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Drought is a devastating natural disaster, during which water shortage often manifests itself in the health of vegetation. Unfortunately, it is difficult to obtain high-resolution vegetation drought impact information that is spatially and temporally consistent. While remotely sensed products can provide part of this information, they often suffer from data gaps and limitations with respect to their spatial or temporal resolution. A persistent feature among remote-sensing products is the trade-off between the spatial resolution and revisit time: high temporal resolution is met with coarse spatial resolution and vice versa. Machine learning methods have been successfully applied in a wide range of remote-sensing and hydrological studies. However, global applications to resolve drought impacts on vegetation dynamics still need to be made available, as there is significant potential for such a product to aid with improved drought impact monitoring. To this end, this study predicted global vegetation dynamics based on the enhanced vegetation index (evi) and the popular Random forest (RF) regressor algorithm at 0.1°. We assessed the applicability of RF as a gap-filling and downscaling tool to generate global evi estimates that are spatially and temporally consistent. To do this, we trained an RF regressor with 0.1° evi data, using a host of features indicative of the water and energy balances experienced by vegetation, and evaluated the performance of this new product. Next, to test whether the RF is robust in terms of spatial resolution, we downscale the global evi: the model trained on 0.1° data is used to predict evi at a 0.01° resolution. The results show that the RF can capture global evi dynamics at both a 0.1° resolution (RMSE: 0.02-0.4) and at a finer 0.01° resolution (RMSE: 0.04-0.6). Overall errors were higher in the downscaled 0.01° product compared with the 0.1° product. Nevertheless, relative increases remained small, demonstrating that RF can be used to

Global freshwater resources are vital to humanity and Earth’s ecosystems, yet about one third of the global population is affected by water scarcity for at least one month per year. In these areas, the overuse of freshwater resources can lead to the threat of depletion, marking them as the global ‘water scarcity hotspots’. This study combines outputs from a global hydrological model (PCR-GLOBWB 2) with an extensive literature search to provide a comprehensive intercomparison of the key drivers, pressures, states, impacts and responses (DPSIR) that shape the water gap between water demand and availability at the most important water scarcity hotspots worldwide. Hydroclimatic change, population growth, and water use for the industrial, municipal and agricultural sectors are the most important driving and pressuring forces on the water gap, affecting both water quality and quantity. These drivers and pressures have been showing increasing trends at all hotspots, which is concerning for the future development of the water gap. Additionally, we identify and characterize seven clusters of hotspots based on shared DPSIR patterns, revealing their common mechanisms. Our work highlights the diversity of water scarcity related issues at hotspots, especially the variety of impacts involved and governmental responses in place. The results of our DPSIR analysis provide valuable insights for building causal networks representing water gap dynamics at the hotspots. They form a foundation for conceptual models that illuminate human-water interactions, trade-offs, and synergies at the hotspots, while guiding policymakers in addressing the multifaceted challenge of closing the water gap.

Numerous proxy reconstructions have provided general insight into late Quaternary East Asian Monsoon variability. However, challenges persist in precisely assessing absolute temperature impacts on proxy variations. Here, we use two independent paleothermometers, based on bacterial membrane lipids and clumped isotopes of snail shells, in the same section of the western Chinese Loess Plateau to establish a robust land surface temperature record spanning the past approximately 21,000 years. Our independent temperature records consistently reveal (i) similar land surface temperatures between the Last Glacial Maximum and late Holocene and (ii) a gradual cooling Holocene, which contrasts with the climate model predictions. We propose that changes in soil moisture availability over the deglaciation modulates the land surface temperature recorded by the proxies. A land surface energy partitioning model confirms this mechanism, suggesting that effects of soil moisture availability should be properly considered when comparing proxy records with climate model outputs.

Drought events have accelerated and intensified in recent decades. In the 21st century, the number and duration of severe drought events are expected to increase even further. The impact on crops and natural vegetation has already become more pronounced over the last years, as crop yield losses increase and forest fires become more frequent. This creates increasing pressure on water demand and food security globally. Therefore, it is important to understand not only the impact of droughts on vegetation but also the regeneration of vegetation in post-drought periods. In this study, vegetation regeneration rates in Nebraska (United States) are determined using the Standardized Precipitation Evapotranspiration Index (SPEI) and the Enhanced Vegetation Index (EVI). The results show that the correlation coefficient between the SPEI and EVI is generally high for grassland (0.8 - 1.0) and low for cropland (∼ 0.2). The time shift, days between the two datasets at the highest possible correlation coefficient, is between 75 and 125 days for grassland which is mainly dependent on the local topography which is the main driver of groundwater flow and therefore hydraulic gradients in the area. Various factors, including the water table, rooting depth, groundwater recharge delay, recharge area, local depressions filled with water, as well as soil moisture, contribute to deviations in time shifts and recovery times within the study area. The average recovery time of grassland is approximately 3.97 months (121 days), while for cropland it is 3.64 months (111 days). The drought timing is shown to be important in the recovery time. The later a drought ends in the growing season, the longer the vegetation regeneration time. Between the drought time or intensity and the vegetation regeneration time, no significant correlation was found, as soil moisture presumably dominates the drought recovery time. The results of this study provide insight into the vegetation recovery time of both gras