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Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a "bloom," increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms. Significance Statement: Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel strategy to robustly diagnose the onset and demise of the rainy season using daily observed rainfall over seven specific regions across Australia, as demarcated by the Natural Resource Management (NRM) agency of Australia. The methodology lies in developing an ensemble spread of the diagnosed onset and demise from randomly perturbing the observed daily time series of rainfall at synoptic scales to obtain a measure of the uncertainty of the diagnosis. Our results indicate that the spread of the ensemble in the diagnosis of the onset and demise dates of the rainy season is higher in the subtropical region than the tropical region. Secular change of earlier onset, later demise, longer length, and wetter season are also identified in many of these regions. The influence of the PDO at decadal scale, ENSO and Indian Ocean dipole at interannual scale, and MJO at intraseasonal scale also reveals significant influence on the evolution of the rainy season over these regions in Australia. Most important, the covariability of the onset date with the length of the season and seasonal rainfall anomaly of the season is highlighted as a valuable relationship that can be exploited for real-time monitoring and providing an outlook of the forthcoming rainy season, which could serve some of the NRM regions. Significance Statement: We document the rainfall variability during the rainy season over tropical, subtropical, and semiarid regions of Australia and relate them to modes of climate variability spanning from intraseasonal to secular time scales. The study highlights the varied influence of the modes of climate variability on various aspects of the evolution of the rainy season, such as its onset and demise dates and the seasonal rainfall anomaly over these Australian regions. The study uses 114 years of data and shows that the variations in the length of the rainy season and its seasonal rainfall anomaly are strongly dictated by variations of rainy-season onset date. This provides a quick seasonal outlook of the forthcoming rainy season by just monitoring the onset-date evolution in these regions. [ABSTRACT FROM AUTHOR]

This paper concerns the simulation of the water table elevation in shallow unconfined aquifers where infiltration is assumed as the main mechanism of recharge. The main aim is to provide a reliable tool for groundwater management that satisfies water supply managers. Such a tool is a candidate as a physically based alternative to the use of empirical methods or general circulation models. It is based on the use of two widely available sets of data: the water table elevation measurements and soil moisture time series. In fact, the former are usually provided by government agencies on public websites whereas the latter are included in the atmospheric global datasets (reanalysis). It is notable that data from reanalysis are accessible to any citizen and organization around the world on an open-access basis (e.g., Copernicus). In the proposed method, the measured water table elevations are correlated quantitatively with the water fluxes toward the aquifer evaluated using the soil moisture data from ERA5 reanalysis (provided by ECMWF) within a Richards equation-based approach. The analysis is executed using data from the Umbria region (Italy) on both a daily and monthly scale. In fact, these are the time intervals of interest for a proper management of groundwater resources. The proposed relationships include both a logarithmic and linear term and point out the possible different regimes of the shallow aquifers with regard to the recharge due to infiltration. These different mechanisms reflect in the different role played by the water fluxes toward the aquifer in terms of water table elevation changes according to the considered time scale. [ABSTRACT FROM AUTHOR]

This paper concerns the simulation of the water table elevation in shallow unconfined aquifers where infiltration is assumed as the main mechanism of recharge. The main aim is to provide a reliable tool for groundwater management that satisfies water supply managers. Such a tool is a candidate as a physically based alternative to the use of empirical methods or general circulation models. It is based on the use of two widely available sets of data: the water table elevation measurements and soil moisture time series. In fact, the former are usually provided by government agencies on public websites whereas the latter are included in the atmospheric global datasets (reanalysis). It is notable that data from reanalysis are accessible to any citizen and organization around the world on an open-access basis (e.g., Copernicus). In the proposed method, the measured water table elevations are correlated quantitatively with the water fluxes toward the aquifer evaluated using the soil moisture data from ERA5 reanalysis (provided by ECMWF) within a Richards equation–based approach. The analysis is executed using data from the Umbria region (Italy) on both a daily and monthly scale. In fact, these are the time intervals of interest for a proper management of groundwater resources. The proposed relationships include both a logarithmic and linear term and point out the possible different regimes of the shallow aquifers with regard to the recharge due to infiltration. These different mechanisms reflect in the different role played by the water fluxes toward the aquifer in terms of water table elevation changes according to the considered time scale. [ABSTRACT FROM AUTHOR]

Because of Earth's motion, periodic temperature fluctuations occur in soil on daily and yearly time scales, which inevitably lead to pulsation of the air pressure in the soil. The Earth-air pressure monitoring technique called 'hou-qi' is the basis of a calendar formulated by the ancient Chinese. However, the daily/yearly variation of air pressure in the soil is very weak, according to practical monitoring experiments, so hou-qi has long been considered a pseudoscience. To determine the potential maximum change of Earth-air pressure, identify what is causing the underlying Earth-air pressure variation, and reveal the mechanism, we use a closed system, which is a more appropriate system test of the possible validity of hou-qi, to monitor Earth-air pressure variation in this paper. The results show that there are potential air pressure fluctuations of about 120-190 hPa in closed soil over the daily/yearly temperature range of 5°-40°C. This provides ample magnitude for hou-qi. The largest contribution made to Earth-air pressure variation was pressurization due to air warming, with vapor due to water evaporation and desorbed gas having smaller effects. The soil's water content has a significant effect on Earth-air pressure amplitude. Dry soil contributes almost no water vapor, but the adsorbed gases from dry soil have up to a 38-hPa influence on air pressure. The soil's salt content also has an important regulating role on Earth-air pressure and can reduce the influence of vapor pressure. This paper provides a scientific basis for the ancient Earth-air monitoring system of hou-qi and also has important significance for hydrometeorology and research on the ancient Chinese civilization. [ABSTRACT FROM AUTHOR]

The hydrologic response of different climate-change emission scenarios for the twenty-first century were evaluated in 14 basins from different hydroclimatic regions across the United States using the Precipitation-Runoff Modeling System (PRMS), a process-based, distributed-parameter watershed model. This study involves four major steps: 1) setup and calibration of the PRMS model in 14 basins across the United States by local U.S. Geological Survey personnel; 2) statistical downscaling of the World Climate Research Programme''s Coupled Model Intercomparison Project phase 3 climate-change emission scenarios to create PRMS input files that reflect these emission scenarios; 3) run PRMS for the climate-change emission scenarios for the 14 basins; and 4) evaluation of the PRMS output. This paper presents an overview of this project, details of the methodology, results from the 14 basin simulations, and interpretation of these results. A key finding is that the hydrological response of the different geographical regions of the United States to potential climate change may be very different, depending on the dominant physical processes of that particular region. Also considered is the tremendous amount of uncertainty present in the climate emission scenarios and how this uncertainty propagates through the hydrologic simulations. This paper concludes with a discussion of the lessons learned and potential for future work. [ABSTRACT FROM AUTHOR]

Precipitation is a key link in the global water cycle and a proxy for changing climate; therefore, proper assessment of the urban environment’s impact on precipitation (land use, aerosols, thermal properties) will be increasingly important in ongoing climate diagnostics and prediction, Global Water and Energy Cycle (GWEC) analysis and modeling, weather forecasting, freshwater resource management, urban planning–design, and land–atmosphere–ocean interface processes. These facts are particularly critical if current projections for global urban growth are accurate. The goal of this paper is to provide a concise review of recent (1990–present) studies related to how the urban environment affects precipitation. In addition to providing a synopsis of current work, recent findings are placed in context with historical investigations such as Metropolitan Meteorological Experiment (METROMEX) studies. Both observational and modeling studies of urban-induced rainfall are discussed. Additionally, a discussion of the relative roles of urban dynamic and microphysical (e.g., aerosol) processes is presented. The paper closes with a set of recommendations for what observations and capabilities are needed in the future to advance our understanding of the processes. [ABSTRACT FROM AUTHOR]

There is increasing evidence that large coastal cities, like Houston, Texas, can influence weather through complex urban land use-weather-climate feedbacks. Recent work in the literature establishes the existence of enhanced lightning activity over and downwind of Houston. Since lightning is a signature of convection in the atmosphere, it would seem reasonable that urbanized Houston would also impact the distribution of rainfall. This paper presents results using data from the world?s first satellite-based precipitation radar (PR) aboard the Tropical Rainfall Measuring Mission (TRMM) and ground-based rain gauges to quantify rainfall anomalies that we hypothesize to be linked to extensive urbanization in the Houston area. It is one of the first rigorous efforts to quantify an urban-induced rainfall anomaly near a major U.S. coastal city and one of the first applications of space-borne radar data to the problem. Quantitative results reveal the presence of annual and warm season rainfall anomalies over and downwind of Houston. Several hypotheses have surfaced to explain how the sea breeze, coastline curvature, or urbanized Houston environment interacts with the atmospheric system to impact rainfall. This paper presents evidence that the urban heat island?s influence is of primary significance in causing the observed precipitation anomalies. Precipitation is a key link in the global water cycle and a proper understanding of its temporal and spatial character will have broad implicationsin ongoing climate diagnostics and prediction, Global Water and Energy Cycle (GWEC) analysis and modeling, weather forecasting, freshwater resource management, and land-atmosphere-ocean interface processes. [ABSTRACT FROM AUTHOR]

This paper explores the link between the anomalous warming hole in the southeastern United States and a major land-use/land-cover (LULC) change in the region. Land surface and satellite observations were analyzed to estimate the net radiative forcing due to LULC change. Albedo and latent energy were specifically addressed for the dominant LULC change of agriculture to forests. It was assumed that in the energy-limited environment of the region, the partition of changes in available energy due to albedo will mostly impact the sensible heat. The results show that in the southeastern United States, for the period of 1920 to 1992, the changes in sensible (as a result of albedo) and latent energies are in direct competition with each other. In the spring and early summer months, the croplands are in peak production and the latent energy associated with their evapotranspiration (ET) is comparable to that of the forests so the decrease in radiation due to albedo dominates the signal. However, during the late summer and fall months, most major crops have matured, thus reducing their transpiration rate while forests (particularly evergreens) maintain their foliage and with their deep roots are able to continue to transpire as long as atmospheric conditions are favorable. This later influence of latent energy appears to more than offset the increased radiative forcing from the spring and early summer. Overall, a mean annual net radiative forcing resulting from a LULC change from cropland to forests was estimated to be −1.06 W m−2 and thus a probable contribution to the 'warming hole' over the Southeast during the majority of the twentieth century. [ABSTRACT FROM AUTHOR]

Recent decades have witnessed rapid urbanization and urban population growth resulting in urban sprawl of cities. This paper analyzes the spatiotemporal dynamics of the urbanization process (using remote sensing and spatial metrics) that has occurred in Delhi, the capital city of India, which is divided into nine districts. The urban patterns and processes within the nine administrative districts of the city based on raw satellite data have been taken into consideration. Area, population, patch, edge, and shape metrics along with Pearson's chi statistics and Shannon's entropy have been calculated. Three types of urban patterns exist in the city: 1) highly sprawled districts, namely, West, North, North East, and East; 2) medium sprawled districts, namely, North West, South, and South West; and 3) least sprawled districts-Central and New Delhi. Relative entropy, which scales Shannon's entropy values from 0 to 1, is calculated for the districts and time spans. Its values are 0.80, 0.92, and 0.50 from 1977 to 1993, 1993 to 2006, and 2006 to 2014, respectively, indicating a high degree of urban sprawl. Parametric and nonparametric correlation tests suggest the existence of associations between built-up density and population density, area-weighted mean patch fractal dimension (AWMPFD) and area-weighted mean shape index (AWMSI), compactness index and edge density, normalized compactness index and number of patches, and AWMPFD and built-up density. [ABSTRACT FROM AUTHOR]

This paper aims to define atmospheric pathways related with the occurrence of daily winter low temperature episodes (LTE) in England, for the 26-yr period 1974-99, and to reveal possible associations with increased mortality rates. For this purpose, backward airmass trajectories, corresponding to LTE in five regions of England, were deployed. A statistically significant increase in mortality levels, at the 0.05 level, was found for LTE, compared to non-LTE days across all five regions. Seven categories of atmospheric trajectory patterns associated with LTE were identified: east, local, west, North Atlantic, Arctic, southwest, and Scandinavian. Consideration of the link between airmass trajectory patterns and mortality levels by region revealed a possible west-to-east split in the nature of air masses connected with elevated mortality. Specifically, for the West Midlands and northwest regions, relatively warm winter weather conditions from the west, most likely associated with the eastward progression of low pressure systems, are allied with the highest daily average mortality counts, whereas, for the northeast, Humberside/York, and southeast regions, cold continental air advection from northern or eastern Europe, which lasts for several days and is linked with either a blocking pattern over the western margins of Europe or an intense high pressure anomaly over eastern or northern Europe, appears important in mortality terms. This finding confirms that winter weather health associations are complex, such that climate setting and airmass climatology need to be taken into account when considering climate and health relationships. [ABSTRACT FROM AUTHOR]

The Alaskan boreal forest is characterized by frequent extensive wildfires whose spatial extent has been mapped for the past 70 years. Simple predictions based on this record indicate that area burned will increase as a response to climate warming in Alaska. However, two additional factors have affected the area burned in this time record: the Pacific decadal oscillation (PDO) switched from cool and moist to warm and dry in the late 1970s and the Alaska Fire Service instituted a fire suppression policy in the late 1980s. In this paper a geographic information system (GIS) is used in combination with statistical analyses to reevaluate the changes in area burned through time in Alaska considering both the influence of the PDO and fire management. The authors found that the area burned has increased since the PDO switch and that fire management drastically decreased the area burned in highly suppressed zones. However, the temporal analysis of this study shows that the area burned is increasing more rapidly in suppressed zones than in the unsuppressed zone since the late 1980s. These results indicate that fire policies as well as regional climate patterns are important as large-scale controls on fires over time and across the Alaskan boreal forest. [ABSTRACT FROM AUTHOR]

In this paper, monthly, maximum seasonal, and maximum annual hydrometeorological (i.e., evaporation, lake water levels, and rainfall) data series from the Kariba catchment area of the Zambezi River basin, Zimbabwe, have been analyzed in order to determine appropriate probability distribution models of the underlying climatology from which the data were generated. In total, 16 probability distributions were considered and the Kolmogorov-Sminorv (KS), Anderson-Darling (AD), and chi-square (χ2) goodness-of-fit (GoF) tests were used to evaluate the best-fit probability distribution model for each hydrometeorological data series. A ranking metric that uses the test statistic from the three GoF tests was formulated and used to select the most appropriate probability distribution model capable of reproducing the statistics of the hydrometeorological data series. Results showed that, for each hydrometeorological data series, the best-fit probability distribution models were different for the different time scales, corroborating those reported in the literature. The evaporation data series was best fit by the Pearson system, the Lake Kariba water levels series was best fit by the Weibull family of probability distributions, and the rainfall series was best fit by the Weibull and the generalized Pareto probability distributions. This contribution has potential applications in such areas as simulation of precipitation concentration and distribution and water resources management, particularly in the Kariba catchment area and the larger Zambezi River basin, which is characterized by (i) nonuniform distribution of a network of hydrometeorological stations, (ii) significant data gaps in the existing observations, and (iii) apparent inherent impacts caused by climatic extreme events and their corresponding variability. [ABSTRACT FROM AUTHOR]

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies. [ABSTRACT FROM AUTHOR]

As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel consumption and emissions with an open-source version of the Consume model. Mapped fuel moisture is derived from daily meteorological data from remote automated weather stations. In addition to tabular output results, WFEIS produces multiple vector and raster formats. This paper provides an overview of the WFEIS system, including the web-based system functionality and datasets used for emissions estimates. WFEIS operates on the web and is built using open-source software components that work with open international standards such as keyhole markup language (KML). Examples of emissions outputs from WFEIS are presented showing that the system provides results that vary widely across the many ecosystems of North America and are consistent with previous emissions modeling estimates and products. [ABSTRACT FROM AUTHOR]

This paper investigates relationships between storm surge heights and tropical cyclone wind speeds at 3-h increments preceding landfall. A unique dataset containing hourly tropical cyclone position and wind speed is used in conjunction with a comprehensive storm surge dataset that provides maximum water levels for 189 surge events along the U.S. Gulf Coast from 1880 to 2011. A landfall/surge classification was developed for analyzing the relationship between surge magnitudes and prelandfall winds. Ten of the landfall/surge event types provided useable data, producing 117 wind–surge events that were incorporated into this study. Statistical analysis indicates that storm surge heights correlate better with prelandfall tropical cyclone winds than with wind speeds at landfall. Wind speeds 18 h before landfall correlated best with surge heights. Raising wind speeds to exponential powers produced the best wind–surge fit. Higher wind–surge correlations were found when testing a more recent sample of data that contained 63 wind–surge events since 1960. The highest correlation for these data was found when wind speeds 18 h before landfall were raised to a power of 2.2, which provided R2 values that approached 0.70. The R2 values at landfall for these same data were only 0.44. Such results will be useful to storm surge modelers, coastal scientists, and emergency management personnel, especially when tropical cyclones rapidly strengthen or weaken while approaching the coast. [ABSTRACT FROM AUTHOR]

Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn. [ABSTRACT FROM AUTHOR]

Assessing climate change risk to municipal water supplies is often conducted by hydrologic modeling specific to local watersheds and infrastructure to ensure that outputs are compatible with existing planning frameworks and processes. This study leverages the modeling capacity of an operational National Weather Service River Forecast Center to explore the potential impacts of future climate-driven hydrologic changes on factors important to planning at the Salt Lake City Department of Public Utilities (SLC). Hydrologic modeling results for the study area align with prior research in showing that temperature changes alone will lead to earlier runoff and reduced runoff volume. The sensitivity of average annual flow to temperature varies significantly between watersheds, averaging -3.8% °F-1 and ranging from 21.8% to 26.5% flow reduction per degree Fahrenheit of warming. The largest flow reductions occur during the high water demand months of May-September. Precipitation drives hydrologic response more strongly than temperature, with each 1% precipitation change producing an average 1.9% runoff change of the same sign. This paper explores the consequences of climate change for the reliability of SLC's water supply system using scenarios that include hydrologic changes in average conditions, severe drought scenarios, and future water demand test cases. The most significant water management impacts will be earlier and reduced runoff volume, which threaten the system's ability to maintain adequate streamflow and storage to meet late-summer water demands. [ABSTRACT FROM AUTHOR]

In recent years, increasing attention has been paid to hydropower generation, since it is a renewable, efficient, and reliable source of energy, as well as an effective tool to reduce the atmospheric concentrations of greenhouse gases resulting from human activities. At the same time, however, hydropower is among the most vulnerable industries to global warming, because water resources are closely linked to climate changes. Indeed, the effects of climate change on water availability are expected to affect hydropower generation with special reference to southern countries, which are supposed to face dryer conditions in the next decades. The aim of this paper is to qualitatively assess the impact of future climate change on the hydrological regime of the Alcantara River basin, eastern Sicily (Italy), based on Monte Carlo simulations. Synthetic series of daily rainfall and temperature are generated, based on observed data, through a first-order Markov chain and an autoregressive moving average (ARMA) model, respectively, for the current scenario and two future scenarios at 2025. In particular, relative changes in the monthly mean and standard deviation values of daily rainfall and temperature at 2025, predicted by the Hadley Centre Coupled Model, version 3 (HadCM3) for A2 and B2 greenhouse gas emissions scenarios, are adopted to generate future values of precipitation and temperature. Synthetic series for the two climatic scenarios are then introduced as input into the Identification of Unit Hydrographs and Component Flows from Rainfall, Evapotranspiration and Streamflow Data (IHACRES) model to simulate the hydrological response of the basin. The effects of climate change are investigated by analyzing potential modification of the resulting flow duration curves and utilization curves, which allow a site's energy potential for the design of run-of-river hydropower plants to be estimated. [ABSTRACT FROM AUTHOR]

This paper uses the empirical orthogonal function (EOF) analysis to decompose satellite-derived nighttime land surface temperature (LST) for the period of 2003-11 into spatial patterns of different scales and thus to identify whether (i) there is a pattern of LST change associated with the development of wind farms and (ii) the warming effect over wind farms reported previously is an artifact of varied surface topography. Spatial pattern and time series analysis methods are also used to supplement and compare with the EOF results. Two equal-sized regions with similar topography in west-central Texas are chosen to represent the wind farm region (WFR) and nonwind farm region (NWFR), respectively. Results indicate that the nighttime warming effect seen in the first mode (EOF1) in WFR very likely represents the wind farm impacts due to its spatial coupling with the wind turbines, which are generally built on topographic high ground. The time series associated with the EOF1 mode in WFR also shows a persistent upward trend over wind farms from 2003 to 2011, corresponding to the increase of operating wind turbines with time. Also, the wind farm pixels show a warming effect that differs statistically significantly from their upwind high-elevation pixels and their downwind nonwind farm pixels at similar elevations, and this warming effect decreases with elevation. In contrast, NWFR shows a decrease in LST with increasing surface elevation and no warming effects over high-elevation ridges, indicating that the presence of wind farms in WFR has changed the LST-elevation relationship shown in NWFR. The elevation impacts on Moderate Resolution Imaging Spectroradiometer (MODIS) LST, if any, are much smaller and statistically insignificant than the strong and persistent signal of wind farm impacts. These results provide further observational evidence of the warming effect of wind farms reported previously. [ABSTRACT FROM AUTHOR]

Studies seeking to understand the impacts of climate variability and change on the hydrology of a region need to take into account the dynamics of vegetation and its interaction with the hydrologic and energy cycles. Yet, most of the hydrologic models used for these kinds of studies assume that vegetation is static. This paper presents a dynamic, spatially explicit model that couples a vertical energy balance scheme (surface and canopy layer) to a hydrologic model and a forest growth component to capture the dynamic interactions between energy, vegetation, and hydrology at hourly to daily time scales. The model is designed to be forced with outputs from regional climate models. Lateral water transfers are simulated using a 1D kinematic wave model. Infiltration is simulated using the Green and Ampt approximation to Richard's equation. The dynamics of soil moisture and energy drives carbon assimilation and forest growth, which in turn affect the distribution of energy and water through leaf dynamics by altering light interception, shading, and enhanced transpiration. The model is demonstrated in two case studies simulating energy, water, and vegetation dynamics at two different spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Flood simulation models and hazard maps are only as good as the underlying data against which they are calibrated and tested. However, extreme flood events are by definition rare, so the observational data of flood inundation extent are limited in both quality and quantity. The relative importance of these observational uncertainties has increased now that computing power and accurate lidar scans make it possible to run high-resolution 2D models to simulate floods in urban areas. However, the value of these simulations is limited by the uncertainty in the true extent of the flood. This paper addresses that challenge by analyzing a point dataset of maximum water extent from a flood event on the River Eden at Carlisle, United Kingdom, in January 2005. The observation dataset is based on a collection of wrack and water marks from two postevent surveys. A smoothing algorithm for identifying, quantifying, and reducing localized inconsistencies in the dataset is proposed and evaluated showing positive results. The proposed smoothing algorithm can be applied in order to improve flood inundation modeling assessment and the determination of risk zones on the floodplain. [ABSTRACT FROM AUTHOR]

Fire affects virtually all terrestrial ecosystems but occurs more commonly in some than in others. This paper investigates how climate, specifically the moisture regime, influences the flammability of different landscapes in the eastern United States. A previous study of spatial differences in fire regimes across the central Appalachian Mountains suggested that intra-annual precipitation variability influences fire occurrence more strongly than does total annual precipitation. The results presented here support that conclusion. The relationship of fire occurrence to moisture regime is also considered for the entire eastern United States. To do so, mean annual wildfire density and mean annual area burned were calculated for 34 national forests and parks representing the major vegetation and climatic conditions throughout the eastern forests. The relationship between fire activity and two climate variables was analyzed: mean annual moisture balance [precipitation P - potential evapotranspiration (PET)] and daily precipitation variability (coefficient of variability for daily precipitation). Fire activity is related to both climate variables but displays a stronger relationship with precipitation variability. The southeastern United States is particularly noteworthy for its high wildfire activity, which is associated with a warm, humid climate and a variable precipitation regime, which promote heavy fuel production and rapid drying of fuels. [ABSTRACT FROM AUTHOR]

Located on the Bahr el Jebel in South Sudan, the Sudd is one of the largest floodplain wetlands in the world. Seasonal inundation drives the hydrologic, geomorphological, and ecological processes, and the annual flood pulse is essential to the functioning of the Sudd. Despite the importance of the flood pulse, various hydrological interventions are planned upstream of the Sudd to increase economic benefits and food security. These will not be without consequences, in particular for wetlands where the biological productivity, biodiversity, and human livelihoods are dependent on the flood pulse and both the costs and benefits need to be carefully evaluated. Many African countries still lack regional baseline information on the temporal extent, distribution, and characteristics of wetlands, making it hard to assess the consequences of development interventions. Because of political instability in Sudan and the inaccessible nature of the Sudd, recent measurements of flooding and seasonal dynamics are inadequate. Analyses of multitemporal and multisensor remote sensing datasets are presented in this paper, in order to investigate and characterize flood pulsing within the Sudd wetland over a 12-month period. Wetland area has been mapped along with dominant components of open water and flooded vegetation at five time periods over a single year. The total area of flooding (both rain and river fed) over the 12 months was 41 334 km2, with 9176 km2 of this constituting the permanent wetland. Mean annual total evaporation is shown to be higher and with narrower distribution of values from areas of open water (1718 mm) than from flooded vegetation (1641 mm). Although the exact figures require validation against ground-based measurements, the results highlight the relative differences in inundation patterns and evaporation across the Sudd. [ABSTRACT FROM AUTHOR]

The Athabasca oil sands development in northeast Alberta, Canada, has disturbed more than 500 km2 of boreal forest through surface mining and tailings ponds development. In this paper, the authors compare the time series of temperatures and precipitation measured over oil sands and non-oil sands locations from 1994 to 2010. In addition, they analyzed the distribution of lightning strikes from 1999 to 2010. The oil sands development has not affected the number of lightning strikes or precipitation amounts but has affected the temperature regime. Over the past 17 years, the summer overnight minimum temperatures near the oil sands have increased by about 1.2°C compared to the regional average. The authors speculate that this is caused by a combination of the industrial addition of waste heat to the atmosphere above the oil sands and changing the surface type from boreal forest to open pit mines with tailings ponds. [ABSTRACT FROM AUTHOR]

Disturbances spreading through the landscape, like wildfires, are essential processes in modeling landscape structure and dynamics. Like other disturbances, fire may spread from a local epicenter with a propagation rate enhanced or retarded by the spatial arrangement of fuel across the landscape. Therefore, fire ignition and spread are a direct consequence of the presence and arrangement of fire-prone habitats. Generalizing the concept of ''habitat selection'' to every spatially distributed ecological process, the resource selection functions used in zoology to summarize habitat use by wildlife can be also used to characterize the wildfire''s pattern across the landscape. The aim of this paper is thus to quantify the relationship between forest cover and burnt area in Canton Ticino (Switzerland) during 1980--2007 using a bootstrap test of significance: that is, to identify forest types that burn more (or less) than expected from a random null model based on the regional availability of the resource (forest type). The results show that fires behave selectively for most forest types; whereas chestnut stands and broad-leaved forests display overproportional burnt areas, coniferous forests typically burn less than expected by a random null model. [ABSTRACT FROM AUTHOR]

Land-use and land-cover change has been recognized as a key component in global climate change. In the boreal forest ecosystem, fires often cause significant changes in vegetation structure and surface biophysical characteristics, which in turn dramatically change energy and water balances of land surface. Several studies have characterized fire-induced changes in surface energy balance in boreal ecosystem based on site observations. This study provides satellite-observed impacts of a large fire on surface climate in Alaska''s boreal forest. A land surface temperature (LST) product from NASA''s Moderate Resolution Imaging Spectroradiometer (MODIS) is used as the primary data. Five years after fire, surface temperature over the burned area increased by an average of 2.0°°C in the May--August period. The increase reached a maximum of 3.2°°C in the year immediately following the fire. The warm anomaly decreased slightly after the second year but remained until the fifth year of the study. These changes in surface temperature may directly affect surface net radiation and thus partition of surface available energy. By documenting continuous and synoptic surface temperature changes over multiple years, this paper demonstrates the value of Earth Observing System (EOS) observations for land--climate interaction research. The observed characteristics of surface temperature change as well as changes in key surface biophysical parameters such as albedo and leaf area index (LAI) can be used in the next generation of climate models to improve the representation of large-scale ecosystem disturbances. [ABSTRACT FROM AUTHOR]

Recent trends in precipitation and streamflow in the United States have become a particular focus of hydroclimatic research. The U.S. Hydro-Climatic Data Network (HCDN) has proven to be especially useful for the analysis of long-term streamflow trends. The U.S. Geological Survey (USGS) scientists selected sites for inclusion in the HCDN from the USGS stream-gauge network on the basis of streamflows being relatively free of nonclimatic anthropogenic influences. Consequently, most previous analyses of flow trends at those sites have either implicitly or explicitly attributed the trends to climate change and variability. In this paper, trends in seasonal and annual precipitation, and annual 7-day low, mean, and peak flows are examined for 48 medium-sized HCDN streams in the upper Mississippi (UM) water-resource region over 1939--2008. Using the concept of precipitation elasticity of flow, it is shown that the observed magnitudes of statistically significant increases in mean and low flows were up to a factor of 3 greater than expected from observed precipitation increases alone. Peak flows increased less than expected, and in the case of the Driftless Area at the center of the UM basin, decreased despite increased precipitation. It is proposed that the differences between expected and observed changes in streamflow can be explained by rural land-use changes in this principally agricultural region. [ABSTRACT FROM AUTHOR]

This paper focuses on the surface-atmospheric interaction in a tropical coastal city including the validation of an atmospheric modeling and an impact study of land-cover and land-use (LCLU) changes. The Regional Atmospheric Modeling System (RAMS), driven with regional reanalysis data for a 10-day simulation, is used to perform the study in the San Juan metropolitan area (SJMA), one of the largest urban conglomerations in the Caribbean, which is located in the island of Puerto Rico and taken as the test case. The model's surface characteristics were updated using airborne high-resolution remote sensing information to obtain a more accurate and detailed configuration of the SJMA. Surface and rawinsonde data from the San Juan Airborne Thermal and Land Applications Sensor (ATLAS) Mission are used to validate the modeling system, yielding satisfactory results in surface/canopy temperature, near-surface air temperatures, and vertical profiles. The impact analysis, performed with the updated SJMA configuration and a potential natural vegetation (PNV) scenario, showed that the simulation with specified urban LCLU indexes in the bottom boundary produced higher air temperatures over the area occupied by the city, with positive values of up to 2.5°C. The same analysis showed changes in the surface radiative balance in the urban case attributed to modifications in the LCLU. This additional heat seems to motivate additional vertical convection that may be leading to possible urban-induced precipitation downwind of the SJMA. This was evident in a precipitation disturbance when the city is present (∼0.9 mm, 22.5% increase) captured by the model that was accompanied by increases in cloud formation and vertical motions mainly downwind of the city. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between vegetation in the Liangzhou Oasis in the Upper Shiyang River watershed (USRW) of west-central Gansu, China, and within-watershed precipitation, soil water storage, and oasis self-support. Oases along the base of the Qilian Mountains receive a significant portion of their water supply (over 90%) from surface and subsurface flow originating from the Qilian Mountains. Investigation of vegetation control on oasis water conditions in the USRW is based on an application of a process model of soil water hydrology. The model is used to simulate long-term soil water content (SWC) in the Liangzhou Oasis as a function of (i) monthly composites of Moderate Resolution Imaging Spectroradiometer (MODIS) images of land surface and mean air temperature, (ii) spatiotemporal calculations of monthly precipitation and relative humidity generated with the assistance of genetic algorithms (GAs), and (iii) a 80-m-resolution digital elevation model (DEM) of the area. Modeled removal of vegetation is shown to affect within-watershed precipitation and soil water storage by reducing the exchange of water vapor from the land surface to the air, increasing the air’s lifting condensation level by promoting drier air conditions, and causing the high-intensity precipitation band in the Qilian Mountains to weaken and to be displaced upward, leading to an overall reduction of water to the Liangzhou Oasis. [ABSTRACT FROM AUTHOR]

Midlatitude regions experienced frequent droughts during the twentieth century, but their impacts on terrestrial carbon balance are unclear. This paper presents a century-scale study of drought effects on the carbon balance of terrestrial ecosystems in China. The authors first characterized the severe extended droughts over the period 1901–2002 using the Palmer drought severity index and then examined how these droughts affected the terrestrial carbon dynamics using tree-ring width chronologies and a process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM). It is found that China suffered from a series of severe extended droughts during the twentieth century. The major drought periods included 1920–30, 1939–47, 1956–58, 1960–63, 1965–68, 1978–80, and 1999–2002. Most droughts generally reduced net primary productivity (NPP) and net ecosystem productivity (NEP) in large parts of drought-affected areas. Moreover, some of the droughts substantially reduced the countrywide annual NPP and NEP. Out of the seven droughts, three (1920–30, 1965–68, and 1978–80) caused the countrywide terrestrial ecosystems to switch from a carbon sink to a source, and one (1960–63) substantially reduced the magnitude of the countrywide terrestrial carbon sink. Strong decreases in NPP were mainly responsible for the anomalies in annual NEP during these drought periods. Changes in heterotrophic respiration happened in the same direction, but mostly with smaller magnitude. The results show that severe extended droughts had significant effects on terrestrial carbon cycling in China, although future studies should consider other important processes such as drought-induced mortality and regrowth, land-use change, disturbances (e.g., fire), human management (e.g., fertilization and irrigation), and environmental pollution (e.g., ozone pollution, nitrogen deposition). These drought effects are of particular importance in light of projected widespread summer drying in midlatitude regions during the twenty-first century. Future droughts could lead to a reduced terrestrial carbon sink or even a source and exert a positive feedback to the global climate system. [ABSTRACT FROM AUTHOR]

Sea surface height (SSH) variability is presented over the Black Sea during 1993–2005. The 1/4° × 1/4° resolution daily SSH fields are formed using optimal interpolation of available altimeter data. SSH variability reveals distinct maxima in the eastern and western basins, reflecting variations in the corresponding gyres. A joint examination of SSH and sea surface temperature (SST) indicates strong relationship between the two only in winter, with correlations as high as 0.6 or more. This would reflect a steric change in sea surface height due to thermal expansion averaged over a relatively deep winter mixed layer. Newly developed SSH fields also demonstrate a switch to the positive mode of SSH starting from the end of 1996 lasting ≈4 yr. Such a climatic shift is found to be strongly related to large-scale teleconnection patterns. Finally, the daily SSH and SST anomaly fields presented in this paper can supplement various applications in the Black Sea, such as examination of biological production and mesoscale eddy dynamics. [ABSTRACT FROM AUTHOR]

The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate. [ABSTRACT FROM AUTHOR]

This paper uses statistical and analytical techniques to investigate intraseasonal interactions between temperature and vegetation [surrogated by the normalized difference vegetation index (NDVI)] over the boreal forests. Results indicate that interactions between the two fields may be approximated as a coupled second-order system, in which the variability of NDVI and temperature of the current month is significantly regulated by lagged NDVI anomalies from the preceding two months. In particular, the influence from the one-month lagged NDVI anomalies upon both temperature and vegetation variability is generally positive, but the influence from the second-month lagged NDVI anomalies is often negative. Such regulations lead to an intrinsic oscillatory variability of vegetation at growing-season time scales across the study domain. The regulation of temperature variability by NDVI anomalies is most significant over interior Asia (Siberia), suggesting strong vegetation–atmosphere couplings over these regions. Physical mechanisms for these statistical results are investigated further with a stochastic model. The model suggests that the oscillatory variability of the temperature–NDVI system may reflect the dynamic adjustments between the two fields as they maintain a thermal balance within the soil and lower boundary layer of the atmosphere; the particular role vegetation plays in this scenario is mainly to dissipate heat and therefore reduce surface temperatures. [ABSTRACT FROM AUTHOR]

The impact of an asteroid or a very large nuclear explosion in the Black Sea may cause a poisonous hydrogen sulfide gas release into the atmosphere. Some effects of this phenomenon on the western Black Sea coasts are evaluated in this paper. Two simple models are proposed to describe the generation of the H2S cloud. The wind speed plays an important role in H2S cloud dynamics. The land surface area covered by the H2S cloud generated by a 1000-m-sized asteroid during the run-in ranges between about 4480 and 9600 km2. This may affect up to 230 000 people. In the case of a 70-m-sized asteroid, the cloud covers between 70 and 210 km2 of land. This may affect between about 1600 and 5100 people. The evaluations do not include the population of the towns on the seashore and may be a few times underestimated for some particular wind directions. These effects are briefly compared with more usual effects associated with asteroids impacting the sea, such as shock waves and tsunamis. Nuclear explosions of 1 and 50 Mton TNT may be assimilated to the impact by asteroids of about 33- and 120-m diameter, respectively. [ABSTRACT FROM AUTHOR]

Projected climatic warming has direct implications for future disturbance regimes, particularly fire-dominated ecosystems at high latitudes, where climate warming is expected to be most dramatic. It is important to ascertain the potential range of climate change impacts on terrestrial ecosystems, which is relevant to making projections of the response of the Earth system and to decisions by policymakers and land managers. Computer simulation models that explicitly model climate–fire relationships represent an important research tool for understanding and projecting future relationships. Retrospective model analyses of ecological models are important for evaluating how to effectively couple ecological models of fire dynamics with climate system models. This paper uses a transient landscape-level model of vegetation dynamics, Alaskan Frame-based Ecosystem Code (ALFRESCO), to evaluate the influence of different driving datasets of climate on simulation results. Our analysis included the use of climate data based on first-order weather station observations from the Climate Research Unit (CRU), a statistical reanalysis from the NCEP–NCAR reanalysis project (NCEP), and the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). Model simulations of annual area burned for Alaska and western Canada were compared to historical fire activity (1950–2000). ALFRESCO was only able to generate reasonable simulation results when driven by the CRU climate data. Simulations driven by the NCEP and MM5 climate data produced almost no annual area burned because of substantially colder and wetter growing seasons (May–September) in comparison with the CRU climate data. The results of this study identify the importance of conducting retrospective analyses prior to coupling ecological models of fire dynamics with climate system models. The authors’ suggestion is to develop coupling methodologies that involve the use of anomalies from future climate model simulations to alter the climate data of more trusted historical climate datasets. [ABSTRACT FROM AUTHOR]

A better understanding of the interannual variability in temperature and precipitation datasets used as forcing fields for hydrologic models will lead to a more complete description of hydrologic model uncertainty, in turn helping scientists study the larger goal of how the Arctic terrestrial systemis responding to global change. Accordingly, this paper investigates temporal and spatial variability in monthly mean (1992--2000) temperature and precipitation datasets over the Western Arctic Linkage Experiment (WALE) study region. The six temperature datasets include 1) the fifth-generation Pennsylvania State University--National Center for Atmospheric Research MesoscaleModel (MM5); 2) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); 3) the Advanced Polar Pathfinderall-sky temperatures (APP); 4) National Centers for Environmental Prediction--National Center for Atmospheric Research (NCEP--NCAR) reanalyses (NCEP1); 5) the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU); and 6) the Matsuura and Wilmott 0.5° × 0.5° Global Surface Air Temperature and Precipitation (MW). Comparisons of monthly precipitation are examined for MM5, ERA-40, NCEP1, CRU, and MW. Results of the temporal analyses indicate significant differences between at least two datasets (for either temperature or precipitation) in almost every month. The largest number of significant differences for temperature occurs in October, when there are five separate groupings; for precipitation, there are four significantly different groupings from March through June, and again in December. Spatial analyses of June temperatures indicate that the greatest dissimilarity is concentrated in the central portion of the study region, with the NCEP1 and APP datasets showing the greatest differences. In comparison, the spatial analysis of June precipitation datasets suggests that the largest dissimilarity is concentrated in the eastern portion of the study region. These results indicate that the choice of forcing datasets likely will have a significant effecton the output from hydrologic models, and several different datasets should beused for a robust hydrologic assessment. [ABSTRACT FROM AUTHOR]

Isoprene is emitted from vegetation to the atmosphere in significant quantities, and it plays an important role in the reactions that control tropospheric oxidant concentrations. As future climatic and land-cover changes occur, the spatial and temporal variations, as well as the magnitude of these biogenic isoprene emissions, are expected to change. This paper presents a study of the change in biogenic isoprene emissions that would result from both anthropogenic land-cover and climate-driven changes. Annual global isoprene emissions were estimated to be 522 Tg yr-1 under current climatological and land-cover conditions. When climate-driven land-cover changes are predicted, but climate does not change, total global emissions did not change significantly, although regional impacts were important. However, the use of future temperature and land-cover drivers to estimate isoprene emissions produced a global estimate of 889 Tg yr-1. Anthropogenic land-cover changes, such as urbanization and changes of natural vegetation to plantation forests, can also have substantial impacts on isoprene emissions (i.e., up to 717 Tg yr-1, a 37% increase, when land-cover changes but temperature remains at current-day values). The Model for Ozone and Related Tracers, version 2 (MOZART-2) was run with the different isoprene emission scenarios to simulate the potential changes in global atmospheric chemical composition. The simulated regional surface ozone concentrations changed as much as -9 to 55 ppbv under certain emission and climate scenarios. These results were used to evaluate changes in the ozone production chemistry under different emission scenarios. As expected, the impacts of changing isoprene emissions are regionally dependent, with large changes in China, the Amazon, and the United States. [ABSTRACT FROM AUTHOR]

This paper presents a GIS-based analysis of climate variability over Senegal, West Africa. It responds to the need for developing a climate atlas that uses local observations instead of gridded global analyses. Monthly readings of observed rainfall (20 stations) and mean temperature (12 stations) were compiled, digitized, and quality assured for a period from 1971 to 1998. The monthly, seasonal, and annual temperature and precipitation distributions were mapped and analyzed using ArcGIS Spatial Analyst. A north–south gradient in rainfall and an east–west gradient in temperature variations were observed. June exhibits the greatest variability for both quantity of rainfall and number of rainy days, especially in the western and northern parts of the country. Trends in precipitation and temperature were studied using a linear regression analysis and interpolation maps. Air temperature showed a positive and significant warming trend throughout the country, except in the southeast. A significant correlation is found between the temperature index for Senegal and the Pacific sea surface temperatures during the January–April period, especially in the El Niño zone. In contrast to earlier regional-scale studies, precipitation does not show a negative trend and has remained largely unchanged, with a few locations showing a positive trend, particularly in the northeastern and southwestern regions. This study reveals a need for more localized climate analyses of the West Africa region because local climate variations are not always captured by large-scale analysis, and such variations can alter conclusions related to regional climate change. [ABSTRACT FROM AUTHOR]

Understory fires, which burn the floor of standing forests, are one of the most important types of forest impoverishment in the Amazon, especially during the severe droughts of El Niño–Southern Oscillation (ENSO) episodes. However, the authors are aware of no estimates of the areal extent of these fires for the Brazilian Amazon and, hence, of their contribution to Amazon carbon fluxes to the atmosphere. In this paper, the area of forest understory fires for the Brazilian Amazon region is calculated during an El Niño (1998) and a non–El Niño (1995) year based on forest fire scars mapped with satellite images for three locations in eastern and southern Amazonia, where deforestation is concentrated. The three study sites represented a gradient of both forest types and dry season severity. The burning scar maps were used to determine how the percentage of forest that burned varied with distance from agricultural clearings. These spatial functions were then applied to similar forest/climate combinations outside of the study sites to derive an initial estimate for the Brazilian Amazon. Ninety-one percent of the forest area that burned in the study sites was within the first kilometer of a clearing for the non-ENSO year and within the first four kilometers for the ENSO year. The area of forest burned by understory forest fire during the severe drought (ENSO) year (3.9 × 106 ha) was 13 times greater than the area burned during the average rainfall year (0.2 × 106 ha), and twice the area of annual deforestation. Dense forest was, proportionally, the forest type most affected by understory fires during the El Niño year, while understory fires were concentrated in transitional forests during the year of average rainfall. The estimate here of aboveground tree biomass killed by fire ranged from 0.049 to 0.329 Pg during the ENSO and from 0.003 to 0.021 Pg during the non-ENSO year. [ABSTRACT FROM AUTHOR]

This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (landcover category, and static fractional vegetation cover and varying leaf-plusstem- area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe. [ABSTRACT FROM AUTHOR]

The United States is currently responsible for 40%–45% of the world’s corn supply and 70% of total global exports [the U.S. Department of Agriculture–National Agricultural Statistics Service (USDA–NASS)]. Therefore, analyses of the spatial and temporal patterns of historical U.S. corn yields might provide insight into future crop-production potential and food security. In this study, county-level maize yield data from 1910 to 2001 were used to characterize the spatial heterogeneity of yield growth rates and interannual yield variability across the U.S. Corn Belt. Widespread decadal-scale changes in corn yield variability and yield growth rates have occurred since the 1930s across the Corn Belt, but the response has varied substantially with geographic location. Northern portions of the Great Plains have experienced consistently high interannual corn yield variability, averaging 30%–40% relative to the mean. Increasing usage of irrigation in Nebraska, Kansas, and Texas, since the 1950s, has helped boost yields by 75%–90% over rain-fed corn, creating a yield gap of 2–4 T ha-1 between irrigated and nonirrigated corn that could potentially be exploited in other regions. Furthermore, irrigation has reduced interannual variability by a factor of 3 in these same regions. A small region from eastern Iowa into northern Illinois and southern Wisconsin has experienced minimal interannual yield variability, averaging only 6%–10% relative to mean yields. This paper shows that the choice of time period used for statistical analysis impacted conclusions drawn about twentieth-century trends in corn yield variability. Widespread increases in yield variability were apparent from 1950 onward, but were not significant over the entire 1930–2001 period. There is also evidence that yield variability decreased from the early 1990s to 2001. Corn yield growth rates peaked at an annual-average rate of 3%–5% in the 1960s (124.5 kg ha-1 yr-1), but have steadily declined to a relative rate of 0.78% yr-1 (49.2 kg ha-1 yr-1) during the 1990s. A general inverse relationship between increasing corn yield and decreasing yield growth rates was noted after county-level yields reached 4 T ha-1, suggesting that widespread, significant increases in corn yield are not likely to take place in the future, particularly on irrigated land, without a second agricultural revolution. [ABSTRACT FROM AUTHOR]

Fire influences global change and tropical ecosystems through its connection to land-cover dynamics, atmospheric composition, and the global carbon cycle. As such, the climate change community, the Brazilian government, and the Large-Scale Biosphere–Atmosphere (LBA) Experiment in Amazonia are interested in the use of satellites to monitor and quantify fire occurrence throughout Brazil. Because multiple satellites and algorithms are being utilized, it is important to quantify the accuracy of the derived products. In this paper the characteristics of two fire detection algorithms are evaluated, both of which are applied to Terra’s Moderate Resolution Imagine Spectroradiometer (MODIS) data and with both operationally producing publicly available fire locations. The two algorithms are NASA’s operational Earth Observing System (EOS) MODIS fire detection product and Brazil’s Instituto Nacional de Pesquisas Espaciais (INPE) algorithm. Both algorithms are compared to fire maps that are derived independently from 30-m spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A quantitative comparison is accomplished through logistic regression and error matrices. Results show that the likelihood of MODIS fire detection, for either algorithm, is a function of both the number of ASTER fire pixels within the MODIS pixel as well as the contiguity of those pixels. Both algorithms have similar omission errors and each has a fairly high likelihood of detecting relatively small fires, as observed in the ASTER data. However, INPE’s commission error is roughly 3 times more than that of the EOS algorithm. [ABSTRACT FROM AUTHOR]

Process-based models that can represent multiple and interacting processes provide a framework for combining field-based measurements with evolving science-based models of specific hydroecological processes. Use of these models, however, requires that the representation of processes and key assumptions involved be understood by the user community. This paper provides a full description of process implementation in the most recent version of the Regional Hydro-Ecological Simulation System (RHESSys), a model that has been applied in a wide variety of research settings. An overview of the underlying (Geographic Information System) GIS-based model framework is given followed by a description of the mathematical models used to represent various biogeochemical cycling and hydrologic processes including vertical and lateral hydrologic fluxes, microclimate variability, canopy radiation transfer, vegetation and soil microbial carbon and nitrogen cycling. An example application of RHESSys for a small forested watershed as part of the Baltimore Long-Term Ecological Research site is included to illustrate use of the model in exploring spatial-temporal dynamics and the coupling between hydrology and biogeochemical cycling. [ABSTRACT FROM AUTHOR]

There is a strong international demand for quantitative estimates of both carbon sources/sinks, and water availability at the land surface at various spatial scales (regional to global). These estimates can be derived (and usually are) from global biosphere models, which simulate physiological, biogeochemical, and biophysical processes, using a variety of plant functional types. Now, the representation of the large area covered with managed land (e.g., croplands, grasslands) is still rather basic in these models, which were first designed to simulate natural ecosystems, while more and more land is heavily disturbed by man (crops cover ~ 35% and grasslands ~ 30%- 40% of western Europe's area as a result of massive deforestation mainly in the Middle Ages). In this paper a methodology is presented that combines the use of a dynamic global vegetation model (DGVM) known as Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and a generic crop model [the Simulateur Multidisciplinaire pour les Cultures Standard (STICS)]. This association aims at improving the simulation of water vapor and CO2 fluxes at the land-atmosphere interface over croplands, and thereby the calculation of the carbon and water budget. Variables that are much better computed in STICS (e.g., leaf area index, root density profile, nitrogen stress, vegetation height) are assimilated daily into ORCHIDEE, which continues to compute its own carbon and water balance from the fluxes simulated at the half-hourly time step. The allocation of photosynthates in ORCHIDEE was modified in order to maintain the coherence between leaf area index and leaf biomass, as well as between root density and root biomass. Soil moisture stress is computed using a more realistic root density profile. The maximum rates of carboxylation and RuBP (ribulosebisphosphate) regeneration were adjusted to more realistic values, while the actual rates can now be reduced following the nitrogen stress. Finally, harve... [ABSTRACT FROM AUTHOR]

The objectives of this study were to quantify land-cover changes. A short-term projection of land-cover distribution in a 2400-ha (1 ha = 10 000 m2 ) area of northern Ghana was generated. Landsat Thematic Mapper images acquired in 1984, 1992, and 1999 were used for land-cover mapping, whereas land-cover projections were carried out using transition probability techniques. Remote sensing analyses showed that in the first period (1984–92), the dominant land-cover change process was the expansion of the built-up area (26 ha yr-1) as a result of an increase in demand for housing by the increasing population. Expansion of the built-up area continued at the rate of 35 ha yr-1 in the second period (1992–99), as well as development of peri-urban agriculture (24 ha yr-1) to meet the food demand of the rapidly growing population. Projection of land-cover distribution showed that the built-up area would further increase at the expense of cropland and natural vegetation, covering about 39% of the landscape by 2006. Policy implications of this trend are discussed. This paper is part of a special theme issue on land use and ecosystems. [ABSTRACT FROM AUTHOR]

Long-term (20 yr) river discharge records from 30 of the world’s largest river basins have been used to characterize surface hydrologic flows in relation to net precipitation inputs, ocean climate teleconnections, and human land/water use patterns. This groundwork study is presented as a precedent to distributed simulation modeling of surface hydrologic flows in large river basins. Correlation analysis is used as a screening method to classify river basins into categories based on major controls on discharge, for example, climate, land use, and dams. Comparisons of paired station records at upstream and downstream discharge locations within each major river basin suggest that the discharge signals represented in upstream discharge records are sustained in the downstream station records for nearly two-thirds of the drainage basins selected. River basins that showed the strongest localized climate control over historical discharge records, in terms of correlations with net basinwide precipitation rates, are located mainly in the seasonally warm temperate and tropical latitude zones, as opposed to river basins located mainly in the higher latitude zones (above 45°N). Ocean climate indices such as the Niño1+2 and Niño3+4 correlate highly with historical interannual patterns in monthly river discharge for only four of the selected discharge station records, namely, on the Amazon, Congo (Zaire), Columbia, and Colorado (Arizona) Rivers. Historical patterns of cropland development and irrigated areas may explain the weak climate correlations with interannual patterns in monthly river discharge rates for at least one-third of the major river drainages selected from the historical discharge dataset. This paper is part of a special theme issue on land use and ecosystems. [ABSTRACT FROM AUTHOR]

Surface broadband emissivity in the thermal infrared region is an important parameter for the studies of the surface energy balance. This paper focuses on estimating a broadband window emissivity from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectrometer (MODIS) data. Both sensors are on board the NASA Earth Observing System (EOS) Terra satellite, which was launched in 1999. First, several definitions of the broadband emissivity were investigated, and it was found that the emissivity integrated between 8 and 13.5 μm is the best for estimating the net longwave radiation under clear-sky conditions. Then, a method to estimate broadband emissivity at the continental scale was developed. The method uses two regressions. The first regression is to relate the broadband emissivity to the emissivities for the five ASTER channels using measured emissivities in the laboratory from spectral libraries. The second regression relates the broadband emissivity map from ASTER data to the emissivity and reflectance derived from MODIS data. The first regression was used for mapping the broadband emissivity using ASTER data in a 500 km × 1400 km area of North Africa, which includes Tunisia, eastern Algeria, and western Libya. This emissivity map was used to calibrate the second regression, which was applied to MODIS data and generated a broadband emissivity map over North Africa and the Arabian Peninsula. The range of the broadband emissivity was found to be between 0.85 and 0.96 for the desert area. The resulting broadband emissivity maps and the methodology for generating them will contribute to future climate modeling studies. [ABSTRACT FROM AUTHOR]

Increasing atmospheric carbon dioxide concentration and the resulting change in temperature affect vegetation physiologically and structurally. These physiological and structural changes are biospheric feedbacks that may enhance or moderate the impacts due to human-induced land-cover change. It is therefore potentially important to include these biospheric feedbacks in experiments that explore the impact of land-cover change on climate. In this paper, it is shown that the vegetation response to higher carbon dioxide concentrations and warmer temperatures moderates the impacts of historical human-induced land-cover change in Australia. The magnitude of these biospheric feedbacks is explored, and it is shown that including them in climate simulations results in smaller land-cover change impacts on latent heat flux (by about 10–20 W m-2) and temperature (by about 0.3°C), irrespective of the direction of change caused initially by land-cover change. Further, the magnitude of the feedback on temperature is nonnegligible and can be comparable, at the regional scale, to temperature changes due to increasing atmospheric carbon dioxide concentrations. It is also shown that the biospheric feedback effects are not limited to areas of human-induced land-cover change. Higher simulated temperatures of about 0.05°–0.15°C were found in regions remote from areas of human-induced changes when these biospheric feedbacks are included. It is concluded therefore that it is necessary to take biospheric feedbacks into account in climate simulations. Excluding these feedbacks may incorrectly assess the impacts due to land-cover change. [ABSTRACT FROM AUTHOR]

In this paper a scheme is presented for calculating the land surface energy budget based on the first law of thermodynamics. It takes into account the effects of water multiphase changes and hydrologic process. The enthalpy expression for a heterogeneous land surface is deduced, during which latent heats are assumed to be functions of temperature. Also, a solution is derived for calculating ground temperatures during the water phase transition and the hydrologic process. This study results in a more conservative estimate of the ground energy in comparison with many other land surface schemes. [ABSTRACT FROM AUTHOR]