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Numerical Model for the High Plains Aquifer GAM

A Presentation To the Representatives of:

Presented By:

February 18, 2015

GMA-1

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Presentation Outline

Background Brief Summary of Draft Numerical Model Construction/Calibration

(stay for the SAF if you want the full story) Proof-of-concept Ogallala MAG run Elicitation of potential MAG run characteristics Schedule

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Groundwater Availability Models as a Planning Tool

Aim of the TWDB GAM program: Develop groundwater flow models for the major and minor aquifers of Texas.

Create tools that can be used to aid in groundwater resources management by stakeholders

These models are updated periodically as new data or science becomes available

GAMs Used For: TWDB provides groundwater conservation districts with water budget

data for their management plans. Groundwater management areas can use to assist in determining

desired future conditions. TWDB uses when calculating estimated Modeled Available

Groundwater.

Paraphrased from TWDB slides what will be presented in SAF meeting.

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Study Area

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Aquifers in the Study Area

High Plains AquiferSystem

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Model Layer Representation

Jigmond, 2012

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Model Layer Representation

Head Boundaries

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Model Grid 932 rows x 580 columns 2640 ft square grid cells Oriented exactly north-

south in the GAMCS Oriented with previous

Southern Ogallala and Dockum models

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Model Grid Base active areas based

on grid centroids Smoothing to remove

corner connections, small islands and peninsulas

Without smoothing, steady-state model does not converge

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Model Grid Base active areas based

on grid centroids Smoothing to remove

corner connections, small islands and peninsulas

Without smoothing, steady-state model does not converge

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Structure on Grid

Structure from conceptual model used to set grid cell elevations

IBOUND carries key for what model cells represent

Where Permian is at surface, model is inactive for all layers

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Structure on Grid

Lower DockumUpper Dockum

OgallalaETHP

Structure from conceptual model used to set grid cell elevations

IBOUND carries key for what model cells represent

Where Permian is at surface, model is inactive for all layers

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Ogallala Base Comparison: OGLL_N - HPAS

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Draft Transient Calibration

Model goes from 1929 (SS) to 2012, with 84 annual SPs

Kh/Kv were modified somewhat from steady-state (fields were shown previously)

Specific storage not changed

Sy not changed Pumping is the big

driver in the Ogallala

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Transient Calibration

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Draft Transient Calibration

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Transient Calibration

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Draft Predictive Simulation: Ogallala Aquifer

Ogallala availability primarily dependent on saturated thickness and specific yield

Specific yield was not modified significantly during calibration (some differences due to scale and interpolation)

Saturated thickness is therefore the primary driver for availability, with contributions from areal and focused recharge, and some dependence on hydraulic conductivity (lateral flow)

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Comparison between OGLL_N (existing GAM) and HPAS GAM for year 2004

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Draft Predictive Simulation

HPAS GAM a slightly “wetter” model than OGLL_N in GMA-1

Area of GMA-1 with Ogallala Aquifer footprint ~10,000,000 acres

10 foot average increase in saturated thickness creates an positive difference of approximately 17,000,000 AF volume in storage, for Sy = 0.17

The Mean Absolute Error has historically been ~30-35 feet for Ogallala models

Volume in Place (AF)County OGLL_N HPAS

Armstrong 3,045,005 5,107,476

Carson 13,781,335 16,680,562

Dallam 22,152,496 23,064,117

Donley 5,334,284 4,674,845

Gray 13,063,030 14,547,173

Hansford 20,994,195 24,455,875

Hartley 25,138,232 22,311,017

Hemphill 14,805,111 17,668,732

Hutchinson 11,069,395 10,845,997

Lipscomb 20,463,052 20,403,691

Moore 11,548,667 11,374,311

Ochiltree 19,767,265 21,610,632

Oldham* 244,180 1,843,091

Potter 2,074,081 1,573,183

Randall* 1,749,823 3,802,573

Roberts 31,121,829 29,883,125

Sherman 18,231,075 22,415,147

Wheeler 7,702,560 7,845,703 Total 242,285,615 260,107,250 2004 Condition

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Draft Predictive Simulation

Initialized predictive simulation with 2004 heads (same as 12-005 MAG report) Used exponential decay to reach target fractions after 50 years on a cell-by-cell

basis

Attempted to keep a minimum 30’ saturated thickness in a cell

MODFLOW-NWT curtails pumping as minimum saturated thickness approaches

Less dry areas than in previous model

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Draft Predictive Simulation: Final Fraction of Initial

Initialized predictive simulation with 2004 heads (same as 12-005 MAG report) Used exponential decay to reach target fractions after 50 years on a cell-by-cell

basis

Goal was to keep a minimum 30’ saturated thickness in a cell

Armstrong 0.50Carson 0.50Dallam 0.40Donley 0.49Gray 0.50Hansford 0.50Hartley 0.40Hemphill 0.79Hutchinson 0.49Lipscomb 0.50Moore 0.40Ochiltree 0.50Oldham 0.55Potter 0.49Randall 0.48Roberts 0.50Sherman 0.40

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Draft Predictive Simulation

Volume in Place by Year

2010 2020 2030 2040 2050 2060Armstrong 5,107,476 4,469,125 3,897,553 3,392,414 2,951,426 2,570,971 Carson 16,680,562 14,728,241 12,894,565 11,216,033 9,707,576 8,369,330 Dallam 23,064,117 19,140,860 15,955,148 13,306,621 11,112,760 9,309,384 Donley 4,674,845 4,088,336 3,546,332 3,071,089 2,664,759 2,324,335 Gray 14,547,173 12,811,881 11,201,228 9,745,751 8,448,603 7,307,935 Hansford 24,455,875 21,541,060 18,868,047 16,453,385 14,293,631 12,371,932 Hartley 22,311,017 18,534,646 15,427,053 12,851,175 10,729,056 8,993,173 Hemphill 17,668,732 16,948,136 16,174,106 15,405,970 14,665,975 13,961,350 Hutchinson 10,845,997 9,629,457 8,421,224 7,297,333 6,282,181 5,380,363 Lipscomb 20,403,691 17,856,495 15,588,687 13,580,507 11,810,745 10,257,284 Moore 11,374,311 9,554,681 7,996,841 6,664,030 5,537,357 4,592,088 Ochiltree 21,610,632 19,068,668 16,719,393 14,582,708 12,663,948 10,952,761 Oldham 1,843,091 1,587,027 1,374,086 1,214,176 1,102,663 1,033,084 Potter 1,573,183 1,370,326 1,187,489 1,029,413 896,343 786,239 Randall 3,802,573 3,294,773 2,845,162 2,454,117 2,125,902 1,862,714 Roberts 29,883,125 26,408,985 23,157,745 20,178,265 17,481,386 15,066,626 Sherman 22,415,147 18,855,800 15,804,857 13,194,818 10,976,366 9,099,733 Wheeler 7,845,703 6,849,980 5,969,529 5,199,264 4,529,369 3,950,495 Total 260,107,250 226,738,477 197,029,045 170,837,069 147,980,046 128,189,797

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Draft Predictive Simulation

Status based on potential for improved calibration to heads

Fraction of volume in place compared to INTERA (2010) reported values

> 1 indicates greater volume in place from HPAS GAM< 1 indicates less volume in place from HPAS GAM

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Draft Predictive Simulation

Rate by Year (AFY)

2010 2020 2030 2040 2050 2060Armstrong 74,191 69,195 62,951 56,490 50,663 45,413 Carson 237,658 236,171 219,032 197,959 176,332 155,250 Dallam 461,265 387,020 335,074 288,611 248,818 212,523 Donley 81,136 88,781 85,911 79,611 72,328 65,112 Gray 204,186 203,471 189,739 172,294 155,118 138,256 Hansford 361,655 342,563 311,783 280,172 250,653 223,314 Hartley 421,416 359,034 310,137 265,484 226,409 191,853 Hemphill 72,786 82,499 90,103 93,226 92,915 91,376 Hutchinson 164,365 161,598 146,991 130,429 114,822 100,426 Lipscomb 289,775 309,572 293,562 270,037 247,136 224,591 Moore 238,964 210,010 185,960 160,016 136,417 115,429 Ochiltree 315,317 306,306 282,803 255,275 228,621 203,162 Oldham 22,181 25,413 24,489 22,809 20,958 18,673 Potter 20,424 19,797 17,839 15,934 14,157 12,461 Randall 77,292 73,985 66,704 58,949 50,580 43,455 Roberts 420,286 466,509 432,350 391,659 351,307 311,201 Sherman 455,693 418,952 371,206 322,841 279,464 240,034 Wheeler 121,886 140,876 139,592 132,526 123,824 114,579

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Draft Predictive Simulation

Status based on potential for improved calibration to heads

Fraction of production rate compared to 12-005 reported values

> 1 indicates greater rate from HPAS GAM< 1 indicates lesser rate from HPAS GAM

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Future MAG Runs for GMA-1

Ogallala Initial year Duration Fraction remaining Decline curve

Rita Blanca Average drawdown, duration

Dockum Average drawdown, duration

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