Emerging trends and spatial shifts of drought potential across global river basins.

Droughts have devastating effects on various sectors and are difficult to quantify and track because of the invisible and slow but prevalent propagation. This dilemma is more significant in the case of the complex interactions between land and atmosphere mechanisms, which are inadequately considered in previous drought metrics. Here, we investigate the spatiotemporal variability of the recently devised metric called 'Drought Potential Index (DPI)', which incorporates the antecedent land water storage and current precipitation. Using the spatial weighted centroid method, we elucidate the emerging spatial movement of the DPI within 168 major global river basins and analyze its influential factors. Improved drought detection and performance disparity of DPI as compared with multi-scale (i.e., 1, 3, 6, 9, 12-month) Standardized Precipitation Index, ensemble soil moisture anomaly, and Total Storage Deficit Index corroborate the robustness and improved insights of DPI. Higher increasing trends in DPI are detected over dryland basins (0.39 ± 0.43 %/a) than in the humid zones (0.15 ± 0.34 %/a). Six hotspot basins, namely, Don, Yellow, Haihe, Rio Grande, Sao Francisco, and Ganges river basins, are identified with increasing (2.1-3.5%/a) DPI during 2003-2021. The interannual occurrence of the highest DPI, spatial shifts, and relative contribution of DPI's constituent variables correspond well to the climatic and anthropogenic changes in humid and dry land basins. The absolute latitudinal/longitudinal shifts of ∼2° (as high as ∼3.2/4.9°) in DPI in 30% (47 out of 168 basins) of the global basins highlight the need for analyzing the water scarcity problems from both the perspectives of long-term trends and spatial shifts. Our findings provide a global assessment of the spatiotemporal shifts of drought potential and will be beneficial to understanding the anthropogenic and climatic influences on water resource management under a changing environment.

Using GRACE to Detect Groundwater Variation in North China Plain after South-North Water Diversion.

The gravity recovery and climate experiment (GRACE) and its Follow-On mission provide a versatile tool for monitoring groundwater depletion in North China Plain (NCP). However, intermittent data gaps and inherent coarse spatial resolution have restricted the continuous detection of regional groundwater storage anomaly (GWSA) after 2014, the period of interest during the implementation of the south-to-north water diversion middle route project (SNWDP). Here, we investigated the spatiotemporal changes of GWSA in the NCP during 2004 to 2020 based on continuous downscaled GRACE data. First, we derived the continuous terrestrial water storage anomaly from six GRACE and Follow-On solutions (i.e., spherical harmonics (SH) and mass concentration [mascon] solutions). Second, we employed a long short-term memory (LSTM) model and water balance equation to downscale GWSA (i.e., 0.25° × 0.25°). Lastly, we investigated its spatiotemporal characteristics before (2004 to 2014) and after (2015 to 2020) the SNWDP operation. We show the applicability of the continuous downscaled GWSA to capture the characteristics of in situ measurements. The GWSA detects groundwater depletion at a significant (p < 0.05) rate of -17.09 ± 1.80 (SH) and -17.87 ± 1.65 (mascon) mm/a during 2004 to 2014, but a recovering trend of 7.18 ± 3.98 (SH) and 8.23 ± 4.99 (mascon) during 2015 to 2018. The subsequent groundwater extraction and precipitation reduction from 2019 to 2020, resulted in the decreasing trend of GWSA from 2015 to 2020, which is -19.11 ± 8.75 (SH) and -19.72 ± 9.08 mm/a (mascon), respectively. Spatially, the overall depletion trends become nonsignificant along the canals of SNWDP compared to the period 2004 to 2014, and groundwater recovering with trends <6 mm/a near Beijing and Tianjin are detected by the mascon solution during 2015 to 2020.

Leveraging machine learning methods to quantify 50 years of dwindling groundwater in India.

Global compilations and regional studies, indicative of the unsustainable extraction and subsequent unremittingly depleting groundwater (GW) in India, either provide bulk estimates or are confined to the river basins and therefore conceal inferences from a nationwide policymaking perspective. Here, we provide the state-wise past (2000-2020) and future (2030-2050) assessment of dwindling groundwater in India utilizing in-situ groundwater levels (GWL) from 54,112 wells, remote sensing products, and hydrological simulations. By employing three machine learning methods, we show a decline in GWL of over 80% in North India with a notable shift towards the eastern state of Uttar Pradesh and a cumulative groundwater loss (169.96 ± 19.67 km3) equivalent to the water storage capacity of the world's biggest dam (Kariba Dam, Zimbabwe). Its likely contribution to sea-level rise (0.47 ± 0.06 mm) is about 64% of that from annual global glacier melt. Our results typically contrast the GW recovery paradox in South India (e.g., a declining trend of -84.48 ± 38.81 mm/a (p < 0.05) in Andhra Pradesh during 2000-2020), reveal high seasonal variability (e.g., up to ~6 m in Maharashtra), and illustrate the skewed effect of survivor bias in the traditional assessments. We infer the significant impact of underlying hydrogeology and the implementation of water-related policies and projects on the GWL dynamic and variability in the region. Projected GWL reveals a likely water scarcity situation for about 2.8 million km2 area and one billion residents of the country up to 2050. Our observation-based analysis offers insights into the state-level monthly GW dynamics, which is critical for efficient interstate resource allocation, development plans, and policy interventions with broad methodological implications for the water-scarce countries.

Projected changes in terrestrial water storage and associated flood potential across the Yangtze River basin.

Terrestrial water storage is a crucial component in water cycle and plays an important role in flood formations process, particularly in a changing environment. In this study, we aim to examine the future variation of terrestrial water storage anomaly (TWSA) and associated flood potential in one of the most flood-prone regions, the Yangtze River basin in China. Using the Gravity Recovery and Climate Experiment (GRACE) data, we perform bias correction for seven general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 under three Shared Socio-economic Pathway (SSP) scenarios: SSP126, SSP245, and SSP585. The spatiotemporal characteristics of changes in future Flood Potential Index are projected and compared between the near (2031-2060) and far (2071-2100) future with reference to the historical period (1985-2014). The results show that GCMs-simulated TWSA generally agrees well with the GRACE results after downscaling and bias correction with the average correlation coefficient of 0.86, Nash-Sutcliffe efficiency of 0.73 and the root mean square error of 21.68 mm. We found that the total variance of projected TWSA is mainly sourced from the internal variability and model uncertainties, while the uncertainties in scenarios contribute relatively less. Moreover, the flood potential is projected to decline during the near future under various scenarios and even lower during the far future under SSP585 scenario. Our findings provide implications for flood control and management under climate change over high flood risk regions worldwide.

ColorCells: a database of expression, classification and functions of lncRNAs in single cells.

Although long noncoding RNAs (lncRNAs) have significant tissue specificity, their expression and variability in single cells remain unclear. Here, we developed ColorCells (http://rna.sysu.edu.cn/colorcells/), a resource for comparative analysis of lncRNAs expression, classification and functions in single-cell RNA-Seq data. ColorCells was applied to 167 913 publicly available scRNA-Seq datasets from six species, and identified a batch of cell-specific lncRNAs. These lncRNAs show surprising levels of expression variability between different cell clusters, and has the comparable cell classification ability as known marker genes. Cell-specific lncRNAs have been identified and further validated by in vitro experiments. We found that lncRNAs are typically co-expressed with the mRNAs in the same cell cluster, which can be used to uncover lncRNAs' functions. Our study emphasizes the need to uncover lncRNAs in all cell types and shows the power of lncRNAs as novel marker genes at single cell resolution.