Abstract:In February 2014 the USDA established regional climate hubs across the United States to assist farmers, ranchers and foresters in adapting to the effects of climate change. The Southwest (SW) region encompasses six states which provide highly diverse agricultural crops including cotton, stone fruit and grapes. Here we report on the hydrologic context within which SW working landowners operate, with focus on regional water resources and likely impacts of climate change. Water is a critical component of agricultural vulnerability in the SW, where high agricultural production can occur with sufficient irrigation. Since 1978, crop yield declines were reported on 11-21% of total irrigated acres, mostly due to surface water shortage. Southwestern agriculture relies on groundwater, using it to supply at least one-third of the agricultural water demand across the region since 1955. Regional groundwater use varies over time, with a decline in the agricultural groundwater fraction in Arizona, but an increase in the fraction in Nevada and Utah. Observed and predicted changes in the southwestern hydrologic cycle can impact regional agriculture. Observed records show an increase in the fraction of precipitation falling as rain, which is expected to continue with future warming, and decrease the natural high-mountain storage reservoir provided by snowpack. Warming causes the snowmelt to peak earlier in the season than observed in historical records, and can reduce water available to crops during the summer months without additional water storage. Observed records indicate streamflow has shifted earlier in the year, most notably in snow dominated watersheds. A continuation of this trend may challenge regional agriculture by further limiting water supplies.
Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.
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