Jairo E. Hernandez

The Quest for Hydrological Signatures: Effects of Data Transformation on Bayesian Inference of Watershed Models

Hydrological models contain parameters, values of which cannot be directly measured in the field, and hence need to be meaningfully inferred through calibration against historical records. Although much progress has been made in the model inference literature, relatively little is known about the effects of transforming calibration data (or error residual) on the identifiability of model parameters and reliability of model predictions. Such effects are analyzed herein using two hydrological models and three watersheds. Our results depict that calibration data transformations significantly influence parameter and predictive uncertainty estimates. Those transformations that distort the temporal distribution of calibration data, such as flow duration curve, normal quantile transform, and Fourier transform, considerably deteriorate the identifiability of model parameters derived in a formal Bayesian framework with a residual-based likelihood function. Other transformations, such as wavelet, BoxCox and square root, while demonstrating some merits in identifying specific model parameters, would not consistently improve predictive capability of hydrological models in a single objective inverse problem. Multi-objective optimization schemes, however, may present a more rigorous basis to extract several independent pieces of information from different data transformations. Finally, data transformations might offer a greater potential to evaluate model performance and assess specific sections of model behavior, rather than to calibrate models in a single objective framework. Findings of this study shed light on the importance and impacts of data transformations in search of hydrological signatures.

Downscaling Surface Temperature Image with TsHARP

Daily evapotranspiration (ET) maps would significantly improve assessing crop water requirements especially in Texas High Plains (THP) where supply of irrigation water is limited. ET maps derived from satellite data with daily coverage such as MODIS and GOES sensors are inadequate, because their thermal pixel size is larger than individual agricultural fields. However, there exists an opportunity to use simultaneously acquired high resolution visible, near-infrared, and shortwave-infrared images from MODIS, and thermal-infrared images from other high resolutions sensors such as LANDSAT 5 Thematic Mapper (TM) or ASTER to improve spatial and temporal resolution of ET maps. Image downscaling methods are useful to improve spatial resolution by examining relationships between simultaneously acquired coarser thermal and finer non-thermal datasets. In this study, the TsHARP, an image downscaling technique, was evaluated for its capability to downscale land surface temperature (LST) images for ET mapping. LANDSAT 5 TM images taken from a southern part of the THP area were utilized to implement TsHARP. For this purpose, we developed a synthetic image with a spatial resolution of 960x960 m using TM based 120x120 m LST image. The 960x960 m resolution was used to mimic a LST image derived from MODIS thermal data. TsHARP was implemented to develop a LST image at 120x120 m resolution using a statistical relationship between LST and normalized difference vegetation index (NDVI). Comparison of downscaled 120x120 m LST image against original LST image from TM data yielded a correlation coefficient of 0.93.Results indicate that TsHARP has the potential to be used to downscale LST images with simultaneously acquired high resolution NDVI image derived from MODIS data.

Calibrating Northern Texas High Plains Groundwater Model

In the Northern High Plains of Texas, irrigated crop production accounts for a major portion of groundwater withdrawals from the Ogallala aquifer. The concern is that diminishing groundwater supplies will severely reduce regional crop and animal production, which in turn would impact the regional economy. The objective of this study was to develop and calibrate a groundwater model for a 4-county area (Dallam, Sherman, Hartley, and Moore counties) in the Northwest region of the Texas High Plains. This study is a major component of a comprehensive regional analysis of groundwater depletion in the Ogallala aquifer region with the purpose of understanding short- and long-term effects of existing and alternative land use scenarios on groundwater changes. A comprehensive geographic information system database was developed for this purpose. Hydrologic simulations were done using MODFLOW-2000 model. The model was calibrated satisfactorily for predevelopment time by reproducing and comparing groundwater levels for the 1950s with a correlation coefficient of 0.9. Predevelopment historical groundwater levels in the 4-county study area ranged from 955 to 1,405 m above MSL and simulated levels ranged from 930 to 1,410 m above MSL. With the calibrated model, the effect of change in land use/land cover on sustainability of the aquifer life will be studied. Our results are expected to be useful to develop and evaluate strategies to conserve groundwater in the Ogallala aquifer beneath Northern High Plains of Texas and improve regional water planning.

Modeling Groundwater Levels in the Northern High Plains of Texas

Future changes in groundwater policy and the use of alternative technologies will have to be adopted for the Northern High Plains of Texas due to the depleting underlying Ogallala aquifer, the major source of water for irrigation. The objective of this study was to calibrate and validate an integrated regional groundwater model using observed groundwater levels between 1939 and 2007 in four heavily irrigated counties (Dallam, Sherman, Hartley, and Moore counties) located in the northwest corner of the Northern High Plains of Texas. For this purpose, the study area was divided into 1km cells and the MODFLOW-2000 model was calibrated and validated. Results indicate that the groundwater model was calibrated and validated satisfactorily by reproducing and comparing groundwater levels with correlation coefficients of 0.98 in both cases and with normalized root mean square errors of 5.0% and 4.3% respectively. The calibrated model is being used to evaluate the implications of five different agricultural management policy scenarios on groundwater levels between 2010 and 2060. Simulation results should assist water planners to estimate specific irrigation water uses and promote the use of alternative technologies in the 4-county area, and to potentially implement new policies for sustainable development of the Ogallala aquifer.