Measuring and modeling deep drainage, streamflow, and soil moisture in Oklahoma

Abstract

This dissertation examines multiple components of the Oklahoma water balance in order to answer three independent research questions:

i) Can long-term soil moisture monitoring data be used to estimate potential groundwater recharge rates? Daily drainage rates from the root zone were estimated for 78 sites using up to 17 years of soil moisture data from the Oklahoma Mesonet. Mean annual drainage rates ranged from 6 to 266 mm yr-1, with a statewide median of 67 mm yr-1. Drainage estimates were also modeled for four focus sites using HYDRUS1-D. Soil moisture-based drainage rates and HYDRUS1-D drainage rates agreed within 10 mm yr-1 at two drier sites but had discrepancies of >150 mm yr-1 at two sites with >1000 mm yr-1 precipitation.

ii) Does incorporating soil moisture information improve seasonal streamflow forecast accuracy? A modified version of the standard Natural Resources Conservation Service (NRCS) principal component analysis and regression (PCR) model was developed to forecast streamflow in four rainfall-dominated watersheds. This model incorporated antecedent precipitation and soil moisture data from long-term monitoring networks into PCR analysis to predict seasonal streamflow volumes at 0-, 1-, 2-, and 3-month lead times. Including soil moisture data improved forecast accuracy by more than 50% over precipitation-based forecasts.

iii) Can root zone soil moisture under diverse land cover types be effectively estimated by integrating ground-based meteorological data and remotely-sensed vegetation index data? Estimates of root zone soil moisture were made for four focus locations - a mixed hardwood forest, a loblolly pine plantation, cropland, and tallgrass prairie - by integrating ground-based meteorological data and basal crop coefficient curves derived from remotely-sensed vegetation index data within a soil water balance model. Results show that the model is able to estimate plant available water dynamics moderately well at the four focus locations, but needs further improvements before it can be used operationally.