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Water resources managers must increasingly consider climate change implications of, whether the concern is floods, droughts, reservoir management, or reliably supplying consumers. Hydrologic and water quality modeling of future climate scenarios requires understanding global climate models (GCMs), emission scenarios and downscaling GCM output, since GCMs generate climate predictions at a resolution too coarse for watershed modeling. Here we present theoretical considerations needed to understand the various downscaling methods. Since most watershed modelers will not be performing independent downscaling, given the resource and time requirements needed, we also present a practical workflow for selecting downscaled datasets. Even given the availability of a number of downscaled datasets, a number of decisions are needed regarding downscaling approach (statistical vs. dynamic), GCMs to consider, options, climate statistics to consider for the selection of model(s) that best predict the historical period, and the relative importance of different climate statistics. Available dynamically-downscaled datasets are more limited in GCMs and time periods considered, but the watershed modeler should consider the approach that best matches the historical observations. We critically assess the existing downscaling approaches and then provide practical considerations (which scenarios and GCMs have been downscaled? What are some of the limitations of these databases? What are the steps to selecting a downscaling approach?) Many of these practical questions have not been addressed in previous reviews. While there is no “best approach” that will work for every watershed, having a systematic approach for selecting the multiple options can serve to make an informed and supportable decision.

Global changes, including climate and land use changes, can result in significant impact to water resources. Planning for these changes requires making projections, even in the face of considerable uncertainties, to make informed management and policy decisions. A number of climate change scenarios and projections at global and regional levels are available that can be used to predict the likely range of outcomes. However, there is a need to translate these projections into potential implications for hydrology and water quality. Since there are dozens of hydrologic models, there is a need to evaluate them critically and to develop guidance regarding selecting the appropriate model for a given objective. We conducted a review of 21 different models commonly used for modeling hydrology (8), water quality (6) or both (7) at the watershed scale. Six of the models are strictly water quality models that depend on a separate model or observed data for hydrology. Seven additional models are useful for estimating hydrology and water quality simultaneously. The models were then evaluated based on ten different criteria, including functionality, scope, ability to model extreme events, data requirements, availability, and technical support, among others. The models were ranked Low, Medium or High in each of the criteria. The results indicate that three hydrologic models, MIKE-SHE, HEC-HMS, and MODHMS, as well as two full hydrology and water quality models, SWAT and WARMF, stand out in terms of functionality, availability, applicability to a wide range of watersheds and scales, ease of implementation, and availability of support. Modelers should carefully select the best model for their application, in part guided by the criteria discussed herein.

This study identifies and summarizes potential risks to operations, regulatory compliance, supply chains, and infrastructure of the electric power industry from changing surface water conditions resulting from global climate change. The results help inform companies/utilities seeking to incorporate climate change risk in their planning and decision‐making processes by ranking risk severity and likelihood of occurrence on both a regional basis and by risk receptor. The assessment includes identification of potential risks to: (1) thermal generating, (2) hydroelectric, (3) land‐based renewable generating, and (4) transmission and distribution assets. These risks may result from such projected changes as reduced water availability (e.g., for hydroelectric or once‐through cooling), increased water temperatures (e.g., decrease in cooling efficiency, inability to meet discharge permit conditions), increased flood severity (e.g., increased streambank erosion and/or damage to river‐adjacent infrastructure), and decreased water quality (e.g., from increased transport of sediment and dissolved solids). The potential risks identified from this qualitative risk‐assessment are documented in a graphical format depicting both severity and likelihood. This approach allows for comparison of risks across a portfolio and for future prioritization of adaptation strategies. A total of 32 risks were identified in the study, including nine risks to infrastructure, six risks to operations, four risks to supply chain, and 13 environmental/regulatory risks. [ABSTRACT FROM AUTHOR]

Objectives: This study compares the effects of an integrated yoga program with brief supportive therapy on distressful symptoms in breast cancer outpatients undergoing adjuvant radiotherapy. Materials and Methods: Eighty-eight stage II and III breast cancer outpatients were randomly assigned to receive yoga (n = 44) or brief supportive therapy (n = 44) prior to their radiotherapy treatment. Intervention consisted of yoga sessions lasting 60 min daily while the control group was imparted supportive therapy once in 10 days during the course of their adjuvant radiotherapy. Assessments included Rotterdam Symptom Check List and European Organization for Research in the Treatment of Cancer-Quality of Life (EORTC QoL C30) symptom scale. Assessments were done at baseline and after 6 weeks of radiotherapy treatment. Results: A GLM repeated-measures ANOVA showed a significant decrease in psychological distress (P = 0.01), fatigue (P = 0.007), insomnia (P = 0.001), and appetite loss (P = 0.002) over time in the yoga group as compared to controls. There was significant improvement in the activity level (P = 0.02) in the yoga group as compared to controls. There was a significant positive correlation between physical and psychological distress and fatigue, nausea and vomiting, pain, dyspnea, insomnia, appetite loss, and constipation. There was a significant negative correlation between the activity level and fatigue, nausea and vomiting, pain, dyspnea, insomnia, and appetite loss. Conclusion: The results suggest beneficial effects with yoga intervention in managing cancer- and treatment-related symptoms in breast cancer patients.

Byline: Raghavendra. Rao, Nagaratna. Raghuram, Hongasandra. Nagendra, Gopinath. Kodaganur, Ramesh. Bilimagga, H. Shashidhara, Ravi. Diwakar, Shekhar. Patil, Nalini. Rao Aims: The aim of this study is to compare the effects [...]

Effects of an integrated yoga program in modulating perceived stress levels, anxiety, as well as depression levels and radiation-induced DNA damage were studied in 68 breast cancer patients undergoing radiotherapy. [...]