Understanding linkages between global climate indices and terrestrial water storage changes over Africa using GRACE products

Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model

Realistic representation of hydrological drought events is increasingly important in world facing decreased freshwater availability. Index-based drought monitoring systems are often adopted to represent the evolution and distribution of hydrological droughts, which mainly rely on hydrological model simulations to compute these indices. Recent studies, however, indicate that model derived water storage estimates might have difficulties in adequately representing reality. Here, a novel Markov Chain Monte Carlo - Data Assimilation (MCMC-DA) approach is implemented to merge global Terrestrial Water Storage (TWS) changes from the Gravity Recovery And Climate Experiment (GRACE) and its Follow On mission (GRACE-FO) with the water storage estimations derived from the W3RA water balance model. The modified MCMC-DA derived summation of deep-rooted soil and groundwater storage estimates is then used to compute 0.5∘ standardized groundwater drought indices globally to show the impact of GRACE/GRACE-FO DA on a global index-based hydrological drought monitoring system. Our numerical assessment covers the period of 2003-2021, and shows that integrating GRACE/GRACE-FO data modifies the seasonality and inter-annual trends of water storage estimations. Considerable increases in the length and severity of extreme droughts are found in basins that exhibited multi-year water storage fluctuations and those affected by climate teleconnections.

A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) have facilitated highly accurate observations of changes in total water storage anomalies (TWSA). However, limited observations of TWSA derived from GRACE in the Yangtze River Basin (YRB) have hindered our understanding of its long-term variability. In this paper, we present a deep learning model called RecNet to reconstruct the climate-driven TWSA in the YRB from 1923 to 2022. The RecNet model is trained on precipitation, temperature, and GRACE observations with a weighted mean square error (WMSE) loss function. The performance of the RecNet model is validated and compared against GRACE data, water budget estimates, hydrological models, drought indices, and existing reconstruction datasets. The results indicate that the RecNet model can successfully reconstruct historical water storage changes, surpassing the performance of previous studies. In addition, the reconstructed datasets are utilized to assess the frequency of extreme hydrological conditions and their teleconnections with major climate patterns, including the El Niño-Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and North Atlantic Oscillation. Independent component analysis is employed to investigate individual climate patterns' unique or combined influence on TWSA. We show that the YRB exhibits a notable vulnerability to extreme events, characterized by a recurrent occurrence of diverse extreme dry/wet conditions throughout the past century. Wavelet coherence analysis reveals significant coherence between the climate patterns and TWSA across the entire basin. The reconstructed datasets provide valuable information for studying long-term climate variability and projecting future droughts and floods in the YRB, which can inform effective water resource management and climate change adaptation strategies.

Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods

The Urmia lake in north-west Iran has dried up to perilously low levels in the past two decades. In this study, we investigate the drivers behind the decline in lake water level with the help of in-situ and remote sensing data. We use total water storage (TWS) changes from the gravity recovery and climate experiment (GRACE) satellite mission. TWS from GRACE includes all the water storage compartments in a column and is the only remote sensing product that can help in estimating groundwater storage (GWS) changes. The coarse spatial (approx. 300 km) resolution of GRACE does not allow us to identify local changes that may have led to the Urmia lake disaster. In this study, we tackle the poor resolution of the GRACE data by employing three machine learning (ML) methods including random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP). The methods predict the groundwater storage anomaly (GWSA), derived from GRACE, as a function of hydro-climatic variables such as precipitation, evapotranspiration, land surface temperature (LST) and normalized difference vegetation index (NDVI) on a finer scale of 0.25° × 0.25°. We found that i) The RF model exhibited highest R (0.98), highest NSE (0.96) and lowest RMSE (18.36 mm) values. ii) The RF downscaled data indicated that the exploitation of groundwater resources in the aquifers is the main driver of groundwater storage and changes in the regional ecosystem, which has been corroborated by few other studies as well. The impact of precipitation and evapotranspiration on the GWSA was found to be rather weak, indicating that the anthropogenic derivers had the most significant impact on the GWSA changes. iii) We generally observed a significant negative trend in GWSA, having also significant positive correlations with the well data. However, over regions with dam construction significant negative correlations were found.

Influence of land use changes on the remaining available aquifer storage (RAAS): A case study of the Taoerhe alluvial-proluvial fan

Groundwater resources are important water sources for people living in arid-semiarid China. To solve the problem of continuously declining groundwater levels, groundwater artificial recharge has been widely conducted by using available aquifers. However, the effects of land use changes on the available aquifer storage, especially on the remaining available aquifer storage (RAAS), have not been fully explored. Here, we quantitatively evaluated the effects of land use changes on the RAAS, exemplifying the Taoerhe alluvial-proluvial fan. Independent component analysis (ICA) is used to determine precipitation- and groundwater extraction-affected RAASs, and regression equations are established for land use type areas and precipitation- and groundwater extraction-affected RAASs through stepwise regression and all-subsets regression. An integrated model combining the future land use simulation (FLUS) model and Markov-chain model is established to predict three land use change scenarios in 2036, and the impacts of land use changes on the precipitation- and groundwater extraction-affected RAASs are evaluated. The results show that land use changes were generally active from 2000 to 2018; during this time, the RAAS showed a fluctuating upward trend. Rational land use changes are critical to the RAAS. In the 2036 baseline scenario, the precipitation-affected RAAS is the smallest and the groundwater extraction-affected RAAS is the largest among the three scenarios, contrary to the economic development scenario results. The woodland conservation scenario shows that the groundwater level can be maintained at a stable level with appropriate woodland protection measures to ensure the stability of the RAAS, providing the most promising results for groundwater development and utilization in the study area. These results temporally quantify the effects of land use changes on the precipitation- and groundwater extraction-affected RAASs and provide a reference for developing artificial recharge schemes in arid-semiarid regions and studying the effects of land use changes on available aquifer storages.

Evaluating the BFAST method to detect and characterise changing trends in water time series: A case study on the impact of droughts on the Mediterranean climate

Mediterranean climate regions are facing increased aridity conditions and water scarcity, thus needing integrated management of water resources. Detecting and characterising changes in water resources over time is the natural first step towards identifying the drivers of these changes and understanding the mechanism of change. The aim of this study is to evaluate the potential of Breaks For Additive Seasonal and Trend (BFAST) method to identify gradual (trend) and abrupt (step- change) changes in the freshwater resources time series over a long-term period. This research shows an alternative to the Pettitt's test, LOESS (locally estimated scatterplot smoothing) filter, Mann-Kendall trend test among other common methods for change detection in hydrological data, and paves the way for further scientific investigation related to climate variability and its influence on water resources. We used the monthly accumulated stored water in three reservoirs, the monthly groundwater levels of three hydrological settings and a standardized precipitation index to show BFAST performance. BFAST was successfully applied, enabling: (1) assessment of the suitability of past management decisions when tackling drought events; (2) detection of recovery and drawdown periods (duration and magnitude values) of accumulated stored water in reservoirs and groundwater bodies after wet and dry periods; 3) measurement of resilience to drought conditions; (4) establishment of similarities/differences in trends between different reservoirs and groundwater bodies with regard to drought events.

Extraction of GRACE/GRACE-FO observed mass change patterns across Antarctica via independent component analysis (ICA)

Here we qualitatively analyse the mass change patterns across Antarctica via independent component analysis (ICA), a statistics-based blind source separation method to extract signals from complex data sets, in an attempt to reduce uncertainties in the glacial isostatic adjustment (GIA) effects and improve understanding of Antarctic Ice Sheet (AIS) mass-balance. We extract the six leading independent components from gravimetric data acquired during the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions. The results reveal that the observed continental-scale mass changes can be effectively separated into several spatial patterns that may be dominated by different physical processes. Although the hidden independent physical processes cannot be completely isolated, some significant signals, such as glacier melt, snow accumulation, periodic climatic signals, and GIA effects, can be determined without introducing any external information. We also observe that the time period of the analysed data set has a direct impact on the ICA results, as the impacts of extreme events, such as the anomalously large snowfall events in the late 2000s, may cause dramatic spatial and temporal changes in the ICA results. ICA provides a unique and informative approach to obtain a better understanding of both AIS-scale mass changes and specific regional-scale spatiotemporal signal variations.

Natural- and Human-Induced Influences on Terrestrial Water Storage Change in Sichuan, Southwest China from 2003 to 2020

A quantitative understanding of changes in water resources is crucial for local governments to enable timely decision-making to maintain water security. Here, we quantified natural-and human-induced influences on the terrestrial water storage change (TWSC) in Sichuan, Southwest China, with intensive water consumption and climate variability, based on the data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-on (GRACE-FO) during 2003–2020. We combined the TWSC estimates derived from six GRACE/GRACE-FO solutions based on the uncertainties of each solution estimated from the generalized three-cornered hat method. Metrics of correlation coefficient and contribution rate (CR) were used to evaluate the influence of precipitation, evapotranspiration, runoff, reservoir storage, and total water consumption on TWSC in the entire region and its five economic regions. The results showed that a significant improvement in the fused TWSC was found compared to those derived from a single model. The increase in regional water storage with a rate of 3.83 ± 0.54 mm/a was more influenced by natural factors (CR was 53.17%) compared to human influence (CR was 46.83%). Among the factors, the contribution of reservoir storage was the largest (CR was 42.32%) due to the rapid increase in hydropower stations, followed by precipitation (CR was 35.16%), evapotranspiration (CR was 15.86%), total water consumption (CR was 4.51%), and runoff (CR was 2.15%). Among the five economic regions, natural influence on Chengdu Plain was the highest (CR was 48.21%), while human influence in Northwest Sichuan was the largest (CR was 61.37%). The highest CR of reservoir storage to TWSC was in Northwest Sichuan (61.11%), while the highest CRs of precipitation (35.16%) and evapotranspiration (15.86%) were both in PanXi region. The results suggest that TWSC in Sichuan is affected by natural factors and intense human activities, in particular, the effect of reservoir storage on TWSC is very significant. Our study results can provide beneficial help for the management and assessment of regional water resources.

Estimation of Terrestrial Water Storage Variations in Sichuan-Yunnan Region from GPS Observations Using Independent Component Analysis

GPS can be used to measure land motions induced by mass loading variations on the Earth’s surface. This paper presents an independent component analysis (ICA)-based inversion method that uses vertical GPS coordinate time series to estimate the change of terrestrial water storage (TWS) in the Sichuan-Yunnan region in China. The ICA method was applied to extract the hydrological deformation signals from the vertical coordinate time series of GPS stations in the Sichuan-Yunnan region from the Crustal Movement Observation Network of China (CMONC). These vertical deformation signals were then inverted to TWS variations. Comparative experiments were conducted based on Gravity Recovery and Climate Experiment (GRACE) data and a hydrological model for validation. The results demonstrate that the TWS changes estimated from GPS(ICA) deformations are highly correlated with the water variations derived from the GRACE data and hydrological model in Sichuan-Yunnan region. The TWS variations are overestimated by the vertical GPS observations the northwestern Sichuan-Yunnan region. The anomalies are likely caused by inaccurate atmospheric loading correction models or residual tropospheric errors in the region with high topographic variability and can be reduced by ICA preprocessing.

Assessing Freshwater Changes over Southern and Central Africa (2002–2017)

In large freshwater river basins across the globe, the composite influences of large-scale climatic processes and human activities (e.g., deforestation) on hydrological processes have been studied. However, the knowledge of these processes in this era of the Anthropocene in the understudied hydrologically pristine South Central African (SCA) region is limited. This study employs satellite observations of evapotranspiration (ET), precipitation and freshwater between 2002 and 2017 to explore the hydrological patterns of this region, which play a crucial role in global climatology. Multivariate methods, including the rotated principal component analysis (rPCA) were used to assess the relationship of terrestrial water storage (TWS) in response to climatic units (precipitation and ET). The use of the rPCA technique in assessing changes in TWS is warranted to provide more information on hydrological changes that are usually obscured by other dominant naturally-driven fluxes. Results show a low trend in vegetation transpiration due to deforestation around the Congo basin. Overall, the Congo (r2 = 76%) and Orange (r2 = 72%) River basins maintained an above-average consistency between precipitation and TWS throughout the study region and period. Consistent loss in freshwater is observed in the Zambezi (−9.9 ± 2.6 mm/year) and Okavango (−9.1 ± 2.5 mm/year) basins from 2002 to 2008. The Limpopo River basin is observed to have a 6% below average reduction in rainfall rates which contributed to its consistent loss in freshwater (−4.6 ± 3.2 mm/year) from 2006 to 2012.Using multi-linear regression and correlation analysis we show that ET contributes to the variability and distribution of TWS in the region. The relationship of ET with TWS (r = 0.5) and rainfall (r = 0.8) over SCA provides insight into the role of ET in regulating fluxes and the mechanisms that drive precipitation in the region. The moderate ET–TWS relationship also shows the effect of climate and anthropogenic influence in their interactions.

The 2019-2020 Rise in Lake Victoria Monitored from Space: Exploiting the State-of-the-Art GRACE-FO and the Newly Released ERA-5 Reanalysis Products

During the period 2019-2020, Lake Victoria water levels rose at an alarming rate that has caused various problems in the region. The influence of this phenomena on surface and subsurface water resources has not yet been investigated, largely due to lack of enough in situ measurements compounded by the spatial coverage of the lake's basin, incomplete/inconsistent hydrometeorological data, and unavailable governmental data. Within the framework of joint data assimilation into a land surface model from multi-mission satellite remote sensing, this study employs the state-of-art Gravity Recovery and Climate Experiment follow-on (GRACE-FO) time-variable terrestrial water storage (TWS), newly released ERA-5 reanalysis, and satellite radar altimetry products to understand the cause of the rise of Lake Victoria on the one hand, and the associated impacts of the rise on the total water storage compartments (surface and groundwater) triggered by the extreme climatic event on the other hand. In addition, the study investigates the impacts of large-scale ocean-atmosphere indices on the water storage changes. The results indicate a considerable increase in water storage over the past two years, with multiple subsequent positive trends mainly induced by the Indian Ocean Dipole (IOD). Significant storage increase is also quantified in various water components such as surface water and water discharge, where the results show the lake's water level rose by ∼1.4 m, leading to approximately 1750 gigatonne volume increase. Multiple positive trends are observed in the past two years in the lake's water storage increase with two major events in April-May 2019 and December 2019-January 2020, with the rainfall occurring during the short rainy season of September to November (SON) having had a dominant effect on the lake's rise.

Decline in terrestrial water recharge with increasing global temperatures

Since 1901, global temperatures have risen by 0.89 °C, seriously impacting precipitation patterns and flow peaks. However, few assessments of changes in global water balance have been conducted. Here we investigate the effect of rising temperatures on water recharge for 31 major river basins across the world using satellite derived terrestrial water storage. We find reductions in Relative Recharge (indicative of the Terrestrial Water Recharge (TWR)) with increasing temperature in 23 of the 31 basins, with 12 basins showing significant reductions (at 90% confidence level). The possible explanation is that increase in temperature reduces the relative recharge due to increased evapotranspiration and reduced snow accumulation. Thus, in a future warmer climate, even an unchanged precipitation would lead to diminished recharge than expected, with reductions in precipitation expected to exacerbate it further. Large-scale changes in recharge would subsequently influence vegetation growth. Reduction in TWR showed clear association with diminished vegetation growth in majority of the river basins analyzed, adding further confirmation to the hypothesis being assessed.

Two Severe Prolonged Hydrological Droughts Analysis over Mainland Australia Using GRACE Satellite Data

In recent years, many droughts have happened over mainland Australia, especially the two severe prolonged droughts, from 2006 to 2009 and 2018 to 2020, resulting in serious water scarcity. Therefore, using the Total Storage Deficit Index (TSDI) from the Gravity Recovery and Climate Experiment (GRACE), we analyzed the two severe prolonged droughts from the perspective of the affected area, spatial evolution, frequency, severity and drought driving factors. The results show that the affected area of Drought 2006–2009 ranged from 57% to 95%, and that of Drought 2018–2020 ranged from 45% to 95%. Drought 2006–2009 took its rise in southeastern Australia and gradually spread to the central part. Drought 2018–2020 originated in the southwest corner of the Northern Territory and northern New South Wales, and gradually expanded to Western Australia and the whole New South Wales respectively. During Drought 2006–2009, Victoria suffered drought all months, including 59% mild drought and 41% moderate drought, North Territory had the highest drought severity of 44.26 and Victoria ranked the second high with the severity of 35.51 (cm months). For Drought 2018–2020, Northern Territory was also dominated by drought all months, including 92% mild drought and 8% moderate drought, the drought severities were in North Territory and Western Australia with 52.19 and 31.44 (cm months), respectively. Finally, the correlation coefficients between the two droughts and Indo-Pacific climate variability including El Niño-Southern Oscillation and Indian Ocean Dipole (IOD) are computed. By comparing the correlation coefficients of Drought 2018–2020 with Drought 2006–2009, we find that the impact of the El Niño on the hydrological drought becomes weaker while IOD is stronger, and the role of Southern Oscillation on droughts is diverse with the quite different spatial patterns. The results from Fourier analysis confirm that the two hydrological droughts are all related to Indo-Pacific climate variability but with slightly different driving mechanisms.

Monthly and Seasonal Drought Characterization Using GRACE-Based Groundwater Drought Index and Its Link to Teleconnections across South Indian River Basins

Traditional drought monitoring is based on observed data from both meteorological and hydrological stations. Due to the scarcity of station observation data, it is difficult to obtain accurate drought distribution characteristics, and also tedious to replicate the large-scale information of drought. Thus, Gravity Recovery and Climate Experiment (GRACE) data are utilized in monitoring and characterizing regional droughts where ground station data is limited. In this study, we analyzed and assessed the drought characteristics utilizing the GRACE Groundwater Drought Index (GGDI) over four major river basins in India during the period of 2003–2016. The spatial distribution, temporal evolution of drought, and trend characteristics were analyzed using GGDI. Then, the relationship between GGDI and climate factors were evaluated by the method of wavelet coherence. The results indicate the following points: GRACE’s quantitative results were consistent and robust for drought assessment; out of the four basins, severe drought was noticed in the Cauvery river basin between 2012 and 2015, with severity of −27 and duration of 42 months; other than Godavari river basin, the remaining three basins displayed significant negative trends at monthly and seasonal scales; the wavelet coherence method revealed that climate factors had a substantial effect on GGDI, and the impact of Southern Oscillation Index (SOI) on drought was significantly high, followed by Sea Surface Temperature (SST) Index (namely, NINO3.4) and Multivariate El Niño–Southern Oscillation Index (MEI) in all the basins. This study provides reliable and robust quantitative result of GRACE water storage variations that shares new insights for further drought investigation.

Twentieth and Twenty-First Century Water Storage Changes in the Nile River Basin from GRACE/GRACE-FO and Modeling

This research assesses the changes in total water storage (TWS) during the twentieth century and future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and a land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (generalized additive model for location, scale, and shape) model. The model performance was tested using standard indicators including coevolution plots, the Nash–Sutcliffe coefficient, cumulative density function, standardized residuals, and uncertainty bounds. All model evaluation results are satisfactory to excellent. The drought and flooding severity/magnitude, duration, and recurrence frequencies were assessed during the studied period. The results showed, (1) The NRB between 2002 to 2020 has witnessed a substantial transition to wetter conditions. Specifically, during the wet season, the NRB received between ~50 Gt./yr. to ~300 Gt./yr. compared to ~30 Gt./yr. to ~70 Gt./yr. of water loss during the dry season. (2) The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS of ~17% during the wet season. Moreover, the TWS storage had witnessed a recovery of ~28% during the dry season. (3) The projected TWSA between 2021 to 2050 unveiled a positive increase in the TWS during the rainy season. While during the dry season, the water storage showed insubstantial TWS changes. Despite these projections, the future storage suggested a reduction between 10 to 30% in TWS. The analysis of drought and flooding frequencies between 1901 to 2050 revealed that the NRB has ~64 dry-years compared to ~86 wet-years. The exceedance probabilities for the normal conditions are between 44 to 52%, relative to a 4% chance of extreme events. The recurrence interval of the normal to moderate wet or dry conditions is ~6 years. These TWSA trajectories call for further water resources planning in the region, especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins.

Exploring groundwater and soil water storage changes across the CONUS at 12.5 km resolution by a Bayesian integration of GRACE data into W3RA

Climate variability and change along with anthropogenic water use have affected the (re)distribution of water storage and fluxes across the Contiguous United States (CONUS). Available hydrological models, however, do not represent recent changes in the water cycle. Therefore, in this study, a novel Bayesian Markov Chain Monte Carlo-based Data Assimilation (MCMC-DA) approach is formulated to integrate Terrestrial Water Storage changes (TWSC) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission into the W3RA water balance model. The benefit of this integration is its dynamic solution that uses GRACE TWSC to update W3RA's individual water storage estimates while rigorously accounting for uncertainties. It also down-scales GRACE data and provides groundwater and soil water storage changes at ~12.5 km resolution across the CONUS covering 2003-2017. Independent validations are performed against in-situ groundwater data (from USGS) and Climate Change Initiative (CCI) soil moisture products from the European Space Agency (ESA). Our results indicate that MCMC-DA introduces trends, which exist in GRACE TWSC, mostly to the groundwater storage and to a lesser extent to the soil water storage. Higher similarity is found between groundwater estimation of MCMC-DA and those of USGS in the southeastern CONUS. We also show a stronger linear trend in MCMC-DA soil water storage across the CONUS, compared to W3RA (changing from ±0.5 mm/yr to ±2 mm/yr), which is closer to independent estimates from the ESA CCI. MCMC-DA also improves the estimation of soil water storage in regions with high forest intensity, where ESA CCI and hydrological models have difficulties in capturing the soil-vegetation-atmosphere continuum. The representation of El Niño Southern Oscillation (ENSO)-related variability in groundwater and soil water storage are found to be considerably improved after integrating GRACE TWSC with W3RA. This new hybrid approach shows promise for understanding the links between climate and the water balance over broad regions.

Detecting Leaders Country from Road Transport Emission Time-Series

Nowadays, climate change and global warming have become the main concerns worldwide. One of the main causes are the greenhouse gas (GHG) emissions produced by human activities, especially by the transportation sector. The adherence to international agreements and the implementation of climate change policy are necessary conditions for reducing environmental problems. This paper investigates the lead–lag relationship between Organization for Economic Co-operation and Development (OECD) and Annex I member countries on road transport emission performance focusing on the statistical analysis of the lead–lag relationships between the road transport emission time-series from 1970–2018 extracted by the Emissions Database for Global Atmospheric Research (EDGAR) database. The analysis was carried out using the cross-correlation function between each pair of the countries’ time-series considered. Empirical results confirm that some nations have been playing a role as leaders, while others as followers. Sweden can be considered the leader, followed by Germany and France. By analyzing their environmental policy history, we can figure out a common point that explains our results.

Influence of global climate on freshwater changes in Africa's largest endorheic basin using multi-scaled indicators

The poor investments in gauge measurements for hydro-climatic research in Africa has necessitated the need to investigate how decision makers can leverage on sophisticated space-borne measurements to improve knowledge on surface water hydrology that can feed directly into water accounting processes, and risk assessment from extreme droughts and its impacts. To demonstrate such potential, a suite of satellite earth observations (Sentinel-2, altimetry, Landsat, GRACE, and TRMM) and model data are combined with the standardized precipitation evapotranspiration index to assess the impacts of global climate on freshwater dynamics over the LCB (Lake Chad basin), Africa's largest endorheic basin. As shown in the results of this study, the significant relationship of climate modes (AMO; r=0.68 and 0.59; and AMM; r=0.42 and 0.47) with drought patterns in the LCB highlights the evidence of global climate influence in the region. The significant declines in drought extents and their intensities (2004 - 2015) over LCB coincide with the rise in surface water extent of the Lake Chad during the same period. Change detection analysis of open water features in the southern pool of Lake Chad during the 2015 - 2019 period shows that on the average, only 28.4% of inundated areas within the vicinity of the Lake persisted during the period. While the association of terrestrial water storage (TWS) with model-derived surface water storage (SWS) is strongest (r=0.89) in the catchments that provide the most nourishment to the Lake Chad, the relationship of rainfall (2002 - 2017) with TWS (r=0.85), model TWS (r=0.87) and SWS (r=0.88) confirm that the LCB's hydrology is predominantly climate-driven. This notion is further reinforced as the predicted SWS over the LCB using a support vector machine regression scheme was found to be strongly correlated (r=0.95 at α=0.05) with observed SWS.

An Iterative ICA-Based Reconstruction Method to Produce Consistent Time-Variable Total Water Storage Fields Using GRACE and Swarm Satellite Data

Observing global terrestrial water storage changes (TWSCs) from (inter-)seasonal to (multi-)decade time-scales is very important to understand the Earth as a system under natural and anthropogenic climate change. The primary goal of the Gravity Recovery And Climate Experiment (GRACE) satellite mission (2002–2017) and its follow-on mission (GRACE-FO, 2018–onward) is to provide time-variable gravity fields, which can be converted to TWSCs with ∼ 300 km spatial resolution; however, the one year data gap between GRACE and GRACE-FO represents a critical discontinuity, which cannot be replaced by alternative data or model with the same quality. To fill this gap, we applied time-variable gravity fields (2013–onward) from the Swarm Earth explorer mission with low spatial resolution of ∼ 1500 km. A novel iterative reconstruction approach was formulated based on the independent component analysis (ICA) that combines the GRACE and Swarm fields. The reconstructed TWSC fields of 2003–2018 were compared with a commonly applied reconstruction technique and GRACE-FO TWSC fields, whose results indicate a considerable noise reduction and long-term consistency improvement of the iterative ICA reconstruction technique. They were applied to evaluate trends and seasonal mass changes (of 2003–2018) within the world’s 33 largest river basins.

Human-Induced and Climate-Driven Contributions to Water Storage Variations in the Haihe River Basin, China

Terrestrial water storage (TWS) can be influenced by both climate change and anthropogenic activities. While the Gravity Recovery and Climate Experiment (GRACE) satellites have provided a global view on long-term trends in TWS, our ability to disentangle human impacts from natural climate variability remains limited. Here we present a quantitative method to isolate these two contributions with reconstructed climate-driven TWS anomalies (TWSA) based on long-term precipitation data. Using the Haihe River Basin (HRB) as a case study, we find a higher human-induced water depletion rate (−12.87 ± 1.07 mm/yr) compared to the original negative trend observed by GRACE alone for the period of 2003–2013, accounting for a positive climate-driven TWSA trend (+4.31 ± 0.72 mm/yr). We show that previous approaches (e.g., relying on land surface models) provide lower estimates of the climate-driven trend, and thus likely underestimated the human-induced trend. The isolation method presented in this study will help to interpret observed long-term TWS changes and assess regional anthropogenic impacts on water resources.

Spatio-temporal groundwater variations associated with climatic and anthropogenic impacts in South-West Western Australia

South-West Western Australia (SWWA) is a critical agricultural region that heavily relies on groundwater for domestic, agricultural and industrial use. However, the behaviours of groundwater associated with climate variability/change and anthropogenic impacts within this region are not well understood. This study investigates the spatio-temporal variability of groundwater in SWWA based on 2997 boreholes over the past 36 years (1980-2015). Results identify the decline in groundwater level (13 mm/month) located in the central coastal region of SWWA (i.e., north and south of Perth) to be caused by anthropogenic impacts (primary factor) and climate variability/change (secondary). In detail, anthropogenic impacts are mainly attributed to substantial groundwater abstraction, e.g., hotspots (identified by above 7 m/month groundwater level change) mostly occur in the central coastal region, as well as close to dams and mines. Impacts of climate variability/change indicate that coupled ENSO and positive IOD cause low-level rainfall in the coastal regions, subsequently, affecting groundwater recharge. In addition, correlation between groundwater and rainfall is significant at 0.748 over entire SWWA (at 95% confidence level). However, groundwater in northeastern mountainous regions hardly changes with rainfall because of very small amounts of rainfall (average 20-30 mm/month) in this region, potentially coupled with terrain and geological impacts. A marked division for groundwater bounded by the Darling and Gingin Scarps is found. This is likely due to the effects of the Darling fault, dams, central mountainous terrain and geology. For the region south of Perth and southern coastal regions, a hypothesis through multi-year analysis is postulated that rainfall of at least 60 and 65-70 mm/month, respectively, are required during the March-October rainfall period to recharge groundwater.

Short-term trends in Africa's freshwater resources: Rates and drivers

The freshwater resources in Africa are vulnerable to natural variabilities as well as anthropogenic interventions. In this study, temporal (April 2002-June 2017) Gravity Recovery and Climate Experiment (GRACE) data are integrated, in a geographic information system environment, with rainfall, temperature, evapotranspiration, and altimetry remote sensing datasets to monitor the short-term trends in terrestrial water storage (TWS) over the African hydrogeologic systems and to explore their origins. Results show that short-term trends over the African continent are largely driven by natural variability such as changes in rainfall, evapotranspiration, and associated variations in lake levels. Exceptions to this observation include central Africa, where deforestation is found to additionally drive changes in TWS, as well as northern Africa, where TWS changes are dominated by anthropogenic groundwater extraction from fossil aquifers. Findings highlight the need for integrative responses at local, national, regional, and international levels by the African nations to overcome current and future challenges related to freshwater availability in Africa.

The newly merged satellite remotely sensed, gauge and reanalysis-based Multi-Source Weighted-Ensemble Precipitation: Evaluation over Australia and Africa (1981-2016)

The Australian and African continents, regions prone to hydroclimate extremes (e.g., droughts and floods), but with sparse distribution of rain-gauge that are limited in time, rely heavily on complementary satellite and reanalysis data to provide important crucial information necessary for informing policies and management. The problem, however, is that satellite products suffer from systematic biases while reanalysis products carry over uncertainties from their forcing parameters. Multi-Source Weighted-Ensemble Precipitation (MSWEP) is a new global rainfall-product that merges satellite, rain-gauge and re-analysis data to exploit their advantages and minimise their disadvantages. Although MSWEP has been validated globally, this product, together with its potential applications, e.g., in water storage fluxes, river discharge and climate impacts studies over Australia and Africa, regions with urgent need of reliable products, has however, not been verified. Using GRACE satellite products, GLDAS model data, GRDC runoff products, and ENSO/IOD climate indices; five rainfall products - FLUXNET, BoM, GPCC, CHIRPS, and AgCFSR; and a suite of statistical methods (Pearson, Kolmogorov-Smirnov, PCA and Three-Corner-Hat (TCH)), this study (i) evaluates monthly MSWEP-V2.1 data (1981-2016), and (ii), assesses its potential applications to water storage flux (within the water balance framework), river discharge analysis, and climate impacts studies. The results show good MSWEP correlations and cumulative distribution with BoM product over most of Australia except in regions with heavy monsoonal rainfall, e.g., northern and north-western Australia where it tends to underestimate. Over Africa, MSWEP has no obvious advantages compared to insitu-GPCC, satellite-CHIRPS or reanalysis-AgCFSR. Furthermore, it is unable to reflect on major hydro-climate extremes over west, east and southern Africa, where it underestimates compared to CHIRPS. Its potential applications to water storage flux, discharge and climate impacts over the two continents show better suitability for water storage flux in Africa, while no advantages are seen compared to other rainfall products on other aspects.

Physical dynamics of Lake Victoria over the past 34 years (1984-2018): Is the lake dying?

Understanding changes in the physical dynamics of lakes (e.g., areas and shorelines) is important to inform policies, planning and management during climate extremes (e.g., floods and droughts). For Lake Victoria, the world's second largest freshwater lake, its physical dynamics and associated changes are not well understood as evidenced, e.g., from the citations of its area 66,400 - 69,485 km², length 300 - 412 km, width 240 - 355 km, and shorelines 3300 - 4828 km. Its sheer size and lack of research resources commitment by regional governments hamper observations. This contribution employs a suite of remotely sensed products for the past 34 years (1984-2018); Landsat, Sentinel-2, MODIS, Google Earth Pro, CHIRPS, Multivariate El' Niño-Southern Oscillation Index and altimetry data together with the physical parameters from 37 publications (1969-2018) to (i) study the lake's dynamics and establish its current (2018) state, (ii) identify and analyse hotspots where significantly dynamic changes occur, and (iii), study the contributions of climate change and anthropogenic activities on these dynamics. Utilizing manual digitisation, MNDWI, NDVI and PCA methods, the study shows the lake's mean surface area to be 69,295 km² (i.e., 812 km² or 1.2% more than that of the 37 publications) and its 2018 value to be 69,216 km² (i.e., ∼733 km² (1.1%) more than that of the 37 publications). As to whether the lake is dying, it shrunk by 203 km² (0.3%) compared to its 1984 value, a decrease noted mainly in four hotspot Gulfs (Birinzi 40%, Winam 20%, Emin Pasha 38% and Mwanza 55%). Correspondingly, the expansion of Nalubaale Dam (2002-2006) decreased the areas by 31%, 10%, 21% and 44%, respectively. Seasonal analysis shows an increase of 9 km² in the lake's area during the heavy rainy season (March-May) while the ENSO enlarged the area by 0.23% (2007) and 0.45% (2010). It is evident, therefore, that both climate variability/change and anthropogenic activities are exerting a toll on the tropical's largest freshwater body thereby necessitating careful exploitation and management plans.

Improved remotely sensed satellite products for studying Lake Victoria's water storage changes

Lake Victoria (LV), the world's second largest freshwater lake, supports a livelihood of more than 42 million people and modulates the regional climate. Studying its changes resulting from impacts of climate variation/change and anthropogenic is, therefore, vital for its sustainable use. Owing to its shear size, however, it is a daunting task to undertake such study relying solely on in-situ measurements, which are sparse, either missing, inconsistent or restricted by governmental red tapes. Remotely sensed products provide a valuable alternative but come with a penalty of being mostly incoherent with each other as they originate from different sources, have different underlying assumptions and models. This study pioneers a procedure that uses a Simple Weighting approach to merge LV's multi-mission satellite precipitation and evaporation data from various sources and then improves them through a Postprocessing Filtering (PF) scheme to provide coherent datasets of precipitation (p), evaporation (e), water storage changes (Δs), and discharge (q) that accounts for its water budget closure. Principal component analysis (PCA) is then applied to the merged-improved products to analyze LV's spatio-temporal changes resulting from impacts of climate variation/change. Compared to the original unmerged data (0.62 and 0.37 average correlation for two samples), the merged-improved products are largely in agreement (0.91 average correlation). Furthermore, smaller imbalances between the merged-improved products are obtained with precipitation (37%) and water storage changes (35%) being the largest contributors to LV's water budget. This data improvement scheme could be applicable to any inland lake of a size similar to LV.

Understanding the global hydrological droughts of 2003-2016 and their relationships with teleconnections

Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003-2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydro-meteorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are strongly interrelated. Our numerical results indicate that the 3- to 6-month hydrological droughts occur more frequently than the other timescales. Longer memory of water storage changes (than water fluxes) has found to be the reason of detecting extended hydrological droughts in regions such as the Middle East and Northern Africa. Through CCA, we show that the El Niño Southern Oscillation (ENSO) has major impact on the magnitude and evolution of hydrological droughts in regions such as the northern parts of Asia and most parts of the Australian continent between 2006 and 2011, as well as droughts in the Amazon basin, South Asia, and North Africa between 2010 and 2012. The Indian ocean Dipole (IOD) and North Atlantic Oscillation (NAO) are found to have regional influence on the evolution of hydrological droughts.