In managing fish populations, especially at-risk species, realistic mathematical models are needed to help predict population response to potential management actions in the context of environmental conditions and changing climate while effectively incorporating the stochastic nature of real world conditions. We provide a key component of such a model for the endangered pallid sturgeon (Scaphirhynchus albus) in the form of an individual-based bioenergetics model influenced not only by temperature but also by flow. This component is based on modification of a known individual-based bioenergetics model through incorporation of: the observed ontogenetic shift in pallid sturgeon diet from marcroinvertebrates to fish; the energetic costs of swimming under flowing-water conditions; and stochasticity. We provide an assessment of how differences in environmental conditions could potentially alter pallid sturgeon growth estimates, using observed temperature and velocity from channelized portions of the Lower Missouri River mainstem. We do this using separate relationships between the proportion of maximum consumption and fork length and swimming cost standard error estimates for fish captured above and below the Kansas River in the Lower Missouri River. Critical to our matching observed growth in the field with predicted growth based on observed environmental conditions was a two-step shift in diet from macroinvertebrates to fish.

To improve the resiliency of designs, particularly for long-lived infrastructure, current engineering practice must be updated to incorporate a range of future climate conditions that are likely to be different from the past. However, a considerable mismatch exists between climate model outputs and the data inputs needed for engineering designs. The present work provides a framework for incorporating climate trends into design standards and applications, including: selecting the appropriate climate model source based on the intended application, understanding model performance and uncertainties, addressing differences in temporal and spatial scales, and interpreting results for engineering design. The framework is illustrated through an application to depth-duration-frequency curves, which are commonly used in stormwater design. A change factor method is used to update the curves in a case study of Pittsburgh, PA. Extreme precipitation depth is expected to increase in the future for Pittsburgh for all return periods and durations examined, requiring revised standards and designs. Doubling the return period and using historical, stationary values may enable adequate design for short duration storms; however, this method is shown to be insufficient to enable protective designs for larger duration storms.

Historical climate data are underutilized in agricultural decision making. We illustrate how long-term climate data and observations from farmers’ fields can be combined to quantify risks from seasonal weather and climate variability for nitrogen fertilizer management for corn (Zea mays L.). We developed a probability model for estimating the risk of deficient corn nitrogen status using within-field late-season plant measurements, field information about previous crop, nitrogen rate, form and application timing in combination with rainfall data. Using three grower risk attitudes (risk-tolerant, risk-neutral, risk-averse) we demonstrate the use of deficient corn nitrogen status probability values for making decisions about nitrogen logistics prior to and within the growing seasons and multi-year investments in more efficient and less risky fertilizer management practices. We find these probabilities could enable growers to explore alternative management scenarios (rates, timing and fertilizer forms) for in-season nitrogen management for each risk attitudes. We conclude that annual surveys of corn nitrogen status across Iowa should be useful not only for field-level logistic decisions but also new in-season weather-based plant status monitoring strategies and tools for evaluating business risk from past weather trends or anticipated changes in weather. While developed for the Midwest United States, the use of annual surveys and on-farm data to translate historical climate data can be implemented in any growing region.

Plant water availability is a key factor that determines maize yield response to excess heat. Lack of available data has limited researchers’ ability to estimate this relationship at regional and global scales. Using a new soil moisture data set developed by running a crop growth simulator over historical data we demonstrate how current estimates of maize yield sensitivity to high temperature are misleading. We develop an empirical model relating observed yields to climate variables and soil moisture in a high maize production region in the United States to develop bounds on yield sensitivity to high temperatures. For the portion of the region with a relatively long growing season, yield reduction per °C is 10% for high water availability and 32.5% for low water availability. Where the growing season is shorter, yield reduction per °C is 6% for high water availability and 27% for low water availability. These results indicate the importance of using both water availability and temperature to model crop yield response to explain future climate change on crop yields.

We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere - ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, climate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our downscaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.

Abstract Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.

Likely changes in precipitation (P) and potential evapotranspiration (PET) resulting from policy-driven expansion of bioenergy crops in the United States are shown to create significant changes in streamflow volumes and increase water stress in the High Plains. Regional climate simulations for current and biofuel cropping system scenarios are evaluated using the same atmospheric forcing data over the period 1979-2004 using the Weather Research Forecast (WRF) model coupled to the NOAH land surface model. PET is projected to increase under the biofuel crop production scenario. The magnitude of the mean annual increase in PET is larger than the inter-annual variability of change in PET, indicating that PET increase is a forced response to the biofuel cropping system land use. Across the conterminous U.S., the change in mean streamflow volume under the biofuel scenario is estimated to range from negative 56% to positive 20% relative to a business-as-usual baseline scenario. In Kansas and Oklahoma, annual streamflow volume is reduced by an average of 20%, and this reduction in streamflow volume is due primarily to increased PET. Predicted increase in mean annual P under the biofuel crop production scenario is lower than its inter-annual variability, indicating that additional simulations would be necessary to determine conclusively whether predicted change in P is a response to biofuel crop production. Although estimated changes in streamflow volume include the influence of P change, sensitivity results show that PET change is the significantly dominant factor causing streamflow change. Higher PET and lower streamflow due to biofuel feedstock production are likely to increase water stress in the High Plains. When pursuing sustainable biofuels policy, decision-makers should consider the impacts of feedstock production on water scarcity.

This presentation will discuss the first in the nation application of utilizing climate science modeling to determine future rainfall events so that future flooding from hydrologic modeling of the basins in Iowa can be predicted. The “future” hydrologic modeling will be analyzed to determine frequency/discharge relationships for sizing bridges and culverts based on “future” flooding. This is a paradigm shift in the way bridges and culverts are currently sized which is based on past flooding. Based on this analysis, policy/design criteria for bridges and roads may change to account for future extreme weather events. This research is an FHWA pilot program to assess and adapt the vulnerability of highway infrastructure due to climate change impacts on extreme weather events.

Large‐scale conversion of traditional agricultural cropping systems to biofuel cropping systems is predicted to have significant impact on the hydrologic cycle. Changes in the hydrologic cycle lead to changes in rainfall and its erosive power, and consequently soil erosion that will have onsite impacts on soil quality and crop productivity, and offsite impacts on water quality and quantity. We examine regional change in rainfall erosivity and soil erosion resulting from biofuel policy‐induced land use/land cover (LULC) change. Regional climate is simulated under current and biofuel LULC scenarios for the period 1979–2004 using the Weather Research Forecast (WRF) model coupled to the NOAH land surface model. The magnitude of change in rainfall erosivity under the biofuel scenario is 1.5–3 times higher than the change in total annual rainfall. Over most of the conterminous United States (~56%), the magnitude of the change in erosivity is between −2.5% and +2.5%. A decrease in erosivity of magnitude 2.5–10% is predicted over 23% of the area, whereas an increase of the same magnitude is predicted over 14% of the area. Corresponding to the changes in rainfall erosivity and crop cover, a decrease in soil loss is predicted over 60% of the area under the biofuel scenario. In Kansas and Oklahoma, the states in which a large fraction of land area is planted with switchgrass under the biofuel scenario, soil loss is estimated to decrease 12% relative to the baseline. This reduction in soil loss is due more to changes in the crop cover factor than changes in rainfall or rainfall erosivity. This indicates that the changes in LULC, due to future cellulosic biofuel feedstock production, can have significant implications for regional soil and water resources in the United States and we recommend detailed investigation of the trade‐offs between land use and management options.

The potential for regional climate change arising from adoption of policies to increase production of biofuel feedstock is explored using a regional climate model. Two simulations are performed using the same atmospheric forcing data for the period 1979–2004, one with present‐day land use and monthly phenology and the other with land use specified from an agro‐economic prediction of energy crop distribution and monthly phenology consistent with this land use change. In Kansas and Oklahoma, where the agro‐economic model predicts 15‐30% conversion to switchgrass, the regional climate model simulates locally lower temperature (especially in spring), slightly higher relative humidity in spring and slightly lower relative humidity in summer, and summer depletion of soil moisture. This shows the potential for climate impacts of biofuel policies and raises the question of whether soil water depletion may limit biomass crop productivity in agricultural areas that are responsive to the policies. We recommend the use of agronomic models to evaluate the possibility that soil moisture depletion could reduce productivity of biomass crops in this region. We conclude, therefore, that agro‐economic and climate models should be used iteratively to examine an ensemble of agricultural land use and climate scenarios, thereby reducing the possibility of unforeseen consequences from rapid changes in agricultural production systems.

Water utilities are increasingly incorporating climate change in their planning activities. A water manager embarking on such a study is often confronted with a large range of climate model projections and the need to incorporate this new source of uncertain information into existing management and operations models. This article discusses these two faces of uncertainty and argues that an increased awareness of both the sources of this uncertainty in climate science and the means to plan for it can help utilities meet this challenge. More specifically, this article investigates the prospects for climate science to provide useful projections, discusses the strategies and challenges faced in translating those projections into effects on water utilities, and presents several decision-support planning methods that are being used or considered for use in several water utilities in the United States. Traditional water utility planning methods are based on an assumption of stationarity—that future hydrology will not significantly deviate from past hydrology. The current scientific understanding of climate change fundamentally challenges this stationarity assumption (Milly et al. 2008). In recognition of the limitations of stationarity, many water utilities are broadening their understanding of climate change and investigating decision-support planning methods to cope with conditions of pervasive climatic uncertainty. Uncertainty in projections of climate change can act as a barrier to the effective use of climate change information. Projections of future climate rely heavily on climate models (also known as general circulation models, or GCMs). On local and regional scales, and for variables such as precipitation and streamflow, the uncertainty in model projections is even greater than for global averages (Hawkins and Sutton 2011), and models may not even agree on the direction of change. Under such conditions, water management and planning must incorporate the new uncertainty of changing climatic conditions, a task for which traditional planning methods are poorly suited. Awater utility’s ability to effectively use this new uncertain scientific information may require adjustments to their management and planning processes, or even the adoption of new approaches. In an important sense, the water resources management community has taken a leadership role in understanding and incorporating climate change into their management, operations, and planning. Much of this effort has occurred at a utility scale, i.e., individual utilities working internally or with an academic and/or consultant partner to investigate the significance of climate change for the resources they manage [East Bay Municipal Utility District (EBMUD) 2009; New York City Department of Environmental Protection (NYCDEP) 2008; Palmer 2007; Palmer and Hahn 2002]. Water resources trade organizations such as the Water Research Foundation (WaterRF), the WateReuse Foundation, and the Water Environment Research Foundation have funded a number of studies investigating the effects of climate change on their member utilities (Miller and Yates 2006; Stratus Consulting and MWH Global 2009; WRF 2010). More recently, some of the largest metropolitan water agencies in the United States formed the Water Utility Climate Alliance (WUCA) to pursue their common objective of understanding and incorporating climate change information into their management, operations, and planning. Water utilities routinely examine the vulnerability of their systems to uncertain factors such as massive supply disruptions, infrastructure failure, potential regulatory requirements, technological changes, and even terrorist attacks. Although climate change may be thought of as simply one more uncertain variable that must be taken into account in water resources management, successfully incorporating this new uncertain information has not proven to be easy, in part because of the assumption of stationary hydrology in existing planning methods. In this article we explore the state of knowledge and practice in several large water utilities in the United States by synthesizing, updating, and greatly condensing the information that the authors developed for four recent studies by the WUCA and the U.S. Environmental Protection Agency (EPA). The first WUCA white paper (Barsugli et al. 2009) focuses on the state of climate modeling, on understanding water utility informational needs and desires, and on assessing which investments in climate science or modeling may yield usability improvements from the perspective of water utilities. The second WUCA white paper (Means et al. 2010) describes five decision-support planning methods that consider multiple future conditions and outcomes to incorporate greater uncertainties into the water planning process.