Groundwater Recharge Response to Reduced Irrigation Pumping: Checkbook Irrigation and the Water Savings Payment Plan
<p>Conceptual diagram of water savings and hypothetical case study. The lag time is defined by the amount of time that elapses following a reduction in pumping but before recharge rates begin to decrease. Lag times are a function of the depth to groundwater, soil water states and fluxes, and soil hydraulic parameters. Also note that the water savings are flat after 3 years, meaning no additional benefit, and that future management decisions can reduce water savings if pumping rates return to their initial rates or if field experiences prolonged periods of dry conditions.</p> "> Figure 2
<p>Location of the three study sites near Brule, NE (red dot on USA). Each site is ~65 ha in area and primarily under irrigated maize production. White outlines are SSURGO soil boundaries. Field sites are S1, S3 and S4 from west to east.</p> "> Figure 3
<p>Results of time-repeat ECa mapping from the Dualem 21S instrument (deep signal ~0–3.2 m) and the corresponding 1st EOF reprojected spatially for each of the three 65 ha study sites (see <a href="#water-16-02910-t001" class="html-table">Table 1</a> for sample dates). Warm EOF colors indicate drier zones/coarser soil texture and cooler colors indicate wetter zones/finer soil texture compared to the field average. White lines are SSURGO soil boundaries. White dots are locations of core extraction (20 November 2017). Red dots are the location of the groundwater observation well (closest well to S1 was ~0.4 km away and not pictured here). Geophysical data layers can be found in <a href="#app1-water-16-02910" class="html-app">Files SI1–SI3</a>.</p> "> Figure 4
<p>Volumetric water content (VWC) and chloride (Cl<sup>−</sup>) concentration profiles of soil cores extracted from the three field sites. Line colors correspond to EOF values determined at the core location (e.g., warm colors correspond to negative EOF values, green colors correspond to near-zero EOF values, and cool colors correspond to positive EOF values; see <a href="#water-16-02910-f003" class="html-fig">Figure 3</a>). Sawtooth patterns observed in VWC and Cl- profiles align with changes in soil textures. Data from this analysis can be found in SI4.</p> "> Figure 5
<p>Numerical modeling results of annual deep drainage; 2012 was an exceptionally dry year with 36% of average precipitation falling for that year. Bar colors correspond to EOF values determined at the core location (e.g., warm colors correspond to negative EOF values, green colors correspond to near-zero EOF values, and cool colors correspond to positive EOF values).</p> "> Figure 6
<p>Volumetric water content profiles from the core analysis overlain onto numerical modeling outputs. Bands are the minimum and maximum of ranges of the simulated VWC profiles and dashed lines are the corresponding simulated mean over the 10-year simulation period. Lines with circles are from the extracted volumetric analysis from core. Line and band colors correspond to the EOF values determined at the core location (e.g., warm colors correspond to negative EOF values, green colors correspond to near-zero EOF values, and cool colors correspond to positive EOF values).</p> "> Figure 7
<p>Correlation between root zone depth integrated VWC for extracted cores and the corresponding simulated root zone depth-integrated VWC (10-year average). EOF values at each core location from the repeat geophysical analysis separate the relative ranges of depth integrated VWC for both the extracted cores and simulated soil profiles. Solid line is 1:1 and dashed line is best fit to data.</p> "> Figure 8
<p>Time series of model output determined at one core (S4C) from two paired simulations that vary only in irrigation scheduling routines. In this case, the lag time is approximately 2.5 years long (determined visually when recharge reductions begin to increase). Water savings are calculated as a cumulative reduction in pumping minus the sum of the cumulative reduction in recharge and ET.</p> "> Figure 9
<p>Time series of simulated water savings calculated from the paired simulations for each core. Cores with coarser soil textures (S1A, S3E, and S4A) had the largest water savings as a result of a reduction in ET.</p> "> Figure 10
<p>Sensitivity analysis of weather year on estimated lag times and water savings. In both panels, simulations were carried out where a continuously repeated dry year is in red, a continuously repeated wet year is in blue, and the 10-year observed weather is in green. The 10th and 90th percentile weather years were selected for this analysis.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of Study Sites
2.2. Electromagnetic Induction Geophysical Mapping
2.3. Sampling Strategy and Soil Core Extraction
2.4. Laboratory Analysis
2.5. Soil Hydraulic Property Measurement
2.6. Chloride Mass Balance
2.7. Numerical Modeling
3. Results
3.1. Electromagnetic Induction
3.2. Laboratory Chemical Analysis
3.3. Laboratory Soil Physics
3.4. Chloride Mass Balance Analysis
3.5. Numerical Modeling
3.5.1. Validation of Numerical Model Water Balance Components
3.5.2. Lag Times and Water Savings
4. Discussion
4.1. Agronomic Reasonableness of Fluxes
4.2. Applications to Subfield Soil Hydrology
4.3. Impact on Water Savings
4.4. Optimizing Water Savings
4.5. Water Savings within Corporate Social Responsibility Programs
4.6. Benefits Other than Water Savings
4.7. Limitations of This Study
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Analysis/Methodology | Description |
---|---|
Geophysical surveys of field sites | Time-lapse electromagnetic induction and epithermal neutron intensity spatial mapping. Used to select 3 soil core sites with different soil texture. Spatial resolution is ~10 m. |
Soil cores | 6 m cores extracted with geoprobe, cut into 30 cm sections and capped for storage for laboratory analysis. Gravimetric water content measured in laboratory. Gravimetric data are used to validate model simulations. Point measurement so may not be representative of field or geophysical area. |
Soil hydraulic properties (θr, θs, n, α, Ksat) | Undisturbed soil cores extracted from 30 cm sections from geoprobe. Van Genuchten–Mualem parameters measured in laboratory using Hyprop, W4PC, and falling head tests on same core. Data from 3 instruments are used to fit to model with Hyprop software. Hydrus model results are highly sensitive to soil hydraulic properties especially n and α for unsaturated flow simulations. |
Meteorological forcing (i.e., rainfall, air temp., air press., rel. hum., wind speed, ref. ET) | Hourly data provided by nearby Nebraska Mesonet station. Data are used to force the Hydrus and crop models. Nebraska Mesonet provides high quality data across the state used by many state and federal sources. Forcing data are highly sensitive on model results. |
Irrigation | Some yearly totals provided by flow meter from producer. Irrigation values used to validate irrigation algorithm in Hydrus model. |
Water table elevation | Static elevation provided by local ground water wells, used to justify free drainage lower boundary conditions at 6 m depth. Free drainage boundary condition should have a minimal impact on Hydrus model simulations. |
Deep drainage | Directly measured with chloride mass balance approach. Used to validate Hydrus model simulations. |
Crop yield | Provided by producer from combine data. Crop data are used as a proxy for annual ET and its year to year variation with weather conditions. For water limited crops, a linear relationship typically exists between ET and yield. |
Crop model | Used to validate crop yield and variable root growth. Crop model results used in Hydrus simulations for partitioning E and T and root water uptake functions with depth. |
Field Site | ECa Survey Dates | Explained Variance of 1st EOF (%) |
---|---|---|
S1 | 2016: 11 March 2017: 2 and 3 May | 96 |
S3 | 2016: 11 March 2017: 2 and 3 May | 91 |
S4 | 2016: 11 March 2017: 2 and 3 May | 69 |
Field Site | ||||
---|---|---|---|---|
Irrigation Depth Summary | S1 | S3 | S4 | |
Data source | Flow meter | 9 | - | - |
Pivot telemetry | - | 2 | 3 | |
Farmer record | - | 3 | 3 | |
Number of years with data | 9 | 5 | 6 | |
Average depth (mm) | 390 | 310 | 460 | |
Depth to groundwater (m) | 6–8 | 6 | 16 | |
Irrigation water summary | S1 | S3 | S4 | |
Number of dates sampled over 2 growing seasons (2016 and 2017) | 5 | 3 | 5 | |
Average Cl− (mg L−1) | 115 (2) | 121 (3) | 122 (3) |
Core | Depth (m) | (cm3 cm−3) | (cm3 cm−3) | n (-) | Ksat (cm day−1) | |
---|---|---|---|---|---|---|
S1A | 0.3 | 0.039 | 0.328 | 0.018 | 2.176 | 30.6 |
S1A | 1.8 | 0 | 0.367 | 0.0158 | 1.644 | 391 |
S1A | 3.4 | 0.054 | 0.399 | 0.0241 | 1.599 | 317 |
S1A | 4 | 0.03 | 0.45 | 0.0132 | 1.486 | 110 |
S1A | 5.2 | 0.044 | 0.547 | 0.0176 | 1.262 | 415 |
S1A | 6.1 | 0 | 0.527 | 0.0517 | 1.182 | 7516 |
S1D | 0.3 | 0.038 | 0.45 | 0.0087 | 1.433 | 5.8 |
S1D | 1.2 | 0.037 | 0.438 | 0.0357 | 1.322 | 2191 |
S1D | 2.1 | 0 | 0.452 | 0.0226 | 1.316 | 109.9 |
S1D | 2.7 | 0.036 | 0.438 | 0.0235 | 1.608 | 102 |
S1D | 3.4 | 0.038 | 0.495 | 0.0168 | 1.263 | 15.6 |
S1D | 4.3 | 0.04 | 0.475 | 0.0035 | 1.399 | 0.5 |
S1D | 5.8 | 0 | 0.36 | 0.0123 | 1.205 | 0.6 |
S1E | 0.3 | 0 | 0.56 | 0.3303 | 1.135 | 10,000.0 * |
S1E | 1.2 | 0.036 | 0.321 | 0.0136 | 1.491 | 36.1 |
S1E | 1.8 | 0.059 | 0.398 | 0.0575 | 1.119 | 10.8 |
S1E | 3 | 0.073 | 0.481 | 0.0232 | 1.213 | 15.6 |
S1E | 4.6 | 0.061 | 0.432 | 0.0041 | 1.308 | 0.3 |
S1E | 5.5 | 0.048 | 0.417 | 0.0046 | 1.378 | 1.5 |
S3A | 0.3 | 0 | 0.499 | 0.0574 | 1.075 | 187.6 |
S3A | 1.2 | 0 | 0.475 | 0.0127 | 1.42 | 333.3 |
S3A | 1.5 | 0.048 | 0.593 | 0.0826 | 1.15 | 89.3 |
S3A | 4 | 0.066 | 0.534 | 0.0518 | 1.079 | 207.8 |
S3A | 4.3 | 0.042 | 0.469 | 0.0044 | 1.15 | 0.6 |
S3A | 5.2 | 0.063 | 0.435 | 0.0372 | 1.847 | 40.1 |
S3C | 0.3 | 0.063 | 0.41 | 0.0653 | 1.133 | 68 |
S3C | 1.2 | 0.041 | 0.446 | 0.0999 | 1.203 | 1806.6 |
S3C | 1.5 | 0 | 0.55 | 0.0389 | 1.379 | 177.2 |
S3C | 2.7 | 0.043 | 0.356 | 0.0566 | 2.54 | 492 |
S3C | 3.4 | 0.064 | 0.583 | 0.011 | 1.118 | 1.8 |
S3C | 5.2 | 0.027 | 0.473 | 0.0525 | 3.564 | 464 |
S3E | 0.3 | 0 | 0.306 | 0.0621 | 1.588 | 401.2 |
S3E | 0.9 | 0.048 | 0.31 | 0.1201 | 1.535 | 1294 |
S3E | 1.5 | 0.029 | 0.38 | 0.1034 | 1.86 | 10.4 |
S3E | 2.4 | 0.027 | 0.436 | 0.0152 | 2.784 | 22.5 |
S3E | 3.7 | 0 | 0.376 | 0.0197 | 1.19 | 0.9 |
S3E | 4.3 | 0.036 | 0.432 | 0.0622 | 2.201 | 7 |
S3E | 5.2 | 0.033 | 0.368 | 0.1029 | 1.556 | 39.8 |
S3E | 6.1 | 0.032 | 0.387 | 0.0808 | 2.577 | 259 |
S4A | 0.3 | 0.036 | 0.325 | 0.0131 | 2.561 | 592 |
S4A | 1.2 | 0.054 | 0.422 | 0.0375 | 1.313 | 3478 |
S4A | 2.4 | 0 | 0.499 | 0.0365 | 1.261 | 54 |
S4A | 3.7 | 0.034 | 0.467 | 0.0336 | 1.391 | 521 |
S4A | 4.6 | 0 | 0.297 | 0.3742 | 1.203 | 311 |
S4A | 5.8 | 0.038 | 0.512 | 0.0116 | 1.316 | 52.5 |
S4C | 0.3 | 0.081 | 0.545 | 0.0564 | 1.176 | 5025 |
S4C | 1.2 | 0.028 | 0.533 | 0.2994 | 1.175 | 10,000.0 * |
S4C | 1.8 | 0.027 | 0.459 | 0.0216 | 1.502 | 33.6 |
S4C | 3.4 | 0.033 | 0.319 | 0.0228 | 1.736 | 21.7 |
S4C | 4.3 | 0 | 0.345 | 0.2216 | 1.485 | 6436 |
S4C | 5.5 | 0 | 0.407 | 0.0475 | 1.375 | 7.8 |
S4C | 6.1 | 0 | 0.402 | 0.0378 | 1.562 | 29.5 |
S4D | 0.3 | 0.033 | 0.522 | 0.1255 | 1.162 | 7635.8 |
S4D | 1.2 | 0.016 | 0.462 | 0.0198 | 1.279 | 5.7 |
S4D | 2.1 | 0.028 | 0.372 | 0.0157 | 2.012 | 59.5 |
S4D | 3.7 | 0.03 | 0.399 | 0.0191 | 2.105 | 353.4 |
S4D | 4 | 0.047 | 0.344 | 0.0249 | 3.217 | 1261 |
S4D | 5.2 | 0 | 0.333 | 0.063 | 1.954 | 36.7 |
S4D | 5.8 | 0 | 0.422 | 0.017 | 2.646 | 321 |
Core | Deep Drainage (mm yr−1) | % of CMB | Pumping Depths (mm yr−1) | % of Observed | Total Stored Years of Cl− | NO3-N Leaching (kg ha−1 yr−1) | ||
---|---|---|---|---|---|---|---|---|
CMB | Model | Observed | Modeled | |||||
S1A | 435 | 336 | 77 | 390 | 427 (H) | 110 | 3 | 39 |
S1D | 135 | 148 | 110 | 7 | 8 | |||
S1E | 187 | 163 | 87 | 7 | 18 | |||
S3A * | 321 | 117 | 36 | 310 | 304 (H) | 98 | 5 | 12 |
S3C | 166 | 131 | 79 | 9 | 3 | |||
S3E | 271 | 216 | 80 | 5 | 10 | |||
S4A | 515 | 384 | 75 | 460 | 420 (H) | 91 | 2 | 21 |
S4C | 205 | 180 | 88 | 3 | 18 | |||
S4D | 215 | 181 | 84 | 5 | 13 | |||
Mean | 272 | 206 | 80 | - | - | 100 | 5 | 16 |
Median | 215 | 180 | - | - | - | - | - | 13 |
Yield at Core Location (Mg ha−1) | |||||
---|---|---|---|---|---|
Field Site | Year | Crop | S1A | S1D | S1E |
S1 | 2015 | Soybean | 2.1 | 4.0 | 4.3 |
2016 | Maize | 9.7 | 12.9 | - | |
2017 | Maize | 7.4 | 10.8 | 13.2 | |
S4A | S4C | S4D | |||
S4 | 2016 | Soybean | 3.9 | 5.9 | 5.9 |
2017 | Maize | 10.0 | 14.0 | 14.6 |
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Gibson, J.; Franz, T.E.; Gilmore, T.; Heeren, D.; Gates, J.; Thomas, S.; Neale, C.M.U. Groundwater Recharge Response to Reduced Irrigation Pumping: Checkbook Irrigation and the Water Savings Payment Plan. Water 2024, 16, 2910. https://doi.org/10.3390/w16202910
Gibson J, Franz TE, Gilmore T, Heeren D, Gates J, Thomas S, Neale CMU. Groundwater Recharge Response to Reduced Irrigation Pumping: Checkbook Irrigation and the Water Savings Payment Plan. Water. 2024; 16(20):2910. https://doi.org/10.3390/w16202910
Chicago/Turabian StyleGibson, Justin, Trenton E. Franz, Troy Gilmore, Derek Heeren, John Gates, Steve Thomas, and Christopher M. U. Neale. 2024. "Groundwater Recharge Response to Reduced Irrigation Pumping: Checkbook Irrigation and the Water Savings Payment Plan" Water 16, no. 20: 2910. https://doi.org/10.3390/w16202910