Contribution of Environmental Forcings to US Runoff Changes for the Period 1950–2010
This study examines the annual and seasonal trends of US runoff for the 1950-2010 period. We used multiple single-factor land surface model (LSM) simulations to conduct detailed detection and attribution (D&A) analysis to assess the causality of changes in US runoff.
We succeeded in applying single-factor land surface model simulations and an adapted version of the classical regression-based methodology for D&A to detect the changing trends and quantify the environmental driving mechanisms for the US runoff during the 1950-2010 period.
Runoff in the United States is changing, and this study finds that the measured change is dependent on the geographic region and varies seasonally. Specifically, observed annual total runoff had an insignificant increasing trend in the US between 1950 and 2010, but this insignificance is due to regional heterogeneity with both significant and insignificant increases in the eastern, northern, and southern US, and a greater significant decrease in the western US. Trends for seasonal mean runoff also differs across regions. By region, the season with the largest observed trend is autumn for the east (positive), spring for the north (positive), winter for the south (positive), winter for the west (negative), and autumn for the US as a whole (positive). Based on the detection and attribution analysis using gridded WaterWatch runoff observations along with semi-factorial land surface model simulations from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we find that while the roles of CO2 concentration, nitrogen deposition, and land use and land cover appear inconsistent regionally and seasonally, the effect of climatic variations is detected for all regions and seasons, and the change in runoff can be attributed to climate change in summer and autumn in the south and in autumn in the west. We also find that the climate-only and historical transient simulations consistently underestimated the runoff trends, possibly due to precipitation bias in the MsTMIP driver or within the models themselves.