Jairo E. Hernández

Groundwater Levels in Northern Texas High Plains: Baseline for Existing Agricultural Management Practices

Jairo E. Hernández et al. — Tue, 26 Mar 2013 14:00:29 PDT

New groundwater policies are being debated for the Northern Texas High Plains because of Ogallala Aquifer depletion. These policies should be evaluated using a calibrated groundwater model for assessing their impact on subsequent groundwater levels. The objective of this study was to calibrate and validate a regional groundwater model for predicting the impact of existing agricultural management practices on groundwater levels beneath 4 counties located in the Northern Texas High Plains. Results indicated that the MODFLOW-2000 groundwater model was calibrated and validated satisfactorily based on reproducing and comparing groundwater levels with coefficients of determination of 0.97 and 0.98, root mean square errors of 28.0 meters (91.9 feet) and 15.5 meters (50.9 feet). The model showed normalized root mean square errors of 6.9% and 4.3%, for calibration and validation, respectively. Analysis of prediction results indicated that 2 zones would become depleted if the current level of aquifer exploitation continues with no modification for the next 50 years. The calibrated model should assist water managers in evaluating alternative agricultural management policy scenarios.

Assessing Canal Structure Automation Rules Using an Accuracy-Based Learning Classifier System, a Genetic Algorithm, and a Hydraulic Simulation Model in the Boise River

Jairo E. Hernández et al. — Tue, 29 May 2012 12:37:02 PDT

Using state-of-the-art computational techniques, a genetic algorithm (GA) and an accuracy-based learning classifier system (XCS) were shown to produce optimal operational solutions for gate structures operation in irrigation canals. An XCS has been successfully developed to generate a set of operational rules for canal gates through the exploration and exploitation of rules using a GA, with the support of an unsteady-state hydraulic simulation model. A computer program which implemented the XCS was used to develop operational rules to operate all canal gate structures simultaneously, while maintaining water depth near target values during variable-demand periods, and with a hydraulically stabilized system when demands were no longer changed. Data from two reaches of the Boise River Project were used for assessing performance of the model. In the tested cases, thousands XCS simulations involving thousands of hydraulic simulations, were required to produce satisfactory rules. However, the overall fitness of the set of rules was increased monotonically as XCS simulations progressed. Simulated water depths approached the respective target depths for variable water delivery demand through turnout structures in the simulated canal systems.

Modeling Groundwater Levels on the Calera Aquifer Region in Central Mexico Using ModFlow

Jairo E. Hernández et al. — Tue, 29 May 2012 12:36:58 PDT

A conceptual model for the Calera Aquifer has been created to represent the aquifer system beneath the Calera Aquifer Region (CAR) in the State of Zacatecas, Mexico. The CAR area was uniformly partitioned into a 500 X 500 m grid generating a high resolution model that represented the natural boundaries of the aquifer. A computer model was calibrated and validated to verify output from the model corresponding to situations that matched the historical aquifer performance. Predicted groundwater levels were compared with measured data collected from nine observation wells between 1954 and 2004 to evaluate model performance. The main objective of this study was to develop and evaluate a groundwater modeling system using ModFlow-2000 for the CAR. Performance statistics indicated that the model performed well in simulating historic groundwater levels in the central part of the CAR where irrigated agriculture was concentrated. Results evaluation yielded average coefficients of determination of 0.81 and 0.67 and root mean square error values lower than 25.1 m and 25.9 m for the calibration and validation processes, respectively. These results are indicative of a good agreement between predicted and observed groundwater levels. However, further improvements in the conceptual model may be needed to improve predictions in other parts of the CAR for evaluating alternative groundwater management strategies.

Downscaling of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines

Wonsook Ha et al. — Fri, 26 Aug 2011 15:16:18 PDT

High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image downscaling has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform downscaling of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for downscaling problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the downscaled 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.

Downscaling Surface Temperature Images with TsHARP

Wonsook Ha et al. — Fri, 26 Aug 2011 15:12:33 PDT

Daily evapotranspiration (ET) maps would significantly improve assessing crop water requirements, especially in the Texas High Plains (THP) where the supply of irrigation water is limited. Evapotranspireation maps derived from satellite data with daily coverage such as MODIS (Moderate Resolution Imaging Spectroradiometer) and GOES (Geostationary Operational Environmental Satellite) 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 (Land Remote-Sensing Satellite) Thematic Mapper (TM) or ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)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. The 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. The 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.

Groundwater Modeling of the Texas High Plains Using MODFLOW

Jairo E. Hernandez et al. — Fri, 26 Aug 2011 14:52:05 PDT

The objective of this study was to develop and calibrate a groundwater model for a 4-county area in the Texas High Plains of the Ogallala Aquifer Region. 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 (GIS) database was developed for this purpose that included a recent land cover map. This 2008 land cover map was developed using Landsat satellite imagery with ground-truth points for Dallam, Sherman, Hartley, and Moore Counties in Texas. Other GIS layers included aquifer elevation contours, surficial geology, hydraulic conductivity contours, saturated thickness areas, well locations and piezometric heads, aquifer discharge and recharge areas, topography, hydrographic data, ecological regions, and soil type data. The hydrologic simulations were done using MODFLOW. Anticipated outcomes from this modeling effort include the effect of change in land use/land cover on sustainability of the aquifer life in the study region. Our results will be used to develop strategies to conserve groundwater in the Ogallala Aquifer beneath Central High Plains and improve regional water planning.

Irrigated Area Mapping in the Northern High Plains of Texas Using Landsat Thematic Mapper Data

Chandrushekar M. Biradar et al. — Fri, 26 Aug 2011 14:47:17 PDT

Irrigated agriculture in the Texas High Plains accounts for a major portion of the groundwater withdrawals from the Ogallala aquifer, and groundwater levels are declining. Accurate information on irrigated acreage and its spatial distribution enhances local groundwater districts’ ability to manage limited water resources. In addition, irrigated land area is one of the important inputs in most surface and groundwater models to evaluate economic feasibility for various crop rotations systems and irrigation management practices. In this study, we used a novel approach to derive an irrigated area map covering a 4-county area (Dallam, Sherman, Hartley, and Moore Counties) in the northwest region of the Texas High Plains from a Landsat 5 Thematic Mapper data image acquired on August 13, 2008. The spectral band ratios and vegetation indices were used to define threshold value for the irrigated pixels. The hierarchical rule-based decision tree classification algorithm was employed to delineate final irrigated class. Ground truth data collected for accuracy assessment included land cover type, irrigation practices and their geographic locations using a global positioning system. Accuracy assessment of the irrigated area map indicated that we achieved an overall mapping accuracy of 96% with omission and commission errors at 9% and 8%, respectively, which are mainly due to clouds and shadows of clouds. Irrigated acreages derived from the TM image closely matched with that from agricultural statistical reports for the 4-county area. This map will be used in the 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.

Validating Northern Texas High Plains Groundwater Model with Data from Observation Wells

Jairo E. Hernandez et al. — Fri, 26 Aug 2011 14:42:16 PDT

Diminishing groundwater supplies will severely reduce regional crop and animal production in the Northern High Plains of Texas where irrigated crop production accounts for a major portion of groundwater withdrawals from the Ogallala Aquifer. The objective of this study was to develop, calibrate and validate a groundwater model for a four-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. Hydrologic simulations were conducted using the MODFLOW-2000. The model was calibrated for predevelopment period by reproducing and comparing groundwater levels of the 1950s using steady state boundary conditions representing no change in the land use. Similarly, the model was calibrated for the period 1950-2000 with a transient model to account for agricultural development occurred during that period. The model was validated by simulating and comparing ground water levels with the observed data for the period 2001-2008. Calibration and validation results indicate that model performed satisfactorily. The calibrated model will be used to evaluate the effects of change in land use/land cover on sustainability of the aquifer life in the Texas High Plains.

Calibrating Northern Texas High Plains Groundwater Model

Jairo E. Hernandez et al. — Fri, 26 Aug 2011 14:35:04 PDT

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.

Vegetation Fraction Mapping with High Resolution Multispectral Data in the Texas High Plains

Susan O’Shaughnessy et al. — Fri, 26 Aug 2011 14:20:33 PDT

Land surface models use vegetation fraction to more accurately partition latent, sensible and soil heat fluxes from a partially vegetated surface as it affects energy and moisture exchanges between the earth's surface and atmosphere. In recent years, there is interest to integrate vegetation fraction data into intelligent irrigation scheduling systems to avoid false positive signals to irrigate. Remote sensing can facilitate the collection of vegetation fraction information on individual fields over large areas in a timely and cost-effective manner. In this study, we developed and evaluated a set of vegetation fraction models using least square regression and artificial neural network (ANN) techniques using RapidEye satellite data (6.5 m spatial resolution and on-demand temporal resolution). Four images were acquired during the 2010 summer growing season, covering bare soil to full crop cover conditions, over the USDA-ARS-Conservation and Production Research Laboratory in Bushland, Texas [350 11' N, 1020 06' W; 1,170 m elevation MSL]. Spectral signatures were extracted from 25 ground truth locations with geographic coordinates. Vegetation fraction information was derived from digital photos taken at the time of image acquisition using a supervised classification technique. Comparison of performance statistics indicate that ANN performed slightly better than least square regression models.

Modeling Groundwater Levels in the Northern High Plains of Texas

Jairo E. Hernandez et al. — Fri, 26 Aug 2011 13:09:37 PDT

Future changes in groundwater policy and the use of alternative technologies will have to be adopted for the Northern High Plains due to the depleting underlying Ogallalaaquifer, a major source of water for irrigation. The objectives of this study were to (1) calibrate and validate an integrated regional groundwater model using 1939-2008 observed water level data and (2) evaluate five agricultural management policyscenarios for four, heavily irrigated counties (Dallam, Sherman, Hartley, and Moorecounties) located in the northwest corner of the Texas Panhandle. For this purpose, the study area was divided into 1000-m cells and the MODFLOW-2000 model wascalibrated and validated using recorded groundwater levels. In order to evaluate the implications of five agricultural management policy scenarios proposed by theEconomics Group of the Ogallala Aquifer Program, the policy scenarios were translated into land use and irrigation management practices to develop input into MODFLOW.Calibration and validation results indicate that the integrated groundwater modelperformed satisfactorily. Currently, we are conducting groundwater simulations toevaluate policy implications. Simulation results from this study should assist groundwatermanagement districts to target specific water uses and promote the use of alternativetechnologies in the four-county area, and to potentially implement new policies for sustainable development of the Ogallala Aquifer.

Evaluation of Four Water Management Policies for Ogallala Aquifer Sustainability in the Texas High Plains

Jairo Hernandez et al. — Fri, 29 Jul 2011 10:17:56 PDT

Diminishing groundwater supply in the Ogallala Aquifer will severely reduce regional crop and animal production in the absence of a sustainable water management policy. It is essential to mitigate adverse impacts on the regional economy due to future withdrawals of the limited groundwater resource. Currently, approximately ten alternative water management policies are being debated by policy makers in the Central and Southern High Plains of the Ogallala Aquifer region. Before implementing any new policy or modifying current policies, newer alternative policies should be evaluated for their impact on groundwater levels with eventual extension to regional economic impacts. The main objective of this study was to evaluate four water management policies, from the debated ones, on future groundwater levels in the Ogallala Aquifer beneath four heavily irrigated counties (Dallam, Sherman, Hartley, and Moore) located in the northwest corner of the Texas High Plains using a calibrated ModFlow model. The four water management policies were (1) voluntary permanent conversion to dry land production up to 10% of the total irrigated area, (2) adoption of advances in biotechnology that allow water use reductions at a rate of 1% per year up to 10% of current use, (3) mandatory water use reduction to decrease the total water pumped by 10% (volume per unit land area per year), and (4) voluntary temporary conversion to dry land production during 15 years for a maximum area of 10% of the total irrigated area. The water management policies were converted into water demand rates for ModFlow model inputs. Simulations were conducted for a 50-year (2010-2060) period. Preliminary results indicate that a combination of more than one policy will be required to produce a significant reduction in the current groundwater depletion rates.

Groundwater Modeling of the Calera Aquifer Region in Central Mexico

Jairo E. Hernandez et al. — Fri, 29 Jul 2011 10:17:55 PDT

Calera Aquifer is the main source of water for irrigated agriculture, industrial and drinking water purposes in the Calera Aquifer Region (CAR) in the State of Zacatecas, Mexico. Irrigated agriculture accounts for 80% of total groundwater extracted from the Calera Aquifer. Limited rainfall and low agricultural water use efficiency in combination with fast growing industrial and urban water demand are contributing to groundwater depletion at an unsustainable rate. The main objective of this study was to develop and evaluate a groundwater modeling system using MODFLOW-2000 for the CAR. Predicted groundwater levels were compared with measured data collected from observation wells between 1954 and 2004. Performance statistics indicated that the model performed well in simulating historic groundwater levels in the central part of the CAR where irrigated agriculture is concentrated. However, further improvements in the conceptual model may be needed to improve predictions in other parts of the CAR.

Canal Structure Automation Rules Using an Accuracy-Based Learning Classifier System, a Genetic Algorithm, and a Hydraulic Simulation Model. I: Design

J. E. Hernandez et al. — Fri, 29 Jul 2011 10:17:53 PDT

Using state-of-the-art computational techniques, a genetic algorithm (GA) and an accuracy-based learning classifier system (XCS) were shown to produce optimal operational solutions for gate structures in irrigation canals. An XCS successfully developed a set of operational rules for canal gates through the exploration and exploitation of rules using a GA, with the support of an unsteady-state hydraulic simulation model. A computer program which implemented the XCS was used to develop operational rules to operate all canal gate structures simultaneously, while maintaining water depth near target values during variable-demand periods, and with a hydraulically stabilized system when demands no longer changed. This model can be applied to canal networks with constant or variable demands within the limits of current hydraulic simulation capabilities. The program output is a set of feasible and optimal operating rules for multiple gate structures, facilitating the automation of open-channel irrigation conveyance systems. Results from sample applications of this technique are presented in the companion paper.

Canal Structure Automation Rules Using an Accuracy-Based Learning Classifier System, a Genetic Algorithm, and a Hydraulic Simulation Model. II: Results

J. E. Hernandez et al. — Fri, 29 Jul 2011 10:17:52 PDT

An accuracy-based learning classifier system (XCS), as described in a companion paper (Part I: Design), was developed and evaluated to produce operational rules for canal gate structures. The XCS was applied together with a genetic algorithm and an unsteady hydraulic simulation model, which was used to predict responses to gate operation rules. In the tested cases, from 100 to 2,000 XCS simulations, each involving thousands of hydraulic simulations, were required to produce satisfactory rules. However, the overall fitness of the set of rules increased monotonically as XCS simulations progressed. Initial fitness started at an arbitrary value, and rules increased in strength by better achieving operational objectives during the training process. Fewer XCS iterations were required to increase the fitness as the rule population evolved. Calculated water depths approached the respective target depths for variable water delivery demand through turnout structures in the simulated canal systems. The water depth achieved stabilization inside a dead band of ± 8% of the target depth after applying different turnout demand hydrographs to each reach. The calculated depth was inside the dead band 92% of the time in Reach 1, and 73% of the time in Reach 2 for the constant supply experiment. The water depth was inside the dead band 100% of the time in Reach 1, and 76% of the time in Reach 2 for the variable-supply experiment.