Abstract

Abstract. Mountainous regions act as the water towers of the world by producing streamflow and groundwater recharge, a function that is particularly important in semiarid regions. Quantifying rates of mountain system recharge is difficult, and hydrologic models offer a method to estimate recharge over large scales. These recharge estimates are prone to uncertainty from various sources including model structure and parameters. The quality of meteorological forcing datasets, particularly in mountainous regions, is a large source of uncertainty that is often neglected in groundwater investigations. In this contribution, we quantify the impact of uncertainty in both precipitation and air temperature forcing datasets on the simulated groundwater recharge in the mountainous watershed of the Kaweah River in California, USA. We make use of the integrated surface water–groundwater model, ParFlow.CLM, and several gridded datasets commonly used in hydrologic studies, downscaled NLDAS-2, PRISM, Daymet, Gridmet, and TopoWx. Simulations indicate that, across all forcing datasets, mountain front recharge is an important component of the water budget in the mountainous watershed, accounting for 9 %–72 % of the annual precipitation and ∼90 % of the total mountain system recharge to the adjacent Central Valley aquifer. The uncertainty in gridded air temperature or precipitation datasets, when assessed individually, results in similar ranges of uncertainty in the simulated water budget. 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 ∘C change in temperature. The total volume of snowmelt is the primary factor creating the high water budget sensitivity, and snowmelt volume is influenced by both precipitation and air temperature 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 hydroclimatic condition of the watershed. Mountain system recharge pathways including mountain block recharge, mountain aquifer recharge, and mountain front recharge are less sensitive to changes in air temperature than changes in precipitation. Mountain front and mountain block recharge are more sensitive to changes in precipitation than other recharge pathways. The magnitude of uncertainty in the simulated water budget reflects the importance of developing high-quality meteorological forcing datasets in mountainous regions.

Highlights

  • As meteorological conditions are the primary drivers of the hydrologic cycle, understanding how groundwater recharge in mountain systems reacts to different meteorological forcings is important

  • We believe that the differences in the resolution of the datasets and interpolation approaches have caused the differences in precipitation and air temperature forcing datasets

  • We examine the propagation of uncertainty in the meteorological forcings, precipitation, and air temperature into groundwater recharge simulated with the integrated hydrologic model, ParFlow.CLM

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Summary

Introduction

Mountainous catchments are known to be important sources of water in semiarid and seasonally dry ecosystems (Viviroli et al, 2007). While it is well understood that mountain systems provide the majority of freshwater resources via streamflow (Viviroli and Weingartner, 2004), the contribution of mountain systems to groundwater resources remains highly uncertain (Ajami et al, 2011). Mountain bedrock aquifer recharge (MAR) consists of snowmelt- or rainfall-derived infiltration. Ajami: Meteorological uncertainty impacts simulated groundwater into the bedrock system of the mountain block, which either discharges to streams or may eventually reach an alluvial aquifer in an adjacent valley as mountain block recharge (MBR). MBR consists of lateral subsurface flow from the mountains to an adjacent valley aquifer. Various efforts have been made to estimate the relative importance of each recharge pathway (Ajami et al, 2011; Mailloux et al, 1999; Manning and Solomon, 2003; Schreiner-McGraw and Vivoni, 2017; Newman et al, 2006), but an analysis of how they respond to uncertainty in atmospheric drivers, such as precipitation or air temperature, is lacking

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