Assessing Potential Winter Weather Response to Climate Change and Implications for Tourism in The U.S. Great Lakes and Midwest

Study Region: Eight U.S. states bordering the North American Laurentian Great Lakes. Study Focus: Variable Infiltration Capacity (VIC) model simulations, based on data from an en- semble of atmospheric-ocean general circulation models (AOGCMs) used for the Intergovernmental Panel on Climate Change\u27s (IPCC\u27s) Fifth Assessment Report (AR5), were used to quantify potential climate change impacts on winter weather and hydrology in the study re- gion and understand implications for its tourism sector. New Hydrologic Insights for the Region: By the 2080s, climate change could result in winters that are shorter by over a month, reductions of over a month in days with snow depths required for many kinds of winter recreation, declines in average holiday snow depths of 50 percent or more, and reductions in the percent area of the study region that would be considered viable for winter tourism from about 22 percent to 0.3 percent. Days with temperatures suitable for artificial snowmaking decline to less than a month annually, making it potentially less feasible as an adaptation strategy. All of the region\u27s current ski resorts are operating in areas that will become non-viable for winter tourism businesses under a high emissions scenario. Given the economic importance of the winter tourism industry in the study region, businesses and communities should consider climate change and potential adaptation strategies in their future planning and overall decision-making

Numerical modeling of thermal bar and stratification pattern in Lake Ontario using the EFDC model

Thermal bar is an important phenomenon in large, temperate lakes like Lake Ontario. Spring thermal bar formation reduces horizontal mixing, which in turn, inhibits the exchange of nutrients. Evolution of the spring thermal bar through Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers, and lake bathymetry. The simulation is performed from April to July, 2011; on a 2-km grid. The numerical model has been calibrated by specifying: appropriate initial temperature and solar radiation attenuation coefficients. The existing evaporation algorithm in EFDC is updated to modified mass transfer approach to ensure correct simulation of evaporation rate and latent heatflux. Reasonable values for mixing coefficients are specified based on sensitivity analyses. The model simulates overall surface temperature profiles well (RMSEs between 1-2°C). The vertical temperature profiles during the lake mixed phase are captured well (RMSEs < 0.5°C), indicating that the model sufficiently replicates the thermal bar evolution process. An update of vertical mixing coefficients is under investigation to improve the summer thermal stratification pattern. Keywords: Hydrodynamics, Thermal BAR, Lake Ontario, GIS

Making the most of data:An information selection and assessment framework to improve water systems operations

Advances in Environmental monitoring systems are making a wide range of data available at increasingly higher temporal and spatial resolution. This creates an opportunity to enhance real-time understanding of water systems conditions and to improve prediction of their future evolution, ultimately increasing our ability to make better decisions. Yet, many water systems are still operated using very simple information systems, typically based on simple statistical analysis and the operator’s experience. In this work, we propose a framework to automatically select the most valuable information to inform water systems operations supported by quantitative metrics to operationally and economically assess the value of this information. The Hoa Binh reservoir in Vietnam is used to demonstrate the proposed framework in a multiobjective context, accounting for hydropower production and flood control. First, we quantify the expected value of perfect information, meaning the potential space for improvement under the assumption of exact knowledge of the future system conditions. Second, we automatically select the most valuable information that could be actually used to improve the Hoa Binh operations. Finally, we assess the economic value of sample information on the basis of the resulting policy performance. Results show that our framework successfully select information to enhance the performance of the operating policies with respect to both the competing objectives, attaining a 40% improvement close to the target trade-off selected as potentially good compromise between hydropower production and flood control

Preparing for Climatic Change: The Water, Salmon, and Forests of the Pacific Northwest

The impacts of year-to-year and decade-to-decade climatic variations on some of the Pacific Northwest’s key natural resources can be quantified to estimate sensitivity to regional climatic changes expected as part of anthropogenic global climatic change. Warmer, drier years, often associated with El Niño events and/or the warm phase of the Pacific Decadal Oscillation, tend to be associated with below-average snowpack, streamflow, and flood risk, below-average salmon survival, below-average forest growth, and above-average risk of forest fire. During the 20th century, the region experienced a warming of 0.8 ◦C. Using output from eight climate models, we project a further warming of 0.5–2.5 ◦C (central estimate 1.5 ◦C) by the 2020s, 1.5–3.2 ◦C (2.3◦C) by the 2040s, and an increase in precipitation except in summer. The foremost impact of a warming climate will be the reduction of regional snowpack, which presently supplies water for ecosystems and human uses during the dry summers. Our understanding of past climate also illustrates the responses of human management systems to climatic stresses, and suggests that a warming of the rate projected would pose significant challenges to the management of natural resources. Resource managers and planners currently have few plans for adapting to or mitigating the ecological and economic effects of climatic change

Preparing for Climatic Change: The Water, Salmon, and Forests of the Pacific Northwest

The impacts of year-to-year and decade-to-decade climatic variations on some of the Pacific Northwest’s key natural resources can be quantified to estimate sensitivity to regional climatic changes expected as part of anthropogenic global climatic change. Warmer, drier years, often associated with El Niño events and/or the warm phase of the Pacific Decadal Oscillation, tend to be associated with below-average snowpack, streamflow, and flood risk, below-average salmon survival, below-average forest growth, and above-average risk of forest fire. During the 20th century, the region experienced a warming of 0.8 ◦C. Using output from eight climate models, we project a further warming of 0.5–2.5 ◦C (central estimate 1.5 ◦C) by the 2020s, 1.5–3.2 ◦C (2.3◦C) by the 2040s, and an increase in precipitation except in summer. The foremost impact of a warming climate will be the reduction of regional snowpack, which presently supplies water for ecosystems and human uses during the dry summers. Our understanding of past climate also illustrates the responses of human management systems to climatic stresses, and suggests that a warming of the rate projected would pose significant challenges to the management of natural resources. Resource managers and planners currently have few plans for adapting to or mitigating the ecological and economic effects of climatic change

Climate change and mountain water resources: overview and recommendations for research, management and policy

Mountains are essential sources of freshwater for our world, but their role in global water resources could well be significantly altered by climate change. How well do we understand these potential changes today, and what are implications for water resources management, climate change adaptation, and evolving water policy? To answer above questions, we have examined 11 case study regions with the goal of providing a global overview, identifying research gaps and formulating recommendations for research, management and policy. &lt;br&gt;&lt;br&gt; After setting the scene regarding water stress, water management capacity and scientific capacity in our case study regions, we examine the state of knowledge in water resources from a highland-lowland viewpoint, focusing on mountain areas on the one hand and the adjacent lowland areas on the other hand. Based on this review, research priorities are identified, including precipitation, snow water equivalent, soil parameters, evapotranspiration and sublimation, groundwater as well as enhanced warming and feedback mechanisms. In addition, the importance of environmental monitoring at high altitudes is highlighted. We then make recommendations how advancements in the management of mountain water resources under climate change could be achieved in the fields of research, water resources management and policy as well as through better interaction between these fields. &lt;br&gt;&lt;br&gt; We conclude that effective management of mountain water resources urgently requires more detailed regional studies and more reliable scenario projections, and that research on mountain water resources must become more integrative by linking relevant disciplines. In addition, the knowledge exchange between managers and researchers must be improved and oriented towards long-term continuous interaction

Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments

We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual forecast components to optimal, adaptive reservoir operation. The framework uses an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity (VIC) hydrology model. We determine the optimal sequence of daily release decisions using the Model Predictive Control (MPC) optimization scheme. We then assess the forecast value by comparing system performance based on the ESP forecasts with the performances based on climatology and perfect forecasts. We distinguish among the relative contributions of the seasonal component of the forecast versus the inter-annual component by evaluating system performance based on hybrid forecasts, which are designed to isolate the two contributions. As an illustration, we first apply the forecast-based adaptive management framework to a specific case study, i.e., Oroville Reservoir in California, and we then modify the characteristics of the reservoir and the demand to demonstrate the transferability of the findings to other reservoir systems. Results from numerical experiments show that, on average, the overall ESP value in informing reservoir operation is 35% less than the perfect forecast value and the inter-annual component of the ESP forecast contributes 20–60% of the total forecast value.</p

Soybean yield and crop stage response to planting date and cultivar maturity in Iowa, USA

Soybean [Glycine max (L.) Merr.] planting date and maturity group are important agronomic decisions. This study quantified how maturity group selection and later than optimal planting dates affected grain yield and crop development across Iowa, US. Field experiments were conducted in seven locations between 2014 and 2016. Cultivar maturities ranged from 2.2 to 3.7 MG and planting dates targeted for 20-day intervals from early May to early July. Soybean grain yield ranged from 0.27 to 7.54 Mg ha-1. Cultivar maturity had little to no effect on grain yield at 4 of 7 sites while planting date was significant at all sites (p\u3c0.001) and the planting date and cultivar maturity interaction was not significant. As planting date was delayed, the VE- R3 and R3-R7 periods were each shortened by up to 15-20 days. The shorter growing period resulted in less radiation and growing degree day accumulation. A exponential-plateau relationship between relative yield and GDD was evident for the VE-R3 phase, with a plateau at 700oC days. A linear relationship between yield and GDD was evident from R3-R7, suggesting greater yield with more accumulated GDD. The opposite relationships were found for photoperiod which had a linear relationship for the VE-R3 and curvilinear for the R3-R7 phases. These results showed that yield potential would be maximized by planting before 20 May. We concluded that planting earlier in the spring was a better management practice than maturity selection to maximize yield and the R3-R7 period duration was critical in determining potential yield