The Lancang-Mekong River (LMR) is a vital transboundary water source in Southeast Asia, supporting ∼70 million people who depend on its streamflow for water, food, and energy. Understanding the multi-decadal and centennial-scale variability in streamflow across the LMR basin is critical for reservoir planning and effective management, as well as for mitigating the risks of droughts and floods that could exacerbate transboundary conflicts. This study reconstructs basin-wide streamflow from 1000 to 2012 CE using a spatiotemporally complete paleoclimate record of the summer Palmer Drought Severity Index over the Asia monsoon region. A log-linear regression model, integrated with a dimension reduction method, was developed and validated across eight gauging stations in the LMR basin, demonstrating robust performance. Our analysis identifies three regional patterns of streamflow variability across the basin over the past millennium. Significant periodic oscillations are observed in the 2–4, 20–40, 60–128, and ∼150 year frequency bands. Associations between low-frequency variability and large-scale climate patterns such as the El Niño-Southern Oscillation, Pacific Decadal Oscillation (PDO), Indian Ocean Dipole, and Atlantic Multidecadal Oscillation are also detected. The PDO emerges as the dominant driver of centennial-scale streamflow variability in the LMR basin. By extending fifty-year instrumental records to millennial-scale data, this study provides valuable insights into the spatiotemporal dynamics of streamflow variability and climate teleconnections at decadal to centennial timescales, thereby enhancing our understanding of the long-term water resource risks faced by the riparian countries.

Groundwater moves slowly, until it doesn't. Two decades of global satellite assimilated data reveal that aquifers in humid, energy-limited environments lose their storage at sub-seasonal timescales, transmitting vadose zone drought signals downward within days to one month, far faster than the months-to-years lag that is frequently observed in drylands. This inverts the established geography of flash droughts, which are characterized by root-zone soil moisture deficit and primarily peak in dry-wet transitional zones, underscoring a noteworthy mechanism. Specifically, the tight hydraulic coupling between the unsaturated and saturated soil zones in wet environments means that when wet-season delivery falters, the system drains at full velocity through baseflow while vegetation pulls simultaneously from above. The aquifer does not buffer the shock but conducts it. In the tropics specifically, ENSO and tropical Atlantic oscillations act as remote dispatchers of this collapse, suppressing monsoon recharge at 3–9 month lead times precisely when baseflow and evaporative demand are highest. Groundwater storage, filtered of high-frequency atmospheric noise, registers these low-frequency oceanic signals with higher fidelity than surface soil moisture, making it a more tractable target for subseasonal prediction in the humid regions where abrupt water shortage carries the greatest human cost.

Frequent glacier surges are a distinctive characteristic of Karakoram glaciers, with their occurrence increasing recently, significantly impacting glacier morphology and dynamics. However, more observations are needed to improve our understanding of surging dynamics and their underlying mechanisms. This study employs extensive multisource remote sensing data to investigate long-term, multi-phase changes in flow velocity, surface elevation, and terminus position of North Kunchhang Glacier I (NKG I) in the Eastern Karakoram. By examining 25 years of changes, we identified the timing of glacier surges, analysed the surging dynamics, and estimated mass transfer during surging events. Historical interpretation of terminus dynamics dating back to 1972 revealed a prior main trunk surge around 1980, enabling an exploration of potential climate change impacts on surge behaviour. Our results indicate that the 2017 main trunk surge lasted four years (June 2015-June 2019), transferring 0.53 +/- 0.013 km3 of glacier mass, inducing significant downstream elevation gain, and leading to a delayed terminus advance starting in 2018. In contrast, the 2004 surge of NKG V (within the NKG basin and connected to NKG I after surge) lasted 2.5 years (November 2002-April 2005), transferring 0.27 +/- 0.011 km3 of glacier mass, destroying a proglacial lake, and raising the glacier surface elevation by similar to 180 m. Flow velocity, surface elevation, and terminus position derived from various sources exhibit strong consistency in both trends and values, confirming the reliability of our results. Notably, the 2017 surge exhibited a shorter rapid advance period compared to the 1980 surge, suggesting that climate change may be influencing surge mechanisms, leading to smaller-scale but more frequent events. These findings provide new insights into the surging dynamics of NKG I and contribute to a deeper understanding of Karakoram glacier behaviours. The integration of multisource remote sensing demonstrates its critical value in deciphering complex glacier dynamics and their responses to a changing climate.

Abstract Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing ∼38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi‐channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.

In light of the rapid advancements in hydrological science research facilitated by cutting-edge remote sensing technologies, such as synthetic aperture radar (SAR), hyperspectral imaging, and Light Detection and Ranging (LiDAR), we have curated a special issue in Remote Sensing of Environment entitled “Emerging remote sensing techniques for hydrological applications”, spanning from October 2022 to April 2024. This special issue comprises 31 publications that highlight methodologies leveraging multi-sensor satellite platforms, unmanned aerial vehicles (UAVs), and advanced physical models and machine learning approaches to improve the monitoring and modeling of key hydrological flux and state variables. These remote sensing retrievals (e.g., river discharge and soil moisture) have been applied to various operational hydrological applications such as real-time flood monitoring and drought risk assessment. To provide a systematic overview, we categorize these publications based upon hydrological themes and the number of publications, covering topics such as water body, soil moisture, river discharge, water level, drought, water storage, and other related areas. Finally, we provide an outlook that envisages how the emerging trends (e.g., multi-sensor integration and machine learning-driven approaches) identified from the published studies will evolve and shape future research directions in hydrological remote sensing.

Abstract Although recent studies have identified the factors contributing to groundwater recovery in the North China Plain (NCP), the magnitude and interactions of these drivers remain poorly quantified. Here we developed an integrated surface–groundwater modeling framework that couples the Community Water Model with MODFLOW and incorporates a priority‐based water allocation module to represent conjunctive water use. Using this modeling framework, we quantify the mechanisms responsible for the observed recovery following decades of depletion. Model simulations indicate that human interventions were the dominant drivers reversing groundwater depletion, while recent wet climate conditions (2021–2024) acted primarily as an amplifier of recovery. During 2015–2024, wet climate conditions, the South‐to‐North Water Diversion, and reductions in agricultural and industrial water use accounted for 45%, 30%, and 25% of the total groundwater level rise, respectively. Groundwater recovery occurred through a two‐step substitution process: imported water initially reduced urban groundwater withdrawals, which subsequently enabled expanded surface water reallocation to agriculture. Overall, the aquifer regained ∼30 km3 of water, restoring a drought buffer comparable to the region's surface reservoir capacity. In addition, the often overlooked lateral groundwater inflow from surrounding mountains supplied a persistent recharge source equivalent to ∼20% of vertical recharge, mitigating seasonal groundwater declines caused by the mismatch between precipitation and irrigation demand. The modeling framework presented here provides a process‐based understanding of groundwater recovery in the NCP and offers a transferable approach for assessing and managing aquifer restoration in other water‐stressed regions globally.

The delayed response of groundwater to surface soil moisture anomalies (Topt), which reflects how quickly surface signals propagate to aquifers, varies across wetness regimes. Understanding its spatiotemporal variability may help diagnose flash droughts, yet it remains underexplored. Here we examine global relationships between modeled Topt and flash droughts using a dynamic exponential filter. We find that background atmospheric aridity generally controls this relationship, since longer Topt occurs with frequent flash droughts in drylands. However, seasonal variability is largely governed by background terrestrial wetness conditions, with stronger bidirectional Topt–flash drought sensitivities during wetter seasons, even in non-hotspots. This is because flash droughts driven by rapid atmospheric dryness propagation to the land are reflected in Topt memory, predominantly shaped by land-atmosphere interplays. Combined evapotranspiration-runoff deficit in dry conditions, especially the dominance of evapotranspiration, rapidly prolongs Topt and reduces its sensitivity to flash droughts. These insights highlight Topt as a promising flash drought diagnostic indicator, offering a practical pathway for improving prediction. Background atmospheric aridity and seasonal terrestrial wetness jointly regulate modeled groundwater–land surface response time, highlighting modeled dynamic groundwater response time as a promising indicator for diagnosing flash droughts over drylands, according to a global analysis using a dynamic exponential filter.

Urban vegetation inequality (UVI) undermines the equitable distribution of ecosystem services such as heat mitigation. However, the role of climate variability in shaping UVI remains unclear. Here we have developed a methodology using satellite, census, and climate data to analyze UVI across 245 major U.S. cities. Our study proposed a vegetation polarization index (VPI), calculated as the normalized difference between the 90th and 10th percentiles of NDVI, to measure UVI. We examined how climate events affect UVI differently in the Sunbelt versus northern cities. Sunbelt cities display exacerbated UVI under drought and warmer climates, while colder and wetter conditions may increase UVI in northern cities. Hot droughts can amplify UVI across almost all cities, with Sunbelt cities showing greater vulnerability. We analyzed UVI trends from 2001 to 2020, revealing that Sunbelt cities exhibit worsening UVI trends, while northern cities show improving trends. These changes are related to climate shifts and socioeconomic factors, underscoring the vulnerability of U.S. cities to fluctuating UVI under climate change. Socioeconomic conditions play a significant role in exacerbating this vulnerability.

The decline in in situ water level measurements since the 1980s has impeded our ability to fully understand hydrological and hydrodynamic processes, particularly in ungauged river reaches, and how global and regional water cycles respond to climate change. Satellite altimetry offers a valuable means of supplementing these gaps in river water level data, both temporally and spatially. However, existing radar waveform retracking techniques often struggle to accommodate rivers with varying morphologies and surrounding environments. This study presents an Improved Multiple Subwaveform Analysis (IMSA) algorithm based on the 50% Threshold and Ice‐1 Combined (TIC) algorithm, incorporating noise filtering into the subwave search module and refining the retracking strategy for multiple subwaves, independent of coarse digital elevation models (DEMs). We validated the IMSA algorithm using in situ data from 23 gauging stations and applied it to Sentinel‐3 and Sentinel‐6 altimetry across 57 virtual stations (VSs) in China, covering rivers with widths ranging from 20 to 1,500 m, generating 79 validation results (each representing an RMSE value comparing altimetry with in situ measurements). The IMSA algorithm demonstrated significant enhancements at over 48 VSs with more than 64 validation results compared to the original TIC, achieving the lowest median RMSE of 0.61 m (0.13–0.50 m lower than the OCOG, Threshold, MWaPP, and TIC algorithms), with strong resilience to environmental noise. Error analysis revealed that the altimetric accuracy is primarily influenced by the underlying surface characteristics of VSs, with built‐up areas exerting significant interference. Additional disturbances stem from surrounding waters, large slopes, river channels running parallel to the satellite's ground track, and unique features such as sandbars, braided and ice‐covered rivers, and hydroelectric stations. The synthetic aperture radar (SAR) mode was found to mitigate some of these land cover impacts, further improving water level retrieval accuracy. Finally, the results show that river width and topography (whether mountainous or flat) do not inherently affect altimetric accuracy, provided that the on‐board tracking system is supported by accurate prior DEMs and minimal slope interference.

Lakes are crucial for ecosystems1, greenhouse gas emissions2 and water resources3, yet their surface-extent dynamics, particularly seasonality, remain poorly understood at continental to global scales owing to limitations in satellite observations4,5. Although previous studies have focused on long-term changes6-8, comprehensive assessments of seasonality have been constrained by trade-offs between spatial resolution and temporal resolution in single-source satellite data. Here we show that seasonality is the dominant driver of lake-surface-extent variations globally. By leveraging a deep-learning-based spatiotemporal fusion of MODIS and Landsat-based datasets, combined with high-performance computing, we achieved monthly mapping of 1.4 million lakes (2001-2023). Our approach yielded basin-level median user's and producer's accuracies of 93% and 96%, respectively, when validated against the Global Surface Water dataset7. Seasonality-dominated lakes constitute 66% of the global lake area and approximately 60% of total lake counts, with over 90% of the world's population residing in regions where such lakes prevail. During seasonality-induced extreme events, the impacts can exceed the combined magnitude of 23-year long-term changes and regular seasonal variations, doubling the contraction of 42% of shrinking lakes and fully offsetting the expansion of 45% of growing lakes. These results uncover previously hidden seasonal dynamics that are crucial for understanding hydrospheric responses to environmental changes9, protecting lacustrine systems10-12 and improving global climate models13,14. Our findings underscore the importance of incorporating seasonality into future research and suggest that advancements in the fusion of multisource remote-sensing data offer a promising path forward.

Abstract: The Haihe River Basin (HRB) in North China, characterized by a warm and humid environment, has witnessed a transformation in agricultural water supply patterns, influenced by both climatic changes and groundwater withdrawal restrictions. Despite the impact of these changes on irrigation activities, comprehensive monitoring of irrigation water use (IWU) is lacking, with existing studies predominantly focusing on the influence of irrigation on climatic factors and crop yield. Few studies address the effects of warming and humidification on IWU, and the impacts of human activities associated with groundwater withdrawal restrictions remain underexplored. This study introduces a novel IWU estimation method and examines changes in IWU across the HRB from 2003 to 2022. By quantifying the contribution of irrigation water to different destinations (evapotranspiration consumption, root zone soil water increment, and groundwater recharge), key drivers of IWU change are revealed. The accuracy of IWU estimates proves high, effectively reflecting spatiotemporal changes in irrigation activities.Results demonstrate declining trends in irrigation water intensity and the proportion of irrigation area, with changes in irrigation water intensity dominating overall IWU variations. Shifts in cropping patterns, such as the southward relocation of winter wheat planting and increased drought-tolerant corn cultivation after 2012, explain regional disparities in IWU values. The proportion of irrigation water consumed by evapotranspiration and root zone water increment was 0.58 and 0.39, respectively. Utilizing the least partial square regression method, cropping pattern changes emerge as common drivers for irrigation water intensity in the three main administrative regions (Hebei Province, Beijing, and Tianjin). Irrigation management factors prevail in Hebei Province and Tianjin, while climate factors, particularly in Beijing, play a significant role. Increased water supply and a wetter climate over the past 20 years contributed to decreased irrigation water intensity, particularly in Hebei Province and Beijing. Additionally, optimization of cropping patterns and the adoption of water-saving agriculture further reduced irrigation water intensity in the HRB. This study provides a thorough understanding of the evolving irrigation landscape and associated mechanisms in the HRB over the past two decades. The findings offer insights into combatting climate change and groundwater depletion, informing strategies for sustainable water resource management.Keywords: Irrigation water use; drivers; cropping patterns; North China Plain

The rate of technological innovation within aquatic sciences outpaces the collective ability of individual scientists within the field to make appropriate use of those technologies. The process of in situ lake sampling remains the primary choice to comprehensively understand an aquatic ecosystem at local scales; however, the impact of climate change on lakes necessitates the rapid advancement of understanding and the incorporation of lakes on both landscape and global scales. Three fields driving innovation within winter limnology that we address here are autonomous real‐time in situ monitoring, remote sensing, and modeling. The recent progress in low‐power in situ sensing and data telemetry allows continuous tracing of under‐ice processes in selected lakes as well as the development of global lake observational networks. Remote sensing offers consistent monitoring of numerous systems, allowing limnologists to ask certain questions across large scales. Models are advancing and historically come in different types (process‐based or statistical data‐driven), with the recent technological advancements and integration of machine learning and hybrid process‐based/statistical models. Lake ice modeling enhances our understanding of lake dynamics and allows for projections under future climate warming scenarios. To encourage the merging of technological innovation within limnological research of the less‐studied winter period, we have accumulated both essential details on the history and uses of contemporary sampling, remote sensing, and modeling techniques. We crafted 100 questions in the field of winter limnology that aim to facilitate the cross‐pollination of intensive and extensive modes of study to broaden knowledge of the winter period.

High Mountain Asia (HMA) is a hotspot for research on global glacier change and its environmental impacts. Over the past few decades, HMA glaciers have undergone relatively slow but accelerating mass loss. However, our current understanding of the inter- and intra- annual variations in these glaciers remains insufficient. In this study, we derived glacier mass changes in HMA at different spatiotemporal scales through the integration of three altimetry products (i.e., ICESat, CryoSat-2, and ICESat-2). We constructed seasonal time series of glacier mass balance in HMA and its subregions and produced multiple elevation change maps for these glaciers over different periods. Our results showed that HMA glaciers experienced heterogeneous glacier ablation with a mean mass loss rate of 26.72 ± 3.30 Gt/yr during 2003 ‒ 2022. Among various subregions, the glaciers in Hengduan Shan experienced the most severe depletion and the most substantial mass loss (3.81 ± 0.47 Gt/yr). The glaciers in Western Himalaya and Eastern Himalaya suffered significant mass loss as well. The melting rate of HMA glaciers over the second decade has significantly accelerated compared to the preceding decade. Furthermore, in 2022, HMA glaciers experienced pronounced mass loss attributed to abnormally high temperatures, with the glacier ablation in the Qilian Mountains being the most severe on record. Our spatially explicit and high-temporal-resolution (monthly to seasonal) features of glaciers would improve understanding of HMA glacier changes and serve as a reference for future research in this field.

Abstract: The depth of groundwater is a critical factor that significantly influences the development and conservation of both surface water and groundwater in lakes located in Northern China, exemplified by Baiyangdian (BYL), the largest lake situated in the North China Plain. It forms a critical foundation for the ecological integrity of BYL by quantifying hydrological fluxes and investigating variations in surface water and groundwater across distinct groundwater depths. To address this inquiry, we established a distributed hydrological model for the basin, enabling the simulation of surface runoff (horizontally) and vertical processes such as evapotranspiration and infiltration. Findings for the period 1966-1980 reveal an overall shallow groundwater condition in the Baiyangdian plain area, with a multi-year average depth of approximately 4 meters. Precipitation recharge, lake evaporation, surface water inflow, surface water outflow, groundwater inflow, and groundwater outflow during this phase were quantified at 187 million m3, 288 million m3, 960 million m3, 683 million m3, 160 million m3, and 40 million m3, respectively. The predominance of horizontal flux (62%) signifies rapid lake water replenishment. Conversely, during the later period of 1981‒2018, groundwater depth in the plain area substantially increased, averaging 23.48 meters. Precipitation recharge, lake evaporation, surface water inflow, surface water outflow, and groundwater outflow were computed at 179 million m3, 227 million m3, 294 million m3, 118 million m3, and 127 million m3, respectively. The horizontal flux contribution diminished to 22%, while the vertical flux surged to 78%, indicating slower lake water renewal and heightened risks of water quality degradation. Climate change and human activities emerged as drivers of rising groundwater depth, subsequently weakening water cycle dynamics over BYL. In the future, the comprehensive recovery of groundwater facilitated by the South-to-North Water Diversion for lake and river replenishment will play a pivotal role in reinstating water cycle dynamics and enhancing ecological integrity. This study establishes a foundation for understanding the intricate interactions between lakes, rivers, and aquifers as groundwater depth evolves over time. It holds significance for water conservation and the preservation of BYL's water quantity and quality in the future.Key words: Baiyangdian Lake; distributed hydrological model; hydrological flux; Groundwater depth

Floating garbage removal is an essential environmental strategy to reduce water pollution and achieve environmental sustainability, and it is a pressing issue for global ecological restoration. Under the interference of complex environments, floating garbage will gather, overlap, and change its shape due to water flow and wind. Efficient and automatic detection and collection of floating garbage gathered on the water surface is a challenging environmental management task. This study proposed an efficient and economical deep learning solution based on YOLOv8 (You Only Look Once v8). By improving the backbone, introducing the Wise-Powerful IoU loss, and adding the AuxHead detection head, the negative impact of complex environmental factors was effectively compressed, and the detection mean Average Precision(mAP) of the surface model aggregated floating garbage was improved to 89.4 %. The Precision(P) was improved to 95.8 %. The model size is only 18.8 MB, and the number of model parameters is reduced by 32.2 % compared with the original model. The proposed model addresses the challenging issue of detecting aggregated floating garbage on the water surface, and the lightweight model is also more conducive to promoting outdoor use. The research results can improve the aggregated floating garbage collection rate by up to 61.5 % compared with the mainstream model Faster R-CNN. It can save up to about 1730.3 kW·h of electricity per ton of recycled waste oil and reduce the emission of 452.7 kg of CO2 and 2328.8t of water pollution. The scheme is superior to the current technical level in terms of detection Precision and mean Average Precision and makes essential scientific contributions to the protection and restoration of water ecosystems, energy conservation, emission reduction, and carbon reduction.

Marine phytoplankton are crucial to oceanic ecosystems, yet trends in their activity, monitored through chlorophyll a, remain uncertain due to observational limitations. We generated an ocean chlorophyll a dataset (2001 to 2023) across low to mid-latitudes (45°N to 45°S) using multisource data and a deep learning approach. Our analysis suggests widespread decline in ocean greenness, with chlorophyll a concentrations decreasing at a rate of (-0.35 ± 0.10) × 10-3 milligrams per cubic meter per year (mg m-3 year-1). The decline is steeper in coastal regions [(-0.73 ± 0.22) × 10-3 mg m-3 year-1]. The frequency of high chlorophyll a concentration events in coastal waters has decreased at a relative rate of -1.78% per year. These trends are predominantly driven by rising sea surface temperatures, which enhance ocean stratification, suppress nutrient upwelling, and limit phytoplankton growth. These findings suggest a long-term decline in marine primary production and a reduced occurrence of phytoplankton blooms, potentially disrupting trophic interactions and oceanic carbon cycling.

A nuanced understanding of crop patterns is pivotal for accurate crop yield and irrigation water use calculations, holding profound implications for national food security and sustainable environmental development. In the water-scarce North China Plain (NCP), where agricultural intensity faces challenges due to groundwater suppression and ecological restoration, this study employs random forest classification on Sentinel-2 Multispectral Instrument (MSI) and Landsat 8 Operational Land Imagery (OLI) time series to reveal the spatial and temporal dynamics of crop patterns from 2013 to 2022. Our classification, featuring a finer scheme (nine categories), higher spatial resolution (10/30 m), and extensive field sampling points, aligns well with China's statistical yearbooks. The annual mapping exposes a shift towards economic forests, mainly from other food crops, across all NCP provinces. Distinct spatial patterns emerge, with wheat-maize rotation decreasing at higher latitudes, countered by an increase in single maize and economic forests. Despite these shifts, wheat-maize rotation remains dominant, and seasonal fallow is concentrated in regions with poor irrigation, notably in groundwater funnel areas. Overall, our crop pattern mapping provides a robust dataset for water conservation and land management, contributing to regional resilience planning.

The North China Plain (NCP) has faced substantial groundwater depletion driven by rapid population growth, socioeconomic development, and high irrigation water demand in recent decades. Responding to this challenge, the Chinese government has implemented significant measures, including the construction of the South-to-North Water Diversion Project's middle route (SNWD-M) and the curtailment of groundwater use, aiming to alleviate water scarcity and overexploitation. The river replenishment initiative, utilizing surplus SNWD-M water, has injected over 9.5 km3 into NCP rivers. Simultaneously, policy-induced shifts in agricultural land use, such as transforming winter wheat and summer maize rotation to single crops through seasonal fallow, have reshaped the landscape. Additionally, extreme events like the record flood in the summer of 2023 have become influential contributors to groundwater recharge in the NCP under changing climate conditions. To evaluate the joint impact of these anthropogenic and natural factors on groundwater levels and surface water-groundwater interactions, we established a coupled surface water-groundwater model across the NCP. Our findings reveal that river replenishment, coupled with the 2023 record flood, played a pivotal role in the rebound of groundwater levels. However, changes in agricultural land use introduce uncertainties. This study provides a holistic understanding of the drivers behind the recovery of groundwater storage in the NCP over the past decade, offering valuable insights for the enhanced management of the SNWD-M initiative.

The Tibetan Plateau (TP), often referred to as the “Water Tower of Asia”, supplies freshwater to nearly 2 billion people, yet its water resources are increasingly threatened by climate change. Terrestrial water storage (TWS) and runoff are key indicators of water security, directly influencing downstream water availability and use. In this review, we assess recent advancements in the estimation of TWS change and runoff over the TP, while identifying both challenges and opportunities for future research. We provide a comprehensive summary of recent developments in satellite-based TWS change retrievals, improvements in runoff modeling, and the integration of data-driven and hybrid approaches for TWS change reconstruction and runoff simulation, emphasizing cutting-edge space observational techniques and interdisciplinary methodologies. We highlight major challenges in the retrieval and simulation of TWS change and runoff, including limited in-situ data, coarse spatial resolution of TWS change observations, the complexity of hydrologic processes that challenge machine learning applications, and the risk of equifinality due to inadequate model calibration. Furthermore, we explore strategies for overcoming these challenges, with a particular focus on the integration of multisource datasets and hybrid modeling approaches. This review aims to offer valuable insights into the estimation of water storage changes and runoff over the TP. The approaches discussed are not only crucial for understanding hydrologic responses to climate change but also essential for informing adaptive water management strategies in this vulnerable high-mountain region.

Groundwater depletion is a critical global challenge, particularly in intensively cultivated drylands, with few documented cases of successful recovery. Here, we report a striking reversal of long-term groundwater decline in the North China Plain, one of the world's most severely depleted aquifers. Based on a comprehensive analysis of groundwater levels from over 2000 monitoring wells spanning the past two decades, we show that groundwater levels have risen at an average rate of ~0.7 m year-1 since 2020, surpassing 2005 levels by 2024. This recovery is driven by a combination of large-scale surface water diversion from the humid south and stringent groundwater pumping regulations, further amplified by wet years (e.g., 2021). From 2005 to 2023, these policies reduced annual groundwater abstraction by ~12 km3 and increased environmental water allocations to over 7 km3 since 2021, promoting aquifer recharge and restoring environmental flows. Our findings demonstrate that rapid, large-scale groundwater recovery is achievable through integrated water management and targeted policy interventions across extensive regions (~130,000 km2).