Management Application: Volume of Water in Storage (An Ogallala Example with Applicability to All Aquifers in Texas) Judy A. Reeves, Ph.D. Hydrogeologist, High Plains Water District Ken Rainwater, Ph.D., P.E., DEE Director, Water Resources Center Texas Tech University

Storage Volumes: Calculation Methods Planimeter method Mass balance GIS MODFLOW (or other) models GAM model with GIS

Texas Tech Modflow Model Pre-GAM model for Region O Contract stipulated high calibration standards, variable S y Emphasized distribution of pumping and irrigation return flow (IRF) High recharge values attributed to IRF Reported unmet demands Dry cell problem

LERWPG “Cedar Pencil Model” Areas between contour lines on saturated thickness map by planimeter Multiplied area by mean saturated thickness, S y = 0.15 Accurate for year mapped Unable to project into the future

GAM Run Steady-state model Predevelopment conditions (1940) Calibrate hydraulic conductivity and recharge Transient model Built on steady-state calibration Uniform pumping distribution on irrigated lands Refined calibration mainly through enhanced recharge (both irrigated and nonirrigated lands) Margins of model domain have many dry or flooded cells

GAM Run Used new demand numbers approved by the TWDB in Sept 2003 No recalibration of previous GAM model

Mass Balance Not a hydrologic model Annual volumetric calculations Begin with initial storage volume Subtract new demand volumes Add average recharge

Water managers selling volumes to their constituents ….

Data Gaps Recharge Pump distribution Specific yield Base of aquifer

Recharge Issues Predevelopment or without cultivation Regional values <0.5 in/yr Playa lakes focus recharge Model calibration Pre-GAM model for <0.5 in/yr in uncultivated lands Average 2.75 in/yr in irrigated, cultivated lands Also calibrated pumpage distribution GAM runs Calibrated recharge, but not pumpage distribution

Recharge Issues Need for field observations Know Winter depth to water (-> saturated thickness) Crop patterns Need Local withdrawal estimates Local precipitation measurements Find Combination of recharge and IRF

Pumping Distribution Greatest uncertainty in planning process Irrigation estimates for maximum yield tend to be much larger than actual Metering underway in several conservation districts Some IRF needed to flush salts Withdrawal will cease when saturated thickness gets too small

Specific Yield Original estimates from Knowles et al. (1984) field and modeling work Range 0.08 < S y < 0.24 Relatively insensitive for head calibration over long periods Directly proportional to storage volume Can be refined if recharge, IRF, and withdrawals are precise

Base of Aquifer Map Thousands of well logs for dataset Occasional debate about pick for aquifer bottom Ogallala/Cretaceous Ogallala/Dockum GIS tools allow easier review of data Bottom topography affects flow

Conclusions Groundwater management plans accept uncertainties Uncertainties can be tested and refined with modern modeling techniques Model results must be compared to real data Real data drive refinement of the modeling tools

Recommendations Local involvement with process Local districts and landowners are closest to local storage and production data Large production areas can be broken down into understandable pieces Interaction with modelers Critical review of model results Refinement of calibrated parameters