How precipitation (P) is translated into streamflow (Q) and over what timescales (i.e., “memory”) is difficult to predict without calibration of site‐specific models or using geochemical approaches, posing barriers to prediction in ungauged basins or advancement of general theories. Here, we used a data‐driven approach to identify regional patterns and exogenous controls on P–Q interactions. We applied an information flow analysis, which quantifies uncertainty reduction, to a daily time series of P and Q from 671 watersheds across the conterminous United States. We first demonstrated that information transfer from P to Q primarily reflects the quickflow component of water‐budgets, based on a watershed model. Readily quantifiable information flows show a functional relationship with model parameters, suggesting utility for model calibration. Second, applied to real watersheds, P–Q information flows exhibit seasonally varying behavior within regions in a manner consistent with dominant runoff generation mechanisms. However, the timing and the magnitude of information flows also reflect considerable subregional heterogeneity, likely attributable to differences in watershed size, baseflow contributions, and variation in aerial coverage of preferential flow paths. A regression analysis showed that a combination of climate and watershed characteristics are predictive of P–Q information flows. Though information flows cannot, in most cases, uniquely determine dominant runoff mechanisms, they provide a means to quantify the heterogeneous outcomes of those mechanisms within regions, thereby serving as a benchmarking tool for models developed at the regional scale. Last, information flows characterize regionally specific ways in which catchment connectivity changes from the wet to dry season.
Understanding the dominant drivers of hydrological change is essential for water resources management. Watersheds in the United States are experiencing different types of changes (e.g., wet gets wetter and dry gets drier); however, few studies have analyzed what drivers are responsible for these changes, and how the dominant drivers vary over time and as a function of the climate/water regime and land cover. This study uses a time‐varying Budyko framework to quantify the relative importance of precipitation, potential evapotranspiration, and other factors (e.g., climate seasonality, agricultural drainage, and urbanization) in 889 watersheds in the contiguous United States from 1950 to 2009. Results show that watersheds that are getting wetter are primarily due to increases in precipitation. However, watersheds in dry climates that are getting drier are primarily due to other factors, while watersheds in wet climates that are getting drier are primarily due to precipitation. The drivers causing statistically significant streamflow trends vary depending on dominant land‐use types. Temporally, the increasing effects of other factors are more pronounced after the 1980s in the Midwest. The dominant drivers of streamflow in the United States are time‐varying instead of constant. This is consistent with non‐stationary patterns of streamflow. The time‐varying drivers provide information on the processes that are increasingly important and require the most attention in water resources management.
more » « less- Award ID(s):
- 2003248
- NSF-PAR ID:
- 10442552
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 57
- Issue:
- 5
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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