Hydrologic models for land-atmosphere retrospective studies of the use of LANDSAT and AVHRR data

The use of a Geographic Information System (GIS) and LANDSAT analysis in conjunction with the Simulator for Water Resources on a Rural Basin (SWRRB) hydrologic model to examine the water balance on the Little Washita River basin is discussed. LANDSAT analysis was used to divide the basin into eight non-contiguous land covers or subareas: rangeland, grazed range, winter wheat, alfalfa/pasture, bare soil, water, woodland, and impervious land (roads, quarry). The use of a geographic information system allowed for the calculation of SWRRB model parameters in each subarea. Four data sets were constructed in order to compare SWRRB estimates of hydrologic processes using two methods of maximum LAI and two methods of watershed subdivision. Maximum LAI was determined from a continental scale map, which provided a value of 4.5 for the entire basin, and from its association with the type of land-cover (eight values). The two methods of watershed subdivision were determined according to drainage subbasin (four) and the eight land-covers. These data sets were used with the SWRRB model to obtain daily hydrologic estimates for 1985. The results of the one year analysis lead to the conclusion that the greater homogeneity of a land-cover subdivision provides better water yield estimates than those based on a drainage properties subdivision

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

Design for validation: An approach to systems validation

Every complex system built is validated in some manner. Computer validation begins with review of the system design. As systems became too complicated for one person to review, validation began to rely on the application of adhoc methods by many individuals. As the cost of the changes mounted and the expense of failure increased, more organized procedures became essential. Attempts at devising and carrying out those procedures showed that validation is indeed a difficult technical problem. The successful transformation of the validation process into a systematic series of formally sound, integrated steps is necessary if the liability inherent in the future digita-system-based avionic and space systems is to be minimized. A suggested framework and timetable for the transformtion are presented. Basic working definitions of two pivotal ideas (validation and system life-cyle) are provided and show how the two concepts interact. Many examples are given of past and present validation activities by NASA and others. A conceptual framework is presented for the validation process. Finally, important areas are listed for ongoing development of the validation process at NASA Langley Research Center

Evaluating the impact of U.S. Historical Climatology Network homogenization using the U.S. Climate Reference Network

Numerous inhomogeneities including station moves, instrument changes, and time of observation changes in the U.S. Historical Climatological Network (USHCN) complicate the assessment of long-term temperature trends. Detection and correction of inhomogeneities in raw temperature records have been undertaken by NOAA and other groups using automated pairwise neighbor comparison approaches, but these have proven controversial due to the large trend impact of homogenization in the United States. The new U.S. Climate Reference Network (USCRN) provides a homogenous set of surface temperature observations that can serve as an effective empirical test of adjustments to raw USHCN stations. By comparing nearby pairs of USHCN and USCRN stations, we find that adjustments make both trends and monthly anomalies from USHCN stations much more similar to those of neighboring USCRN stations for the period from 2004 to 2015 when the networks overlap. These results improve our confidence in the reliability of homogenized surface temperature records