• Energy Research

AbstractThere is considerable interest in the potential impact of climate change on the feasibility and predictability of renewable energy sources, including wind energy. However, relatively little research has been conducted to assess the historical variability of wind energy density across different spatial scales or the degree to which one can derive robust projections of future wind energy density. This article presents analyses of the historical variability of annual wind indices across Europe to provide a context for possible future scenarios, as well as possible tools for use in developing prognoses that are suitable for application to output from global climate models. There is a high degree of covariance and inter‐annual variability of wind indices in northern European countries. Reanalysis data sets suggest that the axis about which the correlations of latitudinally integrated wind indices over Europe change sign is approximately 45°N and that there is evidence of compensating trends in wind energy indices north and south of this latitude. Climate projections from HadCM3 do not indicate evidence for changes in the spatial and temporal variability of annual wind indices, but these results are rather uncertain owing to biases in the variability of wind indices derived from HadCM3 output. Copyright © 2005 John Wiley & Sons, Ltd.

We present empirical downscaling of 5 state-of-the-art AOGCMs (Atmosphere-Ocean General Circulation Models) to investigate potential changes in wind speeds and energy density in northern Europe. The approach is based on downscaling the Weibull parameters of wind speed prob- ability distributions from AOGCM-derived 500 hPa relative vorticity and sea-level pressure gradi- ents, and is demonstrated to generate accurate depictions of the wind climate during the transfer function conditioning period. Bootstrapping is used to develop 100 realizations for each downscaling period and these are used to assess the uncertainty in the results due to stochastic effects in the AOGCM-derived downscaling predictors. Projected changes in the wind speed probability distribu- tion vary with the AOGCMs from which the predictors are derived, but generally it is shown that mean wind speeds, 90th percentile wind speeds and energy density are slightly lower in the 2081-2100 climate projection period than during 1961-1990 at the majority of the 46 stations studied. Conversely it is found that there is no significant difference between conditions during 2046-2065 and 1961-1990 based on the ensemble of downscaling results. Equally, the winter time of 2046-2065 is largely indistinguishable from 1961-1990 for the majority of stations, while the winters of 2081-2100 appear to be associated with lower mean and 90th percentile wind speeds and energy density.

Wind speeds over the Baltic significantly increased over the second half of the 20th century (C20th), with the majority of the increase being focused on the upper quartile of the wind speed distribution and in the southwest of the region. These changes have potentially profound implications for the wind energy resource. For example, based on the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data it is shown that, owing to this non-stationarity, using the normalization period of 1987–98 to determine the wind resource (as in the Danish wind index) leads to overestimation of the wind energy index (and hence the wind energy resource) in western Denmark relative to 1958–2001 by approximately 10%. To address whether the increased prevalence of high wind speeds at the end of the C20th will be maintained in the future, we provide a first prognosis of annual wind indices from the HadCM3 coupled atmosphere–ocean general circulation model. The results suggest the 21st century (C21st) will be similar to the 1958–2001 period with respect to the wind energy density, but that the northeastern Baltic will exhibit slightly higher wind energy indices over the course of the C21st relative to the latter half of the C20th, whereas the southwest of the Baltic exhibits some evidence of declining wind indices towards the end of the C21st. These changes may indicate a tendency in HadCM3 towards more northerly tracking of mid-latitude cyclones in the future, possibly due to evolution of the North Atlantic oscillation. As a caveat to this finding, it should be noted that the NCEP–NCAR and European Centre for Medium-Range Weather Forecasts reanalysis data sets and HadCM3 simulations, although exhibiting commonalities during the period of overlap, differ quantitatively in terms of the spatial fields and empirical cumulative probability distributions at individual grid cells. Copyright  2005 Royal Meteorological Society.

Applications of the Soil and Water Assessment Tool (SWAT) model typically involve delineation of a watershed into subwatersheds/subbasins that are then further subdivided into hydrologic response units (HRUs) which are homogeneous areas of aggregated soil, landuse, and slope and are the smallest modeling units used within the model. In a given standard SWAT application, multiple potential HRUs (farm fields) in a subbasin are usually aggregated into a single HRU feature. In other words, the standard version of the model combines multiple potential HRUs (farm fields) with the same landuse/landcover, soil, and slope, but located at different places of a subbasin (spatially non-unique), and considers them as one HRU. In this study, ArcGIS pre-processing procedures were developed to spatially define a one-to-one match between farm fields and HRUs (spatially unique HRUs) within a subbasin prior to SWAT simulations to facilitate input processing, input/output mapping, and further analysis at the individual farm field level. Model input data such as landuse/landcover (LULC), soil, crop rotation, and other management data were processed through these HRUs. The SWAT model was then calibrated/validated for Raccoon River watershed in Iowa for 2002-2010 and Big Creek River watershed in Illinois for 2000-2003. SWAT was able to replicate annual, monthly, and daily streamflow, as well as sediment, nitrate and mineral phosphorous within recommended accuracy in most cases. The one-to-one match between farm fields and HRUs created and used in this study is a first step in performing LULC change, climate change impact, and other analyses in a more spatially explicit manner.

About 50% of U.S. water pollution problems are caused by non-point source (NPS) pollution, primarily sediment and nutrients from agricultural areas, despite the widespread implementation of agricultural Best Management Practices (BMPs). However, the effectiveness of implementation strategies and type of BMPs at watershed scale are still not well understood. In this study, the Soil and Water Assessment Tool (SWAT) ecohydrological model was used to assess the effectiveness of pollutant mitigation strategies in the Raccoon River watershed (RRW) in west-central Iowa, USA. We analyzed fourteen management scenarios based on systematic combinations of five strategies: fertilizer/manure management, changing row-crop land to perennial grass, vegetative filter strips, cover crops and shallower tile drainage systems, specifically aimed at reducing nitrate and total suspended sediment yields from hotspot areas in the RRW. Moreover, we assessed implications of climate change on management practices, and the impacts of management practices on water availability, row crop yield, and total agricultural production. Our results indicate that sufficient reduction of nitrate load may require either implementation of multiple management practices (38.5% with current setup) or conversion of extensive areas into perennial grass (up to 49.7%) to meet and maintain the drinking water standard. However, climate change may undermine the effectiveness of management practices, especially late in the 21st century, cutting the reduction by up to 65% for nitrate and more for sediment loads. Further, though our approach is targeted, it resulted in a slight decrease (~5%) in watershed average crop yield and hence an overall reduction in total crop production, mainly due to the conversion of row-crop lands to perennial grass. Such yield reductions could be quite spatially heterogeneously distributed (0 to 40%).

Land-based carbon sequestration constitutes a major low cost and immediately viable option in climate change mitigation. Using downscaled data from eight atmosphere-ocean general circulation models for a simulation period between 2015 and 2099, we examine the carbon sequestration potential of alternative agricultural land uses in an intensively farmed Corn Belt watershed and the impact of climate change on crop yields. Our results show that switching from conventional tillage continuous corn to no-till corn-soybean can sequester the equivalent of 192.1 MtCO2 eq of soil organic carbon per hectare with a sequestration rate of 2.26 MtCO2 eq ha-1 yr-1. Our results also indicate that switchgrass can sequester the equivalent of 310.7 MtCO2 eq of soil organic carbon per hectare with a sequestration rate of 3.65 MtCO2 eq ha-1 yr-1. Our findings suggest that, unlike for corn and soybean yields, climate change does not have a significant effect on switchgrass yields, possibly due to the carbon fertilization effect.

Abstract Rapid ongoing development of wind power raises the question of where new wind turbines will be placed. This study uses locational decisions made through 2010 to develop a logistic regression model of wind turbine location among one square kilometer cells in Iowa, the U.S. state with the highest density of wind turbines. An 8-variable model correctly predicts 85 percent of cells. Wind energy density at 50 m and 80 m height are positively related to wind power; so also is population density within a 200 km radius and cropland. Distance from mid-voltage power lines and interstate highways are negatively related as is an airport within 5 km and population density within a 50 km radius. Using map algebra, the logit model generates a map of the likelihood of wind energy development. Locations that would most benefit from augmented electrical transmission are also identified. This form of empirical locational analysis can thus help predict and guide wind power development where spatial data to calibrate a high-fit logit model are sufficient.