Estimating Soil Water Holding Capacity and Runoff in New Mexico to Improve Modeled Recharge Rates

Published Date:
May 2020

Authors:
Daniel Cadol, Gabriel Parrish, Sarah Reuter, Talon Newton, Fred M. Phillips, Jan M.H. Hendrickx

Abstract:
This report summarizes efforts to improve groundwater recharge estimates using the Python Recharge Assessment for New Mexico Aquifers (PyRANA) model by developing new methods to parameterize soil-water holding capacity (SWHC, also called total available water – TAW) and rainfall-runoff relationships in mountainous portions of northern New Mexico. The PyRANA model is a soil-water-balance model that runs on a daily time step and at a 250 m grid resolution across the state of New Mexico, tracking precipitation inflows and evapotranspiration (ET), runoff, and deep percolation outflows at each grid cell. Parameters that partition rainfall into runoff and infiltration, and the amount of water storage available in each cell (i.e., SWHC) were identified as the most poorly constrained of the highly influential model parameters in previous research efforts.

The SWHC parameterization effort explored a depletion-tracking method, which uses independent estimates of precipitation and ET to track soil-water content through time and assumes that the vegetation root zone is naturally adjusted such that SWHC meets the maximum water demand over a medium time range. In this effort, we found that two of the best available monthly ET products, the operational Simplified Surface Energy Balance model (SSEBop) and the Priestly-Taylor-based Jet Propulsion Laboratory product (PT-JPL), were not in adequate agreement with precipitation estimates from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) for use in this exercise. In an effort to parsimoniously correct the ET biases, we scaled the two ET products so that long-term ET matched long-term precipitation from PRSIM. This led to plausible SWHC estimates in natural upland landscapes (nonagricultural and non-riparian) using the scaled PT-JPL, but not using the scaled SSEBop. In an effort to use an ET estimate that is limited by water availability as well as energy availability, we also explored a recursive and iterative approach that employed the ET estimate from PyRANA itself in the depletion-tracking SWHC estimation method. This led to plausible values of SWHC, but spatial patterns that are unverifiable at this time. Finally, we explored an ecosystem-based approach to map SWHC, using landcover classification and a vegetation greenness index (normalized difference vegetation index – NDVI). This approach would require extensive field validation to determine appropriate relationships between NDVI and SWHC for unique vegetation types. But our preliminary exploration yielded promising maps of SWHC that are plausible in both magnitude and spatial variation.

The rainfall-runoff parameterization effort first explored potential biases in the PRISM precipitation dataset. Earlier research had found that PRISM accurately models total seasonal rainfall, but it under-predicts the magnitude of high-rainfall days and over-predicts the number of days with small rainfall amounts during monsoon season in the Walnut Gulch Experimental Watershed (WGEW) in south-east Arizona. In this report, we found a similar, but slightly less pronounced, pattern of bias in the Sangre de Cristo mountains of northern New Mexico. In both WGEW and northern New Mexico we only observed a bias during the monsoon season; winter storms have clear agreement between PRISM and local rain gauges. Next, we used runoff data from small (~ 1 km2) gauged watersheds in the Jemez and Sangre de Cristo mountains to develop a multiple linear regression to predict runoff using rainfall magnitude and intensity. We found that unlike the experience with WGEW, intensity was not a significant predictor of runoff. Instead, the daily rainfall amount alone was an adequate predictor of runoff for use in PyRANA.

Download: tr384.pdf

Keywords:
groundwater recharge, Python, New Mexico aquifers, soil-water-balance model, rainfall-runoff relationships, infiltration, water storage