly enhances the accuracy of watershed runoff simulations，with particularly notable improvements in ungauged basins regions. The shift in perspective from soil physical hydrology to ecosystem hydrology helps clarify the fundamental runoff generation mechanisms in watersheds and reveals the physical meaning of empirical parameters in conceptual hydrological models，thereby advancing the theoretical development of watershed hydrological simulations.