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The intensity and frequency variability of cyclones in the North Indian Ocean (NIO) have been amplified over the last few decades. The number of very severe cyclonic storms (VSCSs) over the North Indian Ocean has increased over recent decades. "Phailin", an extreme severe cyclonic storm (ESCS), occurred during 8-13 October 2013 over the Bay of Bengal and made landfall near the Gopalpur coast of Odisha at 12 UTC on 12 October. It caused severe damage here, as well as in the coastal Odisha, Andhra Pradesh, and adjoining regions due to strong wind gusts (~115 knot/h), heavy precipitation, and devastating storm surges. The fidelity of the WRF model in simulating the track and intensity of tropical cyclones depends on different cloud microphysical parameterization schemes. Thus, four sensitivity simulations were conducted for Phailin using double-moment and single-moment microphysical (MP) parameterization schemes. The experiments were conducted to quantify and characterize the performance of such MP schemes for Phailin. The simulations were performed by the advanced weather research and forecasting (WRF-ARW) model. The model has two interactive domains covering the entire Bay of Bengal and adjoining coastal Odisha on 25 km and 8.333 km resolutions. Milbrandt-Yau (MY) double-moment and WRF single-moment microphysical schemes, with 6, 5, and 3 classes of hydrometeors, i.e., WSM6, WSM5, and WSM3, were used for the simulation. Experiments for Phailin were conducted for 126 h, starting from 00 UTC 8 October to 06 UTC 13 October 2013. It was found that the track, intensity, and structure of Phailin are highly sensitive to the different microphysical parameterization schemes. Further, the precipitation and cloud distribution were studied during the ESCS stage of Phailin. The microphysics schemes (MY, WSM3, WSM5, WSM6), along with Grell-Devenyi ensemble convection scheme predicted landfall of Phailin over the Odisha coast with significant track errors. Supply of moisture remains a more crucial component than SST and wind shear for rapid intensification of the Phailin 12 h before landfall over the Bay of Bengal. Finally, the comparison of cyclone formation between two decades 2001-2010 and 2011-2020 over the Bay of Bengal inferred that the increased numbers of VSCS are attributed to the supply of abundant moisture at low levels in the recent decade 2011-2020. [ABSTRACT FROM AUTHOR]

Climate change and increased urban demand can significantly stress water supply systems, emphasizing the importance of reallocating reservoir storage for the designed uses. Most studies on climate change assessment have analyzed arid region reservoirs due to high interannual variability in streamflows. This study focuses on a within-year reservoir system, Lake Jordan in North Carolina, from a temperate region that has been experiencing rapid growth since the 1990s. Given the interest in utilizing climate change projections for planning purposes, the current operational policies are evaluated, and revised rules for operating the within-year system over 30 year period (2012-2041) are suggested. Downscaled general circulation model (GCM) projections are used to implement the soil and water assessment tool (SWAT) model for the Upper Cape Fear River basin to estimate changes in mean monthly streamflows during 2012-2041 at Lake Jordan. Projected monthly streamflows from four GCMs indicate wet winter conditions and increased interannual variability. The authors forced the reservoir model with multiple streamflow realizations that preserve the projected changes in monthly streamflow using a stochastic scheme. The within-year reservoir system performance was evaluated under stationary climate, climate change under existing and projected water demands, and by investigating interventions to ensure the design reliability under increased demands. These results indicate that the changes in the reliability due to increased urban demands are small because initial reservoir storage ensure the demand for multiple seasons. However, increases in the urban demand and streamflow variability tend to decrease the reservoir resiliency, forcing the within-year reservoir to behave like an over-year system. This could result in increased period of proactive measures such as restrictions and necessitates periodical reevaluation of drought management plans for better managing existing systems. [ABSTRACT FROM AUTHOR]

Despite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infiltration Capacity model is used over the Apalachicola River at Chattahoochee in the southeastern United States. The model is forced with a combination of daily precipitation forcings (temporally disaggregated observed precipitation, spatially downscaled and temporally disaggregated observed precipitation, ESP, ECHAM4.5 forecasts, and observed) and IHCs [simulated and climatological ensemble reverse ESP (RESP)] but with observed air temperature and wind speed at ⅛° resolution. Then, errors in forecasting monthly streamflow at up to a 3-month lead time are decomposed by comparing the forecasted streamflow to simulated streamflow under observed forcings. Results indicate that the errors due to temporal disaggregation are much higher than the spatial downscaling errors. During winter and early spring, the increasing order of errors at a 1-month lead time is spatial downscaling, model, temporal disaggregation, RESP, large-scale precipitation forecasts, and ESP. [ABSTRACT FROM AUTHOR]

Abstract: The Great Lakes are an important source of fresh water, recreation resource and transportation corridor for the Midwestern United States and Canada. The timing and quantity of fresh water inputs and how those may change under projections of future climate change are important for understanding how conditions, including river flows, and lake levels, within the region may be affected. Water quality and the density and diversity of in-stream habitats are responsive to changes in the distribution of daily streamflow, something not typically included in studies of climate change impacts. Projections of precipitation and air temperature changes in the four states surrounding Lake Michigan from the IPCC AR4 were downscaled and bias-corrected before being used to drive a large-scale hydrology model and produce maps of surface runoff and baseflow. These were then routed along drainage networks for regional rivers, and hydrologic metrics describing aspects of the distribution of daily flows important for hydrology and in-stream ecology were computed. The impact of regional climate change projections on early- (water years 2010–2039) and mid-century (water years 2040–2069) streamflow was highly variable; however, by the late-century period (water years 2070–2099) annual streamflow was found to have increased in all rivers. Seasonally, winter and spring flows increased significantly by the late-century period, but summer flows become more variable with a decrease in low-flows and an increase in peak-flows. The number of days with flows above the annual mean-flow (T Qmean) decreased in summer, but flashiness (R-B Index) increased. [Copyright &y& Elsevier]