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: Traditionally, drought indices are calculated under stationary condition, the assumption that is not true in a changing environment. Under non-stationary conditions, it is assumed the probability distribution parameters vary linearly/non-linearly with time or other covariates. In this study, using the GAMLSS algorithm, a time-varying location parameter of lognormal distribution fitted to the initial values ( α 0 ) of the traditional Reconnaissance Drought Index (RDI) was developed to establish a new index called the Non-Stationary RDI (NRDI), simplifying drought monitoring under non-stationarity. The fifteen meteorological stations having the longest records (1951–2014) in Iran were chose to evaluate the NRDI performances for drought monitoring. Trend analysis of the α 0 series at multiple time windows was tested by using the Mann-Kendall statistics. Although all stations detected decreasing trend in the α 0 series, eight of them were significant at the 5% probability level. The results showed that the time-dependent relationship is adequate to model the location parameter at the stations with the significant temporal trend. There were remarkable differences between the NRDI and the RDI, especially for the time windows larger than 6 months, implying monitoring droughts using the NRDI under non-stationarity. The study suggests using the NRDI where the significant time trend appears in the initial values of RDI due to changing climate.

Description: Hydroclimatic drought conditions can affect the hydrological services offered by mountain river basins causing severe impacts on the population, becoming a challenge for water resource managers in Andean river basins. This study proposes an integrated methodological framework for assessing the risk of failure in water supply, incorporating probabilistic drought forecasts, which assists in making decisions regarding the satisfaction of consumptive, non-consumptive and environmental requirements under water scarcity conditions. Monte Carlo simulation was used to assess the risk of failure in multiple stochastic scenarios, which incorporate probabilistic forecasts of drought events based on a Markov chains (MC) model using a recently developed drought index (DI). This methodology was tested in the Machángara river basin located in the south of Ecuador. Results were grouped in integrated satisfaction indexes of the system (DSI G ). They demonstrated that the incorporation of probabilistic drought forecasts could better target the projections of simulation scenarios, with a view of obtaining realistic situations instead of optimistic projections that would lead to riskier decisions. Moreover, they contribute to more effective results in order to propose multiple alternatives for prevention and/or mitigation under drought conditions.

Description: Climate change may have strong impacts on water resources in developing countries. In North Africa, many dams and reservoirs have been built to secure water availability in the context of a strong inter-annual variability of precipitation. The goal of this study is to evaluate climate change impacts on surface water resources for the largest dams in Algeria, Morocco and Tunisia using high-resolution (12 km) regional climate models (RCM) simulations. To evaluate the atmospheric demand (evapotranspiration), two approaches are compared: The direct use of actual evaporation simulated by the RCMs, or estimation of reference evapotranspiration computed with the Hargreaves-Samani (HAR) equation, relying on air temperature only, and the FAO-Penman Monteith (PM) equation, computed with temperature, wind, radiation and relative humidity. Results showed a strong convergence of the RCM simulations towards increased temperature and a decrease in precipitation, in particular during spring and the western part of North Africa. A decrease in actual evapotranspiration, highly correlated to the decrease in precipitations, is observed throughout the study area. On the opposite, an increase in reference evapotranspiration is observed, with similar changes between HAR and PM equations, indicating that the main driver of change is the temperature increase. Since the catchments are rather water-limited than energy-limited, despite opposite projections for actual and reference evapotranspiration a decrease of water availability is projected for all basins under all scenarios, with a strong east-to-west gradient. The projected decrease is stronger when considering reference evapotranspiration rather than actual evaporation. These pessimistic future projections are an incentive to adapt the current management of surface water resources to future climatic conditions.

Description: In this study, a simulation-optimization (SO) model is presented by coupling a meshfree based simulator using radial point collocation method (RPCM) and particle swarm optimization (PSO) as optimizer to identify the unknown groundwater contaminant sources from the measured/simulated contaminant concentration data in the aquifer. To demonstrate the approach, two case studies have been presented. The first example is a hypothetical case which simulates the contaminant releases from several disposal sites in an aquifer during four years release period. The second case considered is a field study where leaching of contaminant, during their storage, from disposal sites at several locations in the aquifer leads to contamination of the groundwater. The goal in both cases was to reconstruct the contaminant release history from the disposal sites and their magnitudes from the given historical concentration data at a few observation wells in the aquifer. It was observed that the source identification model could reconstruct the release histories from the waste disposal sites in both the cases accurately. This study demonstrated that PSO based optimization model with a meshfree flow and transport simulator can be effectively used for groundwater contaminant source identification problems.

Description: Global climate changing and human activities have altered the assumption of stationarity, and intensified the variation of hydrological process in recent decades. It is essential to make progress in accommodating appropriate models to the changing environment where non-stationary models are taken into account. The developing adapted Bayesian inference offers an attractive framework to estimate non-stationary models, when compared with conventional maximum likelihood estimation (MLE). As the inseparable companions of Bayesian inference, an efficient MCMC sampler are expected to be built. However, proper tunings are needed for the sampler to improve the performance by integrating adaptive algorithm and optimization method. A Bayesian approach with the adaptive Metropolis-Hastings optimization (AM-HO) algorithm is adopted to estimate the parameters and quantify the uncertainty in a two-parameter non-stationary Lognormal distribution model. To verify the performance of the developed model, simulation experiments and practical applications are implemented to fit annual maximum flood series of two gauges in Hanjiang River basin. From the point view of parameters estimation, both Bayesian and MLE methods perform similarly. However, Bayesian method is more attractive and reliable than MLE on uncertainty quantification, which provides a relative narrow intervals to be beneficial for risk analysis and water resource management.

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: 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.