Propagation of precipitation and temperature dataset uncertainty from headwaters to groundwater in the Sierra Nevada, California

Authors: Schreiner-Mcgraw, Adam; AJAMI, HOORI - UNIVERSITY OF CALIFORNIA

Submitted to: American Geophysical Union Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/4/2021
Publication Date: 12/13/2021
Citation: Schreiner-Mcgraw, A.P., Ajami, H. 2021. Propagation of precipitation and temperature dataset uncertainty from headwaters to groundwater in the Sierra Nevada, California [abstract]. American Geophysical Union Meeting Abstract. Paper 898283.

Interpretive Summary:

Technical Abstract: Spatially distributed precipitation observations are a key input variable in distributed hydrologic models, and their spatial variability is expected to impact watershed hydrologic response. Gridded precipitation datasets based on gauge observations are plagued by uncertainty, especially in mountainous terrain where gauge networks are sparse. However, their impact on magnitude and variability of simulated groundwater recharge is often neglected. Additionally, it is unclear how these uncertainties affect projected groundwater response to climate change. In this contribution, we quantify the impact of uncertainty in both gridded precipitation and air temperature forcing datasets on the simulated groundwater recharge in the mountainous watershed of the Kaweah River in California, USA. We utilize the integrated groundwater-surface water model ParFlow.CLM and several gridded datasets commonly used in hydrologic studies including downscaled NLDAS-2, PRISM, Daymet, Gridmet, and TopoWx. Our analysis shows high levels of uncertainty in both precipitation and air temperature datasets across the watershed. Total annual precipitation over the watershed varies by 20%, and mean air temperature varies by up to 5 degrees C, depending on the gridded dataset chosen. Variations in simulated recharge to changes in precipitation (elasticities) and air temperature (sensitivities) are larger than 1% change in recharge per 1% change in precipitation or 1-degree C change in temperature. The total volume of snowmelt and topographic redistribution of soil moisture and shallow groundwater are the primary mechanisms creating the high sensitivity of simulated recharge to meteorological forcings. The combined effect of uncertainty in air temperature and precipitation on recharge is additive, and results in uncertainty levels roughly equal to the sum of the individual uncertainties, depending on the hydrolimatic condition of the watershed. Given the uncertainty in historical gridded precipitation datasets, it is unclear whether these uncertainties alter simulated hydrologic responses to climate change. We discuss how the spatial patterns present in the historical gridded precipitation datasets impact the simulated response of groundwater recharge to climate change projections.