Terrestrial Water Storage Research Articles

Unlocking the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage trends across the China-Pakistan Economic Corridor

Studying the influence of vegetation dynamics on water storage is fundamental for efficiently managing ecosystems in dryland areas. Therefore, this study was designed to investigate the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage (TWS) trends across the China-Pakistan Economic Corridor (CPEC). Exploring vegetation growth's effect on TWS utilizes remote sensing and statistical methods such as generalized additive model (GAM), random forest, Mann-Kendall test, Sen's slope, and Partial correlation coefficient. Our results revealed a consistent increase in vegetation cover in semi-arid and dry sub-humid regions, especially in croplands, from 1986 to 2020. However, a noticeable finding was that TWS has declined by approximately 39.65 % in the study area. On the other hand, forests have displayed resilience by mitigating the effects of climate change and human activities through hydraulic memory effects. Additionally, soil moisture has decreased in various land cover types, with croplands experiencing the most significant decrease. Similarly, increased vegetation growth in greening drylands significantly negatively impacts terrestrial water storage, with correlations of −0.31 for terrestrial water storage anomaly (TWSA), −0.26 for root zone soil moisture (RZSM), and −0.29 for surface soil moisture (SSM). Meanwhile, evapotranspiration (ET) also had negative correlations with TWSA, RZSM, and SSM, with standardized coefficients of −0.23, −0.16, and −0.11, respectively. These findings exposed the study area's vegetation interaction with land cover and hydrological dynamics. Addressing these hydrological imbalances will help ensure sustainable ecological management in the study area.

An Abrupt Decline in Global Terrestrial Water Storage and Its Relationship with Sea Level Change

AbstractAs observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions, global terrestrial water storage (TWS), excluding ice sheets and glaciers, declined rapidly between May 2014 and March 2016. By 2023, it had not yet recovered, with the upper end of its range remaining 1 cm equivalent height of water below the upper end of the earlier range. Beginning with a record-setting drought in northeastern South America, a series of droughts on five continents helped to prevent global TWS from rebounding. While back-to-back El Niño events are largely responsible for the South American drought and others in the 2014–2016 timeframe, the possibility exists that global warming has contributed to a net drying of the land since then, through enhanced evapotranspiration and increasing frequency and intensity of drought. Corollary to the decline in global TWS since 2015 has been a rise in barystatic sea level (i.e., global mean ocean mass). However, we find no evidence that it is anything other than a coincidence that, also in 2015, two estimates of barystatic sea level change, one from GRACE/FO and the other from a combination of satellite altimetry and Argo float ocean temperature measurements, began to diverge. Herein, we discuss both the mechanisms that account for the abrupt decline in terrestrial water storage and the possible explanations for the divergence of the barystatic sea level change estimates.

Estimation of Terrestrial Water Storage Changes in Brazil From the Joint Inversion of GRACE‐Based Geopotential Difference and GNSS Vertical Displacement Data

AbstractGravity Recovery and Climate Experiment (GRACE) satellite gravimetry and Global Navigation Satellite System (GNSS) surface displacement measurements offer complementary advantages for monitoring terrestrial water storage (TWS) changes. We propose a new joint inversion model based on the combination of GRACE‐based geopotential difference (GPD) observations using the mascon method and GNSS vertical displacements through the Green's function method to obtain reliable TWS changes in Brazil. The performance of the jointly inverted TWS changes is assessed through closed‐loop simulations and comparisons with hydrometeorological data (precipitation‐P, evapotranspiration‐ET, and runoff‐R) using water budget closure (P‐ET‐R) and river water level observations from satellite altimetry. The simulation results indicate that the joint inversion results exhibit higher accuracy and reliability than GRACE GPD‐based mascon (GPD‐mascon) and GNSS solutions, and the standard deviations (STDs) of joint results decrease by ∼6.98 and ∼37.5 mm compared to those from GPD‐mascon and GNSS solutions. The joint inversion of real GRACE and GNSS data demonstrates notably lower uncertainty than that of GPD‐mascon solutions and exhibits significant improvement than GNSS‐only solutions. The STDs and correlation coefficients between monthly R time series derived from three inversion methods (joint inversion, GPD‐mascon and GNSS solutions, combined with P and ET through water budget closure) and in situ observations are 19.607 mm and 0.912, 20.879 mm and 0.904, and 31.370 mm and 0.778, respectively. The joint estimates also yield better correlation with river water level observations compared to GPD‐mascon and GNSS solutions. Furthermore, at the weekly time scale, the joint inversion results show better consistency with P‐ET‐R data than those from GPD‐mascon and GNSS solutions.

Interannual variations of terrestrial water storage in the East African Rift region

Abstract. The US–German GRACE (Gravity Recovery and Climate Experiment, 2002–2017) and GRACE-FO (GRACE Follow-On, since 2018) satellite missions observe terrestrial water storage (TWS) variations. Over 20 years of data allow for investigating interannual variations beyond linear trends and seasonal signals. However, the origin of observed TWS changes cannot be determined solely with GRACE and GRACE-FO observations. This study focuses on the northern part of the East African Rift around the lakes of Turkana, Victoria, and Tanganyika. It aims to characterise and analyse the interannual TWS variations compared to meteorological and geodetic observations of the water storage compartments (surface water, soil moisture, and groundwater). We apply the STL (Seasonal-Trend decomposition using LOESS) method to decompose the signal into a seasonal signal, an interannual signal, and residuals. By clustering the interannual TWS dynamics for the African continent, we define the exact outline of the study region. We observe a TWS decrease until 2006, followed by a steady rise until 2016, and then the most significant TWS gain in Africa in 2019 and 2020. Besides meteorological variability, surface water storage variations in the lakes explain large parts of the TWS decrease before 2006. The storage dynamics of Lake Victoria alone contribute up to 50 % of these TWS changes. On the other hand, the significant TWS increase around 2020 can be attributed to nearly equal rises in groundwater and surface water storage, which coincide with a substantial precipitation surplus. Soil moisture explains most of the seasonal variability but does not influence the interannual variations. As Lake Victoria dominates the surface water storage variations in the region, we further investigate the lake and the downstream Nile River. The Nalubaale Dam regulates Lake Victoria's outflow. Water level observations from satellite altimetry reveal the impact of dam operations on downstream discharge and on TWS decreases in the drought years before 2006. On the other hand, we do not find evidence for an impact of the Nalubaale Dam regulations on the strong TWS increase after 2019.

Applicability analysis of comprehensive drought index based on GRACE data in ten major river basins in China

As hydroclimatic extremes, drought triggered by climate change and human activities are recurrent over China. Selecting an appropriate comprehensive drought index is an important measure to comprehensively monitor drought characteristics. Precipitation and Terrestrial Water Storage Anomalies (TWSA) are important variables for assessing drought triggered by water deficiency in the atmosphere and terrestrial systems. The study aims to compare and discuss the applicability of drought index based solely on TWSA (DSI) and based on the combined effects of precipitation and TWSA (CCDI) in monitoring comprehensive drought in China. The results reveal that precipitation anomalies do not align with TWSA in arid basins, while they are synchronized in humid basins. CCDI reveals more favorable spatiotemporal agreement with the traditional drought indices, whereas DSI shows more consistency with common drought indices in humid basins. CCDI and DSI are suitable for assessing comprehensive drought in arid and humid basins, respectively. Moreover, almost both DSI and CCDI successfully monitored major drought periods in humid and arid basins during 2002–2006 and 2013–2017, respectively. Overall, this study highlights the applicability of CCDI and DSI in comprehensive drought assessment in arid and humid basins, respectively, providing valuable support for optimizing water resources utilization, management, and drought resistance decision-making.

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Climate change and human activities alter the global freshwater cycle, causing nonstationary processes as its distribution shifting over time, yet a comprehensive understanding of these changes remains elusive. Here, we develop a remote sensing-informed terrestrial reanalysis and assess the nonstationarity of and interconnections among global water cycle components from 2003 to 2020. We highlight 20 hotspot regions where terrestrial water storage exhibits strong nonstationarity, impacting 35% of the global population and 45% of the area covered by irrigated agriculture. Emerging long-term trends dominate the most often (48.2%), followed by seasonal shifts (32.8%) and changes in extremes (19%). Notably, in mid-latitudes, this encompasses 34% of Asia and 27% of North America. The patterns of nonstationarity and their dominant types differ across other water cycle components, including precipitation, evapotranspiration, runoff, and gross primary production. These differences also manifest uniquely across hotspot regions, illustrating the intricate ways in which each component responds to climate change and human water management. Our findings emphasize the importance of considering nonstationarity when assessing water cycle information toward the development of strategies for sustainable water resource usage, enhancing resilience to extreme events, and effectively addressing other challenges associated with climate change.

Impact of Natural and Human Factors on Dryland Vegetation in Eurasia from 2003 to 2022.

Eurasian dryland ecosystems consist mainly of cropland and grassland, and their changes are driven by both natural factors and human activities. This study utilized the normalized difference vegetation index (NDVI), gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) to analyze the changing characteristics of vegetation activity in Eurasia over the past two decades. Additionally, we integrated the mean annual temperature (MAT), the mean annual precipitation (MAP), the soil moisture (SM), the vapor pressure deficit (VPD) and the terrestrial water storage (TWS) to analyze natural factors' influence on the vegetation activity from 2003 to 2022. Through partial correlation and residual analysis, we quantitatively described the contributions of both natural and human factors to changes in vegetation activity. The results indicated an overall increasing trend in vegetation activity in Eurasia; the growth rates of vegetation greenness, productivity and photosynthetic capacity were 1.00 × 10-3 yr-1 (p < 0.01), 1.30 g C m-2 yr-2 (p < 0.01) and 1.00 × 10-3 Wm-2μm-1sr-1yr-1 (p < 0.01), respectively. Furthermore, we found that soil moisture was the most important natural factor influencing vegetation activity. Human activities were identified as the main driving factors of vegetation activity in the Eurasian drylands. The relative contributions of human-induced changes to NDVI, GPP and SIF were 52.45%, 55.81% and 74.18%, respectively. These findings can deepen our understanding of the impacts of current natural change and intensified human activities on dryland vegetation coverage change in Eurasia.

Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States

The Gravity Recovery And Climate Experiment (GRACE) and GRACE-FollowOn (GRACE(−FO)) satellites have been monitoring Earth’s changes in terrestrial water storage (TWS) or surficial mass changes at monthly sampling and a spatial scale longer than ∼330 km (half wavelength) over the past two decades. At monthly sampling or revisit time, the use of satellite gravimetry is difficult to effectively monitor abrupt extreme weather events which are high-frequency, including the climate-induced hurricanes/cyclones, flash floods and droughts. The majority of the contemporary studies have focused on satellite gravimetry spatial downscaling, and not on reducing the temporal resolution of Earth’s mass change. Here, we developed a Deep Learning (DL) algorithm to downscale monthly GRACE/GRACE(−FO) Mass Concentration (Mascon) TWS anomaly (TWSA) solutions to daily sampling over the Contiguous United States (CONUS), with the aim of monitoring rapidly evolving natural hazard episodes. The simulative performance of the DL algorithm is validated by comparing the modeling to an independent observation and the land hydrology model (LHM) predicted TWSA. To confirm that our daily and monthly simulations captured the climatic variations, we first compared our simulations with El Niño/La Niña Southern Oscillation (ENSO) circulation system index, which has a dominant climatological and socioeconomic impact across CONUS, and results reveal high correlations which are statistically significant. Next, we assessed the feasibilities to detect long- and short-term variations in the TWSA signals triggered by hydrological extremes, including the 2011 and 2019 Missouri River Floods, the August 2017 Atlantic Hurricane Harvey landfalls in Texas, the 2012–2017 drought in California, and the flash drought in the Northern Great Plains in 2017. Additional validation results using independent in situ observations reveal that our DL-aided gravimetry downscaled daily simulations are capable of elucidating hazards and water cycle evolutions at high temporal resolution.

Analysis of spatial and temporal variations of vegetation NPP and TWS in the Yangtze River Basin

Net primary productivity (NPP) is an important parameter reflecting vegetation growth, and water is one of the necessary factors for vegetation growth. Investigating the mutual influence between NPP and water is significant for ensuring the stable development of the ecological environment. This study focuses on the Yangtze River Basin (YRB) as the research area, and based on medium-resolution imaging spectrometer (MODIS) data, climate data, and gravity recovery and climate experiment (GRACE) data, the spatiotemporal evolution characteristics of vegetation NPP and terrestrial water storage (TWS) in the YRB from 2000 to 2022 are explored and analyzes the mutual influence of NPP with climate factors and TWS. The results show that vegetation NPP (4.10 gC·m−2·a−1) and TWS (0.55 mm) in the YRB have exhibited an increasing trend from 2000 to 2022, with a strong correlation between the two, which is related to recent environmental policies. Analysis of the impact of climate factors on NPP reveals that temperature and TWS significantly positively impact NPP changes. Furthermore, comparisons between NPP and TWS indicate that changes in TWS substantially promote plant growth. In addition, the comparison between NPP and TWS indicates that changes in TWS have an important promoting effect on plant growth. Surface water (SWS) and soil water (SM) have a significant promoting effect on plant growth, but with a strong lag, while the consumption of groundwater (GWS) has been promoting plant growth without significant lag.

Assessing long-term water storage dynamics in Afghanistan: An integrated approach using machine learning, hydrological models, and remote sensing

Assessment of Terrestrial Water Storage (TWS) components is crucial for understanding regional climate and water resources, particularly in arid and semi-arid regions like Afghanistan. Given the scarcity of ground-based data, this study leverages remote sensing datasets to quantify water storage changes. We integrated Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) data with WaterGap, Global Land Water Storage (GWLS), Catchment Land Surface Model (CLSM), and climate variables (precipitation, temperature, potential evapotranspiration) using artificial neural networks (ANN) and random forests (RF). Additionally, Ice, Cloud, and Land Elevation Satellite (ICESat-1,2) data were utilized to estimate glacier mass changes. Seasonal trend decomposition using Loess (STL) was applied to assess TWS changes from 2003 to 2022. Our methodology reveals a high correlation (R = 0.90–0.97) between reconstructed and observed TWS anomalies across Afghanistan's major basins. Glacier mass decreased by −0.59 and −1.17 Gt/year during 2003–2009 and 2018–2022, respectively, while overall TWS declined by −2.46 Gt/year. The HRB experienced the largest TWS loss (−1.47 Gt/year), primarily due to groundwater depletion (−1.18 Gt/year). These findings underscore the importance of our approach for assessing water resources, providing vital insights for sustainable management under a changing climate in a data-scarce country.

Assessing the stability of terrestrial water storage to drought based on CMIP6 forcing scenarios

Assessing the stability of terrestrial water storage (TWS) under drought conditions is critical for the sustainable development of water resources. In this study, we integrated surface temperature (ST), leaf area index (LAI), and precipitation (P) data from five different scenarios (History, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to develop a standardized temperature vegetation precipitation index (STVPI). The index was then utilized to monitor global drought conditions and investigate the stability of TWS to drought disaster. The results showed that STVPI can not only monitor meteorological drought, but also has a remarkable sensitivity and applicability to drought caused by sparse vegetation. Notably, 21.16% of the global land area will have a drought trend under the SSP1-2.6 scenario, while it will rise to 35.81% under the SSP5-8.5 scenario, which underscored the potential for an expansion of drought-affected regions worldwide as a result of ongoing global warming and escalating emissions. In addition, the results also found that the warm temperate and tropical regions at lower elevations have an advantage in maintaining the stability of TWS. Unfortunately, the stability of TWS to drought will decline in the western Sahara Desert, central China and northern United States in the future, where will face a serious water crisis. The research framework provides an important reference for deeply evaluating and scientifically allocating water resources under climate change.

Observations indicate regionally misleading wetting and drying trends in CMIP6

We evaluate trends in terrestrial water storage over 1950–2100 in CMIP6 climate models against a new global reanalysis from assimilating GRACE and GRACE-FO satellite observations into a hydrological model. To account for different timescales in our analysis, we select regions in which the influence of interannual variability is relatively small and observed trends are assumed to be representative of the development over longer periods. Our results reveal distinct biases in drying and wetting trends in CMIP6 models for several world regions. Specifically, we see high model consensus for drying in the Amazon, which disagrees with the observed wetting. Other regions show a high consensus of models and observations suggesting qualitatively correctly simulated trends, e.g., for the Mediterranean and parts of Central Africa. A high model agreement might therefore falsely indicate a robust trend in water storage if it is not assessed in light of the observed developments. This underlines the potential use of maintaining an adequate observational capacity of water storage for climate change assessments.

Using hydrological modeling and satellite observations to elucidate subsurface and surface hydrological responses to the extreme drought

Climate change and anthropogenic activities have intensified extreme weather events globally. In the summer of 2022, the Yangtze River Basin (YRB) in China experienced an extreme drought, significantly impacting the ecosystems and society. However, the specific effects of this extreme drought on surface and subsurface hydrological dynamics remain unclear. Here we employed satellite-observed terrestrial water storage anomaly (TWSA) and a modified hydrological model with consideration of reservoir operation, human water consumption, and water diversion engineering to quantify how subsurface and surface water in YRB responded to such an extreme drought in 2022. Validation against a series of observations shows that the modified model has good performance in reproducing daily streamflow, reservoir water storage, lake water storage, and snow water equivalent. It achieved more precise GRACE TWSA estimates in the YRB with significant human intervention, and therefore it can accurately quantify both surface and subsurface hydrological responses to the 2022 extreme drought. Compared to the same months (July-December) in 2015–2021, the drought in 2022 resulted in a decrease in precipitation and discharge of 373 km3 (36 %) and 324 km3 (50 %), respectively, while an increase in evapotranspiration of 156 km3 (29 %) in the YRB. In general, the surface water storage (SWS) is relatively low from July 2022, followed by subsurface water storage (SSWS) from August 2022, indicating an approximately one-month lag from the former to the latter. During the latter half year of 2022, the SWS and SSWS reduced by 48 km3 and 83 km3, respectively, suggesting the changes in the latter dominated the TWS variations. This study sheds light on the responses of surface and subsurface hydrology to extreme droughts, and the hydrological modeling framework with consideration of human activities proposed here holds applicability beyond the YRB.

Monitoring terrestrial water storage changes using GNSS vertical coordinate time series in Amazon River basin

Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has better denoising effect than the traditional method; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite’s all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology.

Low-degree gravity field coefficients based on inverse GNSS method: insights into hydrological and ice mass change studies

The relative displacements of stations from a global network of Global Navigation Satellite System (GNSS) sites provide information on global mass transport. In this study, we use 19 years of global GNSS station displacements from the 3rd International GNSS Service reprocessing campaign to estimate the coefficients of the spherical harmonics of the Earth’s gravity field up to degree and order 8 using the inverse GNSS method based on elastic loading theory. The results indicate that the C30 coefficient can be derived based on GNSS station displacements as an alternative to solutions provided by Satellite Laser Ranging (SLR) and Gravity Recovery and Climate Experiment (GRACE). GNSS may support GRACE solutions that face the problems of deriving C30, which has fundamental meaning in estimating ice mass changes in polar regions. The recovery of Antarctic ice sheet mass change from January 2007 to December 2020 based on coefficients replaced by GNSS estimates results in a linear trend of − 152 ± 4 Gt/year, in comparison to − 149 ± 2 Gt/year for the replacement based on SLR from GRACE Technical Note #14. The results indicate that the spatial and seasonal patterns of terrestrial water storage changes derived from GNSS are consistent with those estimated using GRACE/GRACE Follow-On and SLR at a few-millimeter level in the Amazon and Brahmaputra River basin regions.

Analysis of watershed terrestrial water storage anomalies by Bi-LSTM with X-11 time series prediction combined model

Analysis of watershed terrestrial water storage anomalies by Bi-LSTM with X-11 time series prediction combined model

Improving global gross primary productivity estimation using two-leaf light use efficiency model by considering various environmental factors via machine learning

Distinguishing gross primary productivity (GPP) into sunlit (GPPsu) and shaded (GPPsh) components is critical for understanding the carbon exchange between the atmosphere and terrestrial ecosystems under climate change. Recently, the two-leaf light use efficiency (TL-LUE) model has proven effective for simulating global GPPsu and GPPsh. However, no known physical method has focused on integrating the overall constraint of intricate environmental factors on photosynthetic capability, and seasonal differences in the foliage clumping index (CI), which most likely influences GPP estimation in LUE models. Here, we propose the TL-CRF model, which uses the random forest technique to integrate various environmental variables, particularly for terrestrial water storage (TWS), into the TL-LUE model. Moreover, we consider seasonal differences in CI at a global scale. Based on 267 global eddy covariance flux sites, we explored the functional response of vegetation photosynthesis to key environmental factors, and trained and evaluated the TL-CRF model. The TL-CRF model was then used to simulate global eight-day GPP, GPPsu, and GPPsh from 2002 to 2020. The results show that the relative prediction error of environmental stress factors on the maximum LUE is reduced by approximately 52 % when these factors are integrated via the RF model. Thus the accuracy of global GPP estimation (R2 = 0.87, RMSE = 0.94 g C m−2 d−1, MAE = 0.61 g C m−2 d−1) in the TL-CRF model is greater than that (R2 = 0.76, RMSE = 2.18 g C m−2 d−1, MAE = 1.50 g C m−2 d−1) in the TL-LUE model, although this accuracy awaits further investigation among the released GPP products. TWS exerts the greatest control over ecosystem photosynthesis intensity, making it a suitable water indicator. Furthermore, the results confirm an optimal minimum air temperature for photosynthesis. Overall, these findings indicate a promising method for producing a new global GPP dataset, advancing our understanding of the dynamics and interactions between photosynthesis and environmental factors.

The spatial and temporal variation of the terrestrial water storage anomaly (TWSA) of Iraq for the period 2002-2019 based on GRACE gravity data

This study utilized data from the NASA Gravity Recovery and Climate Experiment (GRACE) to examine the variability of the terrestrial water storage anomaly (TWSA) in Iraq between 2002 and 2019. The analysis focused on six grid cells representing the Iraqi territory. The season trend decomposition (SLT) method was used to decompose the signal time series of TWSA to reveal the seasonality, trend, and random noise for the six GRACE blocks. Results proved that Block01 in northwestern side of Iraq, experienced a significant reduction in TWS between 2002 and 2009, followed by a negative linear trend until 2015, and then a positive trend. Block02 which is located in the northeastern part of Iraq showed a decreasing trend in TWS until 2008, after which it had a positive trend. Block03 in the western central side of Iraq reveals a decrease in TWS from 2002 to 2008, followed by a negative linear trend until 2016, and then a positive trend. Block04 in the eastern Mesopotamian plain had a minor increase in TWS until 2006, followed by a substantial decrease until 2016, before stabilizing and showing a positive trend. Block05 in the southwestern side of Iraq demonstrated a continuous decline in TWSA. In contrast, TWS increased in block6 on the southeastern side from 2002 to 2007, then decreased from 2007 to 2009. TWSA levels remained stable from 2009 to 2016, then increased from 2016 to the end of 2019. The seasonal fluctuations in TWS varied across the blocks, but generally, surpluses were observed in winter and spring, while deficits were observed in summer and autumn. The shortage in TWS is attributed to drought and excessive use of groundwater for irrigation. The recent positive trends in TWS in some blocks suggest that the drought may be coming to an end, but further analysis is necessary to reach a definitive conclusion.

Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge

Atmospheric methane concentrations rose rapidly over the past decade and surged in 2020-2022 but the causes have been unclear. We find from inverse analysis of GOSAT satellite observations that emissions from the wet tropics drove the 2010-2019 increase and the subsequent 2020-2022 surge, while emissions from northern mid-latitudes decreased. The 2020-2022 surge is principally contributed by emissions in Equatorial Asia (43%) and Africa (30%). Wetlands are the major drivers of the 2020-2022 emission increases in Africa and Equatorial Asia because of tropical inundation associated with La Niña conditions, consistent with trends in the GRACE terrestrial water storage data. In contrast, emissions from major anthropogenic emitters such as the United States, Russia, and China are relatively flat over 2010-2022. Concentrations of tropospheric OH (the main methane sink) show no long-term trend over 2010-2022 but a decrease over 2020-2022 that contributed to the methane surge.

Dynamic assessment of the impact of compound dry-hot conditions on global terrestrial water storage

Precipitation and temperature are critical factors influencing terrestrial water storage (TWS) can lead to unexpected TWS losses when compounded by dryness and high temperatures. Yet, a dynamic assessment of the individual and combined effects of these conditions on TWS is lacking. This study proposes a framework to assess TWS loss driven by compound dry-hot conditions (CDHC) and dynamically evaluates risk probabilities and thresholds for 2003–2012 and 2013–2022. Results showed that CDHC exert a greater impact on TWS than dry or hot conditions alone. The risk probabilities of global TWS loss are higher in the late period than in the early period, with risk probabilities for light and extreme levels increasing by approximately 9–11 % and 2–7 %, respectively. Although the resilience of water resource systems to CDHC has increased in some regions, it still shows a decreasing trend on a global scale. The decrease in the resilience to TWS in major hyperarid areas is primarily influenced by temperature, whereas that in arid areas is primarily affected by precipitation. These distinct patterns may be the primary factors contributing to the exacerbation of global TWS loss. This study provides a novel approach for the dynamic assessment of global TWS under CDHC. The research findings offer valuable insights for decision-makers developing adaptive strategies to mitigate future CDHC challenges.