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An Earth Observation-Supported Strategy Linking Biophysics and Socio-Economics for Addressing Water Vulnerability

Marc L. Imhoff
Biospheric Sciences Branch
NASA’s Goddard Space Flight Center

Lahouari Bounoua
Earth System Science Interdisciplinary Center
University of Maryland, College Park

Roy Darwin
Resource Economics Division
Economic Research Service, USDA

Robert Harriss and Michael Glantz
Institute for the Study of Society and Environment
NCAR

A Proposal submitted to:
NASA ENERGY and WATER-CYCLE SPONSORED RESEARCH

Including contributions from:
Global Water and Energy Cycle Focus Area
Terrestrial Hydrology Program (THP)
Land Cover and Land Use change (LCLUC) Program
Water Management Program (WMP)

NASA Peer Review Services, Code Y
500 E street, SW suite 200
Washington DC , 20024-2760

ABSTRACT 3
TECHNICAL PLAN 4
Key Background 4
    Objectives 5
Remote Sensing Data and Climate Data 6
Task 1: Inversion of satellite and biophysically-based land surface model 6
    The SiB2 Biophysical Model 7
    Model Inversion for Estimation of Water Balance, Irrigation Water Volume, and Vulnerability 9
    Model Inversion for Identification of Irrigated Land 10
    Determination of the length of the growing season 10
Task 2: The Future Agricultural Resources Model (FARM) 11
    Integrating Satellite and Modeling Results Into FARM 13
    Economic Analyses using FARM in Central Asia 13
Task 3: A Central Asia Strategic Environmental Assessment (CASEA) 13
    Environment, Development, and Conflict Issues in Central Asia 13
    A Research Focus on Cross-Scale Information and the Integrated Assessment of Environmental
    Vulnerabilities
15
Expected Results 16
Capabilities of Research Partners: 16
    Economic Research Service (ERS) of the U.S. Department of Agriculture 16
    National Center for Atmospheric Research (NCAR) 17
References 18
MANAGEMENT PLAN 20
Cost Plan 20
Budget 22
Current and Pending Research Support 27
RESUMES [in alphabetical order] 28
APPENDIX A. Publications from previous LCLUC funding 37
APPENDIX B. Examples of 0.5 degree spatial data used in FARM 39

Human demand for products of photosynthesis strongly influences the water cycle, especially in arid and semi-arid areas, through land transformation and the diversion and extraction of fresh water needed to support agriculture. A powerful way to quantify the interaction between consumption, land cover change, and water use is through satellite driven biophysical modeling of primary production (GPP, NPP etc.) providing quantitative estimates of carbon and water flux as a function of plant functional type, soil properties and climate. In this proposal we will explore an inverse process approach using satellite-driven (MODIS) biophysical modeling to quantitatively assess water resource demand in semi-arid and arid regions by comparing the carbon and water flux modeled under both equilibrium (in balance with prevailing climate) and non-equilibrium (irrigated) conditions. We postulate that the degree to which irrigated lands vary from equilibrium conditions is related to the amount of irrigation water used. The results will provide important 1 st order quantitative parameters required as input to the Future Agricultural Resources Model (FARM) - a climate driven agro-economics model developed by the USDA’s Economic Research Service (ERS). Using FARM we will explore regional vulnerability to water scarcity as a function of the economic consequences of agricultural practices, water use and climate variability. We will evaluate the cross-scale regional robustness of our unique methodologies in a case study of water allocation issues in the Aral and Caspian Sea basins in Central Asia (within the NEESPI boundaries) where the intersection of rapid agricultural/economic development and water availability has had an unprecedented impact on the both the physical and socio-political environments. We will develop and test our methods within the case study and extend the results regionally and globally at different resolutions. The objectives of our proposal are to:

  • Develop a satellite-supported inverse physiological model based approach for quantitatively assessing water flux in vegetated ecosystems and the balance between water supply and demand for food and fiber production in arid and semi-arid regions
  • Use the results of this approach to generate input products that improve the accuracy of FARM and explore the socio-economic consequences of agricultural land cover and land use change related to water resource use and availability and climate change at regional and global scales.
  • Explore the local and regional socio-economic consequences of changes in water resource use and availability in the Caspian and Aral Sea Basins.

The overall approach binds socio-economic, land use/land cover and climate together in an Earth system science modeling framework appropriate for improving our understanding of human interactions with the water and energy cycles. The work will be a collaborative effort between NASA’s Goddard Space Flight Center, the United States Department of Agriculture’s Economic Research Service, and the Institute for the Study of Society and Environment at the National Center for Atmospheric Research.

One of the primary concerns facing humanity is the scarcity of freshwater for human use. It is widely agreed that about one-sixth of the global population (about 1 billion) does not have access to usable fresh water. Even though water is abundant on our planet only about 2.5% of it is fresh and can be used for drinking, food, and domestic use. Of that 2.5%, nearly 70% is frozen and trapped into glaciers and permanent ice lands of Greenland and Antarctica, leaving only a small fraction (< 1 %) of the world’s liquid freshwater available for human use. The scarcity of available fresh water is increasingly the subject of conflicts around the world where political boundaries dissect natural watersheds and aquifers. It is expected that if present water consumption remains unchanged around the world, about 66% of the world population will live in water stressed conditions by 2025 (UNEP).

Human demand for products of photosynthesis strongly influences the water cycle through land transformation and the diversion and extraction of fresh water needed to support agriculture. It is estimated that more than 70% of the planet’s fresh water supply is used for irrigation followed by the industrial sector using around 22% while only 8% is left for all domestic use. Climate, soil properties, crop type and agricultural practices are primary factors influencing both the source of water and amount used for crop production. Even with the development of technologies and innovative irrigation techniques, global agriculture still poses the most challenging problem for managing fresh water availability. Over the next 25 years, population growth, economic development and the subsequent increase in consumption of food and fiber will impact all aspects of agricultural land use as well as increase water demand for domestic and industrial use. For many regions on Earth, such as the semi-arid lands of central and northern Eurasia , socio-economic shifts are likely to eclipse changes in mean climate as a driver of the future relation between water supply and demand (Vorosmarty et al., 2000 ). For these areas in particular, where the balance between supply and demand is fragile, short-term variations in regional climate can have immediate and potentially predictable environmental and socio-economic consequences.

In our previously funded LCLUC work [see Appendix A for list of LCLUC publications] we developed a model that equates food and fiber consumption to landscape level NPP using the same bio-physiology employed in satellite-supported NPP products. This allowed us to produce for the first time a spatially explicit map of human appropriated NPP (HANPP i.e., ‘demand’) and an NPP-carbon balance sheet of ‘supply and demand’. We propose a similar approach for determining water “balance” in arid and semi-arid regions related to food and fiber production as influenced by land cover and climate. MODIS and AVHRR data will be used to identify agricultural land cover and provide vegetation indices (VI) for use in biophysical modeling of primary production and water flux. We will develop an automated, objective satellite and model based method using the basic framework of the Simple Biosphere 2 model (SiB2) that quantifies water flow in vegetated land cover and differentiates between natural climatology (rain-fed) and irrigated conditions.

The method begins with the realistic assumption that, in its “natural” state vegetation density is in quasi-equilibrium with its climate, soil and nutrient resources. Satellite driven biophysical models, such as SiB2 and others, have proven useful for quantifying water and carbon flux for vegetated land cover in this equilibrium state (e.g., Dickinson et al., 1984, Sellers et al., 1986). In these models, the photosynthetic activity of the quantity of living vegetation indicated by the satellite data is modulated by the local climatology in away that is consistent with observations and ecological theory of resource use efficiency (Cowen , 1986). However, irrigated agricultural lands in arid and semi-arid areas are not in equilibrium with the local climate. Despite the high satellite vegetation index (VI) observed for these areas, the modeled photosynthetic activity will be “suppressed” by the lack of adequate precipitation provided by that climate (e.g., Bounoua et al., 2004, Imhoff et al., 2004). We postulate that the degree to which irrigated lands vary from equilibrium conditions is related to the amount of irrigation water used. By inverting the satellite driven biophysical models, it is possible to explore the relationship between observed vegetation (cover type and vegetation index) in the equilibrium state and the amount of additional water required to deviate from it by modulating water input as a unique function of precipitation.

Results of this methodology will provide an objective, satellite supported, detection of irrigated agricultural lands as well as an estimate of the volume of irrigation water required under various conditions. Results will be applied at a local scale using 1 km products for a test site in Central Asia and at regional and global scales using 0.25 and 0.5 degree grids. The socio-economic consequences of agricultural land use change and water demand will be examined across regional and global scales through an interface with the USDA Economics Research Service’s Future Agricultural Resources Model (FARM). FARM is designed to use agriculture and forestry commodity production statistics, land cover, water resources, and climate data for analyzing global changes related to long run agricultural and environmental sustainability. Using a Computable General Equilibrium economic model FARM predicts how changes among the suite of biophysical variables and commodity prices interact and subsequently impact decisions about future agricultural practices that have consequences on water use.

Results from both FARM and the methodologies developed in this proposal will be used to develop cross-scale measures of vulnerability related to the consequences of land use change on land resilience, sustainability, water resources, and the provision of environmental goods and services. The overall approach binds the socio-economic, land use/land cover and climate related aspects in an integrated modeling framework appropriate for improving our understanding of the water and energy cycles.

Objectives

  1. Develop a satellite-supported physiological model based approach for quantitatively assessing water flux in vegetated ecosystems and the balance between water supply and demand for food and fiber production with special focus on arid and semi-arid regions.

  2. Use the results of this approach to generate input products that improve the accuracy of a climate driven agro-economics model used by the USDA’s Economic Research Service, Future Agricultural Resources Model (FARM).

  3. Explore the local and regional socio-economic consequences of changes in water resource use and availability in the Aral Sea Basin.

For this study we intend to use the 2001-2002 MODIS vegetation index (EVI/NDVI) and the MODIS 2001-2002 land cover classification and the AVHRR 1982-1998 calibrated NDVI data set. The MODIS data will be used for all the objectives stated above - for the computation of the biophysical fields necessary for the Simple Biosphere model as well as for length of growing season for a revised 2001-2002 version of FARM. MODIS provides well calibrated, corrected and validated data at spatial and temporal resolution adequate for our proposed study. This data set is available at 1km horizontal resolution and at temporal composition of 16 days. Because of cloud and seasonal snow coverage, data are not systematically retrieved and therefore the entire dataset is not continuous in space and time. We will use the 16-day composite as a baseline dataset and the 8-day composite as a sharpening layer when necessary. However, the procedure can only be applied to a limited region during the process of algorithm development given the cost and computational complexity of the approach. For the global coarser resolution aggregation of the 1kmx1km to the 0.25x0.25 degree grid results in a spatially and temporally complete datasets. Previous research indicates that NDVI may over predict vegetation density in semi-arid environments. The Enhanced Vegetation Index (EVI) has been shown to be more correlated to evapotranspiration than NDVI ( Nagler et al., 2004). Therefore for the biophysical modeling component of our work we will use both NDVI and EVI for our case area in the Aral Sea Basin and compare results. The AVHRR calibrated NDVI will be used for the determining a 1997 length of growing season data layer for the currently used 1997 datum for FARM.

AVHRR NDVI 1982-1998
MODIS 1 km IGBP land cover classification (MOD12Q1) (2001-2002).
MODIS 1 km Global NPP (MOD17A3) (2001-2002).
MODIS 1 km Vegetation Index 16-day (MOD13A2)

The climate data required to drive the Simple Biosphere model consist of surface shortwave and long wave radiations, surface wind speed and temperature and the large scale and convective precipitation. We plan to obtain these data at our working resolutions from the NASA Goddard Space Flight Center Land Information System (LIS). LIS uses two atmospheric data assimilation model outputs as the base forcing: one is the National Center for Environmental Prediction NCEP-GDAS (Global Data Assimilation System) output and the other is the NASA GEOS DAS output; and a collection of other data for precipitation. The model forcing data are then superposed by optional observed datasets such as radiation and precipitation. The LIS runs a suite of land surface models in an assimilation mode and produce components of the water and energy cycles as well as critical land information for numerical weather prediction and climate models on scales from 2 degrees to 1km. We will acquire and use these data as they are already processed and in form directly available to us from the GSFC Global Land Data Assimilation System (GLDAS).

In its “natural” state vegetation density is in quasi-equilibrium with its climate and therefore highly dependent on precipitation as a source of water. Biophysical models driven by satellite observed vegetation indices and climate data have proven useful for quantifying water and carbon flux for vegetated land cover in the equilibrated state. However, irrigated agricultural lands in arid and semi-arid areas are not in equilibrium with the local climate and the degree to which they vary from equilibrium conditions is related to the amount of irrigation water used. Despite the relatively high satellite VI’s, observed for these areas the modeled photosynthetic activity will be suppressed over the actual case because the amount of water shown to be available in the climatology database is insufficient. By inverting the model, it is possible to exploit the conflict between the observed VI and the climate data (through the plant water stress function) and estimate the amount of water required to deviate from the local equilibrium state. This additional water requirement should be roughly equivalent to the amount of water used or needed for irrigation.

This is an entirely new method for remotely identifying irrigated lands because it incorporates a coupled photosynthesis-conductance sub-model that quantifies the flux of carbon uptake as a function of water availability in the root zone. The approach is flexible allowing other models to be incorporated. Initially, we will use the Simple Biosphere model, SiB2 (Sellers et al., 1996) biophysical model. While this model includes a one dimensional hydrological modeling framework, it will provide a reasonable starting point and should not be an issue in our irrigated land detection algorithm where water excess from all model layers including gravitational drainage adds up to total runoff from the model and is reasonably considered as “return flow” to system. Watershed and Catchment models such as the Variable Infiltration Capacity (VIC) (Liang et al., 1996) or the mosaic model (Koster et al., 1996) may be considered in subsequent studies once the technique is tested with the computationally less intensive SiB2 approach. The results will provide quantitative assessments of how irrigation affects water flux within the models parameters as well as provide a remote sensing based methodology for estimating the amount of water used or needed for irrigation.

The SiB2 Biophysical Model

We will use the improved version of the Simple Biosphere model-SiB2 (Sellers et al., 1996a) for the inverse modeling component in this study. In SiB2, the vegetation distribution (Defries and Townshend 1994) as well as its spatial and temporal phenology is described using global satellite observations (Sellers et al., 1996b). Each vegetation class is assigned a set of time-invariant parameters including: 1) morphological parameters such as canopy height, leaf dimensions, leaf angle distribution functions, root depths etc. and 2) optical properties such as leaf and stem reflectance and transmittance for live and dead phyto-elements. The normalized difference vegetation index (NDVI) is used to calculate time varying field of the fraction of photosynthetically active radiation absorbed by the green leaves of the canopy (fpar) and the leaf area index (lai). Fpar is strongly correlated to the ndvi and is used directly in an integrated photosynthesis-conductance model (Collatz et al., 1991, 1992) to calculate the photosynthesis and transpiration rates. In the version of SiB2 used in this study, fpar is prescribed from satellite observations; it then affects the surface water and energy balance but does not respond to it.

Vegetation physiology also responds to climate conditions, mostly temperature and precipitation. Therefore a perturbation to the either the climate or the physiological drivers is expected to result in a positive or negative feedback depending on the intensity of the perturbation. For example, a modest increase (decrease) in fpar is expected to produce more (fewer) leaves and consequently more transpiration. If the perturbation is further amplified however, an increase in fpar beyond a set limit will cause the stomata to close and so result in water stress. The model photosynthetic uptake of CO2 from the atmosphere is coupled to a water loss from the leaf interior and from soil water trough the stomates in a way that is consistent with observations and ecological theory of resource use efficiency (Cowen , 1986). The capacity of vegetation to convert soil moisture into latent heat flux is determined by fpar and

stomatal conductance. The former is prescribed and derived from ndvi while the latter depends on atmospheric conditions and the amount of water available in the root zone layer, thus establishing the required strong and realistic coupling between the climate forcing and the soil hydrology. The photosynthetic-conductance sub-model is controlled by a soil moisture stress factor that reduces the carbon assimilation and consequently transpiration when root zone water is low. The water stress function depends on the root zone soil moisture potential and the critical water potential, both of which are soil type dependent.

The critical part of the model that is of interest to this task is the hydrology module. The SiB2 hydrology module distributes the incoming precipitation into a canopy interception component and a throughfall component. The canopy interception can either evaporate at potential rate or contribute to the throughfall when canopy holding capacity is exceeded (Fig. 1).

The combination of the direct throughfall and water dripping from the canopy is added to the ground liquid water store. There, the water can either evaporate or infiltrate into a shallow surface layer if the ground storage capacity is exceeded. If the infiltration rate is in excess of the infiltration capacity of the soil, the excess water excess contributes to surface runoff. Similarly water from the surface layer can either evaporate or infiltrate into the root zone layer from which it can flow up into the surface layer, infiltrate into the deep soil layer, contribute to runoff, or be used by plants for photosynthesis (transpiration). From the deep soil layer water can diffuse up to the root zone or contribute to runoff though drainage.

Model Inversion for Estimation of Water Balance, Irrigation Water Volume, and Vulnerability.

To explore the potential vulnerability of an area with respect to water resources, we will invert the SiB2 photosynthesis-conductance model and examine the relationship between climate and the water stress function for a given grid-cell and observed vegetation index. For each time step under the prevailing climatology the value of the water stress function will be compared to the permanent wilting value of the dominant cover type. Indicators of vulnerability to climate change will be defined by the relationship between the integrated two values over a period of time.

To estimate the amount of water required for irrigation, we will add precipitation to the prevailing climatology at each time step to minimize the water stress function following the stream of procedure illustrated in Fig.2.

Water input through precipitation will be increased over and above the amounts specified in the observed precipitation amount until the water stress function reaches the minima providing an upper bound for maximum water input. The minimum water stress function as defined in the SiB2 model corresponds to the theoretical equilibrium between the vegetation density and the amount of water in the root zone. Therefore a comparison to the permanent wilting point value will provide a lower bound or minimum amount of water needed over and above what is available from the observed precipitation. Since the model uses precipitation as input, the irrigation component will be determined by subtracting the canopy and ground interception loss terms from the added precipitation. The remainder (i.e., the water infiltrated into the soil) is the amount of water required at the soil surface to meet a specific water stress value. The model will also separately track the amount of water transferred to the atmosphere through ground evaporation and plant transpiration, allowing thus the determination of the “water use efficiency” by plant functional type, as well as the amount of water that runs off or infiltrates into the water table. For our case study, water that runs off or infiltrates will be considered as return flow while that used by plant’s transpiration is referred to as ‘consumptive’, and does not return to the stream after use.

Model Inversion for Identification of Irrigated Land

One of the important data layers in FARM is the location and extent of irrigated land. We will use an approach similar to the one described above to identify grid cells with VI (NDVI/EVI) values that are out of equilibrium with their climatology. For irrigated areas in arid and semi-arid regions, vegetation indices from MODIS will describe a higher density of vegetation than can be supported by the climate. This discrepancy will be identified in the SiB2 model in the water stress function where the water potential of the plants are computed as a function of the available soil moisture (climatology) and the water demand related to leaf area (LAI). When the wilting point of the plants is reached photosynthesis ceases. In our model the VI from the MODIS data set is refreshed every 16 days. The climatology (based on daily updates) runs hourly. At some point in the growing season, irrigated areas will reach the wilting point in the model and it will persist through most of the 16 day period before the next MODIS VI data are entered into the model. If the VI of those cells is unchanged or increasing we will categorize that cell as “irrigated land cover”. This can be repeated throughout the annual cycle to eliminate omissions due to harvesting between MODIS observations. While there is a possibility that some natural areas (on seeps etc.) will be falsely identified as irrigated land, this approach will be a significant improvement over currently available techniques.

Determination of the length of the growing season

The growing season is the period of the year during which the temperature of cultivated vegetation remains sufficiently high enough to allow plant growth. It is also defined as the time interval between the last spring and the first autumn frosts, usually associated with a freezing temperature (32 degrees Fahrenheit). This is rather a static definition which may apply to irrigated cultivation but may leave out significant variability. Previous studies have observed significant variability in the length of the growing season. They noted that ten-day growing season length decreases were characteristically observed over periods of one to two decades throughout an 88-year study period, while increases of the same magnitude occurred in as little as four to six years (White et al., 1999).  Because of the magnitude of this variability and its impact on net primary production (NPP) and therefore water, we propose to evaluate the length of the growing season using a satellite based, dynamic, climatically controlled definition. The potential for monitoring vegetation phenological evolution from satellite has been know ever since the data from the AVHRR NDVI were used in land applications, however well calibrated higher resolution (250m) vegetation indices (vi) data from the NASA’s Moderate Resolution Imaging Spectrometer (MODIS) aboard the Terra and Aqua satellites provide an even better resource to evaluate the growing season for different vegetation types growing in different regions of the world under different climatic conditions. The onsets and ends of growing season will be determined from the normalized difference vegetation index (NDVI) time series composite over a period of sixteen days. A technique based on the derivative of the signal will detect sharp changes in phenological behavior associated with the onset and end of the growing season. An increase (decrease) beyond a fixed vegetation-type dependant threshold will be identified with the onset (end) of the growing season. We will develop a global map describing the length of the growing season and we expect several plant types to have multiples growing seasons. In general, most vegetation has either a unimodal or bimodal growth curves, however plants with 3 growing seasons have also been observed. Annual species can be assumed to have very rapid development during early parts of the season; agricultural areas are expected to have a very sudden drop in NDVI values associated with the harvest. With the dates given, the length of the seasons and their integrated NDVI values are easily calculated. The rates of change in the seasons can also be calculated. These parameters contain important phenological information about land cover and crop yields and implicitly take into account variability of temperature, precipitation, nutrient availability and fires, which are integrated in the vegetation density response.

FARM is an integrated modeling framework specifically designed for analyzing global changes related to long-run agricultural and environmental sustainability. FARM has been used primarily to analyze the impacts of greenhouse gas emissions on agriculture (Darwin et al., 1994, 1995; Darwin, 1999, 2003, 2004; Darwin and Kennedy, 2000). It has also been used to estimate costs of sea level rise ( Darwin and Tol, 2001). Other topics include: the effects of trade deregulation and population growth on tropical forests (Darwin et al., 1996), the costs of protecting global ecosystem diversity (Lewandrowski et al., 1999), and the impacts of technological advance in agriculture on land use (Ianchovichina et al., 2001).

The FARM framework relies on a number of various categories of data in two separate components - an environmental component and an economic component (Fig. 3). The environmental component consists of a geographic information system that links land cover and climate data with land and water resources. Climate is linked with land resources by agro-ecological zones (AEZ) defined primarily in terms of growing season. There are two types of AEZs—irrigated and rainfed. The length of the growing season on irrigated cropland is assumed to be equal to the number of days in a year that the soil temperature is ³ 5 ° C (Fig. 1, Appendix B). It is implicitly assumed that irrigation water is readily available when these soil conditions are met. Thermal regime also helps to define AEZs. Thermal regime, which is also referred to as “temperature regime”, is the average air temperature during the growing season. Thermal regime is calculated once the length of growing season is estimated (Fig. 2, Appendix B). The length of the growing season on rainfed cropland is assumed to be equal to the number of days in a year that the soil temperature is ³ 5 ° C and soil moisture is adequate for plant growth (Fig. 3 Appendix B). Soil moisture conditions are derived from the Newhall model as revised by Van Wambeke and as presented in Eswaran et al. (1995). Only days when both temperature and moisture conditions are suitable for plant growth contribute to length of growing season. Again thermal regime is calculated once the length of growing season is estimated. The meteorological data used to calculate length of growing season and thermal regime are monthly temperature and precipitation at 0.5° lat.-long. grid, 1901-1998 (New et al., 2000).

FARM’s economic component consists of a global computable general equilibrium (CGE) economic model. As a general equilibrium model, it accounts for all production and expenditures within each of its regions. A super household in each region supplies primary factors to producers and maximizes utility with respect to household consumption, government consumption, and saving. Producers maximize profits associated with the utilization of the four primary factors—land, water, labor, and capital. Production functions within the model can be adjusted to simulate technological change. Government policies are typically simulated by imposing charges on inputs and outputs.

Integrating Satellite and Modeling Results Into FARM

FARM’s assumption regarding length of growing season on irrigated land, e.g., that enough water is readily available to extend the growing season to its temperature-based maximum, may lead to overextension of the growing season and uncertainties in the determination of crop production. Currently FARM estimates the length of the growing season based on climate data and a soil temperature function. We propose to improve FARM’s database by using vegetation indices (NDVI, EVI etc) from MODIS and AVHRR to provide significantly improved data on the actual length of growing season. Length of growing season data from the MODIS 2001-2002 and the AVHRR 1982-1998 (selected years) calibrated data sets will be; 1) compared to FARM’s estimates of rainfed and irrigated growing season, and 2) integrated into a revised FARM and the results compared to previous performance as a means of assessing improvement. We expect major improvements in length of growing season particularly on irrigated land. The results will be evaluated globally and regionally.

Economic Analyses using FARM in Central Asia

Once these data are integrated into FARM’s database a series of economic analyses related to water consumption will be conducted. A global analysis will focus on how global increases in the technical efficiency of agricultural production might affect food security and the use of land and water resources. Another analysis will focus on agricultural problems in Central Asia, e.g., Afghanistan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. It will evaluate how adapting water-saving technologies, greater sharing between upstream and downstream users, and changes in the availability of water resources due to changes in climate or transfers from Siberian rivers might affect food security and the use of land and water resources in the region. Both current and NASA-improved FARM models will be used and the economic results compared so as to indicate how incorporating NASA’s data into FARM led to improved economic analyses. Economic results will also help inform NCAR's Central Asia strategic environmental assessment.

We propose to conduct a case study that will demonstrate how to effectively utilize global environmental information from NASA satellite observations and the USDA FARM model to conduct a strategic environmental assessment in the Aral Sea and Caspian Sea basins. Our Central Asian Strategic Environmental Assessment (CASEA) will address two core objectives identified in the Environmental and Security Initiative of the U.N. Development Program (UNDP), U.N. Environment Program (UNEP), and Organization for Security and Cooperation in Europe (OSCE). These objectives are to: (1) improve the methodologies for mapping environmental risks for use in conducting integrated regional assessments of vulnerability to environmental stress posing threats to security, and (2) to focus on capacity building in areas open to cooperation (Carius, 2003). The ultimate aim of our studies is to provide unique information that will contribute to building regional capacity for sustainable resource management, crisis prevention, and peace promotion.

Environment, Development, and Conflict Issues in Central Asia

Central Asia will be a very different region a generation from now. America, Russia, and China are all placing a high priority on establishing strategic positions in the region for both economic and security reasons. American interests in Central Asia escalated rapidly following the fall of the Former Soviet Union, with an initial emphasis on economic interests in oil and gas development in the Caspian Sea area being replaced more recently by security concerns. New American military bases in Kyrgyzstan and Uzbekistan may reflect the beginning of a major repositioning of U.S. forces to an improved tactical position for insuring a free flow of oil and gas from the Persian Gulf and Caspian Sea. NATO recently appointed a Special Representative for Central Asia and the Caucasus, with the expressed interest of expanding its Partnership for Peace programs in the region (McDermott, 2004). China is aggressively seeking access to Caspian Sea oil to fuel its rapidly growing economy. Russia, concerned by American and Chinese intrusions into its “near abroad”, is using discounted oil and gas and the presence of additional military forces in Kyrgyzstan and Tajikistan to maintain its sphere of influence. Does the increased geopolitical prominence of Central Asia offer an important opportunity for creating better futures for a region that is best known for its poverty, drug trading, warlords, and severe environmental degradation?

Central issues associated with the stressed Central Asian environments include a widely acknowledged inefficient use of water for relatively low value cotton crops, extensive pollution from agricultural chemicals, multi-year droughts, food shortages, and the declining extent of glaciers in the headwaters of major river basins (e.g., Glantz, 1999; 2002). A dramatic decline in water levels and devastating environmental degradation in the Aral Sea basin is the most dramatic symbol of a region suffering from extreme environmental and social mismanagement. A planning meeting on “Water, Climate, and Development Issues in the Amudarya Basin” emphasized the urgent and strong need for capacity building in the areas of water resources management and climate studies for states in the basin. Solving cross-scale and transboundary environmental issues requires that effective environmental management and governance mechanisms must be in place for science and technology interventions to be useful.

In the shadows of the considerable economic and military developments in Central Asia, new efforts aimed at addressing environmental risks are emerging with a special focus on the Central Asian Republics (CARs)—Kazakhstan, Kyrgystan, Tajikistan, Turkmenistan, and Uzbekistan (e.g., Carius et al., 2003). It is becoming increasingly obvious that bringing these fragile and failed states into the global economy is a robust, long-term strategy for winning the “war on terror”. The CARs must simultaneously improve their institutions, human conditions, economies, and the productivity of their natural resources to improve their chances for sustainable development. Satellite observing technologies, elaborate computer models, and sophisticated data systems are a sine qua non for the design of environmental management systems in the globalizing world economy. However, these tools have yet to play a major constructive role in improving the well-being of highly stressed regions like the CARs. We believe that our combination of remote sensing and hydrologic skills (NASA GSFC), agricultural and land management skills (USDA), and social science and capacity building skills (NCAR ISSE) offers a unique opportunity for gaining fundamental understanding for how to craft a “usable science” that will serve the needs of the CAR development community. We emphasize that this project is aimed at research on connecting global environmental information to local and regional scales and at evolving strategies for effective capacity building in the CAR development community. The research we propose also provides the foundation for future technology transfer and applications programs.

A Research Focus on Cross-Scale Information and the Integrated Assessment of Environmental Vulnerabilities

As described in previous sections of this proposal, NASA GSFC and USDA ERS will provide an integrated land remote sensing and hydrologic modeling toolkit for global and regional environmental monitoring and assessment. NCAR will foster regional stakeholder dialogues on “usable” monitoring and early warning mechanisms, information sharing and data exchange, and knowledge networking. We will focus on three spatial scales that are crucial to the ecology and sustainable of the region--the Amudarya River Delta, the Karakum Canal, and the Aral Sea Basin. The Amudarya is formed by the Pyanj River that has its headwaters in Afghanistan and the Vaksh River that originates in Tajikistan. The Amudarya River is the biggest river in Central Asia with estimates of its virgin flow starting coming from the Pamir Mountains estimated at about 70 cubic km per year. This river serves as the border between Afghanistan and several of the Central Asian Republics. NCAR ISSE has an unpublished baseline study the mid-1950s ecology and land use in the Amudarya Delta obtained from Russian colleagues that will be use for a comparison to contemporary land uses. The Karakum Canal diverts significant amounts of water from the Amudarya to irrigate expanding agricultural activities in the desert of the Karakum region. The 1,450 km Karakum Canal is a significant source of contention among the CARs, especially between Turkmenistan and Uzbekistan (Glantz, 2002). An assessment at the Aral Basin scale is especially relevant and necessary to a future dialogue on establishing regional water and land management strategies.

The AmudaryaRiver Delta. In the mid-1990s, the World Bank and the Uzbek government pledged to cooperate to projects aimed at saving the Amudarya Delta from complete ecological destruction. This delta had supported robust fisheries, fur pelt production, fodder production and agricultural activities as well as a wide range of unique natural ecosystems. Our strategic assessment will evaluate land use change between the mid-1950s and mid-1990s to estimate changes in water demand necessary to support human activities. Our assessment will provide the first estimate of contemporary water demand using the integration of the NASA satellite data and FARM model as described above.

The KarakumCanal in Turkmenistan. The Karakum Canal is the longest manmade canal in the world. In essence, it is the region’s third major river. The canal takes water out of the Amudarya and diverts it into the Caspian Basin. As it transits Turkmenistan’s Karakum desert the water is used for the production of wheat and cotton and for water supply to human settlements.

Using the observational and modeling methods described above, we will estimate water demand for the land uses associated with the Karakum Canal. The Turkmenistan government is very reluctant to say exactly how much water it is taking from the Amudarya river at Kerki (where it enters their territory). Estimates run from 10 to 20 cubic km from a high discharge of 70 cubic km in the Amudarya River in good water years. Turkmen water withdrawals are very controversial politically. The Turkmen signed a military treaty with the Russians out of fear of Uzbek reprisals against their water diversions. The Uzbek population is over 20 million and Turkmen population is about 4-5 million. The Turkmen are thought to have the highest water per capita water usage in the CARs.

The Aral Sea. The tragic story of how multiple creeping environmental problems have essentially destroyed the Aral Sea is well described by various authors in Glantz (1999). More recent qualitative assessments of the cumulative environmental degradation in the Aral Sea Basin are described at ccb.ucar.edu/centralasia and in the NCAR 2002 workshop report titled “Water, Climate, and Development Issues in the Amudarya Basin”. The current anthropogenic water uses in the basin leave little, if any, freshwater drainage for the Aral Sea. The natural and human ecology in the immediate areas around the Aral Sea and the Amudarya River Delta are devastated. This existing situation is critical with regard to water supply, especially considering the very high population growth rates in the region. Water demand for agricultural uses is destined to become more critical in coming decades as Afghanistan begins to take its legitimate share of water from the Amudarya. Climate change is likely to exacerbate the water crisis due to the gradual melting of glaciers in the headwaters region of the Aral Sea Basin.

This work will use satellite data and a biophysical model developed under NASA’s ESE to develop a quantitative satellite-supported approach for understanding the socio-economic connections between important drivers of the water cycle on land.

Results will provide quantitative assessments of the relationships between agricultural land use, water flux components, irrigation, human consumption of NPP and climate in an integrated framework. We expect the research to produce:

  • The first ever spatially explicit map of irrigated land and the volume of irrigation at high spatial resolution over the Central Asia semi arid lands and a coarse global resolution.
  • A land use and vulnerability map of future impacts and changes to the water balance for the CASEA region derived from inverse modeling at 1km resolution and FARM.
  • Land resilience and sustainability maps produced as functions land-cover and land-use change, climate and water resources and their impact on environmental goods and services (FARM) at regional (CASEA) and global scales.

Economic Research Service (ERS) of the U.S. Department of Agriculture

ERS’s contribution to this study builds on more than a decade of capacity building and research that Roy Darwin and associates have conducted on global change and sustainability. Roy Darwin and associates began by building a global economic framework, FARM, which implicitly includes land and water resources as inputs to production. The initial version of FARM has since been used to analyze the impacts of greenhouse gas emissions on agriculture, to estimate costs of sea level rise, the effects of trade deregulation and population growth on tropical forests, the costs of protecting global ecosystem diversity, and the impacts of technological advance in agriculture on land use. FARM has recently been updated; it now includes information on land and water resources and production in 58 economic sectors and 78 separate countries (including Afghanistan , Kazakhstan , Kyrgyzstan , Tajikistan , Turkmenistan , and Uzbekistan ) or regions. Once further improvements are made by incorporating NASA’s estimates of length of growing, FARM will be used to conduct various global and regional economic analyses related to water consumption. Both current and NASA-improved FARM models will be used and the economic results compared so as to indicate how incorporating NASA’s data into FARM led to improved economic analyses. No other economic model has this capability.

ERS will use the funding from NASA primarily to hire and provide equipment to staff to integrate NASA’s estimates of length of growing season into FARM, conduct global and regional economic analyses, and write various reports, one evaluating the impacts of using NASA’s estimates and others reporting the results of the economic analyses. Some funds also may be used for travel to NCAR to assist in incorporating ERS results into NCAR’s strategic environmental assessment for Central Asia.

National Center for Atmospheric Research (NCAR).

NCAR’s contribution to this study builds on the decades of research and capacity building that Michael Glantz has conducted in the Central Asian Republics. Glantz has worked with both in-country and international experts to define the origins and potential future conflicts related to water issues in the Aral Sea Basin (see references and Task 3). Robert Harriss will facilitate the integration of the NASA and USDA natural science and NCAR social science information into a strategic assessment framework (SEA). The SEA will be used for both conducting a vulnerability assessment and for constructing scenarios of investments in water efficient irrigation technologies as a key element of economic development, conflict resolution, and environmental protection. Harriss has conducted a number of similar studies in the United States and Latin America (e.g., Harriss, 1976; Horvath et al., 1972; Sanchez-Azofeifa et al, 2002).

Carius, A. 2003. Environment and security initiative addressing environmental risks and promoting peace and stability: The post Kiev process, [www.undp.sk, accessed December 2004].

Carius, A., M. Feil, and D. Tanzler, 2003. Addressing Environmental Risks in Central Asia, UN Development Program, 37pp., [www.undp.sk, accessed December 2004].

Darwin, R. F. 1999. “A FARMer =s View of the Ricardian Approach to Measuring Effects of Climatic Change on Agriculture.” Climatic Change 41(3-4):371-411.

Darwin, R. F., J. Lewandrowski, B. McDonald, and M. Tsigas. 1994. “Global Climate Change: Analyzing Environmental Issues and Agricultural Trade within a Global Context.” Chapter 14 in: Sullivan, J., ed., Environmental Policies: Implications for Agricultural Trade. (Foreign Agricultural Economic Report Number 252) U.S. Department of Agriculture, Economic Research Service, Washington, DC, pp. 122-145.

Darwin, R. F., M. Tsigas, J. Lewandrowski, and A. Raneses. 1996 “Land Use and Cover in Ecological Economics,” Ecological Economics 17(3):157-181.

Darwin, R. F., M. Tsigas, J. Lewandrowski, and A. Raneses. 1995. World Agriculture and Climate Change: Economic Adaptation. ( U.S. Department of Agriculture, Economic Research Service, Agricultural Economic Report No. 703) Washington, DC.

Eswaran, H., E. Van den Berg, P. Reich, R. Almaraz, B. Smallwood, and P. Zdruli. 1995. Global Soil Moisture and Temperature Regimes. World Soil Resources Office, Natural Resources Conservation Service , United States Department of Agriculture, Washington , DC.

Glantz, M.H. (ed.), 1999. Creeping Environmental Problems and Sustainable Development in the Aral SeaBasin, CambridgeUniversity Press, Cambridge, UK.

Glantz, M.H. (ed.), 2002. Water, Climate, and Development Issues in the Amudarya Basin, Workshop Report, 46pp., [www.isse.ucar.edu, accessed December 2004].

Harriss, R., 1976. Energy, ecosystems, and world order, in: International Legal Papers, 2: 104-116, Pontchartrain Press, New Orleans, LA.

Horvath, G., R. Harriss, and H. Mattraw, 1972. Land development and heavy metal distribution in the Florida Everglades, Marine Pollution Bulletin, 3: 182-184.

Ianchovichina, E., R.F. Darwin, and R. Shoemaker. 2001. “Resource Use and Technological Progress in Agriculture: A Dynamic General Equilibrium Analysis.” Ecological Economics 38(2):275-291.

Imhoff, M. L., L. Bounoua, T. Ricketts, C. Loucks, R. Harriss, W. Lawrence. 2004. Global patterns in human appropriation of net primary production. Nature , vol. 429, June 24, 2004.

Koster, R. and M. Suarez, 1996. "Energy and Water Balance Calculations in the Mosaic LSM", NASA Tech. Memo. 104606, Vol. 9.

Lewandrowski, J., R. F. Darwin, M. Tsigas, and A. Raneses. 1999. “Estimating Costs of Protecting Global Ecosystem Diversity,” Ecological Economics 29(1):111-125.

Liang, X., E. F. Wood, and D. P. Lettenmaier, 1996. Surface soil moisture parameterization of the VIC-2L model: Evaluation and modifications, Global and Planetary Change, 13, 195-206.

McDermott, R.N., 2004, NATO deepens its partnership with Central Asia, Central Asia Caucasus Institute Analyst, November 17 issue, [www.cacianalyst.org, accessed December 2004].

Nagler, Pamela L., Glenn, Edward, H. Alfredo, W. Zhengming, J. Cleverly , and S. Russ, “Application of MODIS Enhanced Vegetation Index and Land Surface Temperatures to Predict Riparian Evapotranspiration Across The Semi-Arid Southwest, Using Micrometeorological Tower Data From the Middle Rio Grande, San Pedro River, and the Lower Colorado River” The geological Society of America, 2004 Denver Annual Meeting (November 7–10, 2004) Paper No. 221-10.

New, Mark, Mike Hulme, and Phil Jones. 2000. “Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface Climate.” Journal of Climate 13(1 Jul):2217-2238

Sanchez-Azofeifa, G., R. Harriss, A. Storrier, and T. Comino-Beck, 2002. Water resources and regional land cover in Costa Rica: Impacts and economics, Water Research and Development, 18:409-424.

Thornthwaite, C.W. 1948. “An Approach toward a Rational Classification of Climate.” Geographical Review 38:55-94.

Vorosmarty, C. J., Green, P., Salisbury, J., and Lammers, R. B. 2000. “Global Water Resources:

Vulnerability from Climate Change and Population Growth.” Science vol 289, 14 July, 2000, pp. 284-288.

This project is subdivided into 3 major tasks. The detection of irrigated lands and the amount of irrigation water (GSFC and UMD team), the socio-economic projections using FARM (USDA team) and the sustainability, vulnerability study (NCAR team). The principal investigator (PI) will supervise and coordinate between co-investigators activities, call for regular meetings and finalize publications. Dr. Imhoff will have responsibility for overall project management and will work closely with all co-investigators to meet objectives.

Dr. Bounoua will be responsible for the development of the algorithm for inverting the Simple Biosphere model. He will perform runs and produce the algorithm for detecting irrigated lands and the amount of water used for irrigation. Dr. Bounoua will also develop the data base for the drivers of SiB2 and determine the length of the growing season from MODIS vegetation index datasets. Dr Bounoua will also assist and participate in analyzing model outputs as well as participating in elaboration of publications.

Dr. Roy Darwin from the USDA-ERS will be responsible for integration of MODIS, AVHRR, and inverse modeling products into FARM, evaluating the results and generating the economic analyses for CASEA and global studies.

Drs. Harris and Michael Glantz co-investigators in this project will be responsible for developing and coordinating the CASEA case study with the NASA investigators and the USDA/ERS co-investigator.

The principal investigator and the co-investigators will participate equally in finalizing publications; however the PI will be responsible for defining and organizing the publications themes. We expect at least 3 publications from this project: one, by the end of the 1 st year, describing the automated inversion algorithm and the mapping of irrigated land and the irrigation volume, a second publication toward the end of the third year, dealing with the socio-economic projection, and a third publication by the end of the third year summarizing the environmental and societal impact.

The proposed project requests funds for the PI at 0.2 FTE’s per year. The PI’s budget also includes a $5k per year for travel to one meetings of about 3 days duration in the United States plus one trip overseas in support of the NEESPI component. Include is $6K per year to cover equipment, supplies, computer maintenance and publication charges. The project also requests fund for Dr Bounoua from University of Maryland at 0.25 FTE per year and $2k for travel.

Funds ($50K/year) are also requested for Dr. R. Darwin of the USDA’s ERS to offset salary, data acquisition, and travel costs directly related to the project.

The NCAR budget request will provide one-month of salary support for R. Harriss and M. Glantz in each project year. Funds are also requested to establish a five person Aral Sea Basin science team of experts who work and live in the region. The science team will provide within-basin expertise on agricultural practices, water management practices, and policy issues associated with any proposed changes in water management practices. Each member of the science team will receive a $5K honorarium and reimbursement for travel to two project meetings per year. One meeting will be held in an Aral Sea Basin country and the other meeting in the US. Travel support for R. Harriss and M. Glantz to attend these meetings is also included in the NCAR budget.

Marc L. Imhoff
Current: None
Pending: None

Lahouari Bounoua
Current:
2004-2006, Development and Evaluation of Clouds and Land Parameterization in the DAO/FV-GCM for Climate Studies, $600,000, NASA Multidisciplinary Research in Climate, Chemistry and Global Modeling, PI: Y. Sud, (Bounoua – 0.15 FTE/ $25k/year)

Pending:
2005-2008, A Spatially Complete Global Spectral Surface Albedo Derived from MODIS Land Products for Use in Modeling and Remote Sensing applications, $554,000, NASA Modeling, Analysis and Prediction, PI: M. D. King (Bounoua - 0.28 FTE)

2005-2008, Temporal and Spatial Structure in Terrestrial Primary Production “Hot Spots” and their underlying structural functions, $905,000, NASA Modeling, Analysis and Prediction PI: L. Bounoua and B. Middleton (Bounoua 0.25)

2005-2008, Evaluating the Land Use Forcing on Climate, NASA Modeling, Analysis and Prediction PI: G. Bonan (Bounoua 0.50)

2005-2008, Land Cover Change and Permafrost dynamics-Linkages to the Water and Energy Cycle, NASA NEWS, PI: J. Ranson (Bounoua 0.30)

Roy Darwin
Current: None
Pending: None

Robert HarrissCurrent and Pending Support
Current: None (fill)
Pending: None (fill)

Michael GlantzCurrent and Pending Support
Current: None (fill)
Pending: None (fill)

LAHOUARI BOUNOUA

Education
1992 Ph.D. Florida State University, FL; in Atmospheric Sciences
1990 M.S. Florida State University, FL; in Atmospheric Sciences
1980 B.S Meteorological Institute for Training and Research, Algeria.

Professional Experience
2000–present Research Faculty Assistant Professor, Earth System Science Interdisciplinary Center, Univ. of Maryland
1996–2000 Research Faculty Assistant Professor, Dept. Meteorology, University Maryland
1992–1995 Post-Doctoral Research Scientist University Space Research Association (USRA).

Honors and Awards
1996 Excellence in Research, University Space Research Association

Research Experience
Designs, develops, and conducts global modeling studies to assess the effects of Earth system variability and change on global and regional water, energy and carbon cycles and their implications for global climate. Develops algorithms to use satellite and ground observations in global models to improve climate predictability with focus on carbon, water and energy components. Works in partnership with interdisciplinary teams to improve understanding of land surface processes and advance their prediction. Actively engages in research related to remote sensing observations, analysis, coupled land-atmosphere modeling, and prediction of global water, energy, and carbon cycles.

Selected Publications
Bounoua L., and T. N. Krishnamurti, 1993: Influence of soil moisture on the Sahelian climate prediction II. Meteor. Atmos. Phys., 52, 205-224.

Bounoua L., and T. N. Krishnamurti, 1993: Influence of soil moisture on the Sahelian climate prediction I.Meteor. Atmos. Phys., 52, 183-203.

Sellers, P. J., L. Bounoua, G. J. Collatz, D. Randall, D. A. Dazlich, S. Los, J. Berry, I. Y. Fung, C. J. Tucker, C. Field, T. Jensen, 1996: Comparison of the radiative and physiological effects of doubled atmospheric CO 2 on climate. Science, 271, 1402-1406.

Sellers, P. J., D. Randall, G. J. Collatz, J. Berry, C. B. Field, D. A. Dazlich, C. Zhan, G. Collelo, and L. Bounoua, 1996: A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: model formulation. J. Climate, 9, 676-705.

Randall, D. A., P. J. Sellers, J. Berry, D. A. Dazlich, C. Zhang, G. J. Collatz, A. S. Denning, S. Los, C. B. Field, I. Y. Fung, C. Justice, C. J. Tucker, and L. Bounoua, 1996: A revised land surface parameterization (SiB2) for GCMs. Part II: The greening of the Colorado State University general circulation model. J. Climate, 9, 738-763.

Bounoua, L., G. J. Collatz, P. J. Sellers, D. A. Randall, D. Dazlich, S. Los, J. Berry, I. Fung, C. J. Tucker, C. Field, T. Jensen, 1999: Interactions between vegetation and climate: Radiative and physiological effects of doubled atmospheric CO 2. J. Climate, 12, 309-323

Defries, R. S, C. B. Field, I. Y. Fung, G. J. Collatz, and L. Bounoua, 1999: Combining satellite data and biogeochemical models to estimate global effects of human-induced land cover change on carbon emissions and primary productivity. Global Biogeochem. Cyc., 13, 803-815.

Collatz, G. J., L. Bounoua, S. O. Los, D. A. Randall, P. J. Sellers, I. Y. Fung, 2000: Influence of vegetation on the diurnal temperature range. Geophys. Res. Lett., 27, 3381-3384.

Los, S. O., G. J. Collatz. P. J. Sellers, C. M. Malstrom, N. H. Polack, R. S. Defries, L. Bounoua, M.T. Parris, C. J. Tucker, and D. A. Dazlich, 2000: Global 9-year biophysical land-surface data set from NOAA AVHRR data. J. Hydrometeor.,1, 183-199.

Los, S. O., G J. Collatz, P. J. Sellers, L. Bounoua, and C. J. Tucker, 2000: Interannual variation in global vegetation, sea-surface temperature, land-surface air temperature and precipitation during the 1980s. J. Climate, 14, 1535-1549.

Bounoua, L., G. J. Collatz, S. Los, P. J. Sellers, D. A. Dazlich, C. J. Tucker, and D. A. Randall, 2000: Sensitivity of climate to changes in NDVI. J. Climate, 13, 2277-2292.

Bounoua, L., R. Defries, G. J. Collatz, P. J. Sellers, and H. Khan, 2002: Effects of Land Cover Conversion on Surface Climate. Climatic Change, 52, 29-64.

Defries, R. S., L. Bounoua and G. J. Collatz, 2002: Human modification of the landscape and surface climate in the next fifty years. Global Change Biol., 8, 438-458.

Guillevic P., R. D. Koster, M. J. Suarez, L. Bounoua, G. J. Collatz, S. Los and S. P. P. Mohanama, 2002: Influence of the inter-annual variability of vegetation on the surface energy balance- A global sensitivity study. J. Hydrometeor., 3, 617-629.

Bounoua L., R. S. Defries, M. L. Imhoff and M. K. Steininger, 2003: Land use and local climate: A case study near Santa Cruz, Bolivia. Meteor. Atmos Phys. Vol. 86, 2004.

Imhoff L. M., L. Bounoua, R. Defries, W. T. Lawrence, D. Stutzer, C. J. Tucker and T. Ricketts, 2004: The consequence of urban land transformation on net primary productivity in the United States. Remote Sens. Environ., 89, 434-443.

Imhoff, M. L., L. Bounoua, T. Ricketts, C. Loucks, R. Harris, and W. T. Lawrence, 2004: Human appropriation of net primary production. Nature, 429, 2004.

ROY F. DARWIN
U.S. Department of Agriculture 1444 Rhode Island Ave, NW
Economic Research Service Apartment 519
1800 M Street, NW, Room 4081 Washington, DC 20005
Washington, DC 20036-5831
(202) 483-0127
(202) 694-5513

Education
Ph.D. Economics, University of Missouri, Columbia 1990
M.A. Science Education (Biology Emphasis), Northeast Missouri
State University 1977
B.A. Russian Language and Literature, University of Connecticut 1972

Recent Professional Experience
October 1991 - Present
U.S. Department of Agriculture
Economic Research Service
1301 New York Ave, NW, Room 408
Washington, DC 20005-4788

Agricultural Economist leading an effort to evaluate the relationships between global changes, land and water resources, agricultural production, and world welfare. Also conduct research on the economic implication of technological change on the utilization of natural resources. Provide policy makers with economic analysis of issues related to these topics in timely summaries, in official government publications, and in peer reviewed journals.

October 1984 - October 1991
Battelle - Pacific Northwest Laboratories
P.O.Box 999, K6-57
Richland, Washington99352

Research Scientist involved in analyzing economic phenomena related to global environmental changes, energy consumption, radioactively contaminated sites, and acid deposition in eastern U.S. forests.

Relevant Publications
Darwin, R.F. 2004. “Effects of Greenhouse Gas Emissions on World Agriculture, Food Consumption, and Economic Welfare”, Climatic Change 66(1/2):191-238.

Darwin, R.F. 2003. “Impacts of Rising Concentrations Greenhouse Gases.” Chapter 7.2 in: Agricultural Resources and Environmental Indicators, 2003. U.S. Department of Agriculture, Economic Research Service, Washington, DC.

Darwin, R. F. 1999. “A FARMer =s View of the Ricardian Approach to Measuring Effects of Climatic Change on Agriculture.” Climatic Change 41(3-4):371-411.

Darwin, R. F. and D. Kennedy. 2000. “Economic Effects of CO 2 Fertilization of Crops: Transforming Changes in Yield into Changes in Supply.” Environmental Modeling and Assessment 5(3):157-168.

Darwin, R. F., J. Lewandrowski, B. McDonald, and M. Tsigas. 1994. “Global Climate Change: Analyzing Environmental Issues and Agricultural Trade within a Global Context.” Chapter 14 in: Sullivan, J., ed., Environmental Policies: Implications for Agricultural Trade. (Foreign Agricultural Economic Report Number 252) U.S. Department of Agriculture, Economic Research Service, Washington, DC, pp. 122-145.

Darwin, R. F. and R. S. J. Tol. 2001. “Estimates of the Economic Effects of Sea Level Rise.” Environmental and Resource Economics 19(2):113-129

Darwin, R. F., M. Tsigas, J. Lewandrowski, and A. Raneses. 1996 “Land Use and Cover in Ecological Economics,” Ecological Economics 17(3):157-181.

Darwin, R. F., M. Tsigas, J. Lewandrowski, and A. Raneses. 1995. World Agriculture and Climate Change: Economic Adaptation. ( U.S. Department of Agriculture, Economic Research Service, Agricultural Economic Report No. 703) Washington, DC.

Ianchovichina, E., R.F. Darwin, and R. Shoemaker. 2001. “Resource Use and Technological Progress in Agriculture: A Dynamic General Equilibrium Analysis.” Ecological Economics 38(2):275-291.

Lewandrowski, J., R. F. Darwin, M. Tsigas, and A. Raneses. 1999. “Estimating Costs of Protecting Global Ecosystem Diversity,” Ecological Economics 29(1):111-125.

MICHAEL H. GLANTZ
Institute for the Study of Society and Environment
National Center for Atmospheric Research
Boulder, CO. 80307-3000

Professional Preparation

University of Pennsylvania Metallurgical Engineering B.S., 1961
University of Pennsylvania Political Science M.A., 1963
University of Pennsylvania Political Science PhD, 1970

Appointments

Senior Scientist, Environmental and Societal Impacts Group, National Center for Atmospheric Research (1983-present).
Program Director, Environmental and Societal Impacts Group, National Center for Atmospheric Research (1979–1997).
Commissioned Study Author for International Federation of Institutes for Advanced Study ( Stockholm, Sweden) (1974–1976).
Senior Postdoctoral Fellow, National Center for Atmospheric Research (1974–1975).

Publications

(i) Five closely related

Glantz, M.H., 2003: Climate Affairs: A Primer. Covelo, CA: Island Press. 290 pp.

Glantz, M.H., 2002: The Aral Sea, contribution to Vol. 5, ‘Responding to Global Environmental Change,” Encyclopedia of Global Environmental Change (T. Munn, ed.). New York: John Wiley & Sons, 534–536.

Glantz, M.H. (ed.), 1999: Creeping Environmental Problems and Sustainable Development in the Aral SeaBasin. Cambridge, UK: Cambridge University Press. 291 pp.

Glantz, M.H., 1999: Sustainable development and creeping environmental problems in the Aral Sea region. In: Creeping Environmental Problems and Sustainable Development in the Aral SeaBasin (M.H. Glantz, ed.). Cambridge, UK: Cambridge University Press, 1-25.

Glantz, M.H., 1998: Creeping environmental problems in the Aral Sea Basin. In: Central Eurasian Water Crisis: Caspian, Aral, and DeadSeas ( I. Kobori and M.H. Glantz, eds.). Tokyo, Japan: United Nations University Press, 25–52.

(ii) Five other significant

Glantz, M.H. (ed.), 2002: La Niña and Its Impacts: Facts and Speculation. Tokyo, Japan: United Nations University Press. 271 pp.

Glantz, M.H., 2001: Currents of Change: Impacts of El Niño and La Niña on Climate and Society. Cambridge, UK: Cambridge University Press. 252 pp.

Glantz, M.H. (ed.), 2001: Once Burned, Twice Shy? Lessons Learned from the 1997–98 El Niño. Tokyo, Japan: United Nations University Press. 294 pp.

Glantz, M.H. and D.G. Streets, 2000: Exploring the concept of climate surprise. Global Environmental Change, 10, 97-107.

Barnston, A.G., M.H. Glantz, and Y. He, 1999: Predictive skill of statistical and dynamical climate models in SST forecasts during the 1997–98 El Niño episode and the 1998 La Niña onset. Bulletin of the American Meteorological Society, 80(2), 217–242.

d. Synergistic Activities
Principal Investigator, “Reducing the Impact of Environmental Emergencies through Early Warning and Preparedness: The Case of El Niño (19-month-long study with the World Meteorological Organization, UN University, International Decade for Natural Disaster Reduction, and the UN Environment Programme).

e. Collaborators
Zafar Adeel and Ralph Daley, International Network on Water, Environment and Heath (INWEH), McMasters University, Hamilton, Ontario, Canada

Igor Zonn, Desert Development Expert, Sojuzvodproyect, Moscow, Russia
Rene Gommes, Food and Agricultural Organization of the UN, Rome, Italy
Leslie Malone, World Meteorological Organization, Geneva, Switzerland

ROBERT HARRISS
Institute for the Study of Society and Environment
National Center for Atmospheric Research
Boulder, CO. 80307-3000
303-497-8106
harriss@ucar.edu

Professional Education:
B.S., Earth Sciences, Florida State University, Tallahassee (1959 - 1962)
M.A., Geochemistry, Rice University, Houston (1962 - 1963)
Ph.D., Geochemistry, Rice University, Houston (1963 - 1965)

Appointments:
1999 – Present: Director, Environmental and Societal Impacts Group (ESIG), National Center for Atmospheric Research (NCAR), Boulder, CO. and NCAR Associate Director for Strategic Planning, (2000-2001).

2000 – Present: Adjunct Professor and member of the Graduate Faculty, College of Architecture and Planning, University of Colorado, Boulder, CO.

1999 – Present: Distinguished Fellow, Houston Advanced Research Center (HARC)

1998 – 1999: Director, The Sustainable Enterprise Institute, Texas Engineering Experiment Station, Texas A&M University, College Station, TX.

1997 – 1999: Professor and Holder of the A.P. and Florence Wiley Chair in Civil Engineering with joint appointments in the Departments of Meteorology, Agricultural Engineering, Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX

1994 – 1997: Director, Science Division, Office of Mission to Planet Earth, NASA Headquarters, Washington, D.C.

1988 – 1994: Professor of Earth Science & Natural Resources, University of New Hampshire.

1983 – 1993: Mission Scientist, NASA Global Tropospheric Experiment ABLE Expeditions to the Tropical Atlantic (ABLE 1), Amazon Basin (ABLE 2), and Arctic (ABLE 3).

1980 – 1988: Project Scientist, NASA Global Tropospheric Experiment.

1978 – 1988: Senior Scientist, NASA Langley Research Center, Hampton, VA.

1978 Research Professor (visiting), Program on Science, Technology, and Society, Clark University.

1976 – 1977: Senior Research Fellow, University of London (part-time).

1975 – 1978: Professor of Oceanography, Florida State University.

1974 – 1975: Energy Research Coordinator, U.S. National Science Foundation

1971 – 1975: Director, Florida State University Marine Laboratory.

1968 – 1975: Assistant &Associate Professor of Oceanography, Florida State University.

1966 – 1968: Assistant Professor of Geochemistry, McMaster University ( Canada)

1965 – 1966: Postdoctoral Fellow, Harvard University.

Recent Publications:
Wilhelmi, O.V., K.L. Purvis, and R.C. Harriss (2004) Designing a geospatial information infrastructure for the mitigation of heat wave hazards in urban areas. Natural Hazards Review (in press).

Li, C., Y. Zhuang, S. Frolking, J. Galloway, R. Harriss, B. Moore III, D. Schimel, and X. Wang (2003) Modeling soil organic carbon change in croplands of China, Ecological Applications13(2): 327-336.

Czepiel, P.M., J. Shorter, B. Mosher, E. Allwine, J. McManus, R. Harriss, C. Kolb, and B. Lamb (2003) The influence of atmospheric pressure on landfill methane emissions, Waste Management23: 593-598.

Dilling, L., S. Doney, J. Edmonds, K. Gurney, R. Harriss, D. Schimel, B. Stephens, and G. Stokes (2003) The role of carbon cycle observations and knowledge in carbon management, Annual Reviews Environment Resources28: 521-558.

Sanchez-Azofeifa, G.A., K.L. Castro, B. Rivard, M.R. Kalascka, and R.C. Harriss (2003) Remote sensing research priorities in tropical dry forest environments. Biotropica, 35(2), 134-142.

Chou, W.W., S.C. Wofsy, R.C. Harriss, J.C. Lin, C. Gerbig, and G. Sache (2002) Net fluxes of carbon dioxide in Amazonia derived for aircraft observations, J. Geophys. Res., 107, D22, 4614, 10.1029/2001D001295.

Wofsy, S.C. and R.C. Harriss (Eds.) (2002) The North American Carbon Program (NACP). Report of the NACP Committee of the U.S. Interagency Carbon Cycle Science Program. Washington, D.C: U.S. Global Change Research Program.

Sanchez-Azofeifa, G.A., R.C. Harriss, A.L. Storrier, and T. de Camino-Beck (2002) Water resources and regional land cover change in Costa Rica: Impacts and economics. Water Resour. Dev., 18: (3) 409-424.

Sanchez-Axofeifa, G.A., R.C. Harriss, and D. Skole (2001) Deforestation in Costa Rica: A quantitative analysis using remote sensing imagery. Biotropica, 33: 378-384.

Nobre, A.D., M. Keller, P.M. Crill, and R.C. Harriss (2001) Short-term nitrous oxide profile dynamics and emissions response to water, nitrogen and carbon additions in two tropical soils. Bio. Fertility Soils, 34: 363-373.

Collaborators:
Co-authors on above publications, Marc Imhoff, Gary Kronrad, Ron Sass.

Disclosures:
I have no financial or other conflicts of interest associated with this proposed project.

MARC L. IMHOFF, Ph.D.
Aerospace Technology/Earth Scientist
Biospheric Sciences Branch, Goddard Space Flight Center
National Aeronautics and Space Administration
Email: Marc.L.Imhoff@nasa.gov
Phone: 301.614.6628
FAX: 301.614.6695

Current Interests:
Using Earth observation satellite data and land surface climate models to explore socio-economically important human interactions with Earth’s bio-geochemical systems.

Current Position:
1989-Present: Biologist / Earth Scientist / Aerospace Technologist - Biospheric Sciences Branch, Code 923, Laboratory for Terrestrial Physics, National Aeronautics and Space Administration's Goddard Space Flight Center (NASA/GSFC), Greenbelt, MD 20771.

Education
Ph.D. Biological Sciences, Stanford University 1993.
M.S. Agronomy, The Pennsylvania State University, 1980.
B.S. Geography, The Pennsylvania State University, 1977.

Relevant Publications :
Marc L. Imhoff et al., 2004. “Global patterns in human consumption of net primary production.” Nature, vol 429, 24 June 2004, pp. 870-873.

Marc L. Imhoff et al., 2004. “The consequences of urban land transformation for net primary productivity in the United States,” Rem. Sensing of Environment, Vol. 89, Issue 4, pp. 434-443.

Luck, G.W., T.H. Ricketts, G.C. Daily, M. Imhoff, 2004. “Spatial conflict between people and biodiversity”., 2004. Proceedings National Academy of Sciences, vol. 101, No. 1, pp 182-186 (www.pnas.org/cgi/doi/10.1073/pnas.2237148100).

Ricketts, T. and M. Imhoff. 2003. “Biodiversity, urban areas, and agriculture: locating priority ecoregions for conservation”.. Conservation Ecology 8(2): 1. [online] URL: http://www.consecol.org/vol8/iss2/art1

M. L. Imhoff, et al., 2000. “The Use of Multi-source Satellite and Geospatial Data to Study the Effect of Urbanization on Primary Productivity in the United States”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 6, November 2000.

LCLUC99-0004-0016
“Measuring Human Impacts on the Biodiversity and Carrying Capacity of Ecosystems” &

CARBON-0000-0009
“Locating Human Risks to Biodiversity: A Carbon Balance Approach”.

Ricketts, T.H., M. Imhoff, and J.P. Fay (in preparation). Determinants of species endangerment: relative importance of intrinsic and extrinsic factors.

Loucks, C., T. Ricketts, M. Imhoff, L. Bounoua, and J. Lamoreux. (in preparation) Concordance of human consumption patterns with biodiversity distribution: implications for conservation.

Imhoff, M. L., L. Bounoua, T. Ricketts, C. Loucks, R. Harriss, W. Lawrence. 2004. Global patterns in human appropriation of net primary production. Nature , vol. 429, June 24, 2004.

Luck, G.W., T.H. Ricketts, G.C. Daily, M. Imhoff , 2004. Spatial conflict between people and biodiversity. Proceedings National Academy of Sciences, vol. 101, No. 1, pp 182-186 (www.pnas.org/cgi/doi/10.1073/pnas.2237148100).

Marc L. Imhoff , Lahouari Bounoua, Ruth DeFries, William T. Lawrence, David Stutzer, Compton J. Tucker, and Taylor Ricketts (2004). The consequences of urban land transformation for net primary productivity in the United States. Remote Sensing of Environment, Vol. 89, Issue 4, pp. 434-443.

Ricketts, T. and M. Imhoff. 2003. Biodiversity, urban areas, and agriculture: locating priority ecoregions for conservation. Conservation Ecology 8(2): 1. [online] URL: http://www.consecol.org/vol8/iss2/art1

L. Bounoua, R. S. Defries, M. L. Imhoff, and M. K. Steininger, 2003.Land use and local climate: A case study near Santa Cruz, Bolivia. Meteorology and Atmospheric Physics, Publisher: Springer-Verlag Wien, ISSN: 0177-7971, DOI: 10.1007/s00703-003-0616-8.

Rosenqvist, A., Milne T. Lucas R., Imhoff, M. and Dobson C., 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environmental Science & Policy, (October 2003) Vol. 6, No. 5, pp 441-455.

W. T. Lawrence; M. L. Imhoff; N. Kerle; D. Stutzer (2002). Quantifying urban land use and impact on soils in Egypt using diurnal satellite imagery of the Earth surface. Int. J. Rem. Sens., Volume 23 Number 19 October 2002 3921 – 3938.

Elvidge CD, Imhoff ML, Baugh KE, Hobson VR, Nelson I, Safran J, Dietz JB, Tuttle BT (2001). Night-time lights of the world: 1994-1995. ISPRS Journal Of Photogrammetry And Remote Sensing 56 (2): 81-99 December 2001.

J. Hansen, R. Ruedy, M Sato, M. Imhoff, W. Lawrence, D. Easterling, T. Peterson, and T. Karl A closer look at U.S. and global surface temperature change (2001). JGR Atmospheres, Vol. 106, D20, pages 23947-23963, October 27, 2001.

Nizeyimana EL, Petersen GW, Imhoff ML, Sinclair HR, Waltman SW, Reed-Margetan DS, Levine ER, Russo JM (2001). Assessing the impact of land conversion to urban use on soils with different productivity levels in the USA. Soil Science Society Of America Journal, 65: (2) 391-402 MAR-APR 2001.

M. L. Imhoff, C. J. Tucker, W. T. Lawrence, D. Stutzer, and Robert Rusin (2000). The use of multi-source satellite and geospatial data to study the effect of urbanization on primary productivity in the United States. IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 6, November 2000.

APPENDIX B. Examples of 0.5 degree spatial data used in FARM

Figure B3: Current representation of Rainfed Agro-Ecological Zones in 1997. Map is based on climate data.

 

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