Estimating climate change effects on net primary production of rangelands in the United States

The potential effects of climate change on net primary productivity (NPP) of U.S. rangelands were evaluated using estimated climate regimes from the A1B, A2 and B2 global change scenarios imposed on the biogeochemical cycling model, Biome-BGC from 2001 to 2100. Temperature, precipitation, vapor pressure deficit, day length, solar radiation, CO2 enrichment and nitrogen deposition were evaluated as drivers of NPP. Across all three scenarios, rangeland NPP increased by 0.26 % year−1 (7 kg C ha−1 year−1) but increases were not apparent until after 2030 and significant regional variation in NPP was revealed. The Desert Southwest and Southwest assessment regions exhibited declines in NPP of about 7 % by 2100, while the Northern and Southern Great Plains, Interior West and Eastern Prairies all experienced increases over 25 %. Grasslands dominated by warm season (C4 photosynthetic pathway) species showed the greatest response to temperature while cool season (C3 photosynthetic pathway) dominated regions responded most strongly to CO2 enrichment. Modeled NPP responses compared favorably with experimental results from CO2 manipulation experiments and to NPP estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS). Collectively, these results indicate significant and asymmetric changes in NPP for U.S. rangelands may be expected

A comparison of methods for calculating population exposure estimates of daily weather for health research

BACKGROUND: To explain the possible effects of exposure to weather conditions on population health outcomes, weather data need to be calculated at a level in space and time that is appropriate for the health data. There are various ways of estimating exposure values from raw data collected at weather stations but the rationale for using one technique rather than another; the significance of the difference in the values obtained; and the effect these have on a research question are factors often not explicitly considered. In this study we compare different techniques for allocating weather data observations to small geographical areas and different options for weighting averages of these observations when calculating estimates of daily precipitation and temperature for Australian Postal Areas. Options that weight observations based on distance from population centroids and population size are more computationally intensive but give estimates that conceptually are more closely related to the experience of the population. RESULTS: Options based on values derived from sites internal to postal areas, or from nearest neighbour sites – that is, using proximity polygons around weather stations intersected with postal areas – tended to include fewer stations' observations in their estimates, and missing values were common. Options based on observations from stations within 50 kilometres radius of centroids and weighting of data by distance from centroids gave more complete estimates. Using the geographic centroid of the postal area gave estimates that differed slightly from the population weighted centroids and the population weighted average of sub-unit estimates. CONCLUSION: To calculate daily weather exposure values for analysis of health outcome data for small areas, the use of data from weather stations internal to the area only, or from neighbouring weather stations (allocated by the use of proximity polygons), is too limited. The most appropriate method conceptually is the use of weather data from sites within 50 kilometres radius of the area weighted to population centres, but a simpler acceptable option is to weight to the geographic centroid

Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) Terrestrial Primary Production to the Accuracy of Meteorological Reanalyses

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA\u27s satellites, Terra and Aqua, dramatically improves our ability to accurately and continuously monitor the terrestrial biosphere. MODIS information is used to estimate global terrestrial primary production weekly and annually in near-real time at a 1-km resolution. MODIS terrestrial primary production requires daily gridded assimilation meteorological data as inputs, and the accuracy of the existing meteorological reanalysis data sets show marked differences both spatially and temporally. This study compares surface meteorological data sets from three well-documented global reanalyses, NASA Data Assimilation Office (DAO), European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA-40) and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis 1, with observed weather station data and other gridded data interpolated from the observations, to evaluate the sensitivity of MODIS global terrestrial gross and net primary production (GPP and NPP) to the uncertainties of meteorological inputs both in the United States and the global vegetated areas. NCEP tends to overestimate surface solar radiation, and underestimate both temperature and vapor pressure deficit (VPD). ECMWF has the highest accuracy but its radiation is lower in tropical regions, and the accuracy of DAO lies between NCEP and ECMWF. Biases in temperature are mainly responsible for large VPD biases in reanalyses. MODIS NPP contains more uncertainties than GPP. Global total MODIS GPP and NPP driven by DAO, ECMWF, and NCEP show notable differences (\u3e20 Pg C/yr) with the highest estimates from NCEP and the lowest from ECMWF. Again, the DAO results lie somewhere between NCEP and ECMWF estimates. Spatially, the larger discrepancies among reanalyses and their derived MODIS GPP and NPP occur in the tropics. These results reveal that the biases in meteorological reanalyses can introduce substantial error into GPP and NPP estimations, and emphasize the need to minimize these biases to improve the quality of MODIS GPP and NPP products

A flexible, integrated system for generating meteorological surfaces derived from point sources across multiple geographic scales

The generation of meteorological surfaces from point-source data is a difficult but necessary step required for modeling ecological and hydrological processes across landscapes. To date, procedures to acquire, transform, and display meteorological information geographically have been specifically tailored to individual studies. Here we offer a flexible, integrated system that employs a relational database to store point information, a modular system incorporating a choice of weather data interpolation methods, and a matrix inversion method that speeds computer calculations to display information on grids of any specified size, all with minimal user intervention. We demonstrate the power of this integrated approach by cross-validating projected daily meteorological surfaces derived from ∼1200 weather stations distributed across the continental United States for a year. We performed cross-validations for five meteorological variables (solar radiation, minimum and maximum temperatures, humidity, and precipitation) with a truncated Gaussian filter, ordinary kriging and inverse distance weighting and achieved comparable success among all interpolation methods. Cross-validation computation time for ordinary kriging was reduced from 1 h to 3 min when we incorporated the matrix inversion method. We demonstrated the system\u27s flexibility by displaying results at 8-km resolution for the continental USA and at one-degree resolution for the globe

Spatial Data Model for Rural Planning and Land Management in Turkey

In Turkey, in the areas of rural planning and land management, problems regarding data retrieval, data quality, implementation scenario and legal base (law or regulation) have long been experienced. In this study, in order to contribute to resolving such problems, a conceptual/semantic data model was designed which focuses on the definition of required data, determination of their basic qualities and also their relations. As the preparation step for the model development, interviews, and discussions with authorized people were carried out. In addition, for the definitions of the data in the model, the Land Parcel Identification System and Infrastructure for Spatial Information in the European Community (INSPIRE) are considered. For the model design, an object-oriented modelling method with the Unified Modelling Language (UML) notation was used. In the model, planning activities were focused on. It is envisaged that the model will guide work for the preparation of a technical regulation which may enable a standardized implementation throughout Turkey. It has also the potential to be an example for the implementation of laws related to spatial data both in Turkey and also worldwide

Assessment of Global and Regional Reanalyses Data for Hydro- Climatic Impact Studies in the Upper Thames River Basin

This study evaluates NCEP-NCAR reanalyses hydro-climatic data as an initial check for assessment of climate change studies and hydrologic modeling on the basin scale. Reanalysis data set for daily precipitation, and temperature from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) (a) global (NNGR) and (b) regional (NARR) reanalysis project are used as input into the semi-distributed hydrologic model (HEC-HMS) during the period of 1980-2005. First, the precipitation and temperature data are interpolated to selected stations to check for their trends and similarity in means and variances. Although NARR shows some over-estimated values, mainly in estimating temperature during the summer months, it has been able to capture the trends. NNGR, on the other hand, has produced inferior results in many cases, especially in generating precipitation when compared with the observed values. With its improved atmospheric analytical ability, NARR appears to have performed better than the NNGR, suggesting that with coarse resolution NNGR may not be applied in climate change studies for medium or small watersheds. Next, an extensive analysis is performed for assessing the performance of the reanalysis data generated flows by comparing it with the observed inputs during May-November. The stream flows generated from the NARR dataset show encouraging results for simulating summertime low flows with less variability and error. NNGR dataset, have proven to be less accurate and highly variable. This study suggests that NARR can be adequately used as either an additional source of data or as an alternative to observations in data scarce regions.https://ir.lib.uwo.ca/wrrr/1033/thumbnail.jp

Future potential net primary production trends of contiguous United States rangelands

Rangelands are an important ecosystem covering nearly 24% of the earth’s terrestrial vegetation. Climate change is predicted to affect many of the factors that influence the production of rangeland vegetation. Understanding future trends and patterns in net primary production (NPP) requires projected potential NPP to better understand how rangelands will be affected by a changing climate. Here, I used climate data projected from a global climate model (GCM) to drive the biogeochemical model (Biome-BGC) in an attempt to simulate future potential NPP trends in rangelands of the contiguous United States from 2001-2100 on a 100 km2 scale. In response to the simulated climate projections, I found an overall slight increase in potential NPP throughout time. However, these increases were not spatially consistent; in some areas, NPP decreased substantially. Biome-BGC found three distinct zones that have similar potential NPP trends and primary correlating climatic factors that drove these trends. The south western portion of the United States may see a decrease in NPP driven mostly by a decrease in moisture. This simulation indicates a rise in NPP in the Great Plains mostly from c4 grasses driven primarily by an increase in temperature. Furthermore, it projects little to no change in The Great Basin driven by a combination of a slight increase in precipitation and maximum temperature

A Hydrometeorological And Geospatial Analysis Of Precipitation Within The Glacial Ridge Wildlife Refuge Using The R2ain-Gis Tool

Weather radar (radio detection and ranging) is a specialized meteorological tool used to sample and track meteorological objects. This tool is critical for meteorologists and public decision-makers to inform and provide for their constituents in a timely manner, often with the protection of lives and property on the line. With the application of using meteorological and geospatial data in the realm of geographic information systems (G.I.S.), the task of blending the two sciences to inhibit further research and dissemination of information occurs. This study focuses on the creation and implementation of a new geospatial tool, the Radar and Rainfall Analyzed in GIS (R2AIn-GIS) tool. The R2AIn-GIS tool was built upon the initial concepts from Zhang and Srinivasan’s (2010) NEXRAD validation and calibration (NEXRAD-VC) tool for G.I.S. R2AIn-GIS is updated to support the latest software features present in the geospatial world as well as analyze dual-polarization radar products. To test the R2AIn-GIS tool, a warm seasonal precipitation study along with statistical analysis was performed over the Glacial Ridge National Wildlife Refuge in Minnesota, the largest prairie and wetland restoration site. Utilizing rain gauges operated by the United States Geological Survey, warm season precipitation events from 24 May 2012 to 31 August 2013 were analyzed using the R2AIn-GIS tool. The R2AIn-GIS tool calculates the values from various dual-polarization radar products in conjunction with the recorded precipitation gauges to provide a detailed depiction of the weather event. Statistical tests including several iterations of multiple-linear regression of various combinations of dual-polarization radar variables allowed determination of rainfall rate prediction equations over the study area. This contributes to the body of radar literature regarding the best prediction equations for other locations. Unlike treatments in prior literature, most of the various assumptions in multiple linear regression are considered herein. Based off the findings of the various statistical tests that adhere to the linear regression assumptions, regression models utilizing both reflectivity and correlation coefficient were the best models found during this study. These two variables had statistical significant p-values and their Durbin-Watson scores were among the highest even compared with the other radar variables of differential reflectivity and specific differential phase. Models including the radar variables reflectivity and correlation coefficient were found to be heteroscedastic along with the highest R Squared values. While the overall rainfall amounts were too small in terms of effective precipitation sampling, the results still positively contribute to the literature and provides the opportunity for future work

The epidemiology of West Nile virus in Louisiana

West Nile virus (WNV), a member of the genus Flavivirus transmitted by mosquitoes, first appeared in the New York in 1999. Within five years WNV was detected throughout the contiguous 48 states causing disease in reservoirs and accidental hosts alike. In Louisiana, WNV was first detected in 2001 with one human case, ten equine cases, and six dead birds reported. The introduction of WNV into Louisiana presented an unique opportunity to observe an emerging disease unfold, so a study was launched to gain insight into the epidemiology of WNV in Louisiana. The first component, an environmental predictive model for West Nile virus, was developed using geographic information systems and remote sensing in relationship to the prevalence of human cases and the percent of WNV positive dead birds by parish for 2002 and 2003. Linear regression analysis showed a 13 variable model with environmental and human factors for the 2003 human dataset to be the best model. This model was able to explain 74% of the variation in human WNV prevalence by parish. The results of the model along with one-way chi-square analysis of categorical variables indicated largely urban cycle when the mosquito-bird transmission cycle reaches high levels as the main mode of WNV transmission with spillover to humans, and other accidental hosts. A serosurvey of wild birds in East Baton Rouge Parish was conducted from November 2002 to October 2004. A total of 1287 samples were tested by the plaque reduction neutralization test. Overall, 222/1287 (17.25%; CI: 15.19-19.31) tested positive. Species, location, sex, age, and monthly differences were detected. The study identified Northern cardinals (Cardinalis cardninalis) as a statistically significant host for WNV in Louisiana. Mediterranean house geckos (Hemidactylus turcicus) were assessed as a potential reservoir for West Nile virus. Geckos were inoculated orally with West Nile virus and a field study was conducted to determine the prevalence of WNV in naturally infected geckos. Results obtained through virus culture and RT-PCR indicated that geckos could become infected with an oral inoculation of WNV, but that naturally infected geckos do not produce high enough viremias to act as a reservoir