High Plains Aquifer is the largest in the United States

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1 Quantifying the Impacts of Irrigation Technology Adoption on Water Resources in the High Plains Aquifer, USA High Plains Aquifer is the largest in the United States Anthony D. Kendall and David W. Hyndman Hosts 30% of US irrigated agriculture Widespread groundwater irrigaaon began in the 1930s Spans 8 states in the US Great Plains High Plains water levels are declining Irrigation technologies are changing Flood Center Pivot Water levels in the Southern and Central High Plains, in paracular, have steadily declined for decades In response to declining levels, farmers are adopang new irrigaaon technologies data from Haacker et al Low-Pressure Spray Kansas Water Information and Management System (WIMAS) Beginning in 1991, all water users in Kansas report annual water use Metered in most cases For the Kansas High Plains: 46,724 water rights 27,691 points of diversion Also report: Crop type Irrigated acreage Irriga&on system Low Energy Precision ApplicaAon (LEPA) WIMAS irrigation systems in 2014

2 LEPA has taken over the HPA How do these changes impact the hydrologic cycle? 1996 Farm Bill: Environmental Quality IncenAves Program (EQIP) Add mulaple technologies to an irrigaaon module within the Landscape Hydrology Model Landscape Hydrology Model (LHM) Central High Plains LHM Domain LHM is: Process-based, fully-distributed, hourly LHM uses: Widely available climate, soils, GIS data Simulate ~385,000 km 2 with 1 km 2 cells Select LHM inputs: precipitation 7-year simulation period Use model/data fusion products such as NLDAS provides consistent, hourly weather inputs since 1979 Significant climate gradients across the domain Normal: : ~55 cm/yr Drought: : ~35 cm/yr Recovery: : ~46 cm/yr

3 Improved irrigation module in LHM Data: USGS, ca Implement 4 types of spray irrigaaon technologies Each event: 4 cm of 2 cm/hr Two types of automaac irrigaaon ET-based scheduling: replace water used by the crop Soil moisture scheduling: triggered when soil moisture reaches 40% of plant available water Responds to changes in soil moisture, precipitaaon, and plant transpiraaon Fluxes change in response to canopy height variaaons Irrigation conceptualization Implement four technologies LHM already simulates: Return flow Depression and canopy Runoff Need new model for wind drif Wind drift evaporation loss modeling Equa%on Reference!"#$= ! Sadeghi et al. 2015!"#$= ! Playan et al. 2004!"#$= ! King et al. 2012!"#$= ! Dechmi et al. 2003!"#$= !+0.231% Playan et al. 2005!"#$= !+0.47% Faci et al Assumed linear scaling between 2m and 0m of spray height above the canopy FuncAons of windspeed (ms -1 ) and temperature (C) Take an ensemble of staasacal models in the literature A wide range of esamates ET Strong ET gradient from east (weger) to west (dryer) Secondary pagerns relate to land use and soil types groundwater recharge water budget Soil texture is the dominant pagern Land use (wetlands vs. uplands) is a secondary pagern TerAary pagern driven by gradients in precipitaaon ET dominates the water budget, runoff is the second component Upland water budget (shown here) is subsidized by irrigaaon, paracularly in drought years

4 irrigation demand irrigation drift evaporation Highly heterogeneous irrigaaon demand: soil texture, precipitaaon, LAI dependent In the western secaon, irrigaaon can represent up to 80% of annual precipitaaon Complex pagern of total drif Drif as a percent of irrigaaon higher in the east: windspeed gradients? irrigation return flow Evaporation losses across irrigation types IrrigaAon return flow driven by total irrigaaon water applicaaon and soil texture In the western region makes up 60-85% of total groundwater recharge from irrigated areas Why not use LEPA everywhere? LEPA is incredibly efficient for an above-ground irrigaaon method But it requires: Circular plowing CreaAng furrow dikes to help retain water Can only be used in very flat fields, <1% slope on average Finer textured soils to laterally distribute soil moisture Drif dominates losses On average, 16-19% of water lost by highelevaaon spraying LEPA can be as much as 98% efficient Conclusions and Future Work Conclusions Implemented an automated, dynamic, mule-technology irrigaaon module that responds to soil, plant, and climate condiaons Results differenaated by technology, paracularly for wind drif losses These results fall well within the range of those reported in literature Future work SpaAally and temporally distribute technologies: driven by data or technology adopaon model Link to an integrated surface-/ground-water simulaaon

5 High-pressure spray irrigation Low-pressure sprayers Large water loss due to wind drif and from high-pressure, highelevaaon spray heads StarAng in the late 1970s, low pressure technologies began to replace high-pressure pivots Water ApplicaAon Efficiencies as low at 70% in windy condiaons Low-Energy Precision Application (LEPA) Much less wind drif and Can be driven by lower capacity wells Come in a variety of elevaaons Spraying above, below, or within canopy High Plains Aquifer Designed in the mid 1980s Apply directly at ground level to only every other row Greatly reduced losses Requires new management of soil, plants, water Select LHM Inputs: Soils Soil polygons from SSURGO Hydraulic properaes mapped to soil texture An extraordinary but challenging dataset to use Select LHM Inputs: Leaf Area Index LAI data from MODIS sensor Every 8 days at 1 km resoluaon since 2000 Irrigated agriculture clearly visible as higher LAI

6 Model limitations Wind drif modeling: does not account for irrigaaon drop size Very wide range of esamates of, and empirical equaaons to calculate it, within the literature Not all aspects of LEPA fully implemented in the model Automated scheduling needs improvement ET-scheduling rouane under-applies water, Soil moisture scheduling is relaavely insensiave to technology These results produce too much irrigaaon return flow due to drainage Need to adjust scheduling parameters IrrigaAon model validaaon m/year Compare to spaaally explicit pumping esamates for the Republican River Basin, in the NHP Slight biases in averages: High in the East Low in the West IrrigaAon model validaaon Model captures 2012 drought year irrigaaon well, but oversimulates in dryer years OpAmal pumping versus actual behavior model is probably applying too much water Note the years, rush to pump sall present in process model, like it was with the staasacal model