Inferring changes in water cycle dynamics of intensively managed landscapes via the theory of time-variant travel time distributions

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Abstract

Climatic trends and anthropogenic changes in land cover and land use are impacting the hydrology and water quality of streams at the field, watershed, and regional scales in complex ways. In poorly drained agricultural landscapes, subsurface drainage systems have been successful in increasing crop productivity by removing excess soil moisture. However, their hydroecological consequences are still debated in view of the observed increased concentrations of nitrate, phosphorus, and pesticides in many streams, as well as altered runoff volumes and timing. In this study, we employ the recently developed theory of time-variant travel time distributions within the StorAge Selection function framework to quantify changes in water cycle dynamics resulting from the combined climate and land use changes. Our results from analysis of a subbasin in the Minnesota River Basin indicate a significant decrease in the mean travel time of water in the shallow subsurface layer during the growing season under current conditions compared to the pre-1970s conditions. We also find highly damped year-to-year fluctuations in the mean travel time, which we attribute to the “homogenization” of the hydrologic response due to artificial drainage. The dependence of the mean travel time on the spatial heterogeneity of some soil characteristics as well as on the basin scale is further explored via numerical experiments. Simulations indicate that the mean travel time is independent of scale for spatial scales larger than approximately 200 km2, suggesting that hydrologic data from larger basins may be used to infer the average of smaller-scale-driven changes in water cycle dynamics.

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CITATION STYLE

APA

Danesh-Yazdi, M., Foufoula-Georgiou, E., Karwan, D. L., & Botter, G. (2016). Inferring changes in water cycle dynamics of intensively managed landscapes via the theory of time-variant travel time distributions. Water Resources Research, 52(10), 7593–7614. https://doi.org/10.1002/2016WR019091

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