THE INFLUENCE OF SOIL MOISTURE HOLDING CAPACITY ON MODELED ESTIMATES OF EVAPOTRANSPIRATION AND AQUIFER RECHARGE IN NEW MEXICO

Many aquifers in semi-arid regions worldwide are experiencing rapid drawdown associated with unsustainable water extraction. Yet the recharge to most of these aquifers, which effectively defines the sustainable pumping rate, is poorly quantified. As part of a collaborative effort to clearly define the water resources of New Mexico and help close the water budget for the state we have created PyRANA (Python Recharge Assessment for New Mexico Aquifers), a high spatial and temporal resolution soil water balance model. In this model we define recharge as that water which infiltrates below the root zone, either in a diffuse manner across the landscape or in focused zones where surface runoff collects. Our approach uses daily PRISM precipitation estimates as the distributed input to the model cells (250 x 250 m), and estimates evapotranspiration (ET) extractions using the dual crop coefficient method driven by a daily reference ET derived from downscaled NLDAS climate data.

One of the primary controlling parameters in modeling both diffuse recharge and ET using this approach is the soil moisture holding capacity (SMHC) assigned to each cell. The SMHC reflects the amount of moisture extractable between field capacity and wilting point tensions within the rooting zone, and hence is a function of both soil and vegetation properties. Here we explore model results using four different methods of parameterizing SMHC: 1) extracting SMHC data from NRCS soil maps; 2) calculating the maximum range of cumulative soil moisture depletion between the years 2000-2015 based on daily PRISM precipitation inputs and PT-JPL (Priestly Taylor-Jet Propulsion Lab) ET extractions; 3) assigning SMHC based on a combination of vegetation class (from the LandFire existing vegetation landcover estimate) and vegetation density (from a long-term average of the MODIS NDVI product); and 4) iterating the PyRANA model and assigning the maximum range of modeled soil moisture deficit between the years 2000-2015 in each cell as the new SMHC until the SMHC value stabilizes. Each method has strengths and weaknesses, but the resulting ensemble of SMHC estimates helps to reveal relative uncertainties in both ET and recharge estimates across the state.