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Derived from realistic global warming scenarios, long‐term projections of spatial patterns in precipitation and temperature in hydrology and climatology can serve to evaluate climate risk, explore sources of renewable energies and allow local‐scale data to inform decisions regarding agricultural, ecosystem, social, recreational and economic activities. Under the CORDEX‐EA project, the precipitation and temperature projections (2020–2045) for the economically and socially important region of Northwestern China were derived from high‐resolution regional climate model (RCM) simulations for RCP 4.5 and 8.5 scenarios, and compared against a historical period or baseline of 1980–2005. Drawing data from four key RCMs [Weather Research and Forecasting (WRF); Regional Spectral Model (RSM); Regional Climate Model version 4.0 (RegCM4); Mesoscale Model version 5 (MM5)], reliable local‐scale projections were generated by applying suitable bias correction that accords with the multivariate bias correction (MBC) approach. To validate this approach and then evaluate climate change impacts, the adjusted precipitation and temperature estimated from bias‐corrected models for the historical period were compared to the observed data. The results showed that the simulated spatiotemporal distribution of multiyear average precipitation and temperature appear to fit relatively well with the observations, however, the wet‐cold and dry‐warm climate‐related biases were still evident for the high altitude regions. Bias‐corrected future projections of RCMs indicated spatially averaged annual precipitation is expected to rise by about 23.6 and 35.3 mm under the RCP 4.5 and 8.5 scenarios, respectively, while spatially averaged annual temperature is expected to rise by about 1.95 and 1.10°C. Precipitation is projected to increase in all seasons, albeit, more so in the cold season (i.e., boreal winter and spring) than the warm season (i.e., boreal summer and autumn). The annual increase is expected to be about 56.7% under the RCP 4.5 scenario, compared to 67.6% under the RCP 8.5 scenario. The changes of mean temperature in winter are expected to be significant, −37.8% under both RCP scenarios. For spring, the mean temperature will rise by 23.1% (28.9%) under the RCP 4.5 (8.5) scenario. A MBC approach was found to be effective in yielding reliable projected changes in precipitation and temperature variables. The proposed approach has important implications for climatological studies and evaluation of climate change impacts on localized regions, not only in China, but also in other similar areas of the world, where decisions for managing climate risk must be implemented by policy makers, government, industry and stakeholders. [ABSTRACT FROM AUTHOR]

Bangladesh, located in the Bay of Bengal, is a developing nation that is prone to devastating flood events that cause loss of lives and several other forms of humanatarian disasters. Identifying, developing and validating new scientific techniques that can be used for flood-risk warning and regular monitoring, including flood risk mitigation and adaptation, can help reduce the catastrophic effect of floods in Bangladesh and other developing countries. In this study, a daily Flood Index (IF) based on daily effective precipitation (PE) is utilized for quantifying floods for two cites in Bangladesh: Dhaka (23.7° N, 90.38° E) and Bogra (24.51° N; 89.22° E), where flooding is a common phenomenon. Based on total daily precipitation (P) data, in this study the IFis determined by calculating the PE using an exponentially decaying time-reduction function, which considers the gradual depletion of water resources over time. PE per day is normalized and compared with the average and standard deviation of yearly maximums, within the considered hydrological period. The start of a flood event is identified for IF ≥ 0. Subsequently, the IF severity ( I F acc ) as consecutively positive IF, duration (DF) as number of days with positive IF, and peak danger ( I F max ) as maximum IF are estimated for identified flood events, using the run-sum theory. The analysis carried out has accurately identified historical flood events at Dhaka station in 1984 and 2007 as having the largest I F acc value (i.e., greatest severity). Flood severity ( I F acc ) and peak danger ( I F max ) parameters have been verified by accumulated precipitation corresponding to the same period. For Bogra station, the 1998 and 1979 events were found to be the most intense. Seasonality analysis of the flood index shows most floods happened during the summer monsoon, although for the Dhaka station, flood events occurred between early June and November, and in Bogra, from late June to October. A rational study of effective precipitation provides a useful severity and danger indicator. The results indicate the practicality of daily IF for flood-risk assessments where the severity, peak danger, and duration need to be considered as a unified index for flood-risk monitoring. The proposed daily flood index can be useful for hydrologists for the purpose of daily operational flood monitoring in high flood-risk nations. [ABSTRACT FROM AUTHOR]

The Rocky Mountains in North America are comprised of headwater snow catchments that provide sustained seasonal flow downstream. Changes in streamflow over the last half century in these basins may be associated with changing climate with increased temperature and variable precipitation, shifting seasonal hydrology. We investigated potential changes in future hydrology in a Rocky Mountain headwater catchment by simulating water budgets of the Athabasca River located in Jasper National Park, Canada. Potential hydrologic changes were predicted using a calibrated version of the Soil and Water Assessment Tool (SWAT). Future discharge and other parts of the catchment water budget were projected based on the global circulation model (GCM) derived from the Special Report on Emission Scenarios (SRES) for the latter part of the century (2081-2099). A projected decrease in future precipitation resulted in reduced mean annual streamflow, by up to 86%, compared to the baseline period for the catchment. Projected summer streamflow decreased from 58 to 39%. Streamflow increased from 13 to 26% during the spring, dampening the dominance of summer peak-flow hydrology. Colder winters for the future scenarios increase the overall proportion of precipitation as winter snowfall. However, dramatically lower precipitation estimated for this basin will drive water limits for the future. [ABSTRACT FROM AUTHOR]

Summary: In this study, a new method of stand-alone short-term spring snowmelt river flood forecasting was developed based on wavelet and cross-wavelet analysis. Wavelet and cross-wavelet analysis were used to decompose flow and meteorological time series data and to develop wavelet based constituent components which were then used to forecast floods 1, 2, and 6 days ahead. The newly developed wavelet forecasting method (WT) was compared to multiple linear regression analysis (MLR), autoregressive integrated moving average analysis (ARIMA), and artificial neural network analysis (ANN) for forecasting daily stream flows with lead-times equal to 1, 2, and 6 days. This comparison was done using data from the Rideau River watershed in Ontario, Canada. Numerical analysis was performed on daily maximum stream flow data from the Rideau River station and on meteorological data (rainfall, snowfall, and snow on ground) from the Ottawa Airport weather station. Data from 1970 to 1997 were used to train the models while data from 1998 to 2001 were used to test the models. The most significant finding of this research was that it was demonstrated that the proposed wavelet based forecasting method can be used with great accuracy as a stand-alone forecasting method for 1 and 2 days lead-time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year-to-year, and that there is a relatively stable phase shift between the flow and meteorological time series. The best forecasting model for 1 day lead-time was a wavelet analysis model. In testing, it had the lowest RMSE value (13.8229), the highest R 2 value (0.9753), and the highest EI value (0.9744). The best forecasting model for 2 days lead-time was also a wavelet analysis model. In testing, it had the lowest RMSE value (31.7985), the highest R 2 value (0.8461), and the second highest EI value (0.8410). It was also shown that the proposed wavelet based forecasting method is not particularly accurate for longer lead-time forecasting such as 6 days, with the ANN method providing more accurate results. The best forecasting model for 6 days lead-time was an ANN model, with the wavelet model not performing as well. In testing, the wavelet model had an RMSE of 57.6917, an R 2 of 0.4835, and an EI of 0.4366. [Copyright &y& Elsevier]

A water resources index based on weight-accumulated precipitation over the passage of time in heavy rainfall events is used in this study for monitoring flood risk and peak danger, as well as to develop flood warnings. In this research, an hourly water resources index (WRIhr) based on rainfall accumulations over the passage of time is proposed. WRIhr is able to monitor flood risk by taking into account the hourly effective precipitation that accumulates the precipitation (Phr) of both current and antecedent hours, while the contributions from the preceding hours is subjected to a time-dependent reduction function that addresses the depletion of water volume by various hydrological processes (e.g., discharge, runoff, evapotranspiration). By converting rainfall into a water resources index (WRI), the hourly precipitation over a 24-h period is redistributed to formulate a long-term water resources index (WRIhr-L) that monitors flood status based on long-term (more than 1 year) fluctuations in Phr and a short-term water resources index (WRID-hr-S) that considers shorter (D = 24-148 hourly) accumulations of the Phr data. WRI was assessed for its potential in flood monitoring at two hydrologically diverse sites: Dobong (South Korea; August 1998) and Brisbane (Australia; December 2010-January 2011), and its applicability was verified using river water level (H) measurements at hydrological stations. The power spectrum density and spectral coherence of hourly rainfall, river water level, and the corresponding WRI showed good agreements, as did the low and high frequency wavelet components using the discrete wavelet transform algorithm. Importantly, WRI24-hr-S computed over 24 hourly accumulation periods was able to mimic the risk of short-term (flash-style) floods caused by concentrated rainfall, whereas WRIhr-L was more useful for flood risk assessment caused by an event over a long-term period. Dynamical changes in H were closely in-phase with the patterns of change noted in the WRIhr over the respective temporal scale. We conclude that the proposed WRI was able to replicate the flood evolution over the passage of time and, therefore, could possibly aid in the early warning of water-related disasters, demonstrating its practicality for continuous monitoring of the flood risk when a sustained period of rainfall event is observed. [ABSTRACT FROM AUTHOR]

A series of hydroclimatic teleconnection patterns were identified between variations in either Atlantic or Pacific oceanic indices with precipitation and discharge anomalies in the northeastern United States. We hypothesized that temporal annual or seasonal changes in discharge could be explained by variations in extreme phases of the Atlantic Multi-decadal Oscillation (AMO index, SST: Sea Surface Temperature anomalies) and the North Atlantic Oscillation (NAO index, SLP: Sea-Level Pressure anomalies) up to three seasons in advance. The Merrimack River watershed, the fourth largest basin in New England, with a drainage area of 13,000 km 2 , is a compelling study site because it not only provides an opportunity to investigate the teleconnection between hydrologic variables and large-scale climate circulation patterns, but also how those patterns may become obscured by anthropogenic disturbances such as river regulation or urban development. We considered precipitation and discharge data of 21 gauging stations within the Merrimack River watershed, including the Hubbard Brook Experimental Forest (HBEF), NH, with a median record length of 55 years beginning as early as 1904. The discharge anomalies were statistically significant (p-value ≤ 0.2) between extreme positive and negative phases of AMO (1857–2011) and NAO (1900–2011) and revealed the potential teleconnectivity of climate circulation patterns with discharge. Annual and seasonal correlations of discharge were examined with the extreme phases of AMO and NAO at zero-, one-, or two- year/season lags (total of 30 scenarios). When AMO was greater than 0.2, the strongest correlations of AMO and NAO with discharge were observed at headwater catchments. This correlation weakened downstream towards larger regulated and/or developed sub-basins. We introduced a simple approach for near-term prediction of drought and flooding events. An exponential decay function was regressed through the historic occurrence of the relative frequency of wet, average, and dry discharge conditions with regards to the extreme phases of AMO and NAO. While the function was decaying, the tail asymptotically merged into and stabilized at the theoretical probability of the event. As the basin scale increased, the probability of wet, average, and dry discharge conditions decreased. The Merrimack River watershed will most likely experience greater than average discharge as its extreme condition, therefore development should be avoided on flood plains. Furthermore, the current reservoir storage capacity in the Merrimack should be improved in order to accommodate excess water input and minimize flood damage. Future research should target changes in the magnitude and timing of high discharge events in order to develop adaptation strategies for aging hydraulic infrastructure in the region. [ABSTRACT FROM AUTHOR]

Accurate predictions of drought events to plan and manage the adverse effects of drought on agriculture and the environment requires tools that precisely predict standardized drought metrics. Improving on the World Meteorological Organization approved Standardized Precipitation Index (SPI), the multi-scalar Standardized Precipitation and Evapotranspiration Index ( SPE I), a variant of the SPI, is a relatively recent drought index, which takes into account the impacts of temperature change on overall dryness, along with precipitation and evapotranspiration effects. In this paper, an extreme learning machine (ELM) model was applied to predict SPE I in a drought-prone region in eastern Australia, and the quality of the model's performance was compared to that of a multiple linear regression (MLR), an artificial neural network (ANN), and a least support vector regression (LSSVR) model. The SPE I data were derived from climatic variables recorded at six weather stations between January 1915 and December 2012. Model performance was evaluated by means of the normalized root mean square error (NRMSE), normalized mean absolute error (NMAE), coefficients of determination (r 2 ), and the Nash-Sutcliffe efficiency coefficient (NASH) in the testing period. Results showed that the ELM and ANN models outperformed the MLR and LSSVR models, and all four models revealed a greater predictive accuracy for the 12-month compared to the 3-month SPE I predictions. For the 12-month SPE I predictions, optimal models had r 2 that ranged from 0.668 for the LSSVR model (Station 6) to 0.894 for the ANN model (Station 4). The good agreement between observed and predicted SPE I at different locations within the study region indicated the potential of the developed models to contribute to a more thorough understanding of potential future drought-risks in eastern Australia, and their applicability to drought assessments over multiple timescales. The models and findings have useful implications for water resources assessment in drought-prone regions. [ABSTRACT FROM AUTHOR]