Cristina Milesi

This paper examines carbon stocks and their relative balance in terrestrial ecosystems simulated by Biome-BGC, LPJ, and CASA in an ensemble model experiment conducted using the Terrestrial Observation and Prediction System. We developed the Hierarchical Framework for Diagnosing Ecosystem Models to separate the simulated biogeochemistry into a cascade of functional tiers and examine their characteristics sequentially. The analyses indicate that the simulated biomass is usually two to three times higher in Biome-BGC than LPJ or CASA. Such a discrepancy is mainly induced by differences in model parameters and algorithms that regulate the rates of biomass turnover. The mean residence time of biomass in Biome-BGC is estimated to be 40–80 years in temperate/moist climate regions, while it mostly varies between 5 and 30 years in CASA and LPJ. A large range of values is also found in the simulated soil carbon. The mean residence time of soil carbon in Biome-BGC and LPJ is ∼200 years in cold regions, which decreases rapidly with increases of temperature at a rate of ∼10 yr °C−1. Because long-term soil carbon pool is not simulated in CASA, its corresponding mean residence time is only about 10–20 years and less sensitive to temperature. Another key factor that influences the carbon balance of the simulated ecosystem is disturbance caused by wildfire, for which the algorithms vary among the models. Because fire emissions are balanced by net ecosystem production (NEP) at steady states, magnitudes, and spatial patterns of NEP vary significantly as well. Slight carbon imbalance may be left by the spin-up algorithm of the models, which adds uncertainty to the estimated carbon sources or sinks. Although these results are only drawn on the tested model versions, the developed methodology has potential for other model exercises.

This paper examines carbon stocks and their relative balance in terrestrial ecosystems simulated by Biome-BGC, LPJ, and CASA in an ensemble model experiment conducted using the Terrestrial Observation and Prediction System. We developed the Hierarchical Framework for Diagnosing Ecosystem Models to separate the simulated biogeochemistry into a cascade of functional tiers and examine their characteristics sequentially. The analyses indicate that the simulated biomass is usually two to three times higher in Biome-BGC than LPJ or CASA. Such a discrepancy is mainly induced by differences in model parameters and algorithms that regulate the rates of biomass turnover. The mean residence time of biomass in Biome-BGC is estimated to be 40–80 years in temperate/moist climate regions, while it mostly varies between 5 and 30 years in CASA and LPJ. A large range of values is also found in the simulated soil carbon. The mean residence time of soil carbon in Biome-BGC and LPJ is ∼200 years in cold regions, which decreases rapidly with increases of temperature at a rate of ∼10 yr °C−1. Because long-term soil carbon pool is not simulated in CASA, its corresponding mean residence time is only about 10–20 years and less sensitive to temperature. Another key factor that influences the carbon balance of the simulated ecosystem is disturbance caused by wildfire, for which the algorithms vary among the models. Because fire emissions are balanced by net ecosystem production (NEP) at steady states, magnitudes, and spatial patterns of NEP vary significantly as well. Slight carbon imbalance may be left by the spin-up algorithm of the models, which adds uncertainty to the estimated carbon sources or sinks. Although these results are only drawn on the tested model versions, the developed methodology has potential for other model exercises.

Estimates of carbon emissions from factors such as deforestation, degradation and afforestation pose a high degree of uncertainty due to the spatial variability of carbon that is stored in forest biomass. To improve confidence in emissions estimates, we need the spatial distribution of the amounts of carbon stored in the forest. In this study, we aim to quantify the sources of uncertainty, while testing and evaluating multi-modal models to quantify forest aboveground biomass. We initially build a multi-dimensional data layer approach to assess explanatory behavior of vegetation photosynthetic variables (e.g. LAI, NDVI), climate variables (e.g. temperature and precipitation) and topography (e.g. slope and elevation from SRTM) to vegetation height as estimated from the ICESat GLAS data. Our initial pilot project is for North America and the output biomass map is derived at a high spatial resolution of 30m. The vegetation photosynthetic variables are derived from the 30m Landsat atmospherically corrected surface reflectance product and land cover stratification is performed utilizing the 30-m NLCD land cover data. Climate variables corresponding to minimum/maximum temperature and mean annual precipitation are obtained from the Terrestrial Observation and Prediction System (TOPS) modeling framework. We use relational functions developed between measured forest heights from GLAS, vegetation photosynthesis and climate variables, to estimate heights for pixels where GLAS heights are not available. This will create a wall-to-wall continuous height map. Furthermore, we develop empirical models between the continuous height map and the available forest inventory aboveground biomass data from the FIA for different land cover types to create an aboveground biomass map. Uncertainty analysis will involve (a) comparing the continuous height map to FIA tree height data and LVIS obtained heights; (b) comparison of the generated biomass map with already available satellite derived biomass maps; (c) assessing the sensitivity of biomass estimates with respect to the empirical functions utilized in the multivariate models; (d) assessing variability in height distributions by incorporating high resolution species maps and land cover maps.

Estimates of carbon emissions from factors such as deforestation, degradation and afforestation pose a high degree of uncertainty due to the spatial variability of carbon that is stored in forest biomass. To improve confidence in emissions estimates, we need the spatial distribution of the amounts of carbon stored in the forest. In this study, we aim to quantify the sources of uncertainty, while testing and evaluating multi-modal models to quantify forest aboveground biomass. We initially build a multi-dimensional data layer approach to assess explanatory behavior of vegetation photosynthetic variables (e.g. LAI, NDVI), climate variables (e.g. temperature and precipitation) and topography (e.g. slope and elevation from SRTM) to vegetation height as estimated from the ICESat GLAS data. Our initial pilot project is for North America and the output biomass map is derived at a high spatial resolution of 30m. The vegetation photosynthetic variables are derived from the 30m Landsat atmospherically corrected surface reflectance product and land cover stratification is performed utilizing the 30-m NLCD land cover data. Climate variables corresponding to minimum/maximum temperature and mean annual precipitation are obtained from the Terrestrial Observation and Prediction System (TOPS) modeling framework. We use relational functions developed between measured forest heights from GLAS, vegetation photosynthesis and climate variables, to estimate heights for pixels where GLAS heights are not available. This will create a wall-to-wall continuous height map. Furthermore, we develop empirical models between the continuous height map and the available forest inventory aboveground biomass data from the FIA for different land cover types to create an aboveground biomass map. Uncertainty analysis will involve (a) comparing the continuous height map to FIA tree height data and LVIS obtained heights; (b) comparison of the generated biomass map with already available satellite derived biomass maps; (c) assessing the sensitivity of biomass estimates with respect to the empirical functions utilized in the multivariate models; (d) assessing variability in height distributions by incorporating high resolution species maps and land cover maps.

Land surface phenology is widely used as a diagnostic of ecosystem response to global change and influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, a robust framework in classifying the ground phenological events to compare with remotely sensed phenology is lacking. In this research, we develop a methodology to compare the latest Collection 5 500m MODIS phenology data (cardinal dates corresponding to greenup, maturity, senescence and dormancy) with all available ground observed data for North America for the period 2001-2006. Inter-comparison results show that the MODIS phenology product performs well in capturing the “earliest events” corresponding to each of the phenological phases (e.g. budburst, first leaf, first leaf color change) of plant growth in a community. Of all the ground phenological stages, the leaf greenup events are best correlated to the MODIS greenup onset. Establishing confidence in the MODIS greenup onset data through our inter-comparison exercise and exploiting the long-term nature of the data set (2001-present) led us to build a seasonal phenology forecast model within the Terrestrial Observation and Prediction System (TOPS) framework. The available MODIS greenup data serves as a training data to our climate based phenology forecast model. We parameterize the empirical model for each 500m pixel based on the relationship between local climate provided by TOPS and MODIS greenup. As a test case, we have predicted the greenup for 2010 spring by using the seasonal climate forecast data from NECP Climate Forecast System (CFS) to our forecast model. The phenology forecast portal is now available online, which provides a unique platform for community participation in monitoring local phenology forecast events and uploading observed phenological events through the web-based interface.

Land surface phenology is widely used as a diagnostic of ecosystem response to global change and influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, a robust framework in classifying the ground phenological events to compare with remotely sensed phenology is lacking. In this research, we develop a methodology to compare the latest Collection 5 500m MODIS phenology data (cardinal dates corresponding to greenup, maturity, senescence and dormancy) with all available ground observed data for North America for the period 2001-2006. Inter-comparison results show that the MODIS phenology product performs well in capturing the “earliest events” corresponding to each of the phenological phases (e.g. budburst, first leaf, first leaf color change) of plant growth in a community. Of all the ground phenological stages, the leaf greenup events are best correlated to the MODIS greenup onset. Establishing confidence in the MODIS greenup onset data through our inter-comparison exercise and exploiting the long-term nature of the data set (2001-present) led us to build a seasonal phenology forecast model within the Terrestrial Observation and Prediction System (TOPS) framework. The available MODIS greenup data serves as a training data to our climate based phenology forecast model. We parameterize the empirical model for each 500m pixel based on the relationship between local climate provided by TOPS and MODIS greenup. As a test case, we have predicted the greenup for 2010 spring by using the seasonal climate forecast data from NECP Climate Forecast System (CFS) to our forecast model. The phenology forecast portal is now available online, which provides a unique platform for community participation in monitoring local phenology forecast events and uploading observed phenological events through the web-based interface.

Urban places may be broadly defined as the settlements where most people live and work. Human beings worldwide tend to cluster in spatially limited habitats occupying less than 5% of the world’s land area. Urbanization may be defined as those environment altering activities that create and maintain urban places. This includes the processes of construction, habitation, transportation, energy and water use, communication, industrialization, commercial and manufacturing services, plus civic activities associated with education and governance. The physical patterns of urban areas produce distinctive spatial and spectral signatures that are recorded by many types of remotely sensed data.

Urban places may be broadly defined as the settlements where most people live and work. Human beings worldwide tend to cluster in spatially limited habitats occupying less than 5% of the world’s land area. Urbanization may be defined as those environment altering activities that create and maintain urban places. This includes the processes of construction, habitation, transportation, energy and water use, communication, industrialization, commercial and manufacturing services, plus civic activities associated with education and governance. The physical patterns of urban areas produce distinctive spatial and spectral signatures that are recorded by many types of remotely sensed data.

One of the major challenges we face on our planet is increasing agricultural production to meet the dietary requirements of an additional 2.5 billion people by the mid of the century while limiting cropland expansion and other damages to natural resources. This problem is even more so challenging given that nearly all the population growth will take place where the majority of the hungry live today and where ongoing and future climate changes are projected to most negatively impact agricultural production, the semi-arid tropics (SAT). The SAT contain 40% of the global irrigated and rainfed croplands in over 50 developing countries and a growing population of over a billion and half people, many of which live in absolute poverty and strongly depend on agriculture that is constrained by chronic water shortages. Rates of food grain production in many of the countries of the SAT have progressively increased since the mid 1960s aided by the Green Revolution and relatively favourable climatic conditions. However, aggregated agricultural production statistics indicate that the rate of food grain production has recently stalled or declined in several of the countries in this region, escalating the concerns over matters of food security, that is availability of food and one’s access to it, in a region where many people live in extreme poverty, depend on an agrarian economy and are expected to face increasingly worse climatic conditions in the near future. In this paper we analyze the agricultural deceleration and its drivers over the country of India, which faces the daunting challenge of needing a 50-100% increase in yields of major crops by the middle to the 21st century to feed its growing population. We analyze the long term (1982-2006) record of the Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA/AVHRR) together with climate, land use, and crop production statistics. We show that while there are no significant changes in long-term precipitation, there are geographically matching patterns of enhanced crop production and irrigation expansion with groundwater, initially, but both of which have levelled off in the past decade. In addition to increasing pressure on water resources, a decline in expansion of cropland area, a warming climate and increasing air pollution compound to challenging long term food security in India.

One of the major challenges we face on our planet is increasing agricultural production to meet the dietary requirements of an additional 2.5 billion people by the mid of the century while limiting cropland expansion and other damages to natural resources. This problem is even more so challenging given that nearly all the population growth will take place where the majority of the hungry live today and where ongoing and future climate changes are projected to most negatively impact agricultural production, the semi-arid tropics (SAT). The SAT contain 40% of the global irrigated and rainfed croplands in over 50 developing countries and a growing population of over a billion and half people, many of which live in absolute poverty and strongly depend on agriculture that is constrained by chronic water shortages. Rates of food grain production in many of the countries of the SAT have progressively increased since the mid 1960s aided by the Green Revolution and relatively favourable climatic conditions. However, aggregated agricultural production statistics indicate that the rate of food grain production has recently stalled or declined in several of the countries in this region, escalating the concerns over matters of food security, that is availability of food and one’s access to it, in a region where many people live in extreme poverty, depend on an agrarian economy and are expected to face increasingly worse climatic conditions in the near future. In this paper we analyze the agricultural deceleration and its drivers over the country of India, which faces the daunting challenge of needing a 50-100% increase in yields of major crops by the middle to the 21st century to feed its growing population. We analyze the long term (1982-2006) record of the Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA/AVHRR) together with climate, land use, and crop production statistics. We show that while there are no significant changes in long-term precipitation, there are geographically matching patterns of enhanced crop production and irrigation expansion with groundwater, initially, but both of which have levelled off in the past decade. In addition to increasing pressure on water resources, a decline in expansion of cropland area, a warming climate and increasing air pollution compound to challenging long term food security in India.

The Global Land Survey data from Landsat provide the best source of information about global landscapes as they evolved since the 1970s. These carefully constructed datasets are ready for further analysis, though lack of computing resources has so far precluded comprehensive global analyses. With increasing interest in biological sequestration of CO2 into vegetation biomass, we believe these data offer a valuable resource for characterizing global vegetation. The ability to locate physical features on the ground as well as a long publication record showing many successful attempts at converting Landsat data into vegetation biophysical variables such as leaf area index (LAI) and biomass make these data highly suitable for promoting Clean Development Mechanisms (CDM) negotiated under the United Nations Framework Convention on Climate Change (UNFCCC). We created a first generation global LAI product at 30m globally by combining the temporally rich MODIS products with spatially extensive Landsat data. Our methodology used atmospherically corrected Landsat surface reflectances to generate LAI in two ways: 1) using published relations between spectral vegetation indices and LAI in various land cover types and 2) applying the MODIS LAI algorithm with its look-up-table adjusted for Landsat reflectance data. The global 30m LAI dataset is available for the community to explore, verify and validate the spatial patterns and relative magnitudes. Additionally, we would like to offer the processing resources that we built for researchers with innovative algorithms that would produce products of high value to the earth science community.

The Global Land Survey data from Landsat provide the best source of information about global landscapes as they evolved since the 1970s. These carefully constructed datasets are ready for further analysis, though lack of computing resources has so far precluded comprehensive global analyses. With increasing interest in biological sequestration of CO2 into vegetation biomass, we believe these data offer a valuable resource for characterizing global vegetation. The ability to locate physical features on the ground as well as a long publication record showing many successful attempts at converting Landsat data into vegetation biophysical variables such as leaf area index (LAI) and biomass make these data highly suitable for promoting Clean Development Mechanisms (CDM) negotiated under the United Nations Framework Convention on Climate Change (UNFCCC). We created a first generation global LAI product at 30m globally by combining the temporally rich MODIS products with spatially extensive Landsat data. Our methodology used atmospherically corrected Landsat surface reflectances to generate LAI in two ways: 1) using published relations between spectral vegetation indices and LAI in various land cover types and 2) applying the MODIS LAI algorithm with its look-up-table adjusted for Landsat reflectance data. The global 30m LAI dataset is available for the community to explore, verify and validate the spatial patterns and relative magnitudes. Additionally, we would like to offer the processing resources that we built for researchers with innovative algorithms that would produce products of high value to the earth science community.

We conducted an ensemble modeling exercise using the Terrestrial Observation and Prediction System (TOPS) to evaluate sources of uncertainty in carbon flux estimates resulting from structural differences among ecosystem models. The experiment ran public-domain versions of biome-bgc, lpj, casa, and tops-bgc over North America at 8 km resolution and for the period of 1982–2006. We developed the Hierarchical Framework for Diagnosing Ecosystem Models (HFDEM) to separate the simulated biogeochemistry into a cascade of three functional tiers and sequentially examine their characteristics in climate (temperature–precipitation) and other spaces. Analysis of the simulated annual gross primary production (GPP) in the climate domain indicates a general agreement among the models, all showing optimal GPP in regions where the relationship between annual average temperature (T,  °C) and annual total precipitation (P, mm) is defined by P=50T+500. However, differences in simulated GPP are identified in magnitudes and distribution patterns. For forests, the GPP gradient along P=50T+500 ranges from ∼50 g C yr−1 m−2  °C−1 (casa) to ∼125 g C yr−1 m−2  °C−1 (biome-bgc) in cold/temperate regions; for nonforests, the diversity among GPP distributions is even larger. Positive linear relationships are found between annual GPP and annual mean leaf area index (LAI) in all models. For biome-bgc and lpj, such relationships lead to a positive feedback from LAI growth to GPP enhancement. Different approaches to constrain this feedback lead to different sensitivity of the models to disturbances such as fire, which contribute significantly to the diversity in GPP stated above. The ratios between independently simulated NPP and GPP are close to 50% on average; however, their distribution patterns vary significantly between models, reflecting the difficulties in estimating autotrophic respiration across various climate regimes. Although these results are drawn from our experiments with the tested model versions, the developed methodology has potential for other model exercises.

Turf grasses are ubiquitous in the urban landscape of the United States and are often associated with various types of environmental impacts, especially on water resources, yet there have been limited efforts to quantify their total surface and ecosystem functioning, such as their total impact on the continental water budget and potential net ecosystem exchange (NEE). In this study, relating turf grass area to an estimate of fractional impervious surface area, it was calculated that potentially 163,800 km2 (± 35,850 km2) of land are cultivated with turf grasses in the continental United States, an area three times larger than that of any irrigated crop. Using the Biome-BGC ecosystem process model, the growth of warm-season and cool-season turf grasses was modeled at a number of sites across the 48 conterminous states under different management scenarios, simulating potential carbon and water fluxes as if the entire turf surface was to be managed like a well-maintained lawn. The results indicate that well-watered and fertilized turf grasses act as a carbon sink. The potential NEE that could derive from the total surface potentially under turf (up to 17 Tg C/yr with the simulated scenarios) would require up to 695 to 900 liters of water per person per day, depending on the modeled water irrigation practices, suggesting that outdoor water conservation practices such as xeriscaping and irrigation with recycled waste-water may need to be extended as many municipalities continue to face increasing pressures on freshwater.

We conducted an ensemble modeling exercise using the Terrestrial Observation and Prediction System (TOPS) to evaluate sources of uncertainty in carbon flux estimates resulting from structural differences among ecosystem models. The experiment ran public-domain versions of biome-bgc, lpj, casa, and tops-bgc over North America at 8 km resolution and for the period of 1982–2006. We developed the Hierarchical Framework for Diagnosing Ecosystem Models (HFDEM) to separate the simulated biogeochemistry into a cascade of three functional tiers and sequentially examine their characteristics in climate (temperature–precipitation) and other spaces. Analysis of the simulated annual gross primary production (GPP) in the climate domain indicates a general agreement among the models, all showing optimal GPP in regions where the relationship between annual average temperature (T,  °C) and annual total precipitation (P, mm) is defined by P=50T+500. However, differences in simulated GPP are identified in magnitudes and distribution patterns. For forests, the GPP gradient along P=50T+500 ranges from ∼50 g C yr−1 m−2  °C−1 (casa) to ∼125 g C yr−1 m−2  °C−1 (biome-bgc) in cold/temperate regions; for nonforests, the diversity among GPP distributions is even larger. Positive linear relationships are found between annual GPP and annual mean leaf area index (LAI) in all models. For biome-bgc and lpj, such relationships lead to a positive feedback from LAI growth to GPP enhancement. Different approaches to constrain this feedback lead to different sensitivity of the models to disturbances such as fire, which contribute significantly to the diversity in GPP stated above. The ratios between independently simulated NPP and GPP are close to 50% on average; however, their distribution patterns vary significantly between models, reflecting the difficulties in estimating autotrophic respiration across various climate regimes. Although these results are drawn from our experiments with the tested model versions, the developed methodology has potential for other model exercises.

Turf grasses are ubiquitous in the urban landscape of the United States and are often associated with various types of environmental impacts, especially on water resources, yet there have been limited efforts to quantify their total surface and ecosystem functioning, such as their total impact on the continental water budget and potential net ecosystem exchange (NEE). In this study, relating turf grass area to an estimate of fractional impervious surface area, it was calculated that potentially 163,800 km2 (± 35,850 km2) of land are cultivated with turf grasses in the continental United States, an area three times larger than that of any irrigated crop. Using the Biome-BGC ecosystem process model, the growth of warm-season and cool-season turf grasses was modeled at a number of sites across the 48 conterminous states under different management scenarios, simulating potential carbon and water fluxes as if the entire turf surface was to be managed like a well-maintained lawn. The results indicate that well-watered and fertilized turf grasses act as a carbon sink. The potential NEE that could derive from the total surface potentially under turf (up to 17 Tg C/yr with the simulated scenarios) would require up to 695 to 900 liters of water per person per day, depending on the modeled water irrigation practices, suggesting that outdoor water conservation practices such as xeriscaping and irrigation with recycled waste-water may need to be extended as many municipalities continue to face increasing pressures on freshwater.

Forest inventories from the intact rainforests of the Amazon indicate increasing rates of carbon gain over the past three decades. However, such estimates have been questioned because of the poor spatial representation of the sampling plots and the incomplete understanding of purported mechanisms behind the increases in biomass. Ecosystem models, when used in conjunction with satellite data, are useful in examining the carbon budgets in regions where the observations of carbon flows are sparse. The purpose of this study is to explain observed trends in normalized difference vegetation index (NDVI) using climate observations and ecosystem models of varying complexity in the western Amazon basin for the period of 1984–2002. We first investigated trends in NDVI and found a positive trend during the study period, but the positive trend in NDVI was observed only in the months from August to December. Then, trends in various climate parameters were calculated, and of the climate variables considered, only shortwave radiation was found to have a corresponding significant positive trend. To compare the impact of each climate component, as well as increasing carbon dioxide (CO2) concentrations, on evergreen forests in the Amazon, we ran three ecosystem models (CASA, Biome-BGC, and LPJ), and calculated monthly net primary production by changing a climate component selected from the available climate datasets. As expected, CO2 fertilization effects showed positive trends throughout the year and cannot explain the positive trend in NDVI, which was observed only for the months of August to December. Through these simulations, we demonstrated that the positive trend in shortwave radiation can explain the positive trend in NDVI observed for the period from August to December. We conclude that the positive trend in shortwave radiation is the most likely driver of the increasing trend in NDVI and the corresponding observed increases in forest biomass.

ABSTRACT In the last 50 years, the Mediterranean Basin has experienced a doubling of its population. This demographic growth has been the cause of extensive land use changes that have undermined the ecological stability of large portions of its fragile ecosystems. The population of the Mediterranean countries is expected to grow by another 20 percent in the next 25 years, further increasing the pressure on the natural resources. In this paper, we present a methodology combining photosynthetic activity and human settlements both derived from satellite data for monitoring the effects of human settlements on the environment. We found photosynthesis decreasing as one moves from rural to urban settings in the north and increasing in the south Mediterranean countries. Regional scale assessments using this approach may help policy makers in designing appropriate measures to combat further environmental degradation.

Forest inventories from the intact rainforests of the Amazon indicate increasing rates of carbon gain over the past three decades. However, such estimates have been questioned because of the poor spatial representation of the sampling plots and the incomplete understanding of purported mechanisms behind the increases in biomass. Ecosystem models, when used in conjunction with satellite data, are useful in examining the carbon budgets in regions where the observations of carbon flows are sparse. The purpose of this study is to explain observed trends in normalized difference vegetation index (NDVI) using climate observations and ecosystem models of varying complexity in the western Amazon basin for the period of 1984–2002. We first investigated trends in NDVI and found a positive trend during the study period, but the positive trend in NDVI was observed only in the months from August to December. Then, trends in various climate parameters were calculated, and of the climate variables considered, only shortwave radiation was found to have a corresponding significant positive trend. To compare the impact of each climate component, as well as increasing carbon dioxide (CO2) concentrations, on evergreen forests in the Amazon, we ran three ecosystem models (CASA, Biome-BGC, and LPJ), and calculated monthly net primary production by changing a climate component selected from the available climate datasets. As expected, CO2 fertilization effects showed positive trends throughout the year and cannot explain the positive trend in NDVI, which was observed only for the months of August to December. Through these simulations, we demonstrated that the positive trend in shortwave radiation can explain the positive trend in NDVI observed for the period from August to December. We conclude that the positive trend in shortwave radiation is the most likely driver of the increasing trend in NDVI and the corresponding observed increases in forest biomass.

ABSTRACT In the last 50 years, the Mediterranean Basin has experienced a doubling of its population. This demographic growth has been the cause of extensive land use changes that have undermined the ecological stability of large portions of its fragile ecosystems. The population of the Mediterranean countries is expected to grow by another 20 percent in the next 25 years, further increasing the pressure on the natural resources. In this paper, we present a methodology combining photosynthetic activity and human settlements both derived from satellite data for monitoring the effects of human settlements on the environment. We found photosynthesis decreasing as one moves from rural to urban settings in the north and increasing in the south Mediterranean countries. Regional scale assessments using this approach may help policy makers in designing appropriate measures to combat further environmental degradation.

In this study, we aim to generate global 30-m Leaf Area Index (LAI) from Landsat surface reflectance data using the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). Furthermore, canopy spectral invariants introduce an efficient way for incorporating multiple bands for retrieving LAI. We incorporate a 3-band retrieval scheme including the Red, NIR and SWIR bands, the SWIR band being specifically useful in low LAI regions and thus compensating for background effects. The initial results have satisfactory agreement with MODIS LAI, although with spatially more detailed structure and variability. A future exercise will be to introduce field measured LAI estimates to minimize the differences between model-simulated LAI's and in-situ observations.

Weather forecasting models have been shown to exhibit a strong sensitivity to land surface conditions, particularly soil moisture. However, the lack of robust estimates of soil moisture at appropriate time and space scales has been a persistent problem. Terrestrial Observation and Prediction System (TOPS) integrates surface weather observations and satellite data with ecosystem simulation models to produce spatially and temporally consistent nowcasts and forecasts of land surface conditions such as soil moisture, evapotranspiration, vegetation stress and photosynthesis. To extend TOPS capabilities beyond estimating ecosystem rocesses, we integrated TOPS with Weather Research Forecasting (WRF) model to evaluate the utility of TOPS-derived surface conditions such as soil moisture in weather forecasting. TOPS land surface schemes are based on a well-calibrated ecosystem model, Biome-BGC, for simulating water and carbon budgets. One of the advantages of TOPS is its flexibility, which enables it to ingest data from a variety of sensors and surface networks, and thus we can provide the surface conditions to users from historical to near real-time, and for spatial scales ranging from 1km and up. We ran the TOPS-WRF system over California for several days during 2007. The results show TOPS-WRF simulations are consistently better than default WRF simulations, particularly over the dry season when spatial variability in soil moisture becomes a significant factor in influencing local energy balance.

The construction and maintenance of impervious surfaces-buildings, roads, parking lots, roofs, etc.-constitutes a major human alteration of the land surface, changing the local hydrology, climate, and carbon cycling. Three types of national coverage data were used to model the spatial distribution and density of impervious surface area (ISA) for the conterminous U.S.A. The results (Figure 1) indicate that total ISA of the 48 states and Washington, D.C., is 112,610 km2 (+/- 12,725 km2), which is slightly smaller than the state of Ohio (116,534 km2) and slightly larger than the area of herbaceous wetlands (98,460 km2) of the conterminous United States. The same characteristics that make impervious surfaces ideal for use in construction produce a series of effects on the environment. Impervious surfaces alter sensible and latent heat fluxes, causing urban heat islands. In heavily vegetated areas, the proliferation of ISA reduces the sequestration of carbon from the atmosphere. ISA alters the character of watersheds by increasing the frequency and magnitude of surface runoff pulses. Watershed effects of ISA begin to be detectable once 10% of the surface is covered by impervious surfaces, altering the shape of stream channels, raising water temperatures, and sweeping urban debris and pollutants into aquatic environments. Consequences of ISA include reduced numbers and diversity of species in fish and aquatic insects, and degradation of wetlands and riparian zones.

The NASA Terrestrial Observation and Prediction System (TOPS) is a modeling framework that integrates satellite observations, meteorological observations, and ancillary data to support monitoring and modeling of ecosystem and land surface conditions in near real-time. TOPS provides spatially continuous gridded estimates of a suite of measurements describing environmental conditions, and these data products are currently being applied to support the development of new models capable of forecasting estimated mosquito abundance and transmission risk for mosquito-borne diseases such as West Nile virus. We present results from the modeling analyses, describe their incorporation into the California Vectorborne Disease Surveillance System, and describe possible implications of projected climate and land use change for patterns in mosquito abundance and transmission risk for West Nile virus in California.

California is in a third year of drought with April snowpack in the Sierra Nevada Mountains, which supplies the majority of water for agriculture in the state, at 60% of normal, impacting growers throughout the San Joaquin Valley. Competing demands from urban and environmental uses create further constraints on agricultural water supplies. Future climate scenarios for the western U.S. consistently predict reduced winter snow pack, earlier snowmelt, and higher temperatures. Such conditions will increase demand for agricultural water use during the summer growing season, while at the same time reducing the reliability of existing water sources for agricultural use. Previous studies have shown that adopting improved irrigation practices can save as much as 10% of agricultural applied water use. Achievement of these potential efficiency gains requires new data and information systems, built upon existing investments, that are capable of delivering estimates of evapotranspiration and irrigation demand to agricultural producers. We present a prototype system for optimization of agricultural water use that utilizes the NASA Terrestrial Observation and Prediction System (TOPS) to integrate satellite observations and meteorological observations with models parameterized for specific crop types to produce forecasts of soil moisture, evapotranspiration, and irrigation demand for multiple crop types in the San Joaquin Valley of California. The system employs wireless sensor networks to validate estimates from the modeling system and calibrate estimates of soil water balance. Irrigation forecasts and in-situ observations are distributed to water districts and agricultural producers via both SMS (text) messages delivered to hand-held devices, as well as via a browser-based irrigation optimization decision support system. A prototype system will be deployed, tested and evaluated in 2010.

In this study, we aim to generate global 30-m Leaf Area Index (LAI) from Landsat surface reflectance data using the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). Furthermore, canopy spectral invariants introduce an efficient way for incorporating multiple bands for retrieving LAI. We incorporate a 3-band retrieval scheme including the Red, NIR and SWIR bands, the SWIR band being specifically useful in low LAI regions and thus compensating for background effects. The initial results have satisfactory agreement with MODIS LAI, although with spatially more detailed structure and variability. A future exercise will be to introduce field measured LAI estimates to minimize the differences between model-simulated LAI's and in-situ observations.