Satellite-derived estimates of potential carbon sequestration through afforestation of agricultural lands in the United States

Afforestation of marginal agricultural lands represents a promising option for carbon sequestration in terrestrial ecosystems. An ecosystem carbon model was used to generate new national maps of annual net primary production (NPP), one each for continuous land covers of ‘forest’, ‘crop’, and ‘rangeland’ over the entire U. S. continental area. Direct inputs of satellite “greenness” data from the Advanced Very High Resolution Radiometer (AVHRR) sensor into the NASA-CASA carbon model at 8-km spatial resolution were used to estimate spatial variability in monthly NPP and potential biomass accumulation rates in a uniquely detailed manner. The model predictions of regrowth forest production lead to a conservative national projection of 0.3 Pg C as potential carbon stored each year on relatively low-production crop or rangeland areas. On a regional level, the top five states for total crop afforestation potential were: Texas, Minnesota, Iowa, Illinois, and Missouri, whereas the top five states for total rangeland afforestation potential are: Texas, California, Montana, New Mexico, and Colorado. Afforestation at this level of intensity has the capacity to offset at least one-fifth of annual fossil fuel emission of carbon in the United States. These projected afforestation carbon gains also match or exceed recent estimates of the annual sink for atmospheric CO₂ in currently forested area of the country.     

RegCM3 regional climatologies for South America using reanalysis and ECHAM global model driving fields

To enable downscaling of seasonal prediction and climate change scenarios, long-term baseline regional climatologies which employ global model forcing are needed for South America. As a first step in this process, this work examines climatological integrations with a regional climate model using a continental scale domain nested in both reanalysis data and multiple realizations of an atmospheric general circulation model (GCM). The analysis presents an evaluation of the nested model simulated large scale circulation, mean annual cycle and interannual variability which is compared against observational estimates and also with the driving GCM for the Northeast, Amazon, Monsoon and Southeast regions of South America. Results indicate that the regional climate model simulates the annual cycle of precipitation well in the Northeast region and Monsoon regions; it exhibits a dry bias during winter (July–September) in the Southeast, and simulates a semi-annual cycle with a dry bias in summer (December–February) in the Amazon region. There is little difference in the annual cycle between the GCM and renalyses driven simulations, however, substantial differences are seen in the interannual variability. Despite the biases in the annual cycle, the regional model captures much of the interannual variability observed in the Northeast, Southeast and Amazon regions. In the Monsoon region, where remote influences are weak, the regional model improves upon the GCM, though neither show substantial predictability. We conclude that in regions where remote influences are strong and the global model performs well it is difficult for the regional model to improve the large scale climatological features, indeed the regional model may degrade the simulation. Where remote forcing is weak and local processes dominate, there is some potential for the regional model to add value. This, however, will require improvments in physical parameterizations for high resolution tropical simulations.     

The Effect of the MAPS Weather Model on GPS-Determined Ellipsoidal Heights

We investigated a current numerical weather model, known as MAPS (Mesoscale Analysis and Prediction System), to determine if it could precisely define the behavior of GPS signals in the tropospere, ultimately leading to improved GPS-determined ellipsoidal heights. MAPS is the research version of the Rapid Update Cycle (RUC2) generated by NOAA's Forecast System Laboratory. MAPS is generated on an hourly basis and provides coverage in the contiguous United States at a 40-km grid spacing. We processed numerous subsets of GPS data collected over a months-long period on 23 static baselines ranging in length from 62 to 304 km. The GPS data were processed in 1/2-hr, 1-hr, 2-hr, and 4-hr session lengths. The primary effort was to compare the precision of heights obtained using a commonly adopted seasonal weather model with the precision of heights obtained using the MAPS weather model. Our analysis shows that the current version of MAPS can lead to improvement in GPS height precision when session lengths are shorter than two hours. For sessions longer than two hours, comparably precise heights may be obtained using a less accurate seasonal model by introducing appropriate nuisance parameters into the height estimation process. ? 2001 John Wiley & Sons, Inc.     

Application of remote sensing techniques for hydrologic investigations of upper yamuna catchment

The demands of water in various spheres of life, and its insufficient supply to fulfill these demands, specially in the developing countries, has become day by day a great challange to the hydrologist. In such a situation it becomes imperative to search a suitable methodology which can be used efficiently to predict the stream flows using minimum ground data, time and funds. Recently Remote Sensing Techniques have become a more popular and suitable tool for such studies. Keeping in view this aspect of problem, an analysis of Upper Yamuna Catchment, using Landsat imagery, was carried out to obtain the catchment characteristics which affect stream flows. Landsat imagery in Bands 5 and 7 on 1:1 million scale were analysed to get landuse and vegetal cover classification in categories (1) thick forest (ii) thin forest (iii) bare land and cultivation and (iv) snow cover. Using this data, alongwith rainfall data from 20 raingauge stations and stream flow data at Dakpather and Lakhwar, the stream flows were estimated by using Lumped system and Distributed system Models based on Rational Method. The computed 10 day period stream flows of Tons Sub-Basin were compared with observed surface flows with the help of hydrographs, flow comparison graphs and average monsoon period and monthly flow values. Using the coefficients evaluated from Tons sub-basin, the stream flow of Yamuna sub-basin were computed and compared with the observed surface flows.     

Managing cropland and rangeland for climate mitigation: an expert elicitation on soil carbon in California

Understanding the magnitude of and uncertainty around soil carbon flux (SCF) is important in light of California’s efforts to increase SCF (from the atmosphere to soils) for climate change mitigation. SCF depends, to a great extent, on how soils are managed. Here, we summarize the results of an elicitation of soil science and carbon cycle experts aiming to characterize understanding of current SCF in California’s cropland and rangeland, and how it may respond to alternative management practices over time. We considered four cropland management practices—biochar, compost, cover crops, and no-till—and two rangeland management practices, compost and high-impact grazing. Results across all management practices reveal underlying uncertainties as well as very modest opportunities for soil carbon management to contribute meaningfully to California’s climate mitigation. Under median scenarios, experts expect all the surveyed management practices to reverse SCF from negative to positive, with direct carbon additions via biochar and compost offering the best potential for boosting the soil carbon pool.