There is a growing concern about limited water supply and water scarcity in many river basins across the world. The agricultural sector is the largest user of freshwater on the planet, with a growing amount of water extracted for livestock systems. Here, we use data from the GLEAM model to advance previous studies that estimated livestock water footprints by quantifying water use for feed production, animal drinking water, and animal service water. We additionally account for the role of trade in accounting for feed water allocations to different animals in different countries and make use of a hydrologic model to estimate feed irrigation water requirements for individual crops at a high spatial resolution. Lastly, we estimate the contribution of livestock water abstractions to water stress at a small river basin scale for the entire globe. We find that feed production water accounts for the majority (>90%) of global livestock water withdrawals, though there is regional variation. Similarly, we find large regional variation in the water consumption per head by livestock species. Despite consuming >200 km³ of water per year, we find that reducing water use in the livestock system alone will rarely reduce water stress in high-stress basins. This study highlights the need for quantifying locally relevant water use and water stress metrics for individual livestock systems.

Samal, N. R., W. Wollheim, S. Zuidema, R. Stewart, Z. Zhou, M. M. Mineau, M. Borsuk, K. H. Gardner, S. Glidden, T. Huang, D. Lutz, G. Mavrommati, A. M. Thorn, C. P. Wake, and M. Huber. 2017. A coupled terrestrial and aquatic biogeophysical model of the Upper Merrimack River watershed, New Hampshire, to inform ecosystem services evaluation and management under climate and land-cover change. Ecology and Society 22(4):18. https://doi.org/10.5751/ES-09662-220418

The MERIT-Hydro networks re-gridded by the Iterative Hydrography Upscaling (IHU) algorithm do not retain exo- or endorheic basin attributes from the original data. Here we developed methods to assign such attributes to those and any other digital river networks. The motivation is that endorheic inland drainage basins are essential for hydrologic modelling of global and regional water balances, land surface water storage, gravity anomalies, sea level rise, etc. First, we create basin attributes that explicitly label endorheic and exorheic catchments by the criteria of direct or hidden connectivity to the ocean without changing their flow direction grid. In the second step we alter the delineation of endorheic basins by the merging algorithm that eliminates small inland watersheds to the adjacent host basins. The resulting datasets have a significantly reduced number of endorheic basins while preserving the total land portion and topology of the inland basins. The data was validated using the Water Balance Model by comparing volume of endorheic inland depressions with modelled water accumulation in their inland lakes.

Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope.