Dynamic correlation functions and Boltzmann Langevin approach for driven one dimensional lattice gas

We study the dynamics of the totally asymmetric exclusion process with open boundaries by phenomenological theories complemented by extensive Monte-Carlo simulations. Upon combining domain wall theory with a kinetic approach known as Boltzmann-Langevin theory we are able to give a complete qualitative picture of the dynamics in the low and high density regime and at the corresponding phase boundary. At the coexistence line between high and low density phases we observe a time scale separation between local density fluctuations and collective domain wall motion, which are well accounted for by the Boltzmann-Langevin and domain wall theory, respectively. We present Monte-Carlo data for the correlation functions and power spectra in the full parameter range of the model.Comment: 10 pages, 9 figure

Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing

Remote sensing is used for monitoring the impacts of meteorological drought on ecosystems, but few large-scale comparisons of the response timescale to drought of different vegetation remote sensing products are available. We correlated vegetation health products derived frompolar-orbiting radiometer observations with a meteorological drought indicator available at different aggregation timescales, the Standardized Precipitation Evapotranspiration Index (SPEI), to evaluate responses averaged globally and over latitude and biome.The remote sensing products are Vegetation Condition Index (VCI), which uses normalized difference vegetation index (NDVI) to identify plant stress, Temperature Condition Index (TCI), based on thermal emission as a measure of surface temperature, and Vegetation Health Index (VHI), the average of VCI and TCI. Globally, TCI correlated best with 2-month timescale SPEI, VCI correlated best with longer timescale droughts (peak mean correlation at 13 months), and VHI correlated best at an intermediate timescale of 4 months. Our results suggest that thermal emission (TCI) may better detect incipient drought than vegetation color (VCI). VHI had the highest correlations with SPEI at aggregation times greater than 3 months and hence may be the most suitable product for monitoring the effects of long droughts

Cross-sectional analyses of climate change impacts

The authors explore the use of cross-sectional analysis to measure the impacts of climate change on agriculture. The impact literature, using experiments on crops in laboratory settings combined with simulation models, suggests that agriculture will be strongly affected by climate change. The extent of these effects varies by country and region. Therefore, local experiments are needed for policy purposes, which becomes expensive and difficult to implement for most developing countries. The cross-sectional technique, as an alternative approach, examines farm performance across a broad range of climates. By seeing how farm performance changes with climate, one can estimate long-run impacts. The advantage of this approach is that it fully captures adaptation as each farmer adapts to the climate they have lived in. The technique measures the full net cost of climate change, including the costs as well as the benefits of adaptation. However, the technique is not concern-free. The four chapters in this paper examine important potential concerns of the cross-sectional method and how they could be addressed, especially in developing countries. Data availability is a major concern in developing countries. The first chapter looks at whether estimating impacts using individual farm data can substitute using agricultural census data at the district level that is more difficult to obtain in developing countries. The study, conducted in Sri Lanka, finds that the individual farm data from surveys are ideal for cross-sectional analysis. Another anticipated problem with applying the cross-sectionalapproach to developing countries is the absence of weather stations, or discontinued weather data sets. Further, weather stations tend to be concentrated in urban settings. Measures of climate across the landscape, especially where farms are located, are difficult to acquire. The second chapter compares the use of satellite data with ground weather stations. Analyzing these two sources of information, the study reveals that satellite data can explain more of the observed variation in farm performance than ground station data. Because satellite data are readily available for the entire planet, the availability of climate data will not be a constraint. A continuing debate is whether farm performance depends on just climate normals-the average weather over a long period of time-or on climate variance (variations away from the climate normal). Chapter 3 reveals that climate normals and climate variance are highly correlated. By adding climate variance, the studies can begin to measure the importance of weather extremes as well as normals. A host of studies have revealed that climate affects agricultural performance. Since agriculture is a primary source of income in rural areas, it follows that climate might explain variations in rural income. This is tested in the analysis in Chapter 4 and shown to be the case. The analysis reveals that local people in rural areas could be heavily affected by climate change even in circumstances when the aggregate agricultural sector in the country does fine.Climate Change,Environmental Economics&Policies,Wetlands,Global Environment Facility,Montreal Protocol,Environmental Economics&Policies,Climate Change,Wetlands,Global Environment Facility,Montreal Protocol

AVHRR-Based Spectral Vegetation Index for Quantitative Assessment of Vegetation State and Productivity: Calibration and Validation

The goal of the work was to estimate, quantitatively, vegetation state and productivity using AVHRR-based Vegetation Condition Index (VCI). The VCI algorithm includes application of post-launch calibration to visible channels, calculation of NDVI from channels’ reflectance, removal of high-frequency noise from NDVI’s annual time series, stratification of ecosystem resources, and separation of ecosystem and weather components in the NDVI value. The weather component was calculated by normalizing the NDVI to the difference of the extreme NDVI fluctuations (maximum and minimum), derived from multi-year data for each week and land pixel. The VCI was compared with wheat density measured in Kazakhstan. Six test fields were located in different climatic (annual precipitation 150 to 700 mm) and ecological (semi-desert to steppe-forest) zones with elevations from 200 to 700 m and a wide range of NDVI variation over space and season from 0.05 to 0.47. Plant density (PD) was measured in wheat fields by calculating the number of stems per unit area. PD deviation from year to year (PDD) was expressed as a deviation from median density calculated from multi-year data. The correlation between PDD and VCI for all stations was positive and quite strong (r2 \u3e 0.75) with the Standard Errors of Estimates (SEE) of PDD less than 16 percent; for individual stations, the SEE was less than 11 percent. The results indicate that VCI is an appropriate index for monitoring weather impact on vegetation and for assessment of pasture and crop productivity in Kazakhstan. Because satellite observations provide better spatial and temporal coverage, the VCI-based system will provide efficient tools for management of water resources and the improvement of agricultural planning. This system will serve as a prototype in the other parts of the world where ground observations are limited or not available

Using AVHRR Data for Quantitative Estimation of Vegetation Conditions: Calibration and Validation

NDVI-derived Vegetation Condition Index (VCI) was compared with vegetation density, biomass and reflectance measured in the fields. The VCI numerically estimates fluctuation of NDVI related to intra-annual weather change only and is a measure of weather impact on vegetation. Test fields were located in different climatic (annual precipitation 150-700 mm) and ecological zones (semi-desert to steppe-forest) with elevation from 200 to 700 m in Kazakhstan. A range of NDVI variation was from 0.05 to 0.47. The determination coefficient between AVHRR-derived vegetation state and actually measured vegetation density of more than 0.76 was achieved. For the first time it was shown that the VCI-derived vegetation condition data can be effectively used for quantitative assessments of both vegetation state and productivity (density and biomass) over large areas

Signatures of critical full counting statistics in a quantum-dot chain

We consider current shot noise and the full counting statistics in a chain of quantum dots which exhibits a continuous non-equilibrium phase transition as a function of the tunnel couplings of the chain with the electrodes. Using a combination of analytical and numerical methods, we establish that the full counting statistics is conventional away from the phase transition, but becomes, in a well-defined sense, essentially non-Gaussian on the critical line, where the current fluctuations are controlled by the dynamic critical exponent $z$. We find that signatures of the critical full counting statistics persist in quantum-dot chains of finite length.Comment: 7 pages, 7 figure

Gamma-ray flares from red giant/jet interactions in AGN

Non-blazar AGN have been recently established as a class of gamma-ray sources. M87, a nearby representative of this class, show fast TeV variability on timescales of a few days. We suggest a scenario of flare gamma-ray emission in non-blazar AGN based on a red giant interacting with the jet at the base. We solve the hydrodynamical equations that describe the evolution of the envelope of a red giant blown by the impact of the jet. If the red giant is at least slightly tidally disrupted by the supermassive black hole, enough stellar material will be blown by the jet, expanding quickly until a significant part of the jet is shocked. This process can render suitable conditions for energy dissipation and proton acceleration, which could explain the detected day-scale TeV flares from M87 via proton-proton collisions. Since the produced radiation would be unbeamed, such an events should be mostly detected from non-blazar AGN. They may be frequent phenomena, detectable in the GeV-TeV range even up to distances of $\sim 1$ Gpc for the most powerful jets. The counterparts at lower energies are expected to be not too bright.} {M87, and nearby non-blazar AGN in general, can be fast variable sources of gamma-rays through red giant/jet interactions.Comment: 8 pages, 4 figure

Removing Long-Term Errors from the AVHRR–Based Brightness Temperature (BT)

Empirical distribution functions were applied for removing long-term errors from BT data derived from AVHRR sensor on NOAA environmental satellites. This paper investigates BT stability in the NOAA/NESDIS Global Vegetation Index (GVI) data set during 1982-2003. This period includes five NOAA satellites. Degradation of BT over time for each satellite was estimated for geographical location in China. The method of matching empirical distribution function (EDF) improves the time relative stability of BT data for all satellites, especially NOAA-9, -11 and -14

Comparative Analysis on Applicability of Satellite and Meteorological Data for Prediction of Malaria in Endemic Area in Bangladesh

Relationships between yearly malaria incidence and (1) climate data from weather station and (2) satellite-based vegetation health (VH) indices were investigated for prediction of malaria vector activities in Bangladesh. Correlation analysis of percent of malaria cases with Advanced Very High Resolution Radiometer- (AVHRR-) based VH indices represented by the vegetation condition index (VCI—moisture condition) and the temperature condition index (TCI—estimates thermal condition) and with rainfall, relative humidity, and temperature from ground-based meteorological stations. Results show that climate data from weather stations are poorly correlated and are not applicable to estimate prevalence in Bangladesh. The study also has shown that AVHRR-based vegetation health (VH) indices are highly applicable for malaria trend assessment and also for the estimation of the total number of malaria cases in Bangladesh for the period of 1992–2001

Comparative Analysis on Applicability of Satellite and Meteorological Data for Prediction of Malaria in Endemic Area in Bangladesh

Relationships between yearly malaria incidence and (1) climate data from weather station and (2) satellite-based vegetation health (VH) indices were investigated for prediction of malaria vector activities in Bangladesh. Correlation analysis of percent of malaria cases with Advanced Very High Resolution Radiometer- (AVHRR-) based VH indices represented by the vegetation condition index (VCI—moisture condition) and the temperature condition index (TCI—estimates thermal condition) and with rainfall, relative humidity, and temperature from ground-based meteorological stations. Results show that climate data from weather stations are poorly correlated and are not applicable to estimate prevalence in Bangladesh. The study also has shown that AVHRR-based vegetation health (VH) indices are highly applicable for malaria trend assessment and also for the estimation of the total number of malaria cases in Bangladesh for the period of 1992–2001