Wetland area and quality have continued to decline worldwide due to the combined impacts of global climate change and human activities, so there is a need for a scientific and standardized framework to assess wetland degradation risk and support conservation and restoration efforts. In this study, a wetland degradation risk assessment framework integrating pressure level and degradation level was developed and applied to the western Songnen Plain, one of China’s major marsh wetland regions, using data from 2000, 2010, and 2020. The results showed that: (1) anthropogenic activity intensity and wetland area change were the most influential indicators. The degradation risk index ranged from 0.20 to 0.68, with mean values of 0.52, 0.46, and 0.46 in 2000, 2010, and 2020, respectively, indicating a slight overall decline. (2) The pressure level increased from 0.56 to 0.59 from 2000 to 2020, suggesting a growing influence of external pressures. In the same period, the degradation level decreased from 0.34 to 0.30, reflecting a reduced effect of internal environmental conditions. (3) Spatially, medium-risk areas have expanded from 47.5% to 64.1% and became the dominant area, while high-risk areas remained relatively stable at 14–16%, whereas relatively high-risk areas decreased sharply from 33.87% to 6.29%. These results provide useful guidance for regional land management for sustaining ecological systems, and the proposed framework provides a transferable assessment logic for wetland degradation risk, but its application to other regions require local calibration of indicators, weights, thresholds, and validation datasets.

Study Region: The Gnangara region in Western Australia, characterized by a Mediterranean climate with rainfall-dominated recharge, supplies approximately 60% of Perth’s drinking water.Study Focus: This study established spatiotemporal models between GLD and 87 climate and non-climate variables based on a 48-year (1977–2024) dataset from 399 monitoring bores to identify the major drivers of groundwater level differences (GLD) using a progressive statistical framework.New Hydrological Insights for the Region: Results showed: 1) GLD exhibited a significant linear decline (cumulative 0.14 m; multi-year average 0.58 m), driven by long-term drought and sustained abstraction, with a spatial gradient increasing from northwest to southeast, shaped by hydrogeological and climatic gradients. 2) Seasonal climate variables outperformed annual-scale indicators in characterizing groundwater dynamics. Rainfall was the dominant factor influencing GLD, with May-September rainfall positively contributing the most (38.73%), and total rainfall above the 95th percentile threshold contributing 29.87%. June–August potential evaporation (Span68) and mean dry spell length (MeDS) were negatively correlated with GLD, contributing 13.74% and 8.70%, respectively. 3) Among non-climate variables, groundwater abstraction contributed negatively (6.20%), while NDVI had the smallest positive impact (2.76%). Both are scale-dependent, with regional impacts weakened by hydrogeological and climatic heterogeneity. These findings advance groundwater management by supporting targeted strategies: prioritizing seasonal and 95th-percentile rainfall monitoring over annual indicators and implementing permeability-adapted groundwater abstraction management for shallow aquifer dynamics.

Spatial and temporal patterns of terrestrial water storage (TWS), and their relationship with groundwater levels, were investigated with the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Data Assimilation System (GLDAS) land surface model results, and climate observations for the Murray–Darling Basin (MDB). The results show that: (1) TWS displays a clear temporal variability: a negative TWS anomaly with a declining trend during 2002–2009, a positive TWS anomaly with a decreasing trend during 2010–2017, and a period of mixed positive and negative TWS anomalies being accompanied by an increasing trend from 2018 to 2025; (2) five dominant cluster patterns were identified that explain the spatial variability of temporal TWS across the MDB; (3) overall, TWS temporal variability is strongly correlated with rainfall, although it is weak at certain locations; (4) TWS is also influenced by evaporation (both actual and potential evapotranspiration, AET and PET) and runoff, and a combined model significantly improves the overall performance in explaining TWS temporal variability; and (5) TWS-derived groundwater storage changes show both similarities and differences in comparison with groundwater level observation changes, reflecting complex hydrogeological processes and the influence of human activities such as groundwater extraction. These findings provide valuable insights to support improved groundwater resource management with GRACE satellite information and land surface models.

Understanding the relationship between streamflow and climate input is important to inform how streamflow characteristics could change under climate change. The first part of this study assessed the relationship between annual streamflow and climate characteristics using rainfall, potential evapotranspiration (PET) and streamflow data from 133 catchments across the Murray–Darling Basin (MDB). The second part assessed the response of key streamflow metrics (mean annual runoff, high-flow days, low-flow days and minimum three-year flow) to changes in rainfall characteristics and PET using a weather generator and five hydrological models. The results indicate that annual streamflow is strongly correlated to many rainfall characteristics. However, these rainfall characteristics are also strongly correlated to annual rainfall, and therefore annual rainfall is generally a good predictor of annual streamflow. Rainfall characteristics that have a secondary influence on annual streamflow include effective rainfall, which is defined as the cumulative positive differences between daily or monthly rainfall and PET, rainfall seasonality, multi-day rainfall totals and multi-year rainfall lows. The change in future mean annual streamflow is driven primarily by the change in mean annual rainfall and PET, followed by some of the rainfall characteristics above. In addition to mean annual rainfall, high flows are also strongly influenced by multi-day rainfall totals, while multi-year hydrological droughts are also influenced by multi-year rainfall lows and annual rainfall variability. These results are useful to identify the key climate characteristics in assessing the impacts of future climate change on water availability, and potentially for developing next-generation hydrological models that can deal with non-stationarity and better predict streamflow under a changing climate.

The Songhua River Basin (SRB), ranking third largest in China in terms of both runoff volume and basin area, has experienced frequent disasters and drastic changes in runoff since the early 20th century. Many studies have analyzed the causes of runoff reduction; however, the spatiotemporal differences in runoff contributions and their underlying mechanisms remain poorly understood, which are crucial for regional water resources management and effective utilization. This study used the Mann-Kendall rank correlation trend test, continuous wavelet analysis, cumulative anomaly, and the slope change ratio of cumulative quantities (SCRCQ) method to explore the runoff changes characteristics and spatiotemporal differences of the contributions of climate change and human activities to runoff changes across three sub-basins of the SRB. The results show that: 1) runoff from 1955 to 2022 in all the three sub-basins exhibit a statistically significant decreasing trend at 0.05 significant level. 2) Four abrupt change points in runoff were detected in Nenjiang River Basin (NRB) and the mainstream of the SRB (MSRB), whereas only two change points in the Second Songhua River (SSRB). 3) Runoff and precipitation series of the NRB and MSRB exhibit similar multi-timescale cycle characteristics with the most dominated cycles of 45-58 yr. In contrast, it is 12-18 yr for SSRB. 4) Anthropogenic activities are the primary factor leading to in the reduction of runoff in NRB (74.33%-91.67%) and MSRB (50.11%-102.12%), whereas it is only 5.38%-33.12% in SSRB. This is attributed to the uneven distribution of regional climate and human activities in the entire SRB. 5) With the growing demand for water diversion for agricultural irrigation, anthropogenic activities in the NRB and MSRB have increased. However, the opposite is found in SSR, where the increased influence of precipitation on runoff and water conservation policies are identified.

As machine learning models become more widely relied on for groundwater predictions, the ability to interpret and explain these predictions is increasingly important. Explainable AI (XAI) tools are addressing this challenge by enhancing model transparency. Importantly, XAI also offers an early indication of its potential in broadening the role of machine learning in groundwater research - shifting it from a predictive tool to one that deepens understanding of system dynamics. This study explores the capacity of XAI to provide comprehensive insights into groundwater system behavior over large geographic scales. Spatiotemporal variations in groundwater levels and trends across Australia's Murray-Darling Basin (MDB) are investigated. Predominant drivers of groundwater changes are identified, revealing differences across subregions and extended timeframes, including during periods of drought. Insights are revealed on a geographic scale that would be difficult to obtain using physics-based or conceptual models, though the approach is equally applicable to surrogates and emulators of these models. This framework advances the interpretability of spatiotemporal environmental predictions through the incorporation of machine learning with explainability and visualisations-demonstrating the potential for machine learning to add value in hydrological research beyond the production of accurate predictions. Although the application of explainability in hydrological machine learning models is still relatively new, it is poised to become a standard component of future analyses. Through the considered adaptation of XAI methods to hydrological settings, researchers will enhance the acceptance and applicability of machine learning models for sustainable water resource management.

The ability of a general circulation model (GCM) to capture the variability of El Niño–Southern Oscillation (ENSO) is not only a scientific issue of climate model performance, but also critical for climate change and variability impact studies. Here, we assess 48 CMIP5 GCMs for their skill in simulating ENSO interdecadal variability and its teleconnection with precipitation globally. The results show that (1) only 22 out of 48 GCMs display interdecadal variability that is similar to the observations; (2) the ensemble of the 48 GCMs captures the ENSO–precipitation teleconnection at the global scale; (3) no single GCM can capture the observed ENSO–precipitation teleconnection globally; and (4) a GCM that can realistically simulate ENSO variability does not necessarily capture the ENSO-precipitation teleconnection, and vice versa. The results could also be used by climate change impact studies to select suitable GCMs, especially for regions with a statistically significant teleconnection between ENSO and precipitation, as well as for the comparison of CMIP5 and CMIP6.

Study region: In the Murray-Darling Basin (MDB) of Australia, climate change is leading to shifts in the availability of surface water which is driving an increased reliance on groundwater resources. Projected declines in rainfall and rising climate variability are expected to amplify this trend, heightening the role of groundwater in supplementing water demand. Study focus: Quantifying this change is important for ensuring water resource resilience and sustainability into the future. This study explores hydroclimatic conditions associated with periods of elevated groundwater use and evaluates how future reductions in surface water reliability may influence extraction patterns. Relationships between surface water and groundwater dependence are analysed and groundwater requirements under a range of hypothetical future scenarios are simulated. The deep learning-based stress-testing framework used here accounts for simultaneous changes in important surface water components amid the inherent uncertainty of future conditions. New hydrological insights for the region: Results show groundwater demand could increase by up to 16 % under plausible future reductions in rainfall and surface water storage, compared with modelled predictions based on 2010-2020 data. The study demonstrates the utility of machine learning for scenario testing under uncertainty and at multiple-aquifer scale. Findings emphasize the interconnected nature of surface and groundwater systems in the MDB and highlight the importance of conjunctive water management strategies to ensure long-term water security under changing climate conditions.

The long-term trends and variability of hydroclimate variables are critical for water resource management, as well as adaptation to climate change. Three popular methods were used in this study to explore the trends and variability of hydroclimate variables during last 122 years in the Songhua River (SHR), one of most important river systems in China. Results show the followings: (1) There was an obvious pattern of decadal oscillations, with three positive and three negative precipitation and streamflow anomalies. The lengths of these phases vary from 11 to 36 years. (2) Annual temperature demonstrated a statistically significant increasing trend in the last 122 years, and the trend magnitude was 0.30 °C/10 years in the last 50–60 years, being larger than that of the global surface temperature. It has increased much faster since 1970. (3) Monthly precipitation in the winter season in recent years was almost the same as that in earlier periods, but a significantly increasing monthly streamflow was observed due to snowmelt under a warming climate. (4) A statistically significant correlation between hydroclimate variables and climate indices can be determined. These results could be used to make better water resource management decisions in the SHR, especially under future climate change scenarios.

To investigate the dominant species and interspecific association in the phytoplankton community of the Feiyun River basin in Zhejiang Province, East China, the main stream and the Shanxi-Zhaoshandu Reservoir in the downstream were chosen as the study area, for which 22 sampling sites were designated. Sampling was conducted in September 2021, January, May, and July 2022. Phytoplankton species were identified from both quantitative samples and in-vivo observations. Phytoplankton was quantified by direct counting. Results show that there were 98 species belonging to 6 phyla and 78 genera. In addition, to clarify the niches of the dominant phytoplankton species and their interspecific association, the dominance index was calculated, and a comprehensive analysis was conducted including niche width, niche overlap value, ecological response rate, overall association, chi-square test, and the stability. The phytoplankton community exhibited characteristics of a Cyanobacteria-Chlorophyta-Diatom type community, showing higher diversity in spring and lower diversity in summer. Among 11 dominants phytoplankton species from 3 phyla, both frequency and dominance degree varied seasonally, of which Microcystis sp. was the dominant species in Spring, Autumn, and Winter. The niche widths of the dominant species ranged from 0.234 to 0.933, and were categorized into three groups. The niche overlap values of the 11 dominant species ranged from 0.359 to 0.959, exhibiting significant seasonal differences—highest in winter followed by autumn, spring, and summer in turn. The overall correlation among dominant species in all four seasons revealed a non-significant negative association, resulting in an unstable community structure. A significant portion (84.2%) of species pairs displayed positive associations, suggesting a successional pattern where Diatoms dominated while other dominant species shared resources and space. Despite this pattern, stability measurements indicated that the dominant species community remained unstable. Therefore, careful monitoring is recommended for potential water environment issues arising from abnormal proliferation of dominant species in the watershed during winter. This research built a theoretical foundation with a data support to the early warning of eutrophication and provided a reference for water resources management in similar watersheds along the eastern coast of China.

We assessed the ability of Regional Climate Models (RCMs) to reproduce observed means, inter-annual variance and trends for rainfall indices that were important for runoff generation over southeast Australia. To establish the benefit of the RCM without being penalized or rewarded by the performance of the forcing GCM, we used ECMWF Re-Analysis-interim (ERA-Interim) to force the Weather Research and Forecasting model (WRF) and the Conformal Cubic Atmospheric Model (CCAM). The performance of two different configurations of each RCM was evaluated against an observational dataset (Australian Gridded Climate Data, AGCD) and compared with that of ERA-Interim. The assessments were carried out at both observational and ERA-Interim grid resolutions: 5 km and 80 km, respectively. As hypothesised, the RCMs outperformed ERA-Interim in representing spatial patterns and magnitude of mean rainfall, because of higher spatial resolution. RCMs also accurately represented the general spatial patterns of variance, but systematically underestimated the inter-annual variability over much of the domain. RCMs performed better than ERA-Interim in reproducing the magnitude of trends in some cases, especially in the decline of cool season rainfall in southeast Australia, which has had significant impacts on water resources. One of the two CCAM runs generally performed best across all rainfall indices, in part because of atmospheric spectral nudging and an improved land-surface model. Based on the findings, we have provided suggestions on where new research on the development of RCMs could be focused and recommendations relevant to the next generation of Australian hydro-climate projections generated from the sixth phase of the Coupled Model Intercomparison Project (CMIP6).

Attributions of rainfall anomalies to weather systems and their spatio-temporal variability in Victoria, southeast Australia are investigated with a multimethod weather type dataset and two popularly used gridded daily rainfall datasets for the period 1979-2015. The rainfall anomalies before, during and after the Millennium Drought (1997-2009) are compared to quantify the temporal variability of rainfall responses to weather type changes. The results show: (1) Three weather systems (Front, Cyclone and Thunderstorm) and their combinations contribute 89% of total rainfall; (2) Contributions of weather types to rainfall vary from month to month with winter season rainfall coming from more diverse weather types than summer rainfall; (3) The contributions of weather types to rainfall in three periods show temporal variabilities and there is a clear shift of contribution pattern after the Millennium Drought, such as front-thunderstorm (FT) is now the largest contributor to rainfall compared with cyclone-frontal-thunderstorm (CFT) before and during the Millennium Drought; (4) A seasonal shift in the post-drought period is found with higher rainfall in February and March and lower rainfall in September and October. The increased rainfall in February mainly results from Front-Thunderstorm (FT) and Thunderstorm-only (TO), while rainfall declines in September from all weather types; (5) Several rainfall characteristics that are important for streamflow generation, such as rainfall intensity, probability of rainfall occurrence, number of rainfall days and the maximum daily rainfall, do depend on the weather types; (6) The results are similar with different rainfall datasets, but differences do exist, especially at the local scale. The conclusions of this study are drawn from an Australian case study but have implications for other regions to investigate the attributions of rainfall characteristic changes to weather systems. Attributions of rainfall anomalies to weather systems and their spatial and temporal variabilities are investigated in this article and results show that three weather systems (Front, Cyclone and Thunderstorm) and their combinations contribute 89% of total rainfall and the contributions display spatial and temporal heterogenous patterns. Contributions of rainfall intensity, rainy days (RD) and their interaction to total rainfall anomaly (mm) before, during and after the Millennium Drought (1997-2009) over the long-term (1979-2015) for each weather type.image

Accurate and punctual precipitation data are fundamental to understanding regional hydrology and are a critical reference point for regional flood control. The aims of this study are to evaluate the performance of three widely used precipitation datasets—CRU TS, ERA5, and NCEP—as potential alternatives for hydrological applications in the Bahr el Ghazal River Basin in South Sudan, Africa. This includes examining the spatial and temporal evolution of regional precipitation using relatively accurate precipitation datasets. The findings indicate that CRU TS is the best precipitation dataset in the Bahr el Ghazal Basin. The spatial and temporal distributions of precipitation from CRU TS reveal that precipitation in the Bahr el Ghazal Basin has a clear wet season, with June–August accounting for half of the annual precipitation and peaking in July and August. The long-term annual total precipitation exhibits a gradual increasing trend from the north to the south, with the southwestern part of the Basin having the largest percentage of wet season precipitation. Notably, the Bahr el Ghazal Basin witnessed a significant precipitation shift in 1967, followed by an increasing trend. Moreover, the spatial and temporal precipitation evolutions reveal an ongoing risk of flooding in the lower part of the Basin; therefore, increased engineering counter-measures might be needed for effective flood prevention.

To investigate the structural characteristics and driving factors of the metazooplankton community in the cascade reservoirs of Feiyun River Basin, monitoring surveys were conducted at 18 sampling sites across different seasons: September 2021 (autumn), January 2022 (winter), May 2022 (spring), and July 2022 (summer). These surveys identified 51 metazooplankton species, with the highest species abundance observed in summer and the lowest in winter. Notably, metazooplankton densities and biomasses peaked in spring, averaging 13.57 ind./l (where ind./l stands for individuals per liter) and 0.362 mg/l, respectively, while the lowest average densities (9.20 ind./l) and biomasses (0.262 mg/l) occurred in summer and winter, respectively. Seasonal variation had a notable influence on the community composition of metazooplankton, with Rotifera predominating in summer and autumn, and Copepoda in winter and spring. Cyclops larva and Nauplius species consistently dominated throughout. Correlation analyses revealed a significant negative association between metazooplankton richness and Pielou evenness indices with phosphate and total phosphorus concentrations, respectively. Redundancy analyses identified chlorophyll a, water temperature, total phosphorus, conductivity, pH, and phosphate as key environmental factors influencing the seasonal distribution of the metazooplankton community. This study provides a preliminary assessment of the seasonal dynamics of metazooplankton communities in the cascade reservoirs of the Feiyun River Basin, offering foundational insights for evaluating the ecological health of the basin waters.

Study region The Ganjiang River Basin, China Study focus Parameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insights Compared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

Methods to produce seasonal to annual forecasts of surface and groundwater availability have been developed and implemented nationally and globally. Such forecasts allow water managers to create effective irrigation schedules and better plan and manage water supply. However, one limitation of existing approaches that the forecasts of surface streamflow and groundwater are independent and therefore neglect inter-variable correlations that allow for reliable forecasting of total water availability. In this study, we develop an approach to jointly forecast streamflow and groundwater level and demonstrate its performance for the Lockyer Valley, Australia, where both surface water and groundwater are critical for irrigation. Informed by analysis of the processes influencing dynamics of groundwater levels in the alluvial aquifer (as a substitute to available groundwater storage) and streamflow dynamics, an existing hydrological model is adapted to better represent the ephemeral nature of streamflow in the catchment and also to simulate changes in groundwater levels. The modified hydrological model is then integrated into a statistical-dynamical forecasting framework to jointly forecast streamflow and groundwater level. Verification results indicate that forecasts skilful to lead times of up to 3 months for streamflow and 12 months for groundwater levels, even though rainfall forecasts are not skilful beyond the first month. Forecasts for both streamflow and groundwater are also statistically reliable with low bias. The approach developed can be extended to forecast the spatial distribution of groundwater levels across a large region, as well as be used to infill missing groundwater observations.

The interferometric synthetic aperture radar (InSAR) technique was used in this study to derive the temporal and spatial information of ground deformation and explore its temporal correlation with groundwater dynamics. The random forest (RF) machine learning method was used to model the spatial variability of the temporal correlation and understand its influential contributors. The results showed that groundwater dynamics appeared to be an important factor in InSAR deformation at some bores where strong and positive correlations were observed. The RF model could explain up to 72% of spatial variances between InSAR deformation and groundwater dynamics. The spatial and temporal InSAR coherence (a proxy for the noise in InSAR results that is strongly related to vegetation) and soil moisture (difference, trend, and amplitude) were the most important factors explaining the spatial pattern of the temporal correlation between InSAR displacements and groundwater levels. This result confirms that noise sources (including deformation model fitting errors and radar signal decorrelation) and perturbation of the InSAR signal related to vegetation and surficial soils (clay content, moisture changes) should be accounted for when interpreting InSAR to support groundwater-related risk assessments and in groundwater resource management activities.

The trend and variability in rainfall characteristics that influence annual streamflow of southeast Australia is assessed using two methods, non‐parametric Kendall test and linear slope, and two gridded daily rainfall datasets (SILO and AWAP) and 1,196 station rainfall data for 1971–2021 period. The rainfall anomaly before, during and after the Millennium Drought (1997–2009) is compared to quantify the variability. The results show: (a) a declining rainfall trend is detected as the recent dry decades were preceded by wet decades. The declining trend largely occurs from autumn through to spring with the largest trend in August to October followed by April and May; (b) the trend is more significant for number of rainfall days and for multi‐day rainfall totals (and more so for longer accumulations) than for annual rainfall; (c) the very high extreme rainfall shows a small increasing trend in summer and mixed signal in the other seasons; (d) the streamflow in the post drought period is lower than the long‐term mean, despite the mean annual rainfall being slightly higher. This can be partly explained by the rainfall‐runoff relationship of catchments not having fully recovered from the prolonged drought and partly by changes in rainfall characteristics, like the wet spell length and multi‐day rainfall, influencing annual streamflow; (e) The general conclusions for analyses with different sources of daily rainfall data are the same. However, some differences do exist, with SILO gridded rainfall and station data having larger areas with statistically significant trend than the AWAP gridded rainfall, as well as larger declining trends in the high elevation areas. The general conclusions of this study are drawn from an Australia case study but could have implications for other regions to investigate the attributions of rainfall characteristics other than annual rainfall to the non‐stationary rainfall‐streamflow relationship.

With rapid economic and social development, human activities, such as the construction of reservoirs, have satisfied many needs. However, these activities have also caused changes in the hydrological ecosystem of the basin, impacting river biodiversity. The establishment of the Shanxi Reservoir within the Feiyun River Basin has inevitably altered the hydrological system, which was previously shaped primarily by precipitation. This study comprehensively evaluates the impact of Shanxi Reservoir on the hydrological alteration of the basin. Two ecological flow indicators, ecosurplus and ecodeficit, were used based on flow duration curves, along with the Shannon Index of river biodiversity (SI) and several hydrological indicators: the Indicator of Hydrological Alteration (IHA), the Degree of Integrated Hydrological Alteration (D0), and the Dundee Hydrological Regime Alteration Method (DHRAM). The results of the study show that: (1) the previously observed correlation between ecological flow and precipitation was disrupted following the construction of the reservoir. As a result, there was a notable ecological deficit in the spring season, with a maximum of 0.43, and a considerable increase in ecological surplus during winter, reaching a maximum of 4.80. (2) The flow regime of the river has undergone significant changes, resulting in a combined hydrological variability of 66. 44% and an ecological hazard level of 4. This indicates a high risk to the river’s ecological environment. (3) The total seasonal ecological surplus has increased and remained consistently high, which has contributed to a decline in the SI of river biodiversity, the river's biology has degraded over time (4) The use of ecological flow indicators is closely tied to IHA32 indicators. Combining ecological flow indicators with ERHIs has shown to be a successful approach for assessing ecohydrological regimes. These findings have significant implications for future studies in similar cases and offer valuable guidance for managing water resources.

Unsustainable groundwater extraction can lead to aquifer compaction, damages to infrastructure, changes of water accumulation in rivers and lakes and to a decrease of the aquifer's ability to store water for future generations. While this phenomenon is well identified across the globe, the potential for groundwater-related ground deformation is still largely unknown for most of the heavily exploited aquifers of Australia. This study fills that science gap by exploring signs of this phenomenon across a large region comprising seven of Australia's most intensively exploited aquifers, in the New South Wales Riverina region. To detect ground deformation, we processed 396 Sentinel-1 swaths acquired during 2015-2020 using a multitemporal spaceborne radar interferometry (InSAR), leading to the production of a near-continuous ground deformation maps covering similar to 280,000 km(2). To explore potential groundwater-induced deformation hotspots, four criteria are used in a multiple-line of evidence approach: (1) the amplitude, shape, and extent of the InSAR ground displacement anomaly, (2) the spatial correspondence with groundwater extraction hotspots. (3) The correlations between InSAR deformation time series and change in head levels in 975 wells. Four areas are identified as potentially prone to inelastic, groundwater-related deformations, with average deformation rates ranging from -10 to -30mm/yr, high rates of groundwater extraction, and ample critical head drops. Comparison of ground deformation and groundwater level time series also suggests potential for elastic deformation in some of these aquifers. This study will help water managers mitigating the groundwater-related ground deformation risk.