Long samples of weather data are frequently needed to evaluate the long-term effects of proposed hydrologic changes. The evaluations are often undertaken using deterministic mathematical models that require daily weather data as input. Stochastic generation of the required weather data offers an attractive alternative to the use of observed weather records. This paper presents an approach that may be used to generate long samples of daily precipitation, maximum temperature, m'mimum temperature, and solar radiation. Precipitation is generated independently of the other variables by using a Markov chain-exponential model. The other three variables are generated by using a multivariate model with the means and standard deviations of the variables conditioned on the wet or dry status of the day as determined by the precipitation model. Daily weather samples that are generated with this approach preserve the seasonal and statistical characteristics of each variable and the interrelations among the four variables that exist in the observed data. ß ß ß ß ß 0 60 120 180 240 :300 :360

Stochastic weather generators are used in a wide range of studies, such as hydrological applications, environmental management and agricultural risk assessments. Such studies often require long series of daily weather data for risk assessment and weather generators can produce time series of synthetic daily weather data of any length. Weather generators are also used to interpolate observed data to produce synthetic weather data at new sites, and they have recently been employed in the construction of climate change scenarios. Any generator should be tested to ensure that the data that it produces is satisfactory for the purposes for which it is to be used. The accuracy required will depend on the application of the data, and the performance of the generator may vary considerably for different climates. The aim of this paper is to test and compare 2 commonly-used weather generators, namely WGEN and LARS-WG, at 18 sites in the USA, Europe and Asia, chosen to represent a range of climates. Statistical tests were selected to compare a variety of different weather characteristics of the observed and synthetic weather data such as, for example, the lengths of wet and dry series, the distribution of precipitation and the lengths of frost spells. The LARS-WG generator used more complex distributions for weather variables and tended to match the observed data more closely than WGEN, although there are certain characteristics of the data that neither generator reproduced accurately. The implications for the development and use of stochastic weather generators are discussed.

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The USDA has provided technical assistance and cost sharing to farmers and ranchers for implementing conservation practices on privately owned working lands since the 1930s. The primary purpose of the assistance programs has been to "improve the productivity of US farms and ranches and to protect the 'natural resource base' that sustained the agricultural enterprise" (Cox 2006). Conservationists have recognized for many years that these conservation programs protect millions of acres of farm and ranch lands from degradation. Research over the past fifty Abstract: The Conservation Effects Assessment Project was established to quantify the environmental impacts of USDA conservation programs. The Conservation Effects Assessment Project involves multiple watershed assessment studies designed to provide a scientific basis for a national assessment. The USDA Agricultural Research Service established 14 research sites-benchmark watersheds-to measure regionally specific environmental quality effects of conservation practices and to improve and validate models used by the USDA Natural Resources Conservation Service for their national assessment. Within each watershed, data were collected and continue to be collected to provide insight into the effects of specific conservation practices implemented under programs such as the Environmental Quality Incentives Program and the Conservation Reserve Program. A data storage and management system, Sustaining the Earth's Watersheds-Agricultural Research Data System (STEWARDS), was developed to provide easy accessibility to these data for analysis. Models were validated using data from many of the watersheds and were shown to be valuable tools for extrapolating the results for a national assessment. The physical process models were also combined with economic models to optimize tradeoffs among environmental and economic objectives of conservation practices. The benchmark watershed studies have begun to identify the effects of selected conservation practices, although additional data are required to provide definitive results. A prototype of a new modular modeling system has been developed that will provide a more powerful tool for future analyses. The initial Conservation Effects Assessment Project findings and products demonstrate progress toward the overall goals of quantifying conservation practice effects and providing tools to transfer the knowledge to points where they are applied under future conservation policy.

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Agricultural source pollution of water resources has been a source of concern in recent years. Research is needed to define mechanisms of chemical and sediment loss in runoff from agricultural land, and to develop management practices that minimize transport of these pollutants. This study was designed to compare the effect of no‐till (NT) and conventional chisel‐till (CT) soil management on runoff water volumes, sediment loss, and N and P loss from small watersheds on a clay soil. Three NT and three CT watersheds located on Houston Black clay vertisol soil (fine, montmorillonitic, thermic, Udic Pellusterts) in east central Texas were used for the study. Wheat (Triticum aestivum L.), corn (Zea mays L.) and sorghum [Sorghum bicolor (L.) Moench] were grown rotationally on the watersheds from 1984 to 1989. Runoff amounts, sediment loss, and N and P losses were measured for each rainfall event that produced runoff. Runoff volume was not changed by tillage system and sediment loss and N and P losses in runoff were less, on average, from NT than from CT. Runoff averaged 1.3 ML ha−1 annually for both CT and NT. Average annual quantities for sediment and nutrient losses were: 160 kg ha−1 and 1575 kg ha−1 for sediment, 3.8 kg ha−1 and 8.1 kg ha−1 for N, and 0.8 kg ha−1 and 1.5 kg ha−1 for P for NT and CT, respectively. These results indicate that the loss of sediment and nutrients from agricultural lands could be minimized by using NT on clay soils.

Large‐area yield prediction early in the growing season is important in agricultural decision‐making. This study derived maize (Zea mays L.) leaf area index (LAI) estimates from spectral data and used these estimates with a simple LAI‐based yield model to forecast yield under irrigated conditions in large areas in Sinaloa, Mexico. Leaf area index was derived from satellite data with the use of an equation developed with LAI measurements from farmers' fields during the 2001–2002 autumn–winter growing season. These measurements were correlated with the normalized difference vegetation index values from 2002 Landsat ETM+ (enhanced thematic mapper) data. The equation was then tested with 2003 Landsat imagery data. A yield model was validated with maximum LAI and yield data measured in farmers' fields in northern and central Sinaloa during three consecutive autumn–winter growing seasons (1999–2000, 2000–2001, and 2001–2002). The yield model was further validated with 2002–2003 autumn–winter ground LAI (gLAI) and satellite‐derived LAI (sLAI) data from 71 farmers' fields in northern and central Sinaloa. Grain yield was predicted with a mean error of −9.2% with maximum gLAI and −11.2% with sLAI. Results indicate that the yield model using LAI can forecast yield in large areas in Sinaloa in the middle of the growing season with a mean absolute error of −1.2 Mg ha−1. The use of sLAI in place of ground measurements increased the mean absolute error by 0.3 Mg ha−1. Nevertheless, the use of sLAI would eliminate laborious LAI measurements for large‐area yield prediction in Sinaloa.

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