Republican River Research Articles

Retiring Land to Save Water: Participation in Colorado’s Republican River Conservation Reserve Enhancement Program

Agricultural land retirement is increasingly used to manage water resources. This study uses well-level enrollment data to explore the factors that influence landowner participation in the Colorado Republican River Conservation Reserve Enhancement Program. An empirical model of enrollment is informed by a theoretical model of participation that incorporates aquifer and soil characteristics in addition to financial incentives. Our results reveal that enrollment is predicted to increase by 0.087 percentage points with a $10 increase in the incentives offered. The probability of enrollment is also influenced by the aquifer’s saturated thickness and the soil characteristics that impact land productivity.

The Heterogeneous Impacts of Groundwater Management Policies in the Republican River Basin of Colorado

AbstractGroundwater is a critical input to agricultural production across the globe. Current groundwater pumping rates frequently exceed recharge, often by a substantial amount, leading to groundwater depletion and potential declines in agricultural profits over time. As a result, many regions reliant on irrigated agriculture have proposed policies to manage groundwater use. Even when gains from aquifer management exist, there is little information about how policies affect individual producers sharing the resource. In this paper, we investigate the variability of groundwater management policy impacts across heterogeneous agricultural producers. To measure these impacts, we develop a hydroeconomic model that captures the important role of well capacity, productivity of water, and weather uncertainty. We use the model to simulate the impacts of groundwater management policies on producers in the High Plains aquifer of eastern Colorado and compare outcomes to a no‐policy baseline. The management policies considered include a pumping fee, a quantity restriction, and an irrigated acreage fee. We find that well capacity and soil type affect policy impacts but in ways that can qualitatively differ across policy type. Model results have important implications for the distributional impacts and political acceptability of groundwater management policies.

On the role of individuals in models of coupled human and natural systems: Lessons from a case study in the Republican River Basin

In models of coupled human and natural systems (CHANS), the role of individuals and human behavior is often overlooked as data are scarce and assumptions hard to verify. To assess this role, we couple an agent-based model simulating farmers' behavior and a groundwater model and apply the models to the case of groundwater-fed irrigation in a river basin in the High Plains Aquifer region. Results show the crucial role of human behavior in driving the interactions between these coupled systems. Conversely, individuals are impacted by the systems’ dynamics in different ways depending on physical, economic and social characteristics. The findings provide implications for local policy making and education and demonstrate that assumptions on human behavior could be treated as an additional source of uncertainty. This work suggests that modeling individuals and human behavior can be an important step to simulate and understand the dynamics of CHANS in a holistic way.

Local Environmental Context Conditions the Impact of Russian Olive in a Heterogeneous Riparian Ecosystem

Local abiotic and biotic conditions can alter the strength of exotic species impacts. To better understand the effects of exotic species on invaded ecosystems and to prioritize management efforts, it is important that exotic species impacts are put in local environmental context. We studied how differences in plant community composition, photosynthetically active radiation (PAR), and available soil N associated with Russian olive presence are conditioned by local environmental variation within a western U.S. riparian ecosystem. In four sites along the South Fork of the Republican River in Colorado, we established 200 pairs of plots (underneath and apart from Russian olive) to measure the effects of invasion across the ecosystem. We used a series of a priori mixed models to identify environmental variables that altered the effects of Russian olive. For all response variables, models that included the interaction of environmental characteristics, such as presence/absence of an existing cottonwood canopy, with the presence/absence of Russian olive canopy were stronger candidate models than those that just included Russian olive canopy presence as a factor. Compared with reference plots outside of Russian olive canopy, plots underneath Russian olive had higher relative exotic cover (exotic/total cover), lower perennial C4 grass cover, and higher perennial forb cover. These effects were reduced, however, in the presence of a cottonwood canopy. As expected, Russian olive was associated with reduced PAR and increased N, but these effects were reduced under cottonwood canopy. Our results demonstrate that local abiotic and biotic environmental factors condition the effects of Russian olive within a heterogeneous riparian ecosystem and suggest that management efforts should be focused in open areas where Russian olive impacts are strongest.

Incorporating Surface Water Operations in an Integrated Hydrologic Model: Model Development and Application to the Lower Republican River Basin, United States

AbstractFew river systems remain unaltered by engineered water management structures. Yet research investigating the interdependence between natural and engineered components of a hydrologic system...

Defining Sustainability in Nebraska’s Republican River Basin

This symposium presents an opportunity to discuss agricultural sustainability. But we have little practical understanding of what agricultural sustainability really means. This is a common problem with sustainability efforts. This Article provides one example of this problem. But it also provides a story of how an effort at defining sustainability served as a catalyst for a group of stakeholders that wanted to make improvements in water management. Understood in this way, sustainability discussions can serve to overcome historic barriers to progress that so often arise with resource management problems, especially in the agricultural sector.

Effects of nonlinear model response on allocation of streamflow depletion: exemplified by the case of Beaver Creek, USA

Anomalies found when apportioning responsibility for streamflow depletion are examined. The anomalies arise when responsibility is assigned to the two states that contribute to depletion of Beaver Creek in the Republican River Basin in the United States. The apportioning procedure for this basin presumes that the sum of streamflow depletions, computed by comparing simulation model runs with and without groundwater pumping from individual states, approximates the streamflow depletion when both states are pumping. In the case study presented here, this presumed superposition fails dramatically. The stream drying and aquifer-storage depletion, as represented in the simulation model used for allocation, are examined in detail to understand the hydrologic and numerical basis for the severe nonlinear response. Users of apportioning procedures that rely on superposition should be aware of the presence and likely magnitude of nonlinear responses in modeling tools.

Model reduction for combined surface water/groundwater management formulations

A methodology for clustering stresses on a groundwater system based on similarity of hydrologic response produced by the stress is presented. The method is relevant to the computationally efficient calculation of response matrixes for use in groundwater management and other applications. The procedure is presented for the case in which the impact of pumping withdrawals on streamflow is of interest. The method uses cluster analysis on multiple, transient responses and a simplified response matrix for the modeled system. It is demonstrated on a field scale hypothetical management problem applied to a portion of the Republican River basin in the High Plains Aquifer of the United States with a multi-decadal planning horizon that maximizes well withdrawal while requiring that minimum streamflows be maintained. For this case, the clustering approach both reduces the computational requirement and helps to produce response coefficients with meaningful precision. The effectiveness of the model reduction is tested by comparing the optimal solutions produced by a full scale formulation and two reduced size formulations.

The Baller biface cache: A possible Clovis site in Hitchcock county, Nebraska

Twenty-six Clovis implement caches are known from western North America. In cases where time-sensitive artifacts (e.g., Clovis projectile points or mammoth ivory rods) or adequate information about their provenience and geological context are absent, assigning temporal and cultural affiliation has been challenging. Such is the case with a cache of eight large bifaces, four of which were donated by Albert E. Baller in the early 1900s to the University of Nebraska State Museum. The cache was discovered along with debitage within a small tributary of the Republican River in south-central Nebraska. The four donated Baller bifaces have been curated since the early 1900s. This study compared the physical properties, metrical attributes, and lithic reduction strategies exhibited by the bifaces with 119 similar large bifaces recovered from 10 Clovis caches. These comparisons suggest that the Baller bifaces may represent yet unreported Clovis cache from western North America.

Optimizing Water Management for Irrigation Under Climate Uncertainty: Evaluating Operational and Structural Alternatives in the Lower Republican River Basin, Kansas, USA

Structural and operational management methods are used to meet water demands in watersheds around the world. Most river systems are affected by reservoirs, dams, or other engineering structures, and decisions regarding their construction and operation are made in advance of knowing what water demands will be. Numerical models are used to predict future water needs and evaluate the effectiveness of water management strategies. It is important to consider a variety of management methods and future environmental conditions to ensure future demands can be met. In this work, a coupled surface water operations and hydrologic model of the Lower Republican River Basin in portions of Nebraska and Kansas, USA is used to evaluate the ability of several water management strategies, including structural and operational, to meet future demands of a water-stressed agricultural basin under a variety of future climate scenarios. Simulations indicate recent administrative and operational changes to the distribution of water between Nebraska and Kansas have significantly decreased water shortages for irrigation districts in Kansas and will continue to do so. Simulations also indicate that structural alternative of reservoir expansion is most effective at minimizing shortages to demands under a repeat of historical climate conditions. However, an operational alternative of increasing water supplies for Kansas' exclusive use, such as those historically purchased under the Warren Act (US Code 43 Section 523–524), is most effective at minimizing shortages to demands under a hotter and drier climate, demonstrating how optimal water management strategies can vary significantly depending upon climate scenario.

Global sensitivity analysis for large-scale socio-hydrological models using Hadoop

A multi-agent system (MAS) model is coupled with a physically-based groundwater model to understand the declining water table in the heavily irrigated Republican River basin. Each agent in the MAS model is associated with five behavioral parameters, and we estimate their influences on the coupled models using Global Sensitivity Analysis (GSA). This paper utilizes Hadoop-based Cloud Computing techniques and Polynomial Chaos Expansion (PCE) based variance decomposition approach for the improvement of GSA with large-scale socio-hydrological models. With the techniques, running 1000 scenarios of the coupled models can be completed within two hours with Hadoop clusters, a substantial improvement over the 42 days required to run these scenarios sequentially on a desktop machine. Based on the model results, GSA is conducted with the surrogate model derived from using PCE to measure the impacts of the spatio-temporal variations of the behavioral parameters on crop profits and the water table, identifying influential parameters.

Data-driven methods to improve baseflow prediction of a regional groundwater model

Physically‐based models of groundwater flow are powerful tools for water resources assessment under varying hydrologic, climate and human development conditions. One of the most important topics of investigation is how these conditions will affect the discharge of groundwater to rivers and streams (i.e. baseflow). Groundwater flow models are based upon discretized solution of mass balance equations, and contain important hydrogeological parameters that vary in space and cannot be measured. Common practice is to use least squares regression to estimate parameters and to infer prediction and associated uncertainty. Nevertheless, the unavoidable uncertainty associated with physically‐based groundwater models often results in both aleatoric and epistemic model calibration errors, thus violating a key assumption for regression-based parameter estimation and uncertainty quantification. We present a complementary data-driven modeling and uncertainty quantification (DDM-UQ) framework to improve predictive accuracy of physically‐based groundwater models and to provide more robust prediction intervals. First, we develop data-driven models (DDMs) based on statistical learning techniques to correct the bias of the calibrated groundwater model. Second, we characterize the aleatoric component of groundwater model residual using both parametric and non-parametric distribution estimation methods. We test the complementary data-driven framework on a real-world case study of the Republican River Basin, where a regional groundwater flow model was developed to assess the impact of groundwater pumping for irrigation. Compared to using only the flow model, DDM-UQ provides more accurate monthly baseflow predictions. In addition, DDM-UQ yields prediction intervals with coverage probability consistent with validation data. The DDM-UQ framework is computationally efficient and is expected to be applicable to many geoscience models for which model structural error is not negligible.

Strategic Planning for Drought Mitigation under Climate Change

AbstractDroughts continue to be a major natural hazard, and mounting evidence of global warming confronts society with a pressing question: Will climate change aggravate the risk of drought at the local scale? Are current infrastructures sufficient to mitigate the damage of future drought, or is in-advance infrastructure expansion needed for future drought preparedness? To address these questions, this study presents a decision-support framework based on a coupled simulation and stochastic optimization model through a case study area, the Frenchman Creek basin (FCB), part of the Republican River basin. A complex watershed simulation model is established and converted into a statistical surrogate model for computational feasibility. Decisions for drought preparedness include traditional short-term tactical measures (e.g., facility operation) and long-term or in-advance strategic measures, which require capital investment. A scenario-based, three-stage stochastic optimization model assesses the roles of str...

Parameter Estimation for Groundwater Models under Uncertain Irrigation Data.

The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.

The role of groundwater trading in spatial water management

Agricultural groundwater use is increasingly being restricted to address the negative impacts of pumping on instream flows for downstream users, endangered species habitat, and recreation. Understanding the spatial heterogeneity of the costs of water use restrictions to farmers is critical to evaluating the effectiveness of current and alternative water management policies. We use a geospatial population dataset of irrigation wells in the Republican River Basin of Nebraska and model the simultaneous crop choice, land and water use decisions at a well level. We estimate the magnitude and distribution of costs of current groundwater restrictions as well as cost savings from alternative market-based policies that allow trading of permits between farmers. Our analysis highlights the importance of the initial distribution of permits and the institutional context in which trading occurs. Both allocated but unused permits and land estimated to move from irrigated to dryland crops provide important trading volume into the water rights market. The results show that the cost savings from allowing trading of groundwater pumping rights are distributed unevenly between wells, counties, and groundwater management institutions.

Assessing groundwater policy with coupled economic‐groundwater hydrologic modeling

AbstractThis study explores groundwater management policies and the effect of modeling assumptions on the projected performance of those policies. The study compares an optimal economic allocation for groundwater use subject to streamflow constraints, achieved by a central planner with perfect foresight, with a uniform tax on groundwater use and a uniform quota on groundwater use. The policies are compared with two modeling approaches, the Optimal Control Model (OCM) and the Multi‐Agent System Simulation (MASS). The economic decision models are coupled with a physically based representation of the aquifer using a calibrated MODFLOW groundwater model. The results indicate that uniformly applied policies perform poorly when simulated with more realistic, heterogeneous, myopic, and self‐interested agents. In particular, the effects of the physical heterogeneity of the basin and the agents undercut the perceived benefits of policy instruments assessed with simple, single‐cell groundwater modeling. This study demonstrates the results of coupling realistic hydrogeology and human behavior models to assess groundwater management policies. The Republican River Basin, which overlies a portion of the Ogallala aquifer in the High Plains of the United States, is used as a case study for this analysis.

Analyzing streamflow changes: irrigation-enhanced interaction between aquifer and streamflow in the Republican River basin

Abstract. Groundwater-fed irrigation has altered surface and groundwater interactions in the Republic River basin (RRB) in the midwestern United States, where agriculture heavily depends on irrigation. The decreasing flow trend recorded at the RRB gauging station since the 1950s reflects the synthetical effect of dynamic interactions between surface water and groundwater systems, which has been enhanced by groundwater pumping and irrigation return flow. This study uses a systematic modeling approach to analyze the conjunctive effects of pumping and return flow on streamflow. A watershed management model, the Soil and Water Assessment Tool (SWAT), is modified and established for the Frenchman Creek basin (FCB), a subbasin of RRB, to examine the causes of streamflow changes. The baseflow component in SWAT is linked to aquifer storage so that the model can simulate the combined effects of groundwater pumping and irrigation return flow on natural streamflow. Results show that irrigation has not only depleted streamflow but also changed the flow pattern and seasonal variability. The changes can be decomposed into decrease in the slow component (baseflow) and increase in the fast components (surface and subsurface flow). Since the fast components are subject to higher variability than the slow component, the annual streamflow variability is amplified. Agricultural water use in this region also has changed the groundwater storage seasonal regime from the pattern of "summer recharge and winter discharge" in the past to "summer discharge and winter recharge" at present. This challenges the existing groundwater modeling, which usually assumes fixed recharge pattern and rates.

Modis-Aided Water-Balance Investigations in the Republican River Basin, USA

Spatially distributed monthly evapotranspiration (ET) ratesover the 2000–2009 period were specified for the three states[Colorado (CO), Kansas (KS), Nebraska (NE)] sharing the Republican River basin with the help of a calibration-free ET-estimation method using Moderate Resolution (of about one km) Imaging Spectroradiometer (MODIS) data. Mean annual ET-rate is 444 mm [i.e., 103% of the mean annual precipitation(P) of 430 mm] in CO, 576 mm in NE (102% of P=563 mm), 540 mm (101% of P = 536 mm) in KS upstream of Hardy, NE;and 568 mm (95% of P = 596 mm) for the entire KS basin area.It is estimated that irrigation (predominantly center pivot) maylead to an additional annual ET of about 200 mm in CO and NE, and to 140 mm in part of KS that is upstream of Hardy, NE over that of native grass. The MODIS-based ET method yields estimates within 10% of water-balance derived mean annual subcatchment ET rates with correlation coecient values of 0.99-0.96, depending on which estimate (low, average or high) of the specific yield value of the unconfined aquifer is applied in the water balance equation. The present ET-estimation method may help decision makers on how to share the Republican River flow among the neighboring states.

Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models

Quantitative analyses of groundwater flow and transport typically rely on a physically-based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data-driven models (DDMs) to reduce the predictive error of physically-based groundwater models. Two machine learning techniques, the instance-based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real-world case studies of the Republican River Compact Administration model and the Spokane Valley-Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root-mean-square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically-based model.

The regulation of a spatially heterogeneous externality: Tradable groundwater permits to protect streams

Groundwater pumping can reduce the flow of surface water in nearby streams. In the United States, recent awareness of this externality has led to intra- and inter-state conflict and rapidly-changing water management policies and institutions. Although the marginal damage of groundwater use on stream flows depends crucially on the location of pumping relative to streams, current regulations are generally uniform over space. We use a population data set of irrigation wells in the Nebraska portion of the Republican River Basin to analyze whether adopting spatially differentiated groundwater pumping regulations leads to significant reductions in farmer abatement costs and costs from damage to streams. We find that regulators can generate most of the potential savings in total social costs without accounting for spatial heterogeneity. However, if regulators need to increase the protection of streams significantly from current levels, spatially differentiated policies will yield sizable cost savings.