The impact of water policy on conserving the Ogallala Aquifer in Groundwater Management District 3 (GMD3) in southwestern Kansas is analyzed using a system-level theoretical approach integrating agricultural water and land use patterns, changing climate, economic trends, and population dynamics. In so doing, we (1) model the current hyper-extractive coupled natural–human (CNH) system, (2) forecast outcomes of policy scenarios transitioning the current groundwater-based economic system toward more sustainable paths for the social, economic, and natural components of the integrated system, and (3) develop public policy options for enhanced conservation while minimizing the economic costs for the region's communities. The findings corroborate previous studies showing that conservation often leads initially to an expansion of irrigation activities. However, we also find that the expanded presence of irrigated acreage reduces the impact of an increasingly drier climate on the region's economy and creates greater long-term stability in the farming sector along with increased employment and population in the region. On the negative side, conservation lowers the net present value of farmers' current investments and there is not a policy scenario that achieves a truly sustainable solution as defined by Peter H. Gleick. This study reinforces the salience of interdisciplinary linked CNH models to provide policy prescriptions to untangle and address significant environmental policy issues.

The impact of water policy on sustaining the Ogallala Aquifer is analyzed using a system-level theoretical approach integrating agricultural water and land use tendencies, changing climate, economic trends, and population dynamics. In so doing, we 1) model the current hyper-extractive coupled natural-human system (CNH), 2) forecast future outcomes of policy scenarios transitioning the current groundwater-based economic system toward more sustainable paths for the social, economic and natural components of the integrated system, and 3) communicate model projections to inform public policies for enhanced sustainability while minimizing the economic pain for the region's communities. The findings corroborate previous studies showing that conservation often leads initially to an expansion of irrigation activities. However, we also find that the expanded presence of irrigated acreage will reduce the impact of an increasingly dryer climate on the region's economy and create greater long-term stability in the farming sector along with increased employment and population in the region. The primary negative aspect of more extensive conservation policies are on the net present value of farmers' current investments in their operations. This study reinforces the salience of interdisciplinary linked CNH models to provide policy prescriptions to untangle and address significant environmental policy issues.

As the volume of collected data continues to increase in the environmental sciences, so too does the need for effective means for accessing those data. We have developed an Open Modeling Interface (OpenMI) data component that retrieves input data for model components from environmental information systems and delivers output data to those systems. The adoption of standards for both model component input–output interfaces and web services make it possible for the component to be reconfigured for use with different linked models and various online systems. The data component employs three techniques tailored to the unique design of the OpenMI that enable efficient operation: caching, prefetching, and buffering, making it capable of scaling to large numbers of simultaneous simulations executing on a computational grid. We present the design of the component, an evaluation of its performance, and a case study demonstrating how it can be incorporated into modeling studies.

As the availability of computing infrastructure continues to increase, so too does the need for accessible means for utilizing those resources. An effective approach is to enable desktop-oriented scientific software tools and frameworks to support execution on high performance cyberinfrastructure in a way that is transparent to the user. We have found this to be the case in our ongoing environmental modeling study in which we are applying multidisciplinary, integrated models to the study of a depleting aquifer. Our models are linked together using the Open Modeling Interface (OpenMI) which provides a composition framework for the sequential execution of model components. In this work we investigate the potential for incorporating parallelism into the OpenMI as a first-class citizen. We present a general solution in which model components may be executed in parallel without requiring changes to their source code. An alternate solution achieves greater parallelism through simultaneous invocations of individual components, but requires them to be modified in some cases. These can result in significant reductions in simulation runtimes on both multi-core desktop machines as well as in high performance computing environments. We demonstrate this potential speedup in a performance study in which the application of the general solution achieved 86% of linear speedup when executed on a high performance machine with 80 cores.

2011 International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, NV, July 18-21, 2011

Integrated environmental modeling enables the development of comprehensive simulations by compositing individual models within and across disciplines. The Simple Script Wrapper (SSW), developed here, provides a foundation for model linkages and integrated studies. The Open Modeling Interface (OpenMI) enables model integration but it is challenging to incorporate scripting languages commonly used for modeling and analysis such as MATLAB, Scilab, and Python. We have developed a general-purpose software component for the OpenMI that simplifies the linking of scripted models to other components. Our solution enables scientists to easily make their scripting language code linkable to OpenMI-compliant models fostering collaborative, interdisciplinary integrated modeling. The simplicity afforded by our solution is presented in a case study set in the context of irrigated agriculture. The software is available online as supplementary material and includes an example that may be followed to employ our methods.

Abstract. Policy for water resources impacts not only hydrological processes, but the closely intertwined economic and social processes dependent on them. Understanding these process interactions across domains is an important step in establishing effective and sustainable policy. Multidisciplinary integrated models can provide insight to inform this understanding, though the extent of software development necessary is often prohibitive, particularly for small teams of researchers. Thus there is a need for practical methods for building interdisciplinary integrated models that do not incur a substantial development effort. In this work we adopt the strategy of linking individual domain models together to build a multidisciplinary integrated model. The software development effort is minimized through the reuse of existing models and existing model-linking tools without requiring any changes to the model source codes, and linking these components through the use of the Open Modeling Interface (OpenMI). This was found to be an effective approach to building an agricultural-groundwater-economic integrated model for studying the effects of water policy in irrigated agricultural systems. The construction of the integrated model provided a means to evaluate the impacts of two alternative water-use policies aimed at reducing irrigated water use to sustainable levels in the semi-arid grasslands overlying the Ogallala Aquifer of the Central US. The results show how both the economic impact in terms of yield and revenue and the environmental impact in terms of groundwater level vary spatially throughout the study region for each policy. Accessible integration strategies are necessary if the practice of interdisciplinary integrated simulation is to become widely adopted.

An integrated foundation is presented to study the impacts of external forcings on irrigated agricultural systems. Individually, models are presented that simulate groundwater hydrogeology and econometric farm level crop choices and irrigated water use. The natural association between groundwater wells and agricultural parcels is employed to couple these models using geographic information science technology and open modeling interface protocols. This approach is used to study the collective action problem of the common pool. Three different policies (existing, regulation, and incentive based) are studied in the semiarid grasslands overlying the Ogallala Aquifer in the central United States. Results show that while regulation using the prior appropriation doctrine and incentives using a water buy‐back program may each achieve the same level of water savings across the study region, each policy has a different impact on spatial patterns of groundwater declines and farm level economic activity. This represents the first time that groundwater and econometric models of irrigated agriculture have been integrated at the well‐parcel level and provides methods for scientific investigation of this coupled natural‐human system. Results are useful for science to inform decision making and public policy debate.

Near-term consumption of groundwater for irrigated agriculture in the High Plains Aquifer supports a dynamic bio-socio-economic system, all parts of which will be impacted by a future transition to sustainable usage that matches natural recharge rates. Plants are the foundation of this system and so generic plant models suitable for coupling to representations of other component processes (hydrologic, economic, etc.) are key elements of needed stakeholder decision support systems. This study explores utilization of the Environmental Policy Integrated Climate (EPIC) model to serve in this role. Calibration required many facilities of a fully deployed decision support system: geo-referenced databases of crop (corn, sorghum, alfalfa, and soybean), soil, weather, and water-use data (4931 well-years), interfacing heterogeneous software components, and massively parallel processing (3.8×109 model runs). Bootstrap probability distributions for ten model parameters were obtained for each crop by entropy maximization via the genetic algorithm. The relative errors in yield and water estimates based on the parameters are analyzed by crop, the level of aggregation (county- or well-level), and the degree of independence between the data set used for estimation and the data being predicted.

Advancements in data collection and high performance computing are making sophisticated model calibration possible throughout the modeling and simulation community. The model calibration process, in which the appropriate input values are estimated for unknown parameters, is typically a computationally intensive task and necessitates the use of distributed software components. These components are often heterogeneous due to the combination of the model and the optimization software, making scalability difficult to achieve. We have developed a hybrid software system for parameter estimation consisting of an optimization algorithm implemented in a mathematical scripting language, a legacy Fortran model, and an MPI client program. Through a series of optimizations, we achieved near-linear speedup when the model is executed as a standalone process, and achieved superlinear speedup when the model is executed as a subroutine. We report on our optimization techniques and performance results of an estimation problem within the context of an ongoing modeling study.