Rohde, M. M., Froend, R. & Howard, J. A global synthesis of managing groundwater dependent ecosystems under sustainable groundwater policy. Groundwater 55, 293–301 (2017).
Huggins, X. et al. Overlooked risks and opportunities in groundwatersheds of the world’s protected areas. Nat. Sustain. 6, 855–864 (2023).
Doody, T. M. et al. Continental mapping of groundwater dependent ecosystems: a methodological framework to integrate diverse data and expert opinion. J. Hydrol. Reg. Stud. 10, 61–81 (2017).
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
Döll, P. Vulnerability to the impact of climate change on renewable groundwater resources: a global-scale assessment. Environ. Res. Lett. 4, 035006 (2009).
Condon, L. E., Atchley, A. L. & Maxwell, R. M. Evapotranspiration depletes groundwater under warming over the contiguous United States. Nat. Commun. 11, 873 (2020).
Kløve, B. et al. Climate change impacts on groundwater and dependent ecosystems. J. Hydrol. 518, 250–266 (2014).
Wada, Y. et al. Global depletion of groundwater resources. Geophys. Res. Lett. 37, L20402 (2010).
Konikow, L. F. & Kendy, E. Groundwater depletion: a global problem. Hydrogeol. J. 13, 317–320 (2005).
Famiglietti, J. S. The global groundwater crisis. Nat. Clim. Change 4, 945–948 (2014).
Jasechko, S. & Perrone, D. Global groundwater wells at risk of running dry. Science 372, 418–421 (2021).
de Graaf, I. E. M., Gleeson, T., van Beek, L. P. H., Sutanudjaja, E. H., & Bierkens, M. F. P. Environmental flow limits to global groundwater pumping. Nature 574, 90–94 (2019).
Jasechko, S., Seybold, H., Perrone, D., Fan, Y. & Kirchner, J. W. Widespread potential loss of streamflow into underlying aquifers across the USA. Nature 591, 391–395 (2021).
Rohde, M. M. et al. Establishing ecological thresholds and targets for groundwater management. Nat. Water 2, 312–323 (2024).
Rohde, M. M., Stella, J. C., Roberts, D. A. & Singer, M. B. Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow. Proc. Natl Acad. Sci. USA 118, e2026453118 (2021).
Nelson, R. L. Water rights for groundwater environments as an enabling condition for adaptive water governance. Ecol. Soc. 27, 28 (2022).
Saito, L. et al. Managing groundwater to ensure ecosystem function. Groundwater 59, 322–333 (2021).
Eamus, D., Froend, R., Loomes, R., Hose, G. & Murray, B. A functional methodology for determining the groundwater regime needed to maintain the health of groundwater-dependent vegetation. Aust. J. Bot. 54, 97 (2006).
Patten, D. T., Rouse, L. & Stromberg, J. C. Isolated spring wetlands in the Great Basin and Mojave Deserts, USA: potential response of vegetation to groundwater withdrawal. Environ. Manage. 41, 398–413 (2007).
Cartwright, J. M. et al. Oases of the future? Springs as potential hydrologic refugia in drying climates. Front. Ecol. Environ. 18, 245–253 (2020).
Murray, B. R., Hose, G. C., Eamus, D. & Licari, D. Valuation of groundwater-dependent ecosystems: a functional methodology incorporating ecosystem services. Aust. J. Bot. 54, 221 (2006).
Howard, J. K., Dooley, K., Brauman, K. A., Klausmeyer, K. R. & Rohde, M. M. Ecosystem services produced by groundwater dependent ecosystems: a framework and case study in California. Front. Water 5, 1115416 (2023).
Eamus, D., Zolfaghar, S., Villalobos-Vega, R., Cleverly, J. & Huete, A. Groundwater-dependent ecosystems: recent insights from satellite and field-based studies. Hydrol. Earth Syst. Sci. 19, 4229–4256 (2015).
Box, J.B. et al. Mapping terrestrial groundwater-dependent ecosystems in arid Australia using Landsat‐8 time‐series data and singular value decomposition. Remote Sens. Ecol. Conservation 8, 464–476 (2022).
Klausmeyer, K. et al. Mapping Indicators of Groundwater Dependent Ecosystems in California: Methods Report (The Nature Conservancy, 2018).
Liu, C. et al. Mapping groundwater-dependent ecosystems in arid Central Asia: implications for controlling regional land degradation. Sci. Total Environ. 797, 149027 (2021).
Duran-Llacer, I. et al. A new method to map groundwater-dependent ecosystem zones in semi-arid environments: a case study in Chile. Sci. Total Environ. 816, 151528 (2022).
Brown, J., Bach, L., Aldous, A., Wyers, A. & DeGagné, J. Groundwater-dependent ecosystems in Oregon: an assessment of their distribution and associated threats. Fron. Ecol. Environ. 9, 97–102 (2011).
Freed, Z., Schindel, M., Ruffing, C. & Scott, S. Oregon Atlas of Groundwater-Dependent Ecosystems (The Nature Conservancy, 2022); www.groundwaterresourcehub.org/content/dam/tnc/nature/en/documents/groundwater-resource-hub/Oregon_Atlas_of_Groundwater_Dependent_Ecosystems_2022.pdf.
Saito, L. et al. Mapping indicators of groundwater dependent ecosystems in Nevada: Important resources for a water-limited state. J. Nevada Water Resources Assoc. 1, 48–72 (2020).
Hoogland, T., Heuvelink, G. B. M. & Knotters, M. Mapping water-table depths over time to assess desiccation of groundwater-dependent ecosystems in the Netherlands. Wetlands 30, 137–147 (2010).
Kilroy, G., Ryan, J., Coxon, C. & Daly, D. A Framework for the Assessment of Groundwater-Dependent Terrestrial Ecosystems under the Water Framework Directive (Environmental Research Centre, 2008); https://www.epa.ie/publications/research/water/a-framework-for-the-assessment-of-groundwater-dependent-terrestrial-ecosystems-under-the-water-framework-directive.php.
Münch, Z. & Conrad, J. Remote sensing and GIS based determination of groundwater dependent ecosystems in the Western Cape, South Africa. Hydrogeol. J. 15, 19–28 (2007).
Martínez-Santos, P., Díaz-Alcaide, S., De la Hera-Portillo, A. & Gómez-Escalonilla, V. Mapping groundwater-dependent ecosystems by means of multi-layer supervised classification. J. Hydrol. 603, 126873 (2021).
Gou, S., Gonzales, S. & Miller, G. R. Mapping potential groundwater-dependent ecosystems for sustainable management. Groundwater 53, 99–110 (2014).
Anderson, M. C., Allen, R. G., Morse, A. & Kustas, W. P. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens. Environ. 122, 50–65 (2012).
Canadell, J. et al. Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583–595 (1996).
Gleeson, T., Wada, Y., Bierkens, M. F. P., van Beek, L. P. H. & Irawan, D. E. Water balance of global aquifers revealed by groundwater footprint. Nature 488, 197–200 (2012).
Rohde, M. M. et al. A machine learning approach to predict groundwater levels in California reveals ecosystems at risk. Front. Earth Sci. 9, 784499 (2021).
Famiglietti, J. S. & Ferguson, G. The hidden crisis beneath our feet. Science 372, 344–345 (2021).
Albano, C. M. et al. A multidataset assessment of climatic drivers and uncertainties of recent trends in evaporative demand across the continental United States. J. Hydrometeorol. 23, 505–519 (2022).
Muhammad, K. et al. Socio-political and ecological stresses on traditional pastoral systems: a review. J. Geogr. Sci. 29, 1758–1770 (2019).
Diffenbaugh, N. S. & Giorgi, F. Climate change hotspots in the CMIP5 global climate model ensemble. Clim. Change 114, 813–822 (2012).
Dardel, C. et al. Re-greening Sahel: 30 years of remote sensing data and field observations (Mali, Niger). Remote Sens. Environ. 140, 350–364 (2014).
Thébaud, B. & Batterbury, S. Sahel pastoralists: opportunism, struggle, conflict and negotiation. A case study from eastern Niger. Global Environ. Change 11, 69–78 (2001).
Benjaminsen, T. A., Maganga, F. P. & Abdallah, J. M. The Kilosa killings: political ecology of a farmer–herder conflict in Tanzania. Dev. Change 40, 423–445 (2009).
Rodella, A.-S., Zaveri, E. & Bertone, F. The Hidden Wealth of Nations: The Economics of Groundwater in Times of Climate Change (World Bank, 2023).
McGuirk, E. & Nunn, N. Transhumant pastoralism, climate change, and conflict in Africa. Rev. Econ. Stud. rdae027 (2024).
Devineni, N., Perveen, S. & Lall, U. Assessing chronic and climate-induced water risk through spatially distributed cumulative deficit measures: a new picture of water sustainability in India. Water Resour. Res. 49, 2135–2145 (2013).
Perrone, D. et al. Stakeholder integration predicts better outcomes from groundwater sustainability policy. Nat. Commun. 14, 3793 (2023).
Elshall, A. S. et al. Groundwater sustainability: a review of the interactions between science and policy. Environ. Res. Lett. 15, 093004 (2020).
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).
Karra, K. et al. Global land use/land cover with Sentinel 2 and deep learning. In Proc. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS 4704–4707 (IEEE, 2021).
Buchhorn, M. et al. Copernicus Global Land Cover Layers—Collection 2. Remote Sens. 12, 1044 (2020).
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).
LANDFIRE Program: Data Products—Public LANDFIRE Reference Database (LFRDB). Landfire https://landfire.gov/lfrdb.php (2016).
Groundwater Dependent Ecosystems Atlas. Bureau of Meteorology www.bom.gov.au/water/groundwater/gde/ (2023).
Sabatini, F. M. et al. sPlotOpenban environmentally balanced, open-access, global dataset of vegetation plots. Global Ecol. Biogeogr. 30, 1740–1764 (2021).
Sayler, K. Landsat 8 Collection 1 (C1) Land Surface Reflection Code (LaSRC) Product Guide, Version 3 (USGS, 2020); https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/LSDS-1368_L8_C1-LandSurfaceReflectanceCode-LASRC_ProductGuide-v3.pdf.
Zhu, Z. & Woodcock, C. E. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sens. Environ. 152, 217–234 (2014).
Zhu, Z. & Woodcock, C. E. Continuous change detection and classification of land cover using all available Landsat data. Remote Sens. Environ. 144, 152–171 (2014).
Zhu, Z. & Woodcock, C. E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118, 83–94 (2012).
Zhu, Z., Wang, S. & Woodcock, C. E. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sens. Environ. 159, 269–277 (2015).
Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016).
Gao, B. NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266 (1996).
McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17, 1425–1432 (1996).
Landsat Modified Soil Adjusted Vegetation Index. USGS www.usgs.gov/landsat-missions/landsat-modified-soil-adjusted-vegetation-index (2024).
Huntington, J. et al. Assessing the role of climate and resource management on groundwater dependent ecosystem changes in arid environments with the Landsat archive. Remote Sens. Environ. 185, 186–197 (2016).
Gan, R. et al. Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems. Ecohydrology 11, e1974 (2018).
Zhang, Y. et al. Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci. Rep. 6, 19124 (2016).
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).
Marthews, T. R., Dadson, S. J., Lehner, B., Abele, S. & Gedney, N. High-resolution global topographic index values for use in large-scale hydrological modelling. Hydrol. Earth Syst. Sci. 19, 91–104 (2015).
Theobald, D. M., Harrison-Atlas, D., Monahan, W. B. & Albano, C. M. Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PLoS ONE 10, e0143619 (2015).
Pastore, M., Loro, P. A. D., Mingione, M. & Calcagni, A. Overlapping: estimation of overlapping in empirical distributions. https://cran.r-project.org/web/packages/overlapping/overlapping.pdf (CRAN, 2022).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Machine Learning. Google https://developers.google.com/machine-learning/decision-forests/random-forests (2024).
Belgiu, M. & Drăguţ, L. Random forest in remote sensing: a review of applications and future directions. ISPRS J. Photogramm. 114, 24–31 (2016).
Maxwell, A. E., Warner, T. A. & Fang, F. Implementation of machine-learning classification in remote sensing: an applied review. Int. J. Remote Sens. 39, 2784–2817 (2018).
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C. & Landerer, F. W. Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons. J. Geophys. Res. Solid Earth 120, 2648–2671 (2015).
Ek, M. B. et al. Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res. Atmos. 108, 8851 (2003).
Liang, X., Lettenmaier, D. P., Wood, E. F. & Burges, S. J. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. Atmos. 99, 14415–14428 (1994).
Rodell, M. & Famiglietti, J. S. The potential for satellite-based monitoring of groundwater storage changes using GRACE: the High Plains aquifer, Central US. J. Hydrol. 263, 245–256 (2002).
Girotto, M. et al. Benefits and pitfalls of GRACE data assimilation: a case study of terrestrial water storage depletion in India. Geophys. Res. Lett. 44, 4107–4115 (2017).
Richey, A. S. et al. Quantifying renewable groundwater stress with GRACE. Water Resour. Res. 51, 5217–5238 (2015).
Rodell, M. et al. Emerging trends in global freshwater availability. Nature 557, 651–659 (2018).
Abell, R. et al. Freshwater ecoregions of the world: a new map of biogeographic units for freshwater biodiversity conservation. BioScience 58, 403–414 (2008).
The World Database on Protected Areas (WDPA). https://data.apps.fao.org/catalog/dataset/bfcb8c96-648c-4c31-9702-20fc5d4d5b49 (FAO, 2023).
Bingham, H.C. et al. User Manual for the World Database on Protected Areas and world database on other effective area-based conservation measures: 1.6 (UNEP & WCMC, 2019); http://wcmc.io/WDPA_Manual.
Rohde, M.M. et al. Data, code, and outputs for: groundwater-dependent ecosystem map exposes global dryland protection needs. Zenodo https://doi.org/10.5281/zenodo.11062894 (2024).
R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2008); www.R-project.org/.
Hijmans, R. J. Spatial data analysis. R package terra v.1.7-71 (R Foundation for Statistical Computing, 2024); https://CRAN.R-project.org/package=terra.
O’Brien, J. rasterDT: Fast Raster Summary and Manipulation (2022).
Wickham, H. ggplot2: elegant graphics for data analysis (Springer, 2016).