Description: Extreme flood estimates for dam safety are routinely obtained from hydrologic simulations driven by selected design storms. The temporal structure of such design storms can be obtained from Rainfall Mass Curves (RMCs), which are adimensionalized curves of the cumulative precipitation depth as a function of event duration. This paper assesses for the first time the spatialand temporal variability of observed RMCs for Switzerland, an Alpine region with complex topography. The relevance of the detected RMC variability for extreme flood estimation is illustrated based on an application to a high elevation catchment, the Mattmark dam catchment in the Swiss Alps. The obtained results underline that quantile RCMs represent a simple yet powerful tool to construct design storms for dam safety verification and that regional, seasonal and event-duration effects on RMCs are small enough to justify the use of a unique set of Swiss-wide quantile RMCs. The presented analysis could be refined in the future by explicitly accounting for orographic, convective or frontal precipitation events.

Description: The Three Gorges Reservoir (TGR) has brought great socio-economic benefits and has had huge effects on the aquatic environment. The large scale and diversity of the TGR result in the variations of the water quality among the mainstream and its tributaries. Comprehensive understanding of the water quality status is crucial for water management and regional development of the TGR. Monthly data of 8 water quality parameters, including potential of hydrogen, biochemical indexes and nutrients indexes, were collected from 14 sampling sites distributed in the Yangtze River and four tributaries. The temporal and spatial distributions of each water quality parameter were presented, and the underlying causes were disclosed. The cluster analysis (CA) and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) were adopted to analyze and assess the water quality statuses and trend. The results showed that most of the water quality parameters presented significant seasonal patterns due to the seasonality of hydrologic/hydraulic variables. Water quality status and pollution type varied among the mainstream and its tributaries, due to the spatial heterogeneity of geology, geomorphology and anthropogenic activities. NO 3 -N, TN and TP were identified as the key pollution indexes, presenting the enriched nutrients in the water body. A large proportion of NO 3 -N in the TN (over 80%) was linked to the abuse of chemical fertilizers. The water quality in the TGR cannot always reach natural or desirable levels at several of the sampling sites where development and urbanization are relatively high, such as those near the main urban area of Chongqing or the inflow section of the Wu River. This study is expected to have major implications for water quality analysis and assessment approaches and water environment protection and management at large scales.

Description: Due to an oversight, the author names were incorrectly captured in the original publication. The first and last names of the authors were inverted. The correct presentation of the author names is shown below.

Description: Reservoirs play a strategic role in the rapid monetary growth of the world by providing numerous benefits. However, the reduction in appropriate sites along with environmental and social apprehensions has resulted in curtailment of new reservoirs around the world in twenty-first century. There is a potential of benefits available from existing reservoirs which can be best capitalized through their optimized operation. Reservoirs Operation Optimization considering Sediment Evacuation (RESOOSE), recently developed model which combines multiple reservoirs operation and sediment evacuation with Genetic Algorithm based optimization module, has been used in the study. The objective of the study was to optimize the irrigation deficit through cascade reservoirs with consideration to hydropower, sediment evacuation and flood damages reduction benefits. The RESOOSE model was applied to optimize the irrigation deficits of Tarbela and Diamer Basha Reservoirs in Pakistan using developed objective function. The article computed and compared the benefits of optimized and existing rule curves. The hydropower benefits of 36.92 Billion Kw, sediment evacuation benefits of 21.534 Million m 3 and flood damages of 616.19 Million US$ due to existing rule curves were considered as minimum benefits for achieving the optimized rule curves to minimize irrigation deficits. The developed optimized rule curves reduced the irrigation shortages of case study reservoirs from 6.9 to 5.8 Billion m 3 (16% enhancement) annually as compared to existing rule curves. The optimized rule curves minimized the irrigation deficits by maintaining the existing benefits and without lowering the minimum operating levels of case study reservoirs. The study suggests change in existing rule curves of Tarbela and Diamer Basha Reservoirs due to less irrigation shortages. The RESOOSE model can be applied to other cascade reservoirs for optimizing the rule curves.

Description: In this paper, the development and evaluation of an entropy based hybrid data driven model coupled with input selection approach and wavelet transformation is investigated for long-term streamflow forecasting with 10 years lead time. To develop and test the models, data including 45 years of monthly streamflow time series from Taleghan basin, located in northwest of Tehran, are employed. For this purpose, first the performance of a maximum entropy forecasting model is evaluated. To boost the accuracy, an auto-correlation method with %95 confidence levels was carried out to determine the optimum order of the entropy model. Nevertheless, the basic entropy model, as expected, was only able to reach Nash-Sutcliffe efficiency (NSE) index of 0.35 during the test period. On the other hand, data driven models such as artificial neural networks (ANN) have shown to yield good accuracy in modeling complicated and nonlinear systems. Thus, to improve the performance of the maximum entropy model, an entropy-based hybrid model using evolutionary ANN (ENN) was proposed for further investigation. The proposed model with seasonality index substantially improved the test NSE to 0.51 and provided more accurate results than the basic entropy model. Moreover, when wavelet transform was applied to preprocess the input data, the model shows a slight improvement (NSE = 0.54).

Description: The Gravity Recovery and Climate Experiment (GRACE) satellites have been used in drought/flood monitoring by observing terrestrial water storage (TWS) change. Meteorological drought indicators or other identified disaster information were usually adopted in association with GRACE-observed changes in TWS for the determination of the occurrence and severity of droughts/floods. Inter-comparisons of dry conditions based on TWS change on a global scale, however, were very difficult because TWS anomalies are not comparable for different hydro-climatic regions. In this paper, we established a global dataset of GRACE-based dimensionless drought index, the Total Storage Deficit Index (TSDI), which is spatially comparable and capable of independently examining the characteristics of dry/wet spells globally. The globally mapped GRACE-based TSDI was examined with some reported extreme hydrologic events, which suggested that the results were fairly consistent with documented drought/flood disaster information. Moreover, comparisons of the GRACE-based TSDI with other frequently used drought indicators, such as the Standardized Precipitation Index (SPI), the Palmer Drought Severity Index (PDSI), and the Palmer Hydrological Drought Index (PHDI), suggested that the TSDI was significantly correlated with the SPI at three different time scales, the PDSI, and the PHDI over most parts of the global surface. The longer the time scale of the selected SPI, the stronger the correlation tended to be with the TSDI. Moreover, the correlation of the TSDI with the PHDI was higher than that with the PDSI over almost the whole global surface. With regard to its performance, this study suggested that the TSDI derived from GRACE-based TWS could be a useful dimensionless index for global and regional hydrological drought monitoring, especially for areas where meteo-hydrological observations are insufficient or human activities are intensive.

Description: Seasonal inflow variability, climate non-stationarity and climate change are matters of concern for water system planning and management. This study presents optimization methods for long-term planning of water systems in the context of a non-stationary climate with two levels of inflow variability: seasonal and inter-annual. Deterministic and stochastic optimization models with either one time-step (intra-annual) or two time-steps (intra-annual and inter-annual) were compared by using three water system optimization models. The first model used one time-step sampling stochastic dynamic programming (SSDP). The other models with two time-steps are long-term deterministic dynamic programming (LT-DDP) and long-term sampling stochastic dynamic programming (LT-SSDP). The study area is the Manicouagan water system located in Quebec, Canada. The results show that there will be an increase of inflow to hydropower plants in the future climate with an increase of inflow uncertainty. The stochastic optimization with two time-steps was the most suitable for handling climate non-stationarity. The LT-DDP performed better in terms of reservoir storage, release and system efficiency but with high uncertainty. The SSDP had the lowest performance. The SSDP was not able to deal with the non-stationary climate and seasonal variability at the same time. The LT-SSDP generated operating policies with smaller uncertainty compared to LT-DDP, and it was therefore a more appropriate approach for water system planning and management in a non-stationary climate characterized by high inflow variability.

Description: Pipe failure often occurs in water distribution networks (WDNs) and results in high levels of water loss and socio-economic damage. Physical-based, statistical and data-driven models have been developed to estimate pipe failure rates (failures per km of pipe per year) to efficiently manage water losses from WDNs and to ensure safe operations. Due to the complexities of pipe failure patterns, we develop a superposed statistical model to depict the relationship between pipe failure rate and pipe age. The model’s level of uncertainty was then quantified by simulating pipe failures as Poisson numbers. Part of Beijing’s WDN is taken as a study case, and pipe failure data for a 4-year period, as well as pipe properties, are collected to develop the pipe failure model. The case study results show that the pipe failure rates vary with time in a non-monotonic manner and that the proposed model captures pipe failure behaviour with an R 2 value of 0.95. A 95% confidence interval of modelled pipe failures for each pipe age group is used to describe the uncertainty level of the model. We find that 88% of the observations fall under the 95% confidence interval. The established model could be applied to prioritize pipes with higher failure rates to optimize pipe replacement/rehabilitation strategies. Our uncertainty analysis of this model can help utility managers understand the model’s reliability and formulate reasonable WDN management plans.

Description: There has been a growing concern on temporal variations on drought characteristics due to climate change. This study compares meteorological drought characteristics for two different periods to quantify the temporal changes in seasonal droughts of 18 weather stations of the country. Fifty-five years rainfall and temperature data are divided into two different thirty-year periods, 1961–1990 and 1985–2014 and standardized precipitation evapotranspiration index (SPEI) for those periods are calculated to assess the changes. Four seasons in this study are selected as two major crop growing seasons namely, Rabi (November to April) and Kharif (May to October) and two critical periods for crop growth in term of water supply namely critical Rabi (March–April) and critical Kharif (May). Results show that moderate, extreme, and severe Rabi droughts has increased in 11, 9, and 4 stations out of 18 stations, respectively, and Kharif severe and extreme droughts has increased in 8 and 9 stations, respectively, In addition, the frequency analysis shows that the return periods have decreased during 1985–2014 at the stations where it was high during 1961–1990 and vice versa. This has made the spatial distribution of return periods of droughts more uniform over the country for most of the seasons. Increased return period of droughts in highly drought prone north and northwest Bangladesh has caused decrease in average frequency of droughts. Consequently, this result corresponds that Bangladesh experiences fewer droughts in recent years. Trend analysis of rainfall and temperature data reveals that significant increase of mean temperature and no significant change in rainfall in almost all months have increased the frequency of droughts in the regions where droughts were less frequent.

Description: An efficiently parameterized and appropriately structured piecewise linear hedging rule is formulated and included within a multi-objective simulation-optimization (S-O) framework that seeks to obtain Pareto-optimal solutions for the long-term hedged operation of a single water supply reservoir. Two conflicting objectives, namely, “minimize the total shortage ratio” and “minimize the maximum shortage” are considered in the S-O framework, while explicit specification of constraints is avoided in the optimization module. Evolutionary search based non-dominated sorting genetic algorithm is used as the driver, which is linked to the simulation engine that invokes the piecewise linear hedging rule within the S-O framework. Preconditioning of the multi-objective stochastic search of the time-varying piecewise linear hedging model is effected by feeding initial feasible solutions sampled from the Pareto-optimal front of a simple constant hedging parameter model, which has resulted in significant improvement of the Pareto-optimality and the computational efficiency.