Larned, S. T., Datry, T., Arscott, D. B. & Tockner, K. Emerging concepts in temporary-river ecology. Freshw. Biol. 55, 717–738 (2010).
Leigh, C. & Datry, T. Drying as a primary hydrological determinant of biodiversity in river systems: a broad-scale analysis. Ecography 40, 487–499 (2017).
Datry, T. et al. A global analysis of terrestrial plant litter dynamics in non-perennial waterways. Nat. Geosci. 11, 497–503 (2018).
Google Scholar
Marcé, R. et al. Emissions from dry inland waters are a blind spot in the global carbon cycle. Earth Sci. Rev. 188, 240–248 (2019).
Google Scholar
Steward, A. L., von Schiller, D., Tockner, K., Marshall, J. C. & Bunn, S. E. When the river runs dry: human and ecological values of dry riverbeds. Front. Ecol. Environ. 10, 202–209 (2012).
Acuña, V. et al. Why should we care about temporary waterways? Science 343, 1080–1081 (2014).
Google Scholar
Fritz, K., Cid, N. & Autrey, B. Governance, legislation, and protection of intermittent rivers and ephemeral streams. In Intermittent Rivers and Ephemeral Streams: Ecology and Management 477–507 (Academic Press, 2017); https://doi.org/10.1016/B978-0-12-803835-2.00019-X.
Sullivan, S. M. P., Rains, M. C., Rodewald, A. D., Buzbee, W. W. & Rosemond, A. D. Distorting science, putting water at risk. Science 369, 766–768 (2020).
Google Scholar
Allen, D. C. et al. River ecosystem conceptual models and non‐perennial rivers: a critical review. Wiley Interdiscip. Rev. Water 7, e1473 (2020).
Datry, T., Larned, S. T. & Tockner, K. Intermittent rivers: a challenge for freshwater ecology. Bioscience 64, 229–235 (2014).
Ficklin, D. L., Abatzoglou, J. T., Robeson, S. M., Null, S. E. & Knouft, J. H. Natural and managed watersheds show similar responses to recent climate change. Proc. Natl Acad. Sci. USA 115, 8553–8557 (2018).
Google Scholar
Jaeger, K. L., Olden, J. D. & Pelland, N. A. Climate change poised to threaten hydrologic connectivity and endemic fishes in dryland streams. Proc. Natl Acad. Sci. USA 111, 13894–13899 (2014).
Google Scholar
Pumo, D., Caracciolo, D., Viola, F. & Noto, L. V. Climate change effects on the hydrological regime of small non-perennial river basins. Sci. Total Environ. 542, 76–92 (2016).
Google Scholar
Stubbington, R. et al. Biomonitoring of intermittent rivers and ephemeral streams in Europe: current practice and priorities to enhance ecological status assessments. Sci. Total Environ. 618, 1096–1113 (2018).
Google Scholar
Acuña, V. et al. Accounting for flow intermittency in environmental flows design. J. Appl. Ecol. 57, 742–753 (2020).
Arthington, A. H. et al. The Brisbane Declaration and Global Action Agenda on Environmental Flows (2018). Front. Environ. Sci. 6, 45 (2018).
Zimmer, M. A. et al. Zero or not? Causes and consequences of zero-flow stream gage readings. Wiley Interdiscip. Rev. Water 7, e1436 (2020).
Beaufort, A., Lamouroux, N., Pella, H., Datry, T. & Sauquet, E. Extrapolating regional probability of drying of headwater streams using discrete observations and gauging networks. Hydrol. Earth Syst. Sci. 22, 3033–3051 (2018).
Google Scholar
Jaeger, K. L. & Olden, J. D. Electrical resistance sensor arrays as a means to quantify longitudinal connectivity of rivers. River Res. Appl. 28, 1843–1852 (2012).
Yu, S. et al. Evaluating a landscape-scale daily water balance model to support spatially continuous representation of flow intermittency throughout stream networks. Hydrol. Earth Syst. Sci. 24, 5279–5295 (2020).
Google Scholar
Snelder, T. H. et al. Regionalization of patterns of flow intermittence from gauging station records. Hydrol. Earth Syst. Sci. 17, 2685–2699 (2013).
Google Scholar
Jaeger, K. L. et al. Probability of Streamflow Permanence Model (PROSPER): a spatially continuous model of annual streamflow permanence throughout the Pacific Northwest. J. Hydrol. X 2, 100005 (2019).
Yu, S., Bond, N. R., Bunn, S. E. & Kennard, M. J. Development and application of predictive models of surface water extent to identify aquatic refuges in eastern Australian temporary stream networks. Water Resour. Res. 55, 9639–9655 (2019).
Google Scholar
Kennard, M. J. et al. Classification of natural flow regimes in Australia to support environmental flow management. Freshw. Biol. 55, 171–193 (2010).
Lane, B. A., Dahlke, H. E., Pasternack, G. B. & Sandoval‐Solis, S. Revealing the diversity of natural hydrologic regimes in California with relevance for environmental flows applications. J. Am. Water Resour. Assoc. 53, 411–430 (2017).
Google Scholar
Müller Schmied, H. et al. Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration. Hydrol. Earth Syst. Sci. 18, 3511–3538 (2014).
Google Scholar
Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. Data 6, 283 (2019).
Google Scholar
Tooth, S. Process, form and change in dryland rivers: a review of recent research. Earth Sci. Rev. 51, 67–107 (2000).
Google Scholar
Costigan, K. H., Jaeger, K. L., Goss, C. W., Fritz, K. M. & Goebel, P. C. Understanding controls on flow permanence in intermittent rivers to aid ecological research: integrating meteorology, geology and land cover. Ecohydrology 9, 1141–1153 (2016).
Benstead, J. P. & Leigh, D. S. An expanded role for river networks. Nat. Geosci. 5, 678–679 (2012).
Google Scholar
Godsey, S. E. & Kirchner, J. W. Dynamic, discontinuous stream networks: hydrologically driven variations in active drainage density, flowing channels and stream order. Hydrol. Processes 28, 5791–5803 (2014).
Google Scholar
Metzger, M. J. et al. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Glob. Ecol. Biogeogr. 22, 630–638 (2013).
Tolonen, K. E. et al. Parallels and contrasts between intermittently freezing and drying streams: From individual adaptations to biodiversity variation. Freshw. Biol. 64, 1679–1691 (2019).
Prancevic, J. P. & Kirchner, J. W. Topographic controls on the extension and retraction of flowing streams. Geophys. Res. Lett. 46, 2084–2092 (2019).
Google Scholar
FAO. AQUAMAPS: Global Spatial Database on Water and Agriculture (Food and Agriculture Organization of the United Nations, accessed 15 October 2020); https://data.apps.fao.org/aquamaps/
Schneider, A. et al. Global-scale river network extraction based on high-resolution topography and constrained by lithology, climate, slope, and observed drainage density. Geophys. Res. Lett. 44, 2773–2781 (2017).
Google Scholar
Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013); erratum 507, 387 (2014).
Google Scholar
Tramblay, Y. et al. Trends in flow intermittence for European rivers. Hydrol. Sci. J. 66, 37–49 (2021).
Döll, P., Douville, H., Güntner, A., Müller Schmied, H. & Wada, Y. Modelling freshwater resources at the global scale: challenges and prospects. Surv. Geophys. 37, 195–221 (2016).
Google Scholar
Hammond, J. C. et al. Spatial patterns and drivers of nonperennial flow regimes in the contiguous United States. Geophys. Res. Lett. 48, e2020GL090794 (2021).
Google Scholar
Döll, P. & Schmied, H. M. How is the impact of climate change on river flow regimes related to the impact on mean annual runoff? A global-scale analysis. Environ. Res. Lett. 7, 014037 (2012).
Google Scholar
Gleeson, T. et al. The water planetary boundary: interrogation and revision. One Earth 2, 223–234 (2020).
Dickens, C. et al. Incorporating Environmental Flows into “Water Stress” Indicator 6.4.2: Guidelines for a Minimum Standard Method for Global Reporting (FAO, 2019); http://www.fao.org/documents/card/en/c/ca3097en/
Sood, A. et al. Global Environmental Flow Information for the Sustainable Development Goals. IWMI Research Report 168 (International Water Management Institute, 2017); https://doi.org/10.5337/2017.201
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The River Continuum Concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).
Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019); correction 572, E9 (2019).
Google Scholar
Stanley, E. H., Fisher, S. G. & Grimm, N. B. Ecosystem expansion and contraction in streams: desert streams vary in both space and time and fluctuate dramatically in size. Bioscience 47, 427–435 (1997).
Datry, T. et al. Flow intermittence and ecosystem services in rivers of the Anthropocene. J. Appl. Ecol. 55, 353–364 (2018).
Google Scholar
Nembrini, S., König, I. R. & Wright, M. N. The revival of the Gini importance? Bioinformatics 34, 3711–3718 (2018).
Google Scholar
Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Processes 27, 2171–2186 (2013).
Google Scholar
Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos 89, 93–94 (2008).
Google Scholar
Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).
Google Scholar
Global Runoff Data Centre. In-situ river discharge data (World Meteorological Organization, accessed 15 May 2015); https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser#dataDownload/Home
Do, H. X., Gudmundsson, L., Leonard, M. & Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata. Earth Syst. Sci. Data 10, 765–785 (2018).
Google Scholar
Gudmundsson, L., Do, H. X., Leonard, M. & Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment. Earth Syst. Sci. Data 10, 787–804 (2018).
Google Scholar
Lehner, B. et al. High‐resolution mapping of the world’s reservoirs and dams for sustainable river‐flow management. Front. Ecol. Environ. 9, 494–502 (2011).
Mackay, S. J., Arthington, A. H. & James, C. S. Classification and comparison of natural and altered flow regimes to support an Australian trial of the Ecological Limits of Hydrologic Alteration framework. Ecohydrology 7, 1485–1507 (2014).
Zhang, Y., Zhai, X., Shao, Q. & Yan, Z. Assessing temporal and spatial alterations of flow regimes in the regulated Huai River Basin, China. J. Hydrol. 529, 384–397 (2015).
Google Scholar
Reynolds, L. V., Shafroth, P. B. & LeRoy Poff, N. Modeled intermittency risk for small streams in the Upper Colorado River Basin under climate change. J. Hydrol. 523, 768–780 (2015).
Google Scholar
Costigan, K. H. et al. Flow regimes in intermittent rivers and ephemeral streams. In Intermittent Rivers and Ephemeral Streams: Ecology and Management 51–78 (Academic Press, 2017); https://doi.org/10.1016/B978-0-12-803835-2.00003-6
Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).
Google Scholar
Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).
Google Scholar
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Trabucco, A. & Zomer, R. Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2. figshare https://doi.org/10.6084/m9.figshare.7504448.v3 (2018).
Bond, N. R. & Kennard, M. J. Prediction of hydrologic characteristics for ungauged catchments to support hydroecological modeling. Water Resour. Res. 53, 8781–8794 (2017).
Google Scholar
Kotsiantis, S. B., Zaharakis, I. D. & Pintelas, P. E. Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26, 159–190 (2006).
Wainer, J. Comparison of 14 different families of classification algorithms on 115 binary datasets. Preprint at https://arxiv.org/abs/1606.00930 (2016).
Malley, J. D., Kruppa, J., Dasgupta, A., Malley, K. G. & Ziegler, A. Probability machines. Methods Inf. Med. 51, 74–81 (2012).
Google Scholar
Wright, M. N. & Ziegler, A. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77, https://doi.org/10.18637/jss.v077.i01 (2017).
Lang, M. et al. mlr3: a modern object-oriented machine learning framework in R. J. Open Source Softw. 4, 1903 (2019).
Google Scholar
Landau, W. M. The drake R package: a pipeline toolkit for reproducibility and high-performance computing. J. Open Source Softw. 3, 550 (2018).
Google Scholar
Hothorn, T., Hornik, K. & Zeileis, A. Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15, 651–674 (2006).
Google Scholar
Hothorn, T. & Zeileis, A. Partykit: a modular toolkit for recursive partytioning in R. J. Mach. Learn. Res. 16, 3905–3909 (2015).
Google Scholar
Wright, M. N., Dankowski, T. & Ziegler, A. Unbiased split variable selection for random survival forests using maximally selected rank statistics. Stat. Med. 36, 1272–1284 (2017).
Google Scholar
Zhang, G. & Lu, Y. Bias-corrected random forests in regression. J. Appl. Stat. 39, 151–160 (2012).
Google Scholar
Japkowicz, N. & Stephen, S. The class imbalance problem: a systematic study. Intell. Data Anal. 6, 429–449 (2002).
Google Scholar
Bischl, B., Mersmann, O., Trautmann, H. & Weihs, C. Resampling methods for meta-model validation with recommendations for evolutionary computation. Evol. Comput. 20, 249–275 (2012).
Google Scholar
Probst, P., Wright, M. N. & Boulesteix, A. L. Hyperparameters and tuning strategies for random forest. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9, e1301 (2019).
Probst, P. & Boulesteix, A. L. To tune or not to tune the number of trees in random forest. J. Mach. Learn. Res. 18, 1–8 (2018).
Google Scholar
Schratz, P., Muenchow, J., Iturritxa, E., Richter, J. & Brenning, A. Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. Ecol. Modell. 406, 109–120 (2019).
Brenning, A. Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: the R package sperrorest. In 2012 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS) 5372–5375 (2012); https://doi.org/10.1109/IGARSS.2012.6352393
Meyer, H., Reudenbach, C., Hengl, T., Katurji, M. & Nauss, T. Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environ. Model. Softw. 101, 1–9 (2018).
Meyer, H., Reudenbach, C., Wöllauer, S. & Nauss, T. Importance of spatial predictor variable selection in machine learning applications – moving from data reproduction to spatial prediction. Ecol. Modell. 411, 108815 (2019).
Brodersen, K. H., Ong, C. S., Stephan, K. E. & Buhmann, J. M. The balanced accuracy and its posterior distribution. In Proc. Int. Conf. Pattern Recognition 3121–3124 (2010); https://doi.org/10.1109/ICPR.2010.764
Altmann, A., Toloşi, L., Sander, O. & Lengauer, T. Permutation importance: a corrected feature importance measure. Bioinformatics 26, 1340–1347 (2010).
Google Scholar
Amaratunga, D., Cabrera, J. & Lee, Y.-S. Enriched random forests. Bioinformatics 24, 2010–2014 (2008).
Google Scholar
Evans, J. S., Murphy, M. A., Holden, Z. A. & Cushman, S. A. Modeling species distribution and change using random forest. In Predictive Species and Habitat Modeling in Landscape Ecology: Concepts and Applications 139–159 (Springer, 2011); https://doi.org/10.1007/978-1-4419-7390-0_8
Jones, Z. M. & Linder, F. J. edarf: Exploratory Data Analysis using Random Forests. J. Open Source Softw. 1, 92 (2016).
Google Scholar
Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).
Google Scholar
Bondarenko, M., Kerr, D., Sorichetta, A. & Tatem, A. J. Census/projection-disaggregated gridded population datasets for 189 countries in 2020 using Built-Settlement Growth Model (BSGM) outputs (WorldPop, University of Southampton, accessed 26 November 2020); https://doi.org/10.5258/SOTON/WP00684
Colvin, S. A. R. et al. Headwater streams and wetlands are critical for sustaining fish, fisheries, and ecosystem services. Fisheries 44, 73–91 (2019).
Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Science & Business Media, 2009).
Clauset, A., Shalizi, C. R. & Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009).
Google Scholar
Fritz, K. M. et al. Comparing the extent and permanence of headwater streams from two field surveys to values from hydrographic databases and maps. J. Am. Water Resour. Assoc. 49, 867–882 (2013).
Google Scholar
Stoddard, J. L. et al. Environmental Monitoring and Assessment Program (EMAP): Western Streams and Rivers Statistical Summary. Report no. EPA/620/R-05/006 (NTIS PB2007-102088) (US Environmental Protection Agency, 2005).
Hafen, K. C., Blasch, K. W., Rea, A., Sando, R. & Gessler, P. E. The influence of climate variability on the accuracy of NHD perennial and nonperennial stream classifications. J. Am. Water Resour. Assoc. 56, 903–916 (2020).
Google Scholar
Colson, T., Gregory, J., Dorney, J. & Russell, P. Topographic and soil maps do not accurately depict headwater stream networks. Natl Wetlands Newsl. 30, 25–28 (2008).
Allen, D. C. et al. Citizen scientists document long-term streamflow declines in intermittent rivers of the desert southwest, USA. Freshw. Sci. 38, 244–256 (2019).
Datry, T., Pella, H., Leigh, C., Bonada, N. & Hugueny, B. A landscape approach to advance intermittent river ecology. Freshw. Biol. 61, 1200–1213 (2016).
McShane, R. R., Sando, R. & Hockman-Wert, D. P. Streamflow observation points in the Pacific Northwest, 1977–2016. U.S. Geological Survey data release https://doi.org/10.5066/F7BV7FSP (2017).
Observatoire National des étiages (ONDE) (French Office for Biodiversity (OFC), accessed 21 June 2020); https://onde.eaufrance.fr/content/t%C3%A9l%C3%A9charger-les-donn%C3%A9es-des-campagnes-par-ann%C3%A9e
Aguas Continentales de Argentina (Argentinian National Geographic Institute (IGN), accessed 11 June 2020); https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG
Australian Hydrological Geospatial Fabric (Geofabric, v. 3.2) (Australian Bureau of Meteorology (BOM), accessed 11 June 2020); ftp://ftp.bom.gov.au/anon/home/geofabric/Geofabric_Metadata_GDB_V3_2.zip
Base Cartográfica Continua do Brasil (BC250, 2019 version) (Brazilian Institute of Geography and Statistics (IBGE); accessed 11 June 2020); https://geoftp.ibge.gov.br/cartas_e_mapas/bases_cartograficas_continuas/bc250/versao2019/
National Hydrography Dataset Plus (NHDPlus, medium resolution, v.2) (US Geological Survey, accessed 11 June 2020); https://www.epa.gov/waterdata/get-nhdplus-national-hydrography-dataset-plus-data
Busch, M. H. et al. What’s in a name? Patterns, trends, and suggestions for defining non-perennial rivers and streams. Water 12, 1980 (2020).
Google Scholar
Datry, T. et al. Science and management of intermittent rivers and ephemeral streams (SMIRES). Res. Ideas Outcomes 3, e21774 (2017).
Trabucco, A. & Zomer, R. J. Global high-resolution soil–water balance. https://doi.org/10.6084/m9.figshare.7707605.v3 (2010).
Hall, D. K. & Riggs, G. A. MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid, Version 6. [2002–2015] (NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 15 February 2017); https://doi.org/10.5067/MODIS/MYD10A1.006
Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).
Google Scholar
Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F. & Hamilton, S. K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ. 158, 348–361 (2015).
Google Scholar
Döll, P., Kaspar, F. & Lehner, B. A global hydrological model for deriving water availability indicators: model tuning and validation. J. Hydrol. 270, 105–134 (2003).
Google Scholar
Bartholomé, E. & Belward, A. S. GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959–1977 (2005).
Google Scholar
GLIMS and National Snow and Ice Data Center. GLIMS Glacier Database V1 (2012); https://doi.org/10.7265/N5V98602
Gruber, S. Derivation and analysis of a high-resolution estimate of global permafrost zonation. Cryosphere 6, 221–233 (2012).
Google Scholar
Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: croplands from 1700 to 1992. Glob. Biogeochem. Cycles 13, 997–1027 (1999).
Google Scholar
Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).
Google Scholar
Robinson, N., Regetz, J. & Guralnick, R. P. EarthEnv-DEM90: a nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data. ISPRS J. Photogramm. Remote Sens. 87, 57–67 (2014).
Google Scholar
Williams, P. W. & Ford, D. C. Global distribution of carbonate rocks. Z. Geomorphol. Suppl. 147, 1–2 (2006).
Hartmann, J. & Moosdorf, N. The new global lithological map database GLiM: a representation of rock properties at the Earth surface. Geochem. Geophys. Geosyst. 13, Q12004 (2012).
Google Scholar
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