Future Weather Scenarios Research Articles

Human‐made structures, vegetation, and weather influence ferruginous hawk breeding performance

ABSTRACTStudies of anthropogenic impacts on wildlife may produce inconclusive or biased results if they fail to account for natural sources of variation in breeding performance and do not use probabilistic sampling at a scale functional for management. We used stratified random sampling and generalized linear mixed models to test hypotheses on relationships of daily nest survival rate (DSR) and fledgling production with anthropogenic and environmental factors that influence reproduction in the ferruginous hawk (Buteo regalis). We conducted the study across ferruginous hawk range in Wyoming, USA, 2010–2012. We performed extensive field surveys of prey, vegetation, and nest substrates, and used spatially explicit data to quantify weather, and the most widespread forms of anthropogenic infrastructure (i.e., roads, oil and gas well pads) in ferruginous hawk territories. We found strong evidence that DSR and productivity were greater for nests on anthropogenic structures (i.e., artificial nest platforms, gas condensation tanks, abandoned windmill platforms, power poles) compared to natural substrates (i.e., trees, cliffs, rock outcrops). Additionally, ferruginous hawks produced more fledglings at territories with greater shrub cover and fewer severe storms during the June brood‐rearing period. Amount of oil and gas development and prey was not associated with either measure of breeding performance. Our results suggest that artificial nest platforms are an effective tool to improve breeding success of ferruginous hawks and nesting on anthropogenic structures does not constitute an ecological trap for this species. Although ferruginous hawks nested in some areas with very little vegetative cover, territories with greater amounts of shrub cover produced more fledglings. The negative impact of severe spring storms on fledgling production illustrates the importance of including future weather scenarios in management planning for this species because storms are predicted to increase in frequency and intensity as a result of climate change. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Simulating regional climate-adaptive field cropping with fuzzy logic management rules and genetic advance

SUMMARYAgriculture is a largely technical endeavour involving complicated managerial decision-making that affects crop performance. Farm-level modelling integrates crop models with agent behaviour to account for farmer decision-making and complete the representation of agricultural systems. To replicate an important part of agriculture in Central Europe a crop model was calibrated for a unique region's predominant crops: winter wheat, winter and spring barley, silage maize and winter rapeseed. Their cultivation was then simulated over multiple decades at daily resolution to test validity and stability, while adding the dimension of agent behaviour in relation to environmental and economic conditions. After validation against regional statistics, simulated future weather scenarios were used to forecast crop management and performance under anticipated global change. Farm management and crop genetics were treated as adaptive variables in the milieu of shifting climatic conditions to allow projections of agriculture in the study region into the coming decades.

Modeling denitrification in a changing climate

Abstract Although uncertainty exists in the specifics of climate change, sufficient consensus has emerged regarding the scale and direction of changes that it is important for managers to consider the implications of these changes for essential ecosystem services such as denitrification. For the northeastern United States, it is expected that by the end of the 21st century, annual precipitation will increase by 10% and annual temperatures by 3 °C. Because denitrification rates are highly influenced by soil moisture and soil temperature, we expect the projected changes in temperature and precipitation to alter the rates and patterns of denitrification. We developed three future weather scenarios based on the B1, A2, and A1F1 emission scenarios and utilized them as inputs to a coupled hydrologic-denitrification model to analyze the effects of changing temperature and soil moisture on denitrification rates, assuming nitrate availability remains unchanged. Our results suggest denitrification rates will increase by 5.1–11.8 kg N ha−1 yr−1 across the watershed. The greatest projected increases are in the areas and seasons with the highest baseline rates, with smaller increases in those with lower baseline rates. We found that changing temperature is likely to be a much stronger driver of change to denitrification rates (7.7 kg N ha−1 yr−1) than changing precipitation (1.4 kg N ha− yr−1). This study utilizes scenario modeling and a field validated model to quantify the future benefits of the ecosystem service denitrification, addressing a lack of ecosystem service quantification studies and providing a model for quantification studies.

Simple future weather files for estimating heating and cooling demand

Estimations of the future energy consumption of buildings are becoming increasingly important as a basis for energy management, energy renovation, investment planning, and for determining the feasibility of technologies and designs. Future weather scenarios, where the outdoor climate is usually represented by future weather files, are needed for estimating the future energy consumption. In many cases, however, the practitioner's ability to conveniently provide an estimate of the future energy consumption is hindered by the lack of easily available future weather files. This is, in part, due to the difficulties associated with generating high temporal resolution (hourly) estimates of future changes in air temperature. To address this issue, we investigate if, in the absence of high-resolution data, a weather file constructed from a coarse (annual) estimate of future air temperature change can provide useful estimates of future energy demand of a building. Experimental results based on both the degree-day method and dynamic simulations suggest that this is indeed the case. Specifically, heating demand estimates were found to be within a few per cent of one another, while estimates of cooling demand were slightly more varied. This variation was primarily due to the very few hours of cooling that were required in the region examined. Errors were found to be most likely when the air temperatures were close to the heating or cooling balance points, where the energy demand was modest and even relatively large errors might thus result in only modest absolute errors in energy demand.

Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions

Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset. Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1% on average and the variability in prediction accuracy decreased by 10.8%. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.

Impacts of climate variability on water quality with best management practices in sub‐tropical climate of USA

AbstractEfficiency of non‐point source pollution control methods may be altered in future climate. This study investigated climate change impacts on sediment and nutrient transport, and efficiency of best management practices (BMPs), in the Upper Pearl River Watershed (UPRW) in Mississippi. The Soil and Water Assessment Tool was applied to the UPRW using observed flow, sediment and nutrient data. Water quality samples were collected at three US geological survey gauging stations. The model was successfully calibrated and validated for daily time steps (Nash Sutcliffe efficiency and coefficient of determination – R2 up to 0.7) using manual and automatic (sequential uncertainty fitting version 2) methods from February 2010 to May 2011. Future weather scenarios were simulated using the LARS‐WG model, a stochastic weather generator, with Community Climate System Model, global climate model, which was developed by the National Center for Atmospheric Research in the USA. On the basis of the Special Report on Emissions Scenarios A1B, A2 and B1 of the Intergovernmental Panel on Climate Change, climate change scenarios were simulated for the mid (2046–2065) and late (2080–2099) century. Effectiveness of four BMPs (Riparian buffer, stream fencing, sub‐surface manure applications and vegetative filter strips) on reducing sediment and nutrient were evaluated in current and future climate conditions. Results show that sediment, nitrogen and phosphorus loadings will be increased up to a maximum of 26.3%, 7.3% and 14.3%, respectively, in future climate conditions. Furthermore, the effectiveness of BMPs on sediment removal will be reduced in future climate conditions, and the efficiency of nitrogen removal will be increased, whereas phosphorus removal efficiency will remain unchanged. Copyright © 2013 John Wiley & Sons, Ltd.

Spatial Application of WEPS for Estimating Wind Erosion in the Pacific Northwest

<abstract> <bold><sc>Abstract.</sc></bold> The Wind Erosion Prediction System (WEPS) is used to simulate soil erosion by wind on cropland and was originally designed to run simulations on a field scale. This study extended WEPS to run on multiple fields (grid cells) independently to cover a large region and conducted an initial investigation to assess how well WEPS performed in that environment by comparing simulations for two historical dust events with field observations and satellite images in the Columbia Plateau region of Washington. We modified the WEPS source code to allow it not only to run on multiple grid cells but also to save the state of the simulation so that it can be re-initiated from that state in future runs, allowing the model to be started and then stepped through time incrementally under various future climate or forecast weather scenarios. We initially ran WEPS on the entire state of Washington, with the entire Pacific Northwest region as our ultimate target area, to provide PM₁₀ and eventually PM_2.5 emissions from wind erosion events as input to the chemical transport model CMAQ, which is used by the AIRPACT regional air quality modeling system for the Pacific Northwest. Three principal inputs to WEPS are meteorological data, soil data, and crop management practices. These data, at a 1 km × 1 km grid cell resolution, are the basic input data for running the spatially distributed model. The climatic data from a three-year period were stochastically generated based on statistical representations of past meteorological measurements from stations in the region and were used for initializing WEPS, and then a three-day set of meteorological data corresponding with historical dust storm events were selected for simulation by WEPS of wind erosion of cropland in the state of Washington. The crop management data were selected based on the land use and USDA Natural Resources Conservation Service (NRCS) crop management zones, and the soil data were derived from the NRCS SSURGO database. We aggregated the outputs from 1 km × 1 km grid cells into 12 km × 12 km grid cells for easier visualization and then mapped the total surface soil erosion, suspension, and PM₁₀ emissions for each 12 km × 12 km grid cell. This study shows that WEPS can be successfully extended to run from one field grid cell to multiple field grid cells, and the model can identify regions with high potential for soil erosion by wind. It also demonstrates that WEPS can be used for real-time monitoring of soil erosion and air quality in a large region if actual and forecast weather inputs are available.

Integrated Reservoir Management System for Flood Risk Assessment Under Climate Change

Operations of existing reservoirs will be affected by climate change. Reservoir operating rules developed using historical information will not provide the optimal use of storage under changing hydrological conditions. In this paper, an integrated reservoir management system has been developed to adapt existing reservoir operations to changing climatic conditions. The reservoir management system integrates: (1) the K-Nearest Neighbor (K-NN) weather generator model; (2) the HEC-HMS hydrological model; and (3) the Differential Evolution (DE) optimization model. Six future weather scenarios are employed to verify the integrated reservoir management system using Upper Thames River basin in Canada as a case study. The results demonstrate that the integrated system provides optimal reservoir operation rule curves that reflect the hydrologic characteristics of future climate scenarios. Therefore, they may be useful for the development of reservoir climate change adaptation strategy.

Thermal mass in new build UK housing: A comparison of structural systems in a future weather scenario

There are growing concerns that the move to lightweight building construction in housing will lead to higher internal temperatures during the summer, particularly in the warmer future, due to lack of thermal mass. A dynamic thermal simulation study using Tas EDSL undertaken by Oxford Brookes University compared the thermal performance of current light, medium and heavy construction techniques for a typical UK three bedroom house. It found little difference in overheating performance for the three constructions in 1990s and 2050s (projected) weather scenarios. It is concluded that current practice in house building concedes little advantage to ‘traditional’ over modern construction techniques. Thermal mass can reduce overheating, primarily in the daytime, but it must be properly exploited by good design (good night ventilation, correct materials in the correct places). It is suggested that it is possible to optimise lightweight housing to provide similar thermal comfort levels during occupied hours using ventilation and shading.

The natural ventilation performance of buildings under alternative future weather projections

Different climate change projections, such as UK Climate Impacts Program (UKCIP02) and 2009 UK Climate projections (UKCP09), have generated a large quantity of data that represent a range of possible future weather scenarios. This article investigates the potential consequences of alternative scenarios for the natural ventilation of non-domestic buildings. The article considers future natural ventilation rates in example buildings, the risk of summer overheating and whether natural ventilation will be a viable thermal control option for future summers. The wind is obviously a key driver of natural ventilation, and a necessary component of building simulation weather files. Problems associated with the generation of wind data from UKCP09 for the natural ventilation analyses are discussed and the influence of differences in weather files on predicted performance considered. These differences are important to the understanding of the consequences for the wider use of UKCP09 derived weather data for building energy evaluation. Practical applications: Weather data are widely used in practice to evaluate the potential and performance of natural ventilation in non-domestic buildings. The predicted differences in future weather data will have direct implications for the design of naturally ventilated buildings, and engineers will need to be aware of the possible implications these climatic differences will create.

The Influence of Glass Types on the Performance of Air-Conditioned Office Buildings in Australia

Windows are one of the most significant elements in the design of buildings. Whether there are small punched openings in the facade or a completely glazed curtain wall, windows are usually a dominant feature of the building's exterior appearance. From the energy use perspective, windows may also be regarded as thermal holes for a building. Therefore, window design and selection must take both aesthetics and serviceability into consideration. In this paper, using building computer simulation techniques, the effects of glass types on the thermal and energy performance of a sample air-conditioned office building in Australia are studied. It is found that a glass type with lower shading coefficient will have a lower building cooling load and total energy use. Through the comparison of results between current and future weather scenarios, it is identified that the pattern found from the current weather scenario would also exist in the future weather scenario, although the scale of change would become smaller. The possible implication of glazing selection in face of global warming is also examined. It is found that compared with its influence on building thermal performance, its influence on the building energy use is relatively small or insignificant.

Application of CERES-Maize for within-season prediction of rainfed corn yields in Delaware, USA

The objective of this study was to determine whether the CERES-Maize crop simulation model can be used to predict rainfed corn yield up to 4 months prior to harvest. Required weather data were provided by combining the observed weather conditions up until the time of the forecast with predictions of future weather conditions. Yield forecasts were issued four times (June 1, July 1, August 1, and September 1) during each growing season (2001, 2002, and 2003). Thirty future weather scenarios were generated for each forecast date based on historical weather conditions and these forecasts were validated using the full season simulations. Not surprisingly, forecasts issued later in the growing season were more accurate than forecasts issued earlier because they incorporate more of the actual weather conditions. It appears that both the June 1 and July 1 forecasts would be of little value to decision makers because they contain too much uncertainty. However, the August 1 yield forecasts were more promising, the mean absolute error (MAE) ranged from 471 to 2407 kg ha −1 and the mean percent error (MPE) ranged from 4.8 to 46.6% and all of the September yield forecasts were extremely accurate. The September forecasts were very accurate (MAE ranged from 29 to 135 kg ha −1 and the MPE ranged from 0.5 to 1.3%) because the crop has often reached maturity by September 1. CERES-Maize would be useful to agencies that require accurate yield estimates prior to harvest. If reliable predictions of future weather conditions are available, CERES-Maize can be employed to accurately forecast yield months before harvest.

Evaluation of management strategies for sustainable rice–wheat cropping system, using DSSAT seasonal analysis

A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and CERES-Wheat in DSSAT 3.5 were calibrated and validated for transplanted rice, direct seeded rice and wheat crops using the soil and weather parameters of Kharagpur, West Bengal, India. The weather generator program, SIMMETEO, was used to generate future weather scenarios based on weather data from 9 consecutive years. These weather scenarios were used in the seasonal analysis program to run each treatment combination with 20 replications. The results of both biophysical and economic analyses of the Seasonal Analysis program predicted an application of 120 kg N/ha along with both rice and wheat crop residues at 4 t/ha for rice, whereas the economical analysis, specifically the Mean-Gini analysis, showed that application of 80 kg N/ha along with both rice and wheat crop residue incorporation at 4 t/ha as the most dominant management options for wheat. The present study revealed that the generated future weather data were reliable and DSSAT could successfully use it to predict the future crop yields under different management practices and select the best one for sustainable production of rice and wheat crops by DSSAT.

WEATHER AND CLIMATE EXTREMES, CLIMATE CHANGE, AND PLANNING: Views of Community Water System Managers in Pennsylvania's Susquehanna River Basin1

ABSTRACT: This research examines the sensitivity and vulnerability of community water systems (CWSs) to weather and climate in the Pennsylvania portion of the Susquehanna River Basin. Three key findings emerge from a survey of 506 CWS managers. First, CWSs are sensitive to extreme weather and climate, but that sensitivity is determined more by type of system than system size. CWSs that rely partly or wholly on surface water face more disruptions than do groundwater systems. Larger systems have more problems with flooding, and size is not a significant determinant of outages from storms or disruptions from droughts. Second, CWS managers are unsure about global warming. Few managers dismiss global warming; most think global warming could be a problem but are unwilling to consider it in their planning activities until greater scientific certainty exists. Third, the nature of the CWS, its sensitivity to weather and climate, and projected risks from weather and climate are insignificant determinants of how managers plan. Experienced, full‐time managers are more likely to consider future weather and climate scenarios in their planning, while inexperienced and part‐time managers are less likely to do so. Implications of these findings include support for efforts to move away from surface water, for clear communication of climate change information, and for the hiring and retention of full‐time professional CWS managers.

Modeling of climate change effects on stream temperatures and fish habitats below dams and near groundwater inputs

A deterministic heat transport model was developed to calculate stream water temperatures downstream of reservoir outlets (tailwaters) and groundwater sources. The model calculates heat exchange between the atmosphere, the water and the sediments and is driven by climate and stream hydrologic parameters. Past and projected climate conditions were used as input to the stream water temperature model. To produce a projected future weather scenario, output from the Columbia University Goddard Institute for Space Studies (GISS) global circulation model (GCM) for a doubling of atmospheric CO2 were used to adjust past (1955–1979) weather parameters. Stream reach lengths, within which water temperatures are suitable for survival or good growth of 28 fish species, were determined for four selected streams. Several alternative upstream inflow conditions were chosen: Discharges from surface (epilimnion) and bottom (hypolimnion) outlets of reservoirs, and two groundwater inflow scenarios. By applying water temperature criteria for fish survival and good growth (Stefanet al., 1993) to simulated stream temperatures, it was possible to estimate stream lengths with suitable habitat. When simulated suitable habitat was compared to actual fish observations, good agreement was found. For projected climate change, the simulations showed how much of the available stream habitat would be lost. In the examples presented the effect of cold hypolimnetic water release from a reservoir or groundwater discharges is felt as far as 48 km (30 miles) downstream from its source, especially in smaller shaded streams. The impact of climate change on stream temperatures below dams is more pronounced when the water release is from the epilimnion (reservoir surface) rather than the hypolimnion (deep water). Examples used for this study show elimination of coldwater habitat for rainbow trout when the upstream release is from the surface of a reservoir, but only reductions of coldwater habitat when the upstream release is from a reservoir hypolimnion.

Climatic variability and the modelling of crop yields

Crop simulation models are used widely to predict crop growth and development in studies of the impact of climatic change. An important problem is the uncertainty inherent in the construction of the future weather scenarios used as inputs to models. In seeking to couple meteorological information to crop-climate models it must be remembered that many interactions between crops and weather are non-linear. Non-linearity of response means it is necessary to preserve the variability of weather sequences to estimate the effect of climate on agricultural production and to assess agricultural risk. To date, only changes in average weather parameters derived from general circulation models (GCMs) and then applied to historical data have been used to construct climatic change scenarios and in only a few studies were changes in climatic variability incorporated. Accordingly, a computer system, AFRCWHEAT 3S, was designed to couple the simulation crop model for wheat, AFRCWHEAT2, with a stochastic weather generator based on the series approach. AFRCWHEAT 3S provides flexible construction of climatic scenarios and allows changes not only in mean values but also in the variance or type of distribution for a wide variety of weather parameters. Analyses of sensitivity to changes in the variability of temperature and precipitation, as compared with changes in their mean values, were made for locations in the UK and France for winter wheat. Results indicated that changes in climatic variability can have a more profound effect on yield and its associated risk than changes in mean climate.