Description:    Understanding the characteristics of historical droughts will benefit water resource managers because it will reveal the possible impacts that future changes in climate may have on drought, and subsequently, the availability of water resources. The goal of this study was to reconstruct historical drought occurrences and assess future drought risk for the drought-prone Blue River Basin in Oklahoma, under a likely changing climate using three types of drought indices, i.e., Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI) and Standardized Runoff Index (SRI). No similar research has been conducted in this region previously. Monthly precipitation and temperature data from the observational period 1950–1999 and over the projection period 2010–2099 from 16 statistically downscaled Global Climate Models (GCM) were used to compute the duration, severity, and extent of meteorological droughts. Additionally, soil moisture, evapotranspiration (ET), and runoff data from the well-calibrated Thornthwaite Monthly Water Balance Model were used to examine drought from a hydrological perspective. The results show that the three indices captured the historical droughts for the past 50 years and suggest that more severe droughts of wider extent are very likely to occur over the next 90 years in the Blue River Basin, especially in the later part of the 21st century. In fact, all three indices display lower minimum values than those ever recorded in the past 50 years. This study also found that SRI and SPI (PDSI) had a correlation coefficient of 0.81 (0.78) with a 2-month (no appreciable) lag time over the 1950–2099 time period across the basin. There was relatively lower correlation between SPI and PDSI over the same period. Although this study recommends that PDSI and SRI are the most suitable indices for assessing future drought risks under an increasingly warmer climate, more drought indices from ecological and socioeconomic perspectives should be investigated and compared to provide a complete picture of drought and its potential impacts on the dynamically coupled nature-human system. Content Type Journal Article Pages 1-19 DOI 10.1007/s11269-012-0044-y Authors Lu Liu, School of Civil Engineering and Environmental Science, University of Oklahoma, 202 W. Boyd St., Room 334, Norman, OK 73019-1024, USA Yang Hong, School of Civil Engineering and Environmental Science, University of Oklahoma, 202 W. Boyd St., Room 334, Norman, OK 73019-1024, USA Christopher N. Bednarczyk, Research Experiences for Undergraduates Program, National Weather Center, University of Oklahoma, Norman, OK, USA Bin Yong, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, 210098 China Mark A. Shafer, Southern Climate Impacts Planning Program, Oklahoma Climatological Survey, University of Oklahoma, 120 David L. Boren Blvd., Suite 2900, Norman, OK 73072, USA Rachel Riley, Southern Climate Impacts Planning Program, Oklahoma Climatological Survey, University of Oklahoma, 120 David L. Boren Blvd., Suite 2900, Norman, OK 73072, USA James E. Hocker, Southern Climate Impacts Planning Program, Oklahoma Climatological Survey, University of Oklahoma, 120 David L. Boren Blvd., Suite 2900, Norman, OK 73072, USA Journal Water Resources Management Online ISSN 1573-1650 Print ISSN 0920-4741

Description:    Qingjiang River, the second largest tributary of the Yangtze River in Hubei Province, has taken on the important tasks for power generation and flood control in Hubei Province. The Qingjiang River watershed has a subtropical monsoon climate and, as a result, has dramatic diversity in its water resources. Recently, global warming and climate change have seriously affected the Qingjiang watershed’s integrated water resources management. In this article, general circulation model (GCM) and watershed hydrological models were applied to analyze the impacts of climate change on future runoff of Qingjiang Watershed. To couple the scale difference between GCM and watershed hydrological models, a statistical downscaling method based on the smooth support vector machine was used to downscale the GCM’s large-scale output. With the downscaled precipitation and evaporation, the Xin-anjiang hydrological model and HBV model were applied to predict the future runoff of Qingjiang Watershed under A2 and B2 scenarios. The preformance of the one-way coupling approach in simulating the hydrological impact of climate change in the Qingjiang watershed is evaluated, and the change trend of the future runoff of Qingjiang Watershed under the impacts of climate change is presented and discussed. Content Type Journal Article Category Original paper Pages 1-12 DOI 10.1007/s00477-011-0524-2 Authors Hua Chen, State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072 China Tiantian Xiang, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, 430072 China Xing Zhou, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, 430072 China Chong-Yu Xu, State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072 China Journal Stochastic Environmental Research and Risk Assessment Online ISSN 1436-3259 Print ISSN 1436-3240