Guillermo A. Baigorria

GiST: A Stochastic Model for Generating Spatially and Temporally Correlated Daily Rainfall Data

Abstract Weather generators are tools that create synthetic daily weather data over long periods of time. These tools have also been used for downscaling monthly to seasonal climate forecasts, from global and regional circulation models to daily values for use as inputs for crop and other environmental models. One main limitation of most weather generators is that they do not take into account the spatial structure of weather. Spatial correlation of daily rainfall is important when one aggregates, for example, simulated crop yields or hydrology in a watershed or region. A method was developed to generate realizations of daily rainfall for multiple sites in an area while preserving the spatial and temporal correlations among sites. A two-step method generates rainfall events at multiple sites followed by rainfall amounts at sites where generated rainfall events occur. The generation of rainfall events was based on a new orthogonal Markov chain for discrete distributions. For generating rainfall amounts, a ...

Assessment of erosion hotspots in a watershed: Integrating the WEPP model and GIS in a case study in the Peruvian Andes

This paper presents a case study in assessment of erosion hotspots in an Andean watershed. To do this, we made use of an interface called Geospatial Modelling of Soil Erosion (GEMSE): a tool that integrates Geographical Information Systems (GIS) with the Water Erosion Prediction Project (WEPP) model. Its advantages are: (i) it is independent of any special GIS software used to create maps and to visualize the results; (ii) the results can be used to produce response surfaces relating outputs (e.g. soil loss, runoff) with simple inputs (e.g. climate, soils, topography); (iii) the scale, resolution and area covered by the different layers can be different among them, which facilitates the use of different sources of information. The objective of this paper is to show GEMSEs performance in a specific case study of soil erosion in La Encanada watershed (Peru) where the hillslope version of WEPP has been previously validated. Resulting runoff and soil loss maps show the spatial distribution of these processes. Though these maps do not give the total runoff and soil loss at the watershed level, they can be used to identify hotspots that will aid decision makers to make recommendations and plan actions for soil and water conservation.

Assessing Predictability of Cotton Yields in the Southeastern United States Based on Regional Atmospheric Circulation and Surface Temperatures

Abstract The potential to predict cotton yields up to one month before planting in the southeastern United States is assessed in this research. To do this, regional atmospheric variables that are related to historic summer rainfall and cotton yields were identified. The use of simulations of those variables from a global circulation model (GCM) for estimating cotton yields was evaluated. The authors analyzed detrended cotton yields (1970–2004) from 48 counties in Alabama and Georgia, monthly rainfall from 53 weather stations, monthly reanalysis data of 850- and 200-hPa winds and surface temperatures over the southeast U.S. region, and monthly predictions of the same variables from the ECHAM 4.5 GCM. Using the reanalysis climate data, it was found that meridional wind fields and surface temperatures around the Southeast were significantly correlated with county cotton yields (explaining up to 52% of the interannual variability of observed yields), and with rainfall over most of the region, especially durin...

Potential influence of land development patterns on regional climate: a summer case study in the Central Florida

Two land development scenarios based on the Central Florida Regional Growth Vision projection for 2050 were used to explore the developments’ potential influence on regional climate. One scenario is a widespread suburban land development plan, and the other is a higher density urban development plan, both for the same location in central Florida. A series of simulation experiments were conducted using a regional climate model upgraded for this study to include an urban scheme. Noticeable differences in simulated regional climate patterns were found between the land development scenarios, which could potentially influence population requirements for energy and water. In our simulations, the aggregated effect of land cover changes over large suburban areas produced a more intense heat island effect than that produced by high-density urban areas.

Assessing the Use of Seasonal Climate Forecasts to Support Farmers in the Andean Highlands

Andean farmers plant their fields before and during the initial months of the rainy season, avoiding planting all of their fields on a specific date or with the same crop. This traditional technique reduces climatic risks that occur as a result of the high interannual climate variability and also assures a minimum production for self-consumption during years of poor production. Farmers make decisions according to their expectation and based on previous experiences of risk and they have developed their own systems for weather and seasonal climate forecasting based on meteorological and astronomical phenomena as well as biological behavior of wild species (Baigorria 2005). However, in comparison to other Andean areas, studies in La Encanada and Tambomayo show that these indicators are more related to short-term decision-making such as when to apply agro-chemicals, than what, when, where and how to plant and crop. Although formal weather and seasonal climate forecasts are available from the Peruvian National Service of Meteorology and Hydrology, these are used only in a few cases, due to the inadequate spatial resolution and the lack of training to interpret them properly. Similarly, but at a different level, the extension offices provide general-purpose recommendations without using these forecasts.