DOI: 10.5061/dryad.kwh70rzfk
title: Resource redistribution mediated by hydrological connectivity modulates vegetation response to aridification in drylands

description: Water scarcity poses a significant life constraint in global drylands that
 determines species adaptations and mosaic of exposed bare areas and
 vegetation patches. Runoff-water redistribution resulting from this
 spatial configuration has been suggested as a key process controlling
 water availability for vegetation and ecosystem functioning. However, the
 potential of this process to ameliorate the negative impacts of
 aridification in drylands remains unclear and there is no empirical
 evidence of its relevance on natural ecosystems under different levels of
 aridity and disturbance regimes. To address this gap, we analysed temporal
 series of the Normalized vegetation index (NDVI, a proxy of vegetation
 functioning) along a regional aridity-disturbance gradient under current
 and future climatic conditions. We found that mean NDVI increases in areas
 of runoff water accumulation (calculated using a water redistribution
 index) until a certain threshold, above which vegetation patches are not
 able to retain extra runoff water. Once threshold values were identified,
 we analysed the role of water redistribution on vegetation dynamics by
 analysing temporal series of monthly NDVI in a space for time substitution
 approach. The obtained results provided further evidence of the runoff
 water redistribution on vegetation, triggering a positive feedback between
 water accumulation and vegetation growth. Results obtained by the
 combination of the obtained model with climatic data from the 6th IPCC
 report, suggest that this feedback could ameliorate the expected negative
 effects of aridification in drylands. However, this effect is partially
 counterbalance in scenarios of high human disturbance and in areas where
 vegetation is not able to trap and retain the extra amount of resources
 given by runoff. Overall, our results provide empirical evidence of the
 relevance of runoff redistribution as a key process linking vegetation
 patterns to climate resistance in drylands that underscore its importance
 in the analysis and modelling of dryland´s responses to aridification.
description: # Resource redistribution mediated by hydrological connectivity modulates
 vegetation response to aridification in drylands
 [https://doi.org/10.5061/dryad.kwh70rzfk](https://doi.org/10.5061/dryad.kwh70rzfk) ## Description of the data and file structure We used R software for data analysis. Long term climatic data (average values for the period 1970-2000) were obtained from the Spanish Climate Atlas developed by the Spanish Meteorological Agency (AEMET, [http://agroclimap.aemet.es](http://agroclimap.aemet.es)). Daily climatic data for the studied period was obtainded from the climatic network of the Instituto Valenciano de Investigaciones Agrarias (IVIA, [http://riegos.ivia.es/listado-de-estaciones](http://riegos.ivia.es/listado-de-estaciones)) and the Instituto Murciano de Investigación y Desarrollo Agrario y Medioambiental (IMIDA, [http://siam.imida.es](http://siam.imida.es)). Climate predictions for the 6th IPCC report were downloaded from [https://esgf-ui.ceda.ac.uk/cog/search/cmip6-ceda/](https://esgf-ui.ceda.ac.uk/cog/search/cmip6-ceda/). Digital Elevation Models (DEMs) were obtained from the Instituto Geográfico Nacional (IGN, [https://www.ign.es/web/ign/portal](https://www.ign.es/web/ign/portal)). Orthoimages were obtained from National Plan of Aerial Orthophotography from Spain (PNOA, [https://pnoa.ign.es/](https://pnoa.ign.es/)). Aridity values were obtained from Global Aridity Index Database (Trabucco and Zomer, 2018). Global Human Influence Index v2 was obtained from [https://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-influence-index-geographic](https://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-influence-index-geographic) . NDVI from Sentinel-2 imagery was calculated in Google Earth Engine code editor ([https://code.earthengine.google.com/](https://code.earthengine.google.com/)) using the COPERNICUS/S2 image collection. --- **General Glossary**: GAI: Global aridity index (Trabucco and Zomer, 2018) ETP: Potential Evapotranspiration (Trabucco and Zomer, 2018) Tmax: Mean annual maximum temperature (AEMET, [http://agroclimap.aemet.es](http://agroclimap.aemet.es)) Tmean: Mean annual temperature (AEMET, [http://agroclimap.aemet.es](http://agroclimap.aemet.es)) PP: Mean annual precipitation (AEMET, [http://agroclimap.aemet.es](http://agroclimap.aemet.es)) COVER: Vegetation cover (Calculated from PNOA, [https://pnoa.ign.es/](https://pnoa.ign.es/)) NDVI: Normalized Difference Vegetation Index (Calcutated from Sentinel-2 images). HI: Global Human Influence Index ([https://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-influence-index-geographic](https://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-influence-index-geographic)) retention: retentioni from BalanR, a water redistribution index from (Rodriguez-Lozano et al., 2023) --- **Dataset description:** POINTID: Identifier of each pixel from SENTINEL-2 images studied CODE: Identifier of each study area CodYear:  Identifier of each hydrological year studied NDVI: Normalized Difference Vegetation Index (Calcutated from Sentinel-2 images). Can contain NA due to cloud masking and low data quality removal. NDVIn-1: NDVI in the previous month. Please note that for the first month of data NDVIn-1 is NA. In addition, it can contain NA due to cloud masking and low data quality removal. PP_n-1 to PP_9month: Precipitation in the previous month to 9 months before NDVI adquisition. This information was obtained from Instituto Valenciano de Investigaciones Agrarias (IVIA, [http://riegos.ivia.es/listado-de-estaciones](http://riegos.ivia.es/listado-de-estaciones)) and the Instituto Murciano de Investigación y Desarrollo Agrario y Medioambiental (IMIDA, [http://siam.imida.es](http://siam.imida.es)). Solar: Potential incoming solar radiation calculated with the hemispherical visual basin algorithm with 100% of atmospheric transmissivity in ArcGis 10.1 (Fu, 2000). Altitude: Altitude (m.a.s.l.) of each study area COVER: Vegetation cover of each study pixel. Please note that you need to multiply by 100 to obtain the % Plot_Altitude: Mean altitude of each study plot Plot_COVER: Mean vegetation cover of each study plot. Please note that you need to multiply by 100 to obtain the % GAI:Global aridity index (Trabucco and Zomer, 2018) Plot_PP, Plot_Tmax and Plot_Tmean: Mean annual precipitation, Mean annual maximum temperature and Mean annual temperature (AEMET, [http://agroclimap.aemet.es](http://agroclimap.aemet.es)) of each study plot Plot_HI: Mean Global Human Influence Index ([https://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-influence-index-geographic](https://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-influence-index-geographic)) of each plot ETP: Potential Evapotranspiration (Trabucco and Zomer, 2018) ETP_1month to ETP_9month:  Potential Evapotranspiration in the previous month  to 9 months before NDVI adquisition. This information was calculated based on potential solar radiation and climatic data from Instituto Valenciano de Investigaciones Agrarias (IVIA, [http://riegos.ivia.es/listado-de-estaciones](http://riegos.ivia.es/listado-de-estaciones)) and the Instituto Murciano de Investigación y Desarrollo Agrario y Medioambiental (IMIDA, [http://siam.imida.es](http://siam.imida.es)). WB1 to WB9: Water balance from month he previous month  to 9 months before NDVI adquisition. This information was calculated based on ETP_1month to ETP_9month and PP_n-1 to PP_9month class: Identifies the pixels below and above the thresholds (breakpoints).