Plain Aquifer Research Articles

A roadblock on the path to aquifer sustainability: underestimating the impact of pumping reductions

Depletion of aquifers across the globe is challenging our ability to maintain critically needed agricultural production and provide potable water supplies for millions. In most cases, the only option to decrease the rate of depletion is to reduce the pumping of groundwater. Although implementation of large-scale pumping reductions in the absence of alternative water sources has proven difficult, recent work has shown that locally based, stakeholder-driven initiatives, coupled with regulatory oversight, can be a promising path forward. A critical question is how much must pumping be reduced to have a significant impact on decline rates. Data limitations and modeling uncertainties, however, have frustrated efforts to answer this question with reliable estimates of the needed reductions. We address this situation using a variant of the water-balance equation to identify a key factor, the misestimation of specific yield, that is limiting our ability to assess the impact of proposed pumping reductions. We find that common modeling practices can lead to large overestimates of the required pumping reductions, thereby inadvertently discouraging conservation efforts. We demonstrate the importance of this general finding using data from the High Plains Aquifer in the central United States where common practices have led to overestimates of required pumping reductions by a factor of three to six. We introduce a new metric, the coefficient of variation of net inflow, to help identify such conditions. The reliability of estimates of the impact of pumping reductions can be greatly improved when the constraints imposed by this new metric are combined with a recently proposed method for estimation of specific yield from field data. The ramifications of these findings are far reaching, as defensible estimates of the impact of proposed pumping reductions are an essential element of efforts to chart more sustainable paths for the world’s heavily stressed aquifers.

Groundwater hydrogeochemical assessment using advanced spatial statistics methods: a case study of Tehran-Karaj plain aquifer, Iran

Geostatistical tools have been increasingly applied to compile spatial distribution maps of groundwater quality. Results of geostatistics can be helpful for decision-makers to carry out appropriate remedial actions to sustain the quality of groundwater sources. The main purpose of this paper is to assess the groundwater hydrogeochemistry of Tehran-Karaj plain aquifer, Iran, by a forward geostatistical method and an inverse geostatistical method called sequential Gaussian simulation. Seven important hydrogeochemical properties of the aquifer including total dissolved solids, sodium adsorption ratio, electrical conductivity, sodium, total hardness, chloride, and sulfate were analyzed and compiled geostatistically. Data were taken from 137 well samples in 2016. After data normalization, variography was compiled, and experimental variograms were plotted; then, the best theoretical model was fitted on each variogram based on the minimum residual sum of squares. Cross-validation was used to determine the accuracy of parameters related to the variograms. Estimation maps of the groundwater hydrogeochemistry were prepared, and the estimation variance map was drawn to assess the accuracy of estimation in each estimated point. Forward geostatistical methods are subjected to smoothing, while inverse geostatistical methods are not subjected to this problem. The results of this study revealed that the utilized inverse geostatistical methods called simulation algorithms are more accurate than forward methods. Eventually, estimation maps of each parameter, as well as error maps, were compiled, and critical regions have been proposed according to the simulated maps.

Property rights and groundwater management in the High Plains Aquifer

Groundwater is an important natural resource that needs to be managed dynamically. Ideally, institutions governing property rights to the groundwater of low-recharge aquifers should not discourage or disincentivize groundwater users from dynamic management. We develop an empirical model to examine whether agricultural groundwater users faced with prior appropriation property rights to groundwater in western Kansas exhibit dynamic, forward-looking behavior consistent with dynamic management. We find that although farmers are allotted a time-invariant maximum amount of groundwater that they can extract each year, they still behave in a manner consistent with dynamic management. Their groundwater extraction decisions are not significantly affected by the quantity they are authorized to extract, but are instead affected by expected future crop prices, expected future energy prices, and groundwater extraction by neighbors. Our results provide evidence that farmers manage their groundwater resource dynamically, even if their property rights do not necessarily encourage or incentivize them to do so.

Self-optimizer data-mining method for aquifer level prediction

Abstract Groundwater management requires accurate methods for simulating and predicting groundwater processes. Data-based methods can be applied to serve this purpose. Support vector regression (SVR) is a novel and powerful data-based method for predicting time series. This study proposes the genetic algorithm (GA)–SVR hybrid algorithm that combines the GA for parameter calibration and the SVR method for the simulation and prediction of groundwater levels. The GA–SVR algorithm is applied to three observation wells in the Karaj plain aquifer, a strategic water source for municipal water supply in Iran. The GA–SVR's groundwater-level predictions were compared to those from genetic programming (GP). Results show that the randomized approach of GA–SVR prediction yields R2 values ranging between 0.88 and 0.995, and root mean square error (RMSE) values ranging between 0.13 and 0.258 m, which indicates better groundwater-level predictive skill of GA-SVR compared to GP, whose R2 and RMSE values range between 0.48–0.91 and 0.15–0.44 m, respectively.

Long-term (1970s–2016) changes in groundwater geochemistry in the High Plains aquifer in south-central Kansas, USA

Changes in groundwater chemistry in the Great Bend Prairie aquifer, a portion of the High Plains aquifer in south-central Kansas (USA), were studied in order to better understand factors influencing groundwater quality and aquifer sustainability. To assess changes, groundwater samples from 22 monitoring wells were analyzed during 2016. Results were then compared to data obtained previously from the same wells in the 1970s and 1980s. Of the wells sampled, 13 wells were screened near the water table (average depth 22 m) and 9 wells were screened near the aquifer base (average depth 41 m). Nitrate levels in 2016 were higher for 20 of 21 wells with data available for comparison. The average increase for shallow-aquifer and aquifer-base samples was 9.5 (standard deviation, SD, 12.9) and 3.4 (SD 3.1) mg/L as N, respectively. Nitrate isotope ratios (δ15N-NO3 and δ18O-NO3) of the 2016 samples are consistent with nitrification of ammonium-based fertilizers as the nitrate source with potential contributions from animal waste. Total dissolved solute levels were also higher in samples from nine of 12 shallow-aquifer wells and four of eight aquifer-base wells, with average increases of 191 (SD 238) and 194 (SD 133) mg/L, respectively. Taken together, the results demonstrate that water quality has decreased considerably over the past 40 years primarily because of fertilizer use, but that groundwater mixing, evapotranspiration, and potentially animal waste inputs also affected groundwater chemistry. These findings help identify the scale of water-quality degradation in the High Plains aquifer.

The use of geochemical ratios in groundwater quality assessment: the case of the Thriassion Plain, Attica, Greece

This paper is an effort to assess the groundwater quality and the geochemical processes mainly using the Chadha’s diagram which classifies natural waters and documents the Piper and extended Durov diagrams. Chadha’s diagram is a useful tool to interpret groundwater geochemical processes because it is produced by simple spreadsheets excel files. The example of hydrochemical analyses were given from groundwater samples of the Thriassion Plain. To attend our objective, 38 groundwater samples were collected. Hydrochemical sections, XY diagrams, distribution maps of ionic ratios as well as the Gibbs diagrams were used to identify origin of salinity and the hydrogeochemical processes that have taken place. The Gibbs diagrams have shown that evapotranspiration (ET) and rock-water interaction play an important role to the increase of groundwater salinity. The interpretation of Chadha diagram highlights that the stratigraphic factors and especially the clay strata occurrence have isolated fresh groundwater from seawater. The abundant occurrence of clay deposits to the depth of the plain work as barriers to direct seawater intrusion. Good quality groundwater identified confirms the important role of clay strata. Reverse cation exchange, is the predominant geochemical process in the Thriassion Plain aquifers, whereas evapotranspiration (ET) and rock-water interaction play an important role to the increase of groundwater salinity.

Assimilating GRACE Into a Land Surface Model in the Presence of an Irrigation‐Induced Groundwater Trend

AbstractAssimilating terrestrial water storage observations from the Gravity Recovery and Climate Experiment (GRACE) mission into land surface models (LSMs) provides an opportunity to disaggregate and downscale GRACE information to finer scales and improve water component estimates in LSMs. However, the performance of GRACE data assimilation (GRACE‐DA) is limited by the lack of representation of human activities in most LSMs. To simultaneously improve GRACE‐DA and reduce the uncertainties in the modeled anthropogenic processes, we assimilate mascon‐based GRACE terrestrial water storage into the Noah‐Multiparameterization LSM that includes groundwater extraction for irrigation. Simulations with and without GRACE‐DA and with and without groundwater pumping for irrigation are performed to study the isolated and combined effects of groundwater irrigation and GRACE‐DA on water and energy fluxes over the High Plains Aquifer (HPA). The results reveal that the DA‐only simulation may erroneously distribute increments across water storage components and affect the related fluxes through biased feedbacks, while the irrigation‐only simulation may overestimate groundwater decline due to shortcomings in the irrigation and groundwater parameterizations. Assimilating GRACE when irrigation is simulated produces the best overall performance for water storage trends over the northern HPA. For the southern HPA, GRACE assimilation with irrigation performs similarly to irrigation‐only simulation for water storage components and evapotranspiration. GRACE assimilation also improves results in nonirrigated regions and can potentially alleviate the overestimation of groundwater trends in regions with greater irrigation uncertainties. This study highlights the potential to advance hydrological data assimilation in the context of anthropogenic water consumption and land‐atmosphere interactions.

Peer effects in the diffusion of water‐saving agricultural technologies

AbstractWe investigate the role of peer effects in the diffusion of an important water‐saving irrigation technology: low energy precise application (LEPA). Using detailed irrigation behavior data for growers in the High Plains Aquifer region of Kansas covering 1990–2014, we find clear evidence of peer influence in adoption of LEPA, net of environmental factors. Specifically, an additional neighboring LEPA installation within 1 km increases the probability of adoption by about 0.3 percentage points, on average, and this effect diminishes with distance. Our empirical estimates indicate that in the absence of peer effects, LEPA adoption would have been about 10% lower (1,000–1,600 fewer installations) per year. In addition, we find that growers install LEPA in response to higher energy prices.

Groundwater vulnerability assessment with using GIS in Hamadan\u2013Bahar plain, Iran

Vulnerability assessment to delineate areas that are more susceptible to contamination from anthropogenic sources has become an important element for sensible resource management and land use planning. It has been recognized for its ability to delineate areas that are more likely than others to become contaminated as a result of anthropogenic activities near the earth’s surface. The main methods of mapping and assessing intrinsic vulnerability in porous media are the following: SI, GOD, SINTACS and DRASTIC. The basic purpose of these maps is to divide an area into more classes, each of which will represent a different dynamic for a specific purpose and use. These models have been used to map groundwater vulnerability to pollution in Hamadan–Bahar aquifer. The results showed in models of DRASTIC, SI, GOD and SINTACS, respectively, 7.1, 44.21, 29.56 and 20.16 percent of the areas are high potential vulnerabilities. According to the model DRASTIC at study area, 33.6% of has a low class of groundwater vulnerability to contamination, whereas a total of 29.4% of the study area has a moderate vulnerability. The final results indicate that the aquifer system in the interested area is relatively protected from contamination on the groundwater surface. The correlation between models shows that DRASTIC model has the highest CI, which is 141, and the GOD model has the highest CI, which is 139. Also, the highest CI for SINTACS and SI is 137 and 136, respectively. Therefore, DRASTIC model is the best model among these models for predicting groundwater vulnerability in Hamadan–Bahar plain aquifer.

An integrated indexical investigation of selected heavy metals in drinking water resources from a coastal plain aquifer in Nigeria

With respect to the rapid changes in climatic conditions, commerce, industrialization, and urbanization, the water quality of many shallow coastal aquifers in different parts of the world is under serious heavy metals pollution threats. In the current study, three water quality indexical models and hierarchical cluster analysis (HCA) were integrated to investigate the heavy metals contamination and drinking quality of groundwater from the coastal plain aquifer in Oshodi-Isolo area (Nigeria). Several groundwater samples were analyzed for pH and selected heavy metals such as Fe, Zn, Pb, Cu, Ni, Cr, Mn, and Cd. The pH values ranged from 5.1 to 6.9, with about 71.43% of the total groundwater samples indicating slightly acidic nature. Among the analyzed heavy metals, Cu contamination was predominant in over 80% of the samples. However, the water quality evaluation models revealed that the majority of the water samples are suitable for drinking. Based on pollution index of groundwater, 80.95% of the samples have insignificant pollution. Nonetheless, both synthetic pollution index and overall index of pollution classified 85.7% of the samples as excellent water suitable for drinking. The HCA was used to resolve the disparity between the results of the models. Two major water quality classes (excellent water and polluted water) were identified in this study based on the HCA. It is, therefore, recommended that the polluted water be treated before human consumption.

Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine

Understanding how irrigated areas change over time is vital to effectively manage limited agricultural water resources, but long-term, high-resolution, and spatially explicit datasets are rare. The High Plains Aquifer (HPA) in the central United States is one of the largest and most stressed aquifer systems in the world. It supports a $20 billion economy, but groundwater use is unsustainable over much of the aquifer. Emerging cloud computing tools like Google Earth Engine (GEE) make it possible to use the full Landsat record to monitor regional systems like the HPA with high spatial and temporal resolution over multiple decades. However, challenges remain to develop irrigation classification methods that are robust to a wide range of climate conditions and crop types, evolving management, and missing data. Here, we addressed these challenges to produce an annual, moderately high resolution (30 m) irrigation map time series from 1984 to 2017 over the aquifer. Leveraging GEE's extensive data catalog, we combined Landsat imagery, environmental covariables, and a large heterogeneous ground truth dataset to create a single random forest classifier applied annually to the entire region. Following classification, we applied the Bayesian Updating of Land-Cover (BULC) algorithm to fill imagery gaps and reduce commission errors in the provisional irrigation time series. Novel neighborhood greenness indices contributed to an overall 91.4% map accuracy across years; county statistics (r2 = 0.86) were similarly well-matched. Trend analysis of irrigated area through time identified regions of stable, expanding, and declining irrigated area. Given declining aquifer storage, we estimate that up to 24% of currently irrigated area may be lost this century. To date, the map dataset is the longest, highest resolution large-scale record of where and when irrigation occurs. It is freely available for stakeholders, managers, and researchers to inform policies and management decisions, as well as for use in hydrology, agronomy, and climate models.

Are Well Owners Unique Environmentalists? An Exploration of Rural Water Supply Infrastructure, Conservation Routines, and Moderation

Enormous demands for irrigation water have depleted groundwater reserves in Kansas, where portions of the High Plains aquifer are in jeopardy. This study examines how private water wells influence conservation routines by comparing the watering practices of well owners to those of non-well owners across the state. My guiding research question is: Does water supply infrastructure moderate the relationship between demographic predictors and pro-environmental behaviors (PEBs)? To answer this, I surveyed well owners and non-well owners throughout Kansas (n = 864). The results indicate that controlling for water supplies uncovers differences across many associations between water conservation efforts and other PEBs with commonly studied demographics (sex, income, political affiliation, education, age, and geography). Researching well owners’ acts of environmental stewardship will be key to aquifer preservation.

Land market valuation of groundwater

We estimate irrigation premiums and implicit marginal valuations of water in-storage using parcel-level transaction data for land sales in the Kansas portion of the High Plains Aquifer. We find that agricultural land values are 53% higher for irrigated parcels than non-irrigated parcels on average and that the irrigation premium has increased at an average rate of 1.0 percentage points per year over the sample period (1988–2015). Spatial heterogeneity in irrigation premiums is explained by differences in saturated thickness of the aquifer. Water in-storage is capitalized into land prices at average marginal values ranging from $3.42/acre-ft to $15.86/acre-ft.

The impact of land cover on groundwater recharge in the High Plains: An application to the Conservation Reserve Program

• A change in land cover from cropland to grassland reduces aquifer storage in portions of the High Plains Aquifer. • Magnitude of the difference varies spatially across the High Plains Aquifer. • Findings reduce the environmental benefit of land retirement programs such as the Conservation Reserve Program.

The Effects of Irrigation and Climate on the High Plains Aquifer: A County‐Level Econometric Analysis

AbstractThe High Plains Aquifer (HPA) underlies parts of eight states and 208 counties in the central area of the United States (U.S.). This region produces more than 9% of U.S. crops sales and relies on the aquifer for irrigation. However, these withdrawals have diminished the stock of water in the aquifer. In this paper, we investigate the aggregate county‐level effect on the HPA of groundwater withdrawal for irrigation, of climate variables, and of energy price changes. We merge economic theory and hydrological characteristics to jointly estimate equations describing irrigation behavior and a generalized water balance equation for the HPA. Our simple water balance model predicts, at average values for irrigation and precipitation, an HPA‐wide average decrease in the groundwater table of 0.47 feet per year, compared to 0.48 feet per year observed on average across the HPA during this 1985–2005 period. The observed distribution and predicted change across counties is in the (−3.22, 1.59) and (−2.24, 0.60) feet per year range, respectively. The estimated impact of irrigation is to decrease the water table by an average of 1.24 feet per year, whereas rainfall recharges the level by an average of 0.76 feet per year. Relative to the past several decades, if groundwater use is unconstrained, groundwater depletion would increase 50% in a scenario where precipitation falls by 25% and the number of degree days above 36°C doubles. Editor’s note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.

A Systems Approach To Assess Trade Dependencies in U.S. Food-Energy-Water Nexus.

We present a network model of the United States (U.S.) interstate food transfers to analyze the trade dependency with respect to participating regions and embodied irrigation impacts from a food-energy-water (FEW) nexus perspective. To this end, we utilize systems analysis methods including the pointwise mutual information (PMI) measure to provide an indication of interdependencies by estimating probability of trade between states. PMI compares observed trade with a benchmark of what is statistically expected given the structure and flow in the network. This helps assess whether dependencies arising from empirically observed trade occur due to chance or preferential attachment. The implications of PMI values are demonstrated by using Texas as an example, the largest importer in the U.S. grain transfer network. We find that strong dependencies exist not only just with states (Kansas, Oklahoma, Nebraska) providing high volume of transfer to Texas but also with states that have comparatively lower trade (New Mexico). This is due to New Mexico's reliance on Texas as an important revenue source compared to its other connections. For Texas, import interdependencies arise from geographical proximity to trade. As these states primarily rely on the commonly shared High Plains aquifer for irrigation, overreliance poses a risk for water shortage for food supply in Texas. PMI values also indicate the capacity to trade more (the states are less reliant on each other than expected), and therefore provide an indication of where the trade could be shifted to avoid groundwater scarcity. However, some of the identified states rely on GHG emission intensive fossil fuels such as diesel and gasoline for irrigation, highlighting a potential tradeoff between crop water footprint and switching to lower emissions pumping fuels.

Optimizing the DRASTIC vulnerability approach to overcome the subjectivity: a case study from Shabestar plain, Iran

Due to the seasonality of Shabestar plain rivers, most of the water demands for agricultural and drinking sectors are provided from groundwater. The existence of agricultural activities has expanded the use of chemical and animal fertilizers that are possible to infiltrate and contaminate the groundwater resources. The purpose of this study is to provide an optimized DRASTIC approach for assessing the vulnerability of the Shabestar plain aquifer located in East Azarbaijan Province, North West of Iran. The effective parameters in this method include the depth to groundwater, net recharge, aquifer media, soil media, impact of the vadose zone, topographic slope, and hydraulic conductivity, which are being provided in seven layers in the ArcGIS software environment. After ranking each of the input parameters and applying their equivalent weight coefficients, the DRASTIC vulnerability index for the study area ranged between 53.3 and 118.3. Nitrate concentration values at 66 water wells were used for the validation of the DRASTIC-based vulnerability maps. The r value between the DRASTIC indices and the concentration of nitrate is 0.38 indicating a low correlation. Sensitivity analysis results showed that the impact of the vadose zone and aquifer media is significant on the intrinsic vulnerability of the study area. The Wilcoxon rank-sum test (WRST) was used for rate modification. Also, the weights of the DRASTIC approach were optimized by using the ant colony optimization (ACO) and genetic algorithm (GA). All optimized models increased the r compared to the typical DRASTIC method. The results indicate that WRST and GA methods were most successful for optimization of the rates and weights, respectively, where the WRST-GA-DRASTIC model obtained an r of 0.63.

Perceptions of fairness in common-pool resource access: farmer responses to new agricultural water use restrictions in Idaho

ABSTRACTThis study examines farmers’ perceptions of fairness regarding an agricultural water use policy change in Idaho: the 2015 settlement agreement, a compromise between ground- and surface water farmers in Idaho’s Eastern Snake Plain Aquifer (ESPA) that requires reductions in groundwater withdrawals of about 13% for all groundwater farmers. We characterize the agreement as an attempt to solve a common-pool resource social dilemma and draw on Elinor Ostrom’s seven ‘design principles’ as a conceptual framework. Using data from 43 interviews and 265 mail surveys, we find a split between farmers who grudgingly accept the agreement as necessary to keep the region’s agricultural productivity sustainable, and those who consider it unnecessary, unfair, or even illegal. A belief in the importance of protecting groundwater resources and support for local control was cited by farmers backing the agreement, whereas dissenting farmers believed outside interests had exerted too much influence throughout the negotiation process and generally mistrusted the state’s groundwater data. Given that residents of the American West are likely to continue having to address declining water availability in the future due to climate change, our findings can help policymakers design effective and equitable water policies in a way that advances rural environmental justice.

Estimating irrigation demand with geospatial and in-situ data: Application to the high plains aquifer, Kansas, USA

Unsustainable groundwater mining threatens the economic stability of several regions around the world. Developing sustainable water-use policies and integrated water-management plans requires reliably predicting water demand under variable weather and land-use conditions. Here, a water-budget model capable of estimating spatial and temporal variations in daily irrigation demand under variable weather and land-use scenarios is modified to consider irrigation management and groundwater pumping limitations. This model uses a combination of geospatial and in-situ measured data, including land-use and land-cover maps, crop-specific evapotranspiration data, and weather data. A coupled sub-model limits pumping rates based on pump capacity and spatial and temporal variations in the saturated thickness of the aquifer. The ability of this water-budget model to accurately estimate irrigation demand is demonstrated using the High Plains aquifer region of Kansas, USA, an aquifer that has undergone, and continues to undergo, significant depletion due to decades of irrigation. The model was calibrated with reported and measured water use for more than 1200 fields, with ratios of simulated annual irrigation demand to actual reported water use of 1.10, 0.78, 0.75, 0.93, and 1.40 for corn, sorghum, soybean, winter wheat, and alfalfa, respectively. Further simulations at a variety of scales, from field to multi-county levels, demonstrate that the developed water-budget model is capable of simulating the spatial and temporal variability of irrigation demand.

Environmental effects of the groundwater rebound in the eastern plain of Naples (Italy)

Since 1990, in the eastern plain of Naples a generalized groundwater rebound phenomenon has occurred, caused by the sharp reduction of the withdrawals which had overexploited the plain aquifer until 1989.The present study aims to investigate, for 1989-2013 period, the groundwater flooding and ground uplift related to the groundwater rebound, by a coupled analysis of multi-temporal hydrogeological and DInSAR data of this area.Results show a generalized rise of groundwater levels from 1990, varying from 6.0 to 16.0 m, whereas localized impacts of groundwater flooding on buildings and agricultural soils have started since 2006.An uplift trend is observed in ground deformations (up to +40 mm), caused by the increase of pore water pressure in the aquifer system, following to groundwater rebound.This result opens new perspectives in assessment, mitigation and management of geohazards not considered by the Italian legislation.