Testing a model for the prediction of isolated waters in the Sonoran Desert. (July 2015)

• Record Type: Journal Article
• Authors: Drake, Joseph C.; Jenness, Jeffrey S.; Calvert, Jordan; Griffis-Kyle, Kerry L.
• Is Part Of: Journal of arid environments. Volume 118(2015:Jul.)
• Page Start: 1
• Page End: 8
• Publication Date: 2015-07
• Subjects: Ephemeral water -- Mahalanobis distance -- Topographic wetness index -- Slope -- Southwestern United States -- Arizona -- Wetland -- Predictive -- Model; Arid regions ecology -- Periodicals; Arid regions -- Periodicals; Écologie des régions arides -- Périodiques; Régions arides -- Périodiques; 577.54
• DOI: 10.1016/j.jaridenv.2015.02.018
• Languages: English
• ISSNs: 0140-1963

Abstract

Abstract: Water is an extremely limiting resource in arid regions and wildlife managers need accurate inventories of water sources to better manage natural resources. Many of the water sources in the Sonoran Desert are tinajas, solid rock-bottom pools of varying sizes. These and other isolated and ephemeral water resources are essential for desert wildlife. We developed an approach to predict the location of unidentified ephemeral waters in the Sonoran Desert of Arizona, USA. We used Mahalanobis distance based on topographic wetness and slope to indicate groups of pixels in GIS that are the most similar in these aspects to locations of currently known waters. We tested this model in southwestern Arizona at the U.S. Air Force's Barry M. Goldwater Range - East by comparing polygons of predicted waters with random polygons. Seventy-four percent of standing surface water features found were attributed to the predicted polygons derived by our model. The model found a significantly larger water capacity in predicted polygons than in random polygons. This modeling technique could provide a new tool for researchers and land managers to better estimate potential water resources for wildlife conservation objectives in arid landscapes. Highlights: Created model predicting locations of isolated desert waters in the Sonoran Desert. Model was based on attributes of currently known water sites. Model was significantly better at predicating the locations of sites than random. Predicted polygons held significantly more water than random polygons. Model can help enhance ability to maintain water for wildlife on arid lands.