Are the Discharge Changes in an Agricultural Watershed in Iowa Driven by Changes in Climate or Agriculture?
Abstract
River discharge represents a vital resource for many human activities. The improved understanding of the physical processes controlling its regime can lead to large economic and societal benefits, such as improved flood warning and mitigation, and improved water management during droughts. This is particularly true for the agricultural U.S. Midwest, and Iowa more specifically. Iowa is relentlessly plagued by catastrophic flooding, with the spring and summer river floods of 1993 and 2008 and the drought of 2012 being the most recent widespread event affecting the state. These natural disasters also come with a very large price tag, both in terms of economic damage and number of fatalities. During the 20th and 21st centuries, discharge over this area has been changing on a number of temporal scales, from annual to decadal. An outstanding question is whether this variability is related to changes in the climate system or to changes in land use/land cover and agricultural practices. We address this question by developing statistical models to describe the changes in different parts of the discharge distribution. We use rainfall and predictors related to agricultural practices to explain the observed streamflow variability. We focus on the Raccoon River at Van Meter, which is a 9000-km2 watershed with daily discharge measurements covering most of the 20th century up to the present. Our results indicate that variability in the climate system is responsible for the majority of the changes observed in the discharge records. Moreover, the relative contribution of rainfall in explaining the changes in streamflow increases as we move toward the upper tail of the distribution.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2013
- Bibcode:
- 2013AGUFMGC21A0817V
- Keywords:
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- 1616 GLOBAL CHANGE Climate variability;
- 1632 GLOBAL CHANGE Land cover change;
- 1860 HYDROLOGY Streamflow;
- 1872 HYDROLOGY Time series analysis