Enhancing Producer Management Behaviors in Achieving Crop-water Sustainability
A Machine Learning Approach
In-stream water quality assessment often relies on numerical simulation modeling of detailed physical processes in contributing watershed (e.g. using a model such as SWAT, WASP, HSPF, etc.) However, this approach is usually time and data intensive and is not particularly suited to rapid real-time assessments. We propose to investigate approaches for rapid in-stream water quality assessment using existing and newly emerging machine learning techniques to probabilistically characterize the relationship between specific watershed characteristics (associated with terrain, landuse, soils, etc.) and existing water quality observations. The project will include a large data management effort in which water quality observations from existing state and national databases will be organized with geospatial data for watersheds and sub-watersheds in the INRA region. This dataset of water quality and watershed characteristics will then be explored using several machine learning and data mining techniques (e.g. Bayesian Networks, Artificial Neural Networks, and Scalable Vector Machines) in an effort to derive a regional set of analytical tools for rapid assessment of water quality from watershed characteristics.
Investigating Environmental Impacts and Climate Change of Creating a Lake in the Hyperarid Sahara Desert
The unusually active hurricane seasons in the Atlantic Ocean and specifically the tragic destruction in New Orleans from Hurricane Katrina illustrate the importance of considering environmental factors that may impact climate change. One factor that might affect climate on a local to regional scale and can lead to better understanding of the link between the environment and global change is linking the Qattara Depression, in the Western Desert of Egypt, to the Mediterranean Sea. The Qattara Depression, the world's fifth deepest natural depression and the second lowest point in Africa, is located in one of most hyperarid zones in the world, the Sahara desert. It has been proposed to flood the Qattara Depression with water diverted from the Mediterranean Sea for hydro-power generation.
The Qattara Depression as well as many of the great deserts of the world corresponds to almost the latitudes of central and north Florida and the South/Central United States. Yet, even though Florida exists at the same latitude as these great deserts, Florida has a humid, subtropical climate. Central Florida exhibits a long dry season tempered only by the transition to a summer sea-breeze induced wet season. This brief three month period is responsible for 70% of Florida’s 1.5 meters of annual rainfall. The primary factor contributing to Florida’s high precipitation is the proximity to the warm waters of the Atlantic, Caribbean Sea and Gulf of Mexico. If a substantial body of water is introduced into the Western Desert, what environmental and climatological impact might result with this large-scale change in landforms? Would the area surrounding the water body experience increased humidity and precipitation as does Florida?
The Qattara Depression has a sub-sea level area of 18,130 kmē with a maximum depth of 133 m below sea level. The Depression provides the opportunity to observe environmental and climate changes affected by water availability and evaporation from the desert floor. These attributes make the Qattara Depression a microcosm of contemporary water resource issues and an excellent site to pursue interdisciplinary and integrated hydrologic science. Evaluation of the hydrology and potential changes in micro-climatology of the region is of scientific interest and practical importance. This project was actively studied in Egypt from the 1950s to the 1980s as a hydro-power project in northwestern Egypt. Since this time, Egypt has experienced a significant population increase (presently of about 2.2 % per annum), expansion of agricultural land, and rapid industrialization. The development of new land is now limited by water. It is hypothesized that linking the Qattara Depression to the Mediterranean would bring climatic benefits to Egypt and surrounding areas, including the Delta. The proximity of the future lake to the Mediterranean Sea, it is believed, would set up small-scale pressure gradients similar to the sea breeze effect experienced in peninsular Florida, which is responsible for most of Florida’s annual rainfall. This would be promoted by low pressure rain storms driven by the prevailing winds. The possible climate change may support future development along with ecological benefits perhaps providing a greater net benefit than that of the energy project originally proposed.
It would be of great scientific and practical value to analyze the micro-meteorological and hydrologic consequences of creating this massive lake in the region. The depression is bounded to the north and west by steep escarpments but becomes comparatively flat towards the South and the East. It has a maximum length of 300 km and width of 145 km. The northern edge of the escarpment is bordered by a ridge with an elevation of about 200 m above sea level. This is also the closest point to the Mediterranean Sea at 56 km (Bastiaanseen and Menenti, 1990). The bottom of the depression consists of a salt bog. Evaporation rates from the bare and hyper-saline soils vary widely due to variability in soil types and depths to the shallow groundwater table at the bottom of the depression.
Assessment of Water Resources Degradation in the Tampa Bay Watersheds
Concerns about degradation of water resources and the long-term sustainability of ecological resources have rapidly increased across the United States over the past few decades. Long term sustainability includes water quality issues, such as pollution, as well as water supply issues, such as aquifer overuse. The causes can vary greatly; natural (e.g., climate change) to anthropogenic (e.g., land-use change). Long-term sustainability means that enough water of suitable quality is available to support natural systems and human populations over time, and that the supply of water is replenished. Once a water supply is permanently contaminated, for example, sustainability is affected because the contamination may be difficult or impossible to remediate. The pollutants are carried with water to lakes, rivers, wetlands, oceans, and to underground water deposits (Singleton, 2000).
If water resources decrease in quantity and deteriorate in quality, it is critical to check the sustainability of the system. For example, the southeastern region of United States and particularly Florida is considered a diverse agricultural base and a highly urbanized state. As urbanization increases, land for agriculture, forest and other natural resources are further reduced. Water resources are degraded by pollution from different sources, such as agriculture, urban runoff, industrial waste products, and mining operations. Much of the urban development occurs in areas traditionally devoted to agricultural production and is located adjacent to sensitive inland and coastal environmental areas. West-central Florida has been of particular interest due to specific pollution concerns such as nitrate contamination of ground water adjacent to areas of major citrus production. There are also some areas affected by poultry production wastewater outfall, which may have a deleterious effect on ground and surface water.
Currently, main water bodies such as the Hillsborough River, Blackwater and Cypress creeks among others were included in the 303(d) list in 2004. Water quality impairments in the Tampa Bay Basin are mainly dissolved oxygen, nutrients, fecal and total coliform. The U.S. EPA has developed total maximum daily load (TMDL) specifications for some of these waters.
Simulation models can be used to predict the magnitude of non-point source pollution from different land uses. The Agricultural Non-Point Source Pollution (AGNPS) model is well suited to compare different land parcels in terms of the amount of pollutants and simulate the changes in pollutant production according to the changes of land use and land cover.
The AGNPS simulates the generation and transport of pollutants in overland flow to waterways from upland areas. The continuous simulation Annualized version (AnnAGNPS), part of the AGNPS suite of modeling components, simulates runoff, sediment, nutrient, and pesticide contributions as a result of storm flow (Suttles et al., 2003). It includes a climate generating model (GEM), and a subset of the topographic analysis model (TOPAZ, or TOPographic PArameteriZation). Runoff is calculated in the model using the TR–55 method (NRCS, 1986). Sediment loading is predicted by simulating upland erosion with RUSLE (Renard et al., 1997), along with sediment delivery to the stream (HUSLE) (Theurer and Clarke, 1991). Sediment reach routing is based on a modified Einstein deposition equation using the Bagnold suspended sediment formula for the transport capacity by particle size. Algorithms for nutrient dynamics are largely similar to the EPIC (Williams et al., 1984) and GLEAMS (Leonard et al., 1987) models.
Although AGNPS uses the TR-55 method which is generally accepted, it has a very limited description of the hydrologic system. The sandy soils of Florida have very high infiltration capacities, but when the soil storage capacity between the shallow water table and the ground surface is filled, all remaining rainfall becomes runoff. In order to get more accurate description of the hydrologic system, it is better to track the infiltrated water entering the soil to the water table. The AGNPS considers the infiltration as a sink and as water goes to infiltrate, it exits the system is lost. HYDRUS 1-D (Simunek and van Genuchen, 1998) may be used to partition the water in the vadoze zone.
The Agricultural Contribution to Nitrogen Loading in Tampa Bay Estuary
Significant amount of nutrients are flowing from the agricultural land and rural non-point sources into Tampa Bay Estuary contributing to fish kills and the loss of nearly half of the sea grasses and coral reefs, from the 1950s to the 1980s (U.S. EPA, 2003). However, accurate quantification of these amounts is necessary for water quality trading policy. Tampa Bay is located on the eastern shore of the Gulf of Mexico in west-central Florida. Sources of water into the bay come from Alafia, Hillsborough, Little Manatee, Manatee and over one hundred tributaries that drain more than 5,700 km2 of watershed.
Tampa Bay was accepted into the U.S. EPA National Estuary Program in 1990 (Greening, 2001) and in 1998, local government and agency partners in the Tampa Bay Estuary Program (TBEP) signed an agreement to maintain nitrogen loading at 1992-1994 levels by addressing nitrogen sources from storm-water runoff and municipal point sources.
We hypothesize that in the past decade, change in land use of the North Tampa Bay area (NTB), particularly decreased agricultural land use, increased urbanization, have contributed to higher indirect nitrogen loading to the Tampa Bay Estuary.
While the northern and western parts of the watershed are heavily urbanizing, the eastern and southern parts remain heavily agricultural. Runoff from intensive agricultural land uses contributes about 6 percent of total Tampa Bay nitrogen loadings. Agricultural runoff from pastures and rangelands, which cover roughly 28 percent of the watershed, accounts for another 13 percent of total bay nitrogen loadings (SWFWMD, 1999). Although agricultural nitrogen sources are often identified as significant, limit analysis has been completed. Furthermore, little comparison between atmospheric and land use urbanization has been completed to compare against agricultural sources.
Current estimates of nitrogen run-off are based on literature values reported by land use and by nitrogen measurements made at gauging stations throughout the watershed. A relatively simple model is now used to estimate nitrogen flux every three years.
Impact of Urbanization on Nitrogen Loading to the Tampa Bay Estuary
Nitrogen Export Efficiency for Urban versus Rural Land Use
Tampa Bay, Florida's largest open water estuary is located on the eastern shore of the Gulf of Mexico in west-central Florida. It is bordered by Hillsborough, Manatee and Pinellas counties, which collectively have more than 2 million people with an expected increase of 2 percent by 2010. Sources of water into the bay come from over one hundred tributaries of major rivers that drain more than 5,700 km2 of watershed. Major bay habitats include mangroves, salt marshes, and submerged aquatic vegetation. The bay is also home to more than 200 species of fish and 25 species of birds. There has been a significant reduction in the major habitats since the 1950s (Greening, 2001). Water quality decline contributed to the loss of nearly half of Tampa Bay's sea grass, almost 19,000 acres, from the 1950s to the 1980s (U.S. EPA, 2003).
Nitrogen loading to the bay has been identified as a major contributor the reduction in water quality. In addition to surface water nitrogen input, studies indicate that the water quality of the bay is also affected by atmospheric deposition of nitrogen species (Dixon et al., 1996; Greening, 2001, Poor et al., 2001; Poor, 2002; Pollman, 2003; Poor et al., 2003a&b). A number of factors in addition to nutrient inputs regulate productivity in estuaries, including residence time, tides, temperature, wind, geomorphology, hydrology, and light availability (Borsuk et al., 2002). Two significant steps have been taken to protect the sustainability of Tampa Bay estuarine habitat: 1) Tampa Bay was accepted into the U.S. EPA National Estuary Program in 1990 (Greening, 2001), and, 2) in 1998 local government and agency partners in the Tampa Bay Estuary Program (TBEP) signed an agreement to maintain nitrogen loading at 1992-1994 levels by addressing nitrogen sources from storm-water runoff and municipal point sources. The largest source of nitrogen, storm-water runoff, represents an estimated 62% of the total load. The remaining sources (38%) are wastewater, nitrate-enriched groundwater and direct atmospheric deposition (TBEP, www.tbep.org), primarily originating as emissions from power plants, automobiles and other industrial sources. In 1996, a project for bulk atmospheric deposition assessment to the Tampa Bay Watershed estimated direct nitrogen loading as 32% (Dixon, 1996). We hypothesize that in the past decade, change in land use of the North Tampa Bay area (NTB), particularly decreased agricultural land use, increased urbanization, have contributed to higher indirect nitrogen loading to the Tampa Bay Estuary.
There are many types of nitrogen reduction projects that have been and can be implemented, such as storm-water upgrades, industrial retrofits, and increased agricultural best management practices. However, to efficiently manage nitrogen reduction strategies, a better understanding of the sources and quantities of nitrogen inputs, both direct and indirect, are required, as well as spatial and temporal distributions of nitrogen loading. Because nitrogen inputs are ubiquitous, a unified perspective which incorporates the atmospheric, surface water and groundwater systems together must be utilized.
To help manage the water resources of the Tampa Bay area, Tampa Bay Water, and the Southwest Florida Water Management District commissioned the development and application of an integrated surface water/groundwater model. This model represents the surface water system, the groundwater system and the complex interaction between the two including the Tampa Bay watershed. The model, the Integrated Hydrologic Model (IHM), was specifically developed for the dynamic high water-table environment of west-central Florida (Ross et al., 2004). There is approximately 4,000 square mile domain of the Integrated North Tampa Bay model (INTB). This model can be used to estimate the runoff, streamflow and groundwater discharges to Tampa Bay.
Regional atmospheric modeling and atmospheric chemistry research is being pursued by the Bay Regional Atmospheric Chemistry Experiment (BRACE) study. The estimates of nitrogen loading are subject to large error. We expect the error can be reduced and a better understanding of the complete nitrogen budget gained through integrated hydrologic modeling and simultaneous atmospheric and storm-water process modeling. This will expand the knowledge of the Tampa Bay nitrogen cycle with the objective of understanding the full impact of land use and land management strategies, on air, water and health of the Tampa Bay ecosystem.
Integrating Hydrological Measurements and Remote Sensing across Regional and Small Experimental Scales
The Center for Modeling Hydrologic and Aquatic Systems (CMHAS) in the University of South Florida (USF) has experienced a long history of data collection in a number of small-scale sites. A project is now ongoing that focus in an experimental site adjacent to a planned reservoir in Hillsborough County. This site is a small-scale in Alafia watershed which is a part of a regional watershed of Tampa Bay. The work is focusing mainly on soil moisture measurements with soil probes that have eight sensors at different depths. In addition to soil moisture contents, observations of precipitation, evapotranspiration, and surface runoff have been collected. Parallel to that and since the1980s, CMHAS is concerning in integrated surface water and groundwater modeling. A model was developed in 1988 and was used intensively by several local and regional water resources agencies. The model has been revised and evolved from its first version to a more sophisticated model. The revised model, Integrated Hydrologic Model (IHM), is completed in 2003 and became a highly comprehensive model with a capability of simulating the complex interaction in surface water and groundwater systems. Some problems remained unsolved in all these stages which are related the massive data requirements.
Ecosystems change dramatically on timescales from years to decades, due to natural (e.g., climate change or drought) and anthropogenic processes (e.g., land-use change) that can have drastic impacts on basin-wide water and solute budgets. To address these knowledge gaps, CMHAS developed a physically-based understanding by investigating water-partitioning processes at regional scale. CMHAS also observed the hydrologic parameters in several small-scales. The next step is to identify methods to parameterize these processes at the landscape to regional scale of Tampa Bay. Once this is accomplished, the hydrologic models and parameters can be incorporated into numerical models that can be tested against the actual historical behavior of the integrating system outputs. Finally, these results can be integrated into the integrated model that will incorporate human interactions and be employed to predict responses of the system under a variety of imposed stresses and management responses. The approach is to combine intensive observations at multiple scales with modeling.
Highly-Time Resolved Coupled Measurements of Atmospheric, Surface and Subsurface Water and Nitrogen Fluxes and the Impact of these Fluxes on Stream Water Quality
Tampa Bay is one of the most important ecological systems in west-central Florida, as well as the largest estuary in Florida. Declines in the water quality of the bay have adversely impacted native sea grass and fish populations. One of the identified contributors to the water quality decline is the excessive input of nitrogen compounds. Nitrogen compounds are deposited through a variety of means: direct deposition from the atmosphere as dryfall and precipitation, urban storm-water runoff, streamflow, and groundwater discharge. Sources of nitrogen are automotive emissions, industrial emissions, wastewater, fertilizer manufacturing, and agricultural practices.
This study will employ an interdisciplinary approach incorporating remote sensing, data collection, coupled surface water/groundwater hydrologic modeling, atmospheric transport modeling, and Bayesian Decision Networks (BDN) to estimate the sources and relative contributions of nitrogen deposited into Tampa Bay, and to recommend cost-effective strategies to reduce nitrogen loading from both urban and rural communities. The model domain will be an approximately 200 km x 200 km area of west-central Florida. Remote sensing will be used to refine land use, soil moisture, and biomass information maps. Concentrations of nitrogen in precipitation, surface water, groundwater, and runoff will be sampled. The ambient air concentrations, wet and dry deposition rates will be computed using the CALMET/CALPUFF atmospheric modeling. A generic Bayesian network (BN) will be constructed for all modeling units and a predictive analysis will be performed using a complete BDN to model decision impacts and perform sensitivity analysis.
Determination of the Thresholds and Dynamic Behaviors of Vegetation Cover in Response to Global Change
Over past two centuries a rapid increase in human population coupled with unplanned water management activities has resulted in severe degradation of ecosystems on a global scale (Zalewski, 2000). The primary driving force behind this loss are the mechanistic-hydrotechnical approaches adopted for water management. Human activities such as construction of impoundments for water conservation and conversion of natural land to agricultural or urban land have drastically altered the natural regime causing a significant reduction of pristine ecosystems. Freshwater wetlands, for example, have been reduced by as much as 50% in the United States (Light and Dineen 1994; Davis et al, 1994;Schuller et al 2000).
Hydro-geological studies have shown that evolution of every ecosystem is associated with a series of events that occur over geological timescales and depend on climatic and hydrologic conditions and/or nutrient availability (Zaleweski, 2000; Domenico and Schwartz, 1997). Subsequently, the mechanisms of interaction of the biota with their ambience are the fundamental factors behind the differences among various biomes especially in the development of their space-time pattern (Rodriguez-Iturbe and Porporato, 2004). Hence, any efforts of restoration of these ecosystems should take place in the backdrop of strong knowledge of specie specific interactions with its environment and ways in which the pristine conditions can be restored.
It is well recognized that hydrologic cycle is the most dominant of all the controlling factors in sustaining biota dynamics (Rodriguez-Iturbe et al., 1999; Petts, 1984; Schiemer et al., 1995). However, there are interactions between the vegetation, soils, land cover/land use and the climate system. Thus, sound understanding of spatiotemporal linkage between hydrology and ecology assumes paramount importance in understanding specie dynamics (Rodriguez-Iturbe, 2000). To this end, interdisciplinary work in ecology and hydrology has been initiated. Seminal research – in this new area called ‘Eco-hydrology’– like Zalewiski et al. (1997), Rodriguez-Iturbe (2000), Eagleson (2002), and Rodriguez-Iturbe and Porporato (2004) have shown promising results thereby increasing the confidence in the use of ecohydrological framework for understanding these dynamics. Despite the recent progress, our knowledge about the species interaction especially that of plants in ecotones and response of an ecosystem to the change in ambient conditions remains limited. The approaches currently being pursued are more theory driven and tend to make simplifying assumptions for important hydrological processes (e.g. Wijk and Rodriguez-Iturbe, 2001).
The main aim of this proposal is to further our understanding of eco-hydrological relationships between plants and their climate in a water-controlled environment. The focus will be on quantifying favorable regimes that sustain healthy ecosystems, especially in humid shallow water-table environments like that of Florida and the Southeast United States. The ecosystems in these environments, though recognized to be rich and productive, are most vulnerable to any changes in native hydrology (Myers and Ewels, 1990). Hence, they also provide an ideal ground for monitoring the effect of changes in hydrologic regimes on the vegetation cover. Additionally, the proposal will explore mechanisms of interaction between different ecosystems, their relative composition in an area and how does they change in response to external stresses, be it natural or anthropogenic. Understanding and accurately modeling vegetation dynamics is the key towards sustainability of natural resources and, in the future, it is this knowledge that will provide the backbone of more ecologically sound water resources planning and management, as envisioned in International Hydrological Programme (IHP-V ) by UNESCO (Janauer ,2000).
Climate Change: Evaluation of Sources, Potential Destinations, and Measures for Mitigations
Recent world events confirm that human populations face appreciable risk from natural hazards such as hurricanes, floods, earthquakes, landslides, volcanic eruptions and tsunamis. These hazards may be driven to some degree by changes in global climate. While climate change itself is certainly closely interconnected with natural variability and long-term earth system cycles, research has also indicated the high probability of a direct connection between anthropogenic activities and certain climate change indicators. Although the implicated human activities can be local or regional (e.g. local vehicular or industrial emissions), the effects on climate can be global. Additionally, impacts are not necessarily spatially consistent across the globe as some regions experience warmer wetter conditions and other regions become drier and cooler.
Evidence suggests that climate change associated with global warming is a direct result of greenhouse gas (GHG) emissions produced from burning coal, oil and gasoline – and that this is an increasing trend (Shogren and Toman, 2000; U.S. Department of State, 2002). In addition, some researchers have indicted that increased GHG emissions can result from agriculture and land-use changes (U.S. EPA, 1995). In spite of the growing understanding of GHG emissions and associated apparent climate change, robust accurate predictions of long and short term economic, environmental and social impacts are difficult to make due to significant uncertainties in every link in the cause and effect chain of the global climate change story.
Recognizing this uncertainty, the Intergovernmental Panel on Climate Change (IPCC) developed in 1992 six projections or scenarios of future global emissions to the year 2100. These scenarios attempt to represent possible paths of future emissions based on alternative assumptions about world population growth, economic activities and energy use. They can be used to feed Integrated Assessment Models (IAMs) to evaluate potential climate change under different management options (e.g. reducing greenhouse gas emissions). IAMs are a key tool for assessing the potential physical, ecological, economic, and social impact that can be expected as a result of specific policies. However, IAMs vary widely in size, scope, detail, objectives, and assumptions and can be applied regionally or globally.
As government agencies and policy-makers begin to depend more heavily on IAMs it is critical that they are fully aware of the uncertainties associated with projections and estimations of potential change and impacts. Most IAMs produce a large range of uncertainty making it difficult for policymakers and planners to have confidence in their selected responses or actions.
In addition, under current practice, a trail and error procedure and subjective analysis and evaluation of scenarios is often used. This may require running multiple scenarios with the IAMs until an acceptable scenario is arrived at. While some IAMs are efficient, others can be time consuming to use, making this an ineffective approach for assessing the outcomes of the climate protection strategies and evaluating the respective physical, ecological, economic, and social consequences (Grades, 2003). This “policy evaluation approach” used to evaluate some test policies may not explicitly account for uncertainty in results and may “need to determine the likelihood of the range of impacts” (IPCC, 1966a). The key concept here is that of the “likelihood” of the range of impacts. This suggests a probabilistic approach which may be achieved by providing confidence estimates of outputs. In addition to the likelihood associated with the range of impacts, it is also clear that each scenario used as input for an IAM also has an associated likelihood or probability of occurring, which should be formally addressed in the analysis.
This need motivates the current research proposal, which is to construct a formal probabilistic tool of the climate change problem using Bayesian networks to be combined with appropriate IAMs. The resulting probabilistic tool will attempt to capture all significant variables in the climate change “problem space” including physical variables (wind speed, air pressure, temperature, precipitation, etc.), management variables, growth projections, and end-point indicators of human and environmental sustainability. The Bayesian network framework links all such variables in a clear cause and effect numerical construct that explicitly accounts for the probabilistic (mostly stochastic, non-deterministic) nature of relationships between variables, and serves as a tool for evaluating the likelihood of the range of impacts of climate change in light of policy and management efforts.
Under current practice, climate change scenarios are recognized as having several sources of uncertainty but generally do not acknowledge them explicitly (IPCC, 2001). We propose that for each scenario run through an IAM results should be presented with likelihood or confidence interval corresponding to estimates of outcomes. This will help quantify the broad range of uncertainty associated with each outcome. In some cases the range may be too broad, for example, scenarios indicate possible increases in sea level of less than 20 cm to almost 100 cm by 2100 as a result of a doubling of Earth’s atmospheric greenhouse gas concentrations (Shogren and Toman, 2000). Titus and Narayanan (1996) conclude that a sea-level rise of 10–65 cm by 2100 has an 80% probability of occurring. The uncertainty in these estimates stems from not knowing the exact relationship between variables (e.g., temperature change, greenhouse gas concentrations, and oceans and ice caps). This can be solved using, for example, sets of loss functions attached to a Bayesian network. In 2001, IPCC stated that “IAMs generate outcomes that are plausible but typically contain no information on the likelihood of outcomes or information on confidence in estimates of outcomes. In addition, questions such as how each result fits into broader ranges of uncertainty or what the ranges of uncertainty may be for each outcome still need to be answered.
Reducing the Buildup of Carbon Dioxide in the Atmosphere
In 2005, Idaho State University (ISU) and Idaho Department of Water Resources (IDWR) proposed to develop a geospatial outreach program (GOP) that capitalizes on the collaborative efforts of the NOAA-funded Boise Center Aerospace Laboratory (BCAL), the Department of Geosciences at ISU, IDWR and the US Bureau of Reclamation (USBR). BCAL is an established remote sensing laboratory in southwest Idaho that provides research and educational links between ISU campuses. ISU and IDWR have a strong working relationship and build upon this partnership through the GOP program and the effort to transfer remote sensing and GIS technology to local water delivery organizations (LWDOs). The program started in 2006 with extensive meetings and training on the use and application of these tools and data that empowered LWDOs to solve local water issues.
The establishment of a geospatial outreach program in conjunction with BCAL allowed for better interaction with the community for geospatial training and education, enabled remote sensing research technologies developed at BCAL and its partner (IDWR) to be distributed in decision support systems within the community, and helped facilitate long-term support for BCAL in Idaho by demonstrating BCAL’s expertise and willingness to partner with the community. The current project proposal, Reducing the Buildup of Carbon Dioxide in the Atmosphere, is a natural follow-on to the establishment of the GOP. The purpose of this proposal is to extend this effort by include promotion of community level efforts and awareness to address climate change. There are more than 170 water users associations in Idaho that promote and assist the development, control, conservation, preservation and utilization of the water resources within the state. There are thousands of acres of alfalfa, grain, corn, potatoes, beets and other crops.
Farmers can play a central role to reduce global warming by adopting environmentally friendly farming methods. For example, they can use improved soil management methods to reduce greenhouse gases or foster deep-rooted perennial plant species that have significant biomass in their root systems. In addition, no-till farming, besides helping to meet targeted reductions in atmospheric carbon dioxide and reduce the harmful effects of global warming, it improves soil quality, reduces soil erosion, saves the farmer work, reduces the need for fertilizers and fuel, and increases crop yields. However, not all the framers are aware of their potential role.
Enhancing Producer Management Behaviors in Achieving Crop-water Sustainability
Water resources sustainability has both physical and economic requirements. The physical requirement is achieved if water is available in sufficient quantity and is related to efficient use; while the economic requirement involves balancing the costs and values of water. To help attain sustainability, agricultural producers need to be given tools to assure efficient water use and avoid both water and economic deficit problems. Additionally, making producers involved in and aware of environmental issuesincluding issues of crop-water sustainabilitywill help them identify and mitigate water conservation problems. A previous study by the PIs on the Big Lost River in Southern Idaho identified specific watershed management decisions that can be taken to improve water-use sustainability. However, producer management behavior was not previously addressed. Although it is necessary for producers to identify and prevent future problems, they can also develop ways to achieve sustainable agriculture. The objective of this study is to develop tools to identify, evaluate, and understand producer management behavior that improve agricultural water conservation in crop production and consequently raise surface- water and ground-water sustainability in the Big Lost River. We also have an outreach component in which the water budgets of the Big Lost Valley will be illustrated in a short educational movie, which we will present live and on CD. The ultimate goal is economically and environmentally sustainable agriculture. This research will use Bayesian networks with an optimization model considering all significant variables related to sustainability, such as water supply and demand, water conservation, and the different costs and values of water. The results of this study can be implemented in the Snake River Basin after the appropriate modifications.