2018
DOI: 10.7287/peerj.preprints.26483v1
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Impact of extreme drought and incentive programs on flooded agriculture and wetlands in California’s Central Valley

Abstract: and the winter for WHEP (100%), may have been provided through incentive programs underscoring the contribution of these programs. However, further assessment is needed to know how much the incentive programs directly offset the impact of drought in post-harvest rice or simply supplemented funding for activities that might have been done regardless. Our, first of its kind, landscape analysis documents the significant impacts of the drought on freshwater wetland habitats in the xentral Valley and highlights the… Show more

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“…To represent the local conditions within each waterbird survey area, we used field observations from each survey of the land-cover class surveyed (a categorical predictor) and the proportion of the survey area that was flooded. To represent the surrounding landscape-and particularly the highly dynamic distribution of surface water and crop classes that can provide suitable waterbird habitat-we relied on the USDA's National Agricultural Statistics Service's (NASS) Cropland Data Layer data specific to each year of the waterbird survey (2013-2014; NASS 2018) paired with remotely-sensed surface water data specific to the year and season of each waterbird survey from Point Blue Conservation Science's Water Tracker (https://pointblue.org/watertracker) (Reiter et al 2018;Shuford et al 2019). We grouped the original NASS land-cover classifications into a smaller set of classes likely to be relevant to waterbirds (see Appendix B, Table B1 which shows how NASS classifications were grouped https://doi.org/10.15447/sfews.2023v21iss3art3 into the land-cover classes used in these models), and, where the NASS classification indicated double-cropping, we retained the winter crop classification for use with the winter waterbird distribution models.…”
Section: Waterbird Modelsmentioning
confidence: 99%
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“…To represent the local conditions within each waterbird survey area, we used field observations from each survey of the land-cover class surveyed (a categorical predictor) and the proportion of the survey area that was flooded. To represent the surrounding landscape-and particularly the highly dynamic distribution of surface water and crop classes that can provide suitable waterbird habitat-we relied on the USDA's National Agricultural Statistics Service's (NASS) Cropland Data Layer data specific to each year of the waterbird survey (2013-2014; NASS 2018) paired with remotely-sensed surface water data specific to the year and season of each waterbird survey from Point Blue Conservation Science's Water Tracker (https://pointblue.org/watertracker) (Reiter et al 2018;Shuford et al 2019). We grouped the original NASS land-cover classifications into a smaller set of classes likely to be relevant to waterbirds (see Appendix B, Table B1 which shows how NASS classifications were grouped https://doi.org/10.15447/sfews.2023v21iss3art3 into the land-cover classes used in these models), and, where the NASS classification indicated double-cropping, we retained the winter crop classification for use with the winter waterbird distribution models.…”
Section: Waterbird Modelsmentioning
confidence: 99%
“…For waterbirds during the winter season, we overlaid any distinct winter crop cover data for agricultural fields that were double-cropped (CDWR 2021). Projections of waterbird group presence also required estimates of open surface water, and we used the mean probability of open surface water during the fall and winter seasons, 2013-2019, derived from Point Blue Conservation Science's Water Tracker (Reiter et al 2018).…”
Section: Projected Distributionsmentioning
confidence: 99%