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Given the significant momentum of developing water quality models to simulate water quality variables and support decision-making, the literature recognizes the importance of addressing uncertainties embedded in the water quality models, such as inherent, parametric, and hydrological uncertainties. However, previous studies have not extensively examined the magnitude of hydrological events in terms of their intensity. Therefore, this paper aims to propose an adjusted Monte Carlo event-based scenarios framework that considers four scenarios (1- 35 years of flow rate records, 2- events with ARI ≤ 10 years, 3- events with ARI ≤ 5 years, 4- events with ARI ≤ 1) to evaluate hydrological variabilities and quantify embedded uncertainties. The study employs a Qual2k model that simulates five water quality variables in the Zaroub River, Iran, as a case study. The model's uncertainty boundaries are quantified using five statistical metrics: Plevel, ARIL, SU, CU, and EU. The study's findings reveal that when considering the flow rate time histories during extreme hydrological events, there is a significant increase in uncertainty in the water quality model, highlighting the challenge of incorporating various scenarios within the framework such as high computational demand, integrating different scenarios, sparse data, and its temporal resolution. On the other hand, reducing the ARI values of hydrological events not only minimizes the uncertainty boundaries but also improves the accuracy of the model simulations. These results underscore the critical importance of selecting hydrological scenarios based on the water quality variable under investigation. Furthermore, the proposed framework can be applied to any water quality model and water body. The study's outcomes suggest that the presented methodology reduces uncertainty and provides more reliable simulations for decision-making in water resources conundrums. [ABSTRACT FROM AUTHOR]

Drought is a major natural hazard that can have devastating impacts on regional agriculture, water supply, ecological environment and social economy. Extreme events, including droughts, are expected to be severer and more frequent because of climate warming. This paper presents an analysis of observed changes and future projections of meteorological drought in the Daqing River Basin (DRB), North China, based on the modified Standardized Precipitation Index (SPImod). Observed changes of meteorological drought are analyzed based on the precipitation series of 26 meteorological stations from 1971 to 2011, and future projections are made under two representative concentration pathways (RCPs 4.5 and 8.5) from 2021 to 2050. The result shows a change from wet to dry detected in most areas of the DRB in summer, winter and annual series. The drought risk is higher under the condition of "OR" and "AND" return period during the observation period, and the high-risk regions cover a large area. The drought risk from 2021 to 2050 will increase across the basin under RCP 4.5 and RCP 8.5, compared with which in the base period from 1971 to 2000. Most regions will be at a higher drought risk under the condition of "OR" return period, and the area with high-risk under RCP 4.5 is slightly larger than that under RCP 8.5. Under the condition of "AND" return period, the high-risk regions will cover the area from the northwest to the northeast, while the area of high-risk regions under RCP 8.5 will be larger than that under RCP 4.5. The result of this paper will be helpful for performing an efficient water resource management in the DRB under the impacts of global warming and climate change. [ABSTRACT FROM AUTHOR]

This paper combines a risk-based two-stage stochastic robust programming (RTSRP) model into an agricultural-ecological system dynamics (AESD) to conduct agricultural-ecological water resources management. It integrates the chance- constrained programming, conditional risk-at-value, two-stage stochastic robust programming with the AESD model. The RTSRP model improves upon the TSRP model by introducing the risk control measurements. The agricultural-ecological dynamic system (AEDS) model can build water connections amid wheat, field corn, seed corn, economic crops, woodland, meadow, and city greening and farmland shelterbelts. Besides, the ecological service value is transformed into the irrigation benefit of ecological sector to guide water allocation amid sub-ecological sectors at a more applicable detailed scale. It can tradeoff relationships between economic benefit and ecological recovery by formulating two enlarging ecological areas scenarios. The combinations of the RTSRP model and the AEDS model can get optimal water allocation schemes, and simulate future sustainable degree (SD) of optimal water allocation schemes and explore relationships amid water allocation, economic benefit, and SD and risk and robustness of model. It is used to the middle reaches of the Heihe River Basin in China to verify its application. The results show that water allocation schemes have different responses on various risk scenarios. Higher water allocation corresponds to bigger water shortage risk and higher robustness of the model. The economic benefit positive with water allocation is contradictory with SD. Enlarging ecological areas contributes to improvements of economic benefit and ecological recovery, but not the SD under the condition of enough water resources. [ABSTRACT FROM AUTHOR]

Time series analysis requires powerful and robust tools; at the same time the tools must be intuitive for users. Bayesian networks have been widely applied in static problem modelling, but, in some knowledge areas, Dynamic Bayesian networks are hardly known. Such is the case in the environmental sciences, where the application of static Bayesian networks in water resources research is notable, while fewer than five papers have been found in the literature for the dynamic extension. The aim of this paper is to show how Dynamic Bayesian networks can be applied in environmental sciences by means of a case study in water reservoir system management. Two approaches are applied and compared for model learning, and another two for inference. Despite slight differences in terms of model complexity and computational time, both approaches for model learning provide similar results. In the case of inference methods, again, there were slight differences in computational time, but the selection of one approach over the other is based on the prediction needed: If the aim is just to go one step forward, both Window and Roll out approaches are similar, when we need to go more than one step forward; the most appropriate will be Roll out. [ABSTRACT FROM AUTHOR]

The inequity of water allocation in the Indus Basin by the rotational method has created conflicts of interest leading to water scarcity for irrigated agricultural land. The arid region of the basin is affected the most, which demands a new inventive way of managing water resources. Geo-informatics maps of the Bahawalnagar canal circle were prepared which show groundwater quality and depth to water table (DWT) for 2006 and 2016, crop evapotranspiration (ETc) by using product MOD16A2 for Kharif (April to September) 2017 and Rabi (October to March) 2017–18 cropping season. Results showed that groundwater quality is deteriorating and DWT is rising over 72% of the study area which leads to waterlogging and salinization. Surface water deficit filled through groundwater contribution is 53.66% and 39%, while ETc varies from 0.0 to 931 and 0.0 to 1979 mm/season for the Rabi and Kharif cropping seasons respectively. This paper presents the status of water balance and groundwater characteristics of the arid region of the Indus basin and recommends re-evaluation of the current water allocation system both within and between canal command areas, recommending that water allocation should be allocated as per crop water requirement and groundwater quality. A re-allocation of water-based on groundwater characteristics will not only mitigate waterlogging and soil salinity, which is a threat to the sustainability of agricultural land but also reduces the stress of inequities within the current rotational system. Implementation of this water allocation strategy will require long-lasting monitoring of groundwater quality at a different depth, a variation of depth to the water table, and estimation of crop evapotranspiration. [ABSTRACT FROM AUTHOR]

Demands for water, food, and energy are rising owing to increasing population and growing economy. Agriculture is the main source of food and is the largest consumer of water and has therefore the greatest impact on water security. It also greatly affects energy security and is also the main source of non-point source pollution. Changes in the availability of water, energy, and land are driven by uncertainties in nature and strongly affect food production, with severe implications for the security of water–food–energy nexus (WFEN). Thus, a coordinated and effective management of WFEN in the agricultural sector is needed. This paper therefore proposes an integrated modelling approach for the optimization of agricultural WFEN in a random environment. The approach quantifies the interactions and feedbacks within agricultural water, food and energy subsystems, making tradeoffs between agricultural benefit and environmental impacts, which will serve as sustainability indicators for agricultural systems, including crop farming and livestock farming. The incorporation of stochastic mathematical programming in the modeling framework aids in understanding how strategies and comprehensive benefits change under different scenarios. The approach was applied in a real-world case study in an irrigation district in northeast China. The development and implementation of such an integrated approach are anticipated to be applied in other agriculture-centered regions to guide policies of sustainable water, food, energy and land resources management. [ABSTRACT FROM AUTHOR]

Due to the widespread uncertainties in agricultural water resources systems and climate change projections, the traditional optimization methods for agricultural water management may have difficulties in generating rational and effective optimal decisions. In order to get optimal future agricultural water allocation schemes for arid areas with consideration of climate change conditions, the model framework established in this paper integrates a statistical downscaling model, back propagation neural networks, and an evapotranspiration model (the Hargreaves model) with inexact irrigation water allocation optimization model under future climate change scenarios. The model framework, which integrates simulation models and optimization models, considers the interactions and uncertainties of parameters, thereby reflecting the realities more accurately. It is applied to the Yingke Irrigation Area in the midstream area of the Heihe River Basin in Zhangye city, Gansu Province, northwest China. Then, water allocation schemes in planning year (2047) under multiple future Representative Concentration Pathways (RCP) scenarios and the status quo (2016) are compared, in order to evaluate the practicability of generated water allocation schemes. The results show that the water shortages of economic crops are improved compared with the status quo under all RCP scenarios while those of the grain crops present opposite results. Meanwhile, the economic benefits decrease from the status quo to planning year under all future scenarios. This phenomenon is directly related to the amount of irrigation water allocation and is indirectly related to the changes of meteorological conditions. The model framework can reveal the regular pattern of hydro-meteorological elements with the impact of climate change. Meanwhile, it can generate irrigation water allocation schemes under various RCPs scenarios which could provide valuable decision support for water resources managers. [ABSTRACT FROM AUTHOR]

Drought is a natural hazard which can cause harmful effects on water resources. To monitor drought, the use of an indicator and determination of wet and dry period trend seem to have an important role in quantifying the drought analysis. In this paper, in addition to the comparison of Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI), based on the most appropriate probability distribution function, it was tried to examine the trends of dry and wet periods based on the mentioned indices. Accordingly, the meteorological data of 30 synoptic stations in Iran (1960-2014) was used and the trend was analyzed using the Mann-Kendall test by eliminating the effect of any significant autocorrelation coefficients at 95% confidence level (modified Mann-Kendall). Comparing results between the time series of RDI and SPI drought indices based on statistical indicators (RMSE < 0.434, R2 > 0.819 and T-statistic < 0.419) in all studied stations revealed that the behavior of the two indices was roughly the same and the difference between them was not significant. The trend analysis results of RDI and SPI indices based on modified Mann-Kendall test showed that the variation of dry and wet periods was decreasing in most of the studied stations (five cases were significant). In addition, the results of the trend line slope of dry and wet periods related to the drought indices in the studied area indicated that the slope was negative for SPI and RDI indices in 70% and 50% of stations, respectively. [ABSTRACT FROM AUTHOR]

Leak detectability or leakage awareness refers to the capability of sensing losses from a water supply system. Several methods exist in the technical literature to tackle this problem, but only few address it with a state estimation approach. The aim of this paper is to present a new methodology that enables probabilistic assessment of the extent to which water loss could be detected using state estimation by only analysing a single hydraulic state, i.e. one time period. Significant leaks are sensed by identifying unusually high normalised state estimation residuals, which can be identified based on the largest normalised residual test. More specifically, the probability of detecting leaks is computed here by working with the multivariate distribution among measurements and estimates to take into account the noisy nature of measurements with an analytical approach rather than with sampling experiments, which are time-consuming. The methodology set out herein also provides a procedure to systematically assess the minimum leak that could be detected in different parts of the network for a specific measurement setting and operating condition. The method has been applied to a water transport network case study to show its potential and to highlight the usefulness of such a tool for practitioners. The limitations of such a methodology are also discussed, including its possible use for on-line leak detection strategies. [ABSTRACT FROM AUTHOR]

Water reuse is a viable option to increase urban water supply, especially under new realities of climate change and increasing anthropogenic activities. A sustainable water reuse application should be cost-effective and have acceptable health risk to consumers. Water reuse application evaluation is complex because data acquisitions are usually associated with the problems of uncertainty, hesitancy, and parameterization. In this paper, a generalized intuitionistic fuzzy soft set (GIFSS)-based decision support framework is proposed to provide an effective approach to describe uncertainty and hesitancy in an intuitionistic fuzzy number. In addition, the modified measures of comparison and similarity are proposed to compare water reuse applications. Then, the proposed framework is applied to the City of Penticton (British Columbia, Canada) to evaluate seven water reuse applications. The evaluation results show that the applications of garden flower watering and public parks watering are the most preferred alternatives, which are consistent with the existing practice in the city. Furthermore, the results are highly affected by the generalized parameter and the weights of evaluation criteria. Both the comparison measure-based and similarity measure-based evaluations within the same GIFSS-based framework produce consistent results, indicating an applicable and efficient methodology. [ABSTRACT FROM AUTHOR]

Concepts of Expected Waiting Time (EWT) and Expected Number of Exceedances (ENE) have been presented in much literature for estimating the Design Flood (DF) under non-stationary conditions. The parameters of the EWT and ENE are generally no less than four, which inevitably leads to the uncertainty of the DF estimation. In this paper, the Bayesian method is proposed to analyze the impact of parameter estimation uncertainty on the EWT- and ENE-based estimation of the DF and Corresponding Design Reliability (CDR). In addition, a comparison analysis between the EWT and ENE is conducted in terms of the DF and CDR with or without a consideration being given to the impact of parameter uncertainty. In the case of giving no consideration to the impact of parameter uncertainty, the experiment results indicate that the EWT-based estimations are less than that of ENE in terms of DF and CDR in the case of a decreasing trend. While in the case of an increasing trend, the EWT-based estimations are bigger than that of ENE. In the case of considering the impact of parameter uncertainty, results in the case study show that the distribution of the EWT-based estimations of DF and CDR are left shifted compared to that of the ENE. Overall, the EWT-based estimations are significantly different from that of ENE in terms of DF and CDR. Therefore, it is necessary and open for further discussions about which metric will be optimal between the EWT and ENE for estimating the DF under non-stationarity. [ABSTRACT FROM AUTHOR]

Modelling glacier discharge is an important issue in hydrology and climate research. Glaciers represent a fundamental water resource when melting of ice and snow contributes to runoff. Glaciers are also studied as natural global warming sensors. GLACKMA association has implemented one of their Pilot Experimental Catchment areas at the King George Island in the Antarctica which records values of the liquid discharge from Collins glacier. In this paper, we propose the use of time-varying copula models for analyzing the relationship between air temperature and glacier discharge, which is clearly non constant and non linear through time. A seasonal copula model is defined where both the marginal and copula parameters vary periodically along time following a seasonal dynamic. Full Bayesian inference is performed such that the marginal and copula parameters are estimated in a one single step, in contrast with the usual two-step approach. Bayesian prediction and model selection is also carried out for the proposed model such that Bayesian credible intervals can be obtained for the conditional glacier discharge given a value of the temperature at any given time point. The proposed methodology is illustrated using the GLACKMA real data where there is, in addition, a hydrological year of missing discharge data which were not possible to measure accurately due to problems in the sounding. [ABSTRACT FROM AUTHOR]

Evapotranspiration is an important process in the water cycle that represents a considerable amount of moisture lost to the atmosphere through evaporation from the soil and wet surfaces, and transpiration from plants. Therefore, several water management methods, such as irrigation scheduling and hydrological impact analysis, rely on an accurate estimation of evapotranspiration rates. Often, daily reference evapotranspiration is modelled based on the Penman, Priestley-Taylor or Hargraeves equation. However, each of these models requires extensive input data, such as daily mean temperature, wind speed, relative humidity and solar radiation. Yet, in design studies, such data may be unavailable and therefore, another approach may be needed that is based on stochastically generated time series. More specifically, when rainfall-runoff models are used, these evapotranspiration data need to be consistent with the accompanying (stochastically generated) precipitation time series data. In this paper, such an approach is presented in which the statistical dependence between evapotranspiration, precipitation and temperature is described by three- and four-dimensional vine copulas. Based on a case study of 72 years of evapotranspiration, temperature and precipitation data, observed in Uccle, Belgium, it is shown that canonical vine copulas (C-vines) perform very well in preserving the dependences between variables. [ABSTRACT FROM AUTHOR]

In this paper a composite analysis was used to assess the influence of the North Atlantic Oscillation (NAO) on the winter daily rainfall and seasonal runoff at 28 stations of the Abruzzo region (Central Italy) during the period 1951-2012. Compositing was based on NAO and NAO phases, identified by mean winter values of the normalized NAO index (NAOI) ≤−0.75 and ≥+0.75, respectively. In accordance with previous studies, it was found that NAO phases determine, in general, a greater number of wet days ( N ) and (consequently) higher seasonal rainfall amounts in comparison to NAO phases. However, the NAO influence is characterized by a certain spatial variability, that can mostly be explained by orographic effects due to the Apennine Mountains. This is particularly evident for the mean rainfall depth per event ( P ) that, during NAO phases, increases for the stations to the west of the Apennines, while it decreases for most of the stations to the east. The structure of winter daily rainfall of NAO and NAO type, was described by a simple but effective first-order Markov process, determining the transition probabilities P01 (dry to wet) and P10 (wet to dry) and modelling the rainfall depth on wet days by a Weibull distribution. The most significant influence of NAO concerns P01 and the shape parameter of the Weibull distribution that are both higher during the NAO phase. This means that NAO phases are characterized by less persistent dry periods and less variable daily rainfall depths, in comparison to NAO phases. The effect of these differences on the winter seasonal runoff was explored by applying a Curve Number rainfall-runoff model. Significant increments of the mean seasonal runoff during NAO phases were observed only for few stations (mainly on the west), characterized by corresponding increments of N , P and P .). NAO phases, instead, are characterized by relevant increments of the seasonal runoff variability, particularly on the eastern areas. In this context, the important regulating function of the watershed conditions was also discussed. [ABSTRACT FROM AUTHOR]

The need for irrigation water in arid and semi-arid regions is mostly supplied by groundwater. Furthermore, the agricultural development in these areas is not generally based on a comprehensive plan, which can cause aquifers depletion. On the other hand, to properly manage an aquifer and to have an optimal crop plan, the stochastic nature of the different parameters of a groundwater system such as groundwater recharge and water demands should be taken into consideration. In this paper, we develop an explicit stochastic optimization model for Firouzabad aquifer in Iran. This formulation is based on the first and second moment analysis for groundwater head which has been initially proposed for surface water resources management by Fletcher and Ponnambalam. We extend the model to create a new random withdrawal policy for conjunctive use setting in which the randomness in available precipitation is taken into account. The interesting point is that the model provides the respective probabilities of shortage and surplus without imposing the extra decision variables into the optimization model. A genetic-based algorithm is used to solve the stochastic nonlinear and non-convex formulation. The outcome results indicate that the current crop pattern should be changed, that is, the allocated areas of some crops have to be meaningfully reduced. Finally, to validate our model efficiency, we demonstrate that how much close the statistical characteristics obtained from the optimization model are to those estimated from the Monte Carlo simulation. Furthermore, the optimal benefits obtained using the proposed optimization model are as suitable as the benefits achieved using the corresponding Monte Carlo-based optimization model. [ABSTRACT FROM AUTHOR]

Water resources are influenced by various factors such as weather, topography, geology, and environment. Therefore, there are many difficulties in evaluating and analyzing water resources for the future under climate change. In this paper, we consider climate, land cover and water demand as the most critical factors affecting change in future water resources. We subsequently introduce the procedures and methods employed to quantitatively evaluate the influence of each factor on the change in future water resources. In order to consider the change in land cover, we apply the Multi-Regression approach from the cellular automata-Markov Chain technique using two independent variables, temperature and rainfall. In order to estimate the variation of the future runoff due to climate change, the data of the SRES A2 climate change scenario were entered in the SLURP model to simulate a total of 70 years, 2021-2090, of future runoff in the Han River basin in Korea. However, since a significant amount of uncertainties are involved in predicting the future runoff due to climate change, 50 sets of daily precipitation data from the climate change scenario were generated and used for the SLURP model to forecast 50 sets of future daily runoff. This process was used to minimize the uncertainty that may occur when the prediction process is performed. For future water balance analysis, the future water demand was divided into low demand, medium demand and high demand categories. The three water demand scenarios and the 50 daily runoff scenarios were combined to form 150 sets of input data. The monthly water balance within the Han River basin was then calculated using this data and the Korean version of Water Evaluation and Planning System model. As a result, the future volume of water scarcity of the Han River basin was predicted to increase in the long term. It is mostly due to the monthly shift in the runoff characteristic, rather than the change in runoff volume resulting from climate change. [ABSTRACT FROM AUTHOR]

Most studies on separating the effects of climate change and human activities on runoff are mainly conducted at an annual scale with few analyses over different time scales, which is especially essential for regional water resources management. This paper investigates the impacts of climate change and human activities on runoff changes at annual, seasonal and monthly time scales in the Zhang River basin in North China. Firstly, the changing trends and inflection point are analyzed for hydro-climatic series over different time scales. Then the hydrological modeling based method and sensitivity based method are used to separate the effects. The results show that the effect of climate change is stronger than that of human activities on annual runoff changes. However, the driving factors on runoff are different at seasonal scale. In the wet season, the effect of human activities on runoff, accounting for 57 %, is stronger than that of climate change, while in the dry season climate change is the dominant factor for runoff reduction and the contribution rate is 72 %. Furthermore, the effects of climate change and human activities on monthly runoff changes are various in different months. The separated effects over different time scales in this study may provide more scientific basis for the water resources adaptive management over different time scales in this basin. [ABSTRACT FROM AUTHOR]

In the wake of global and regional climate change and heightened human activities, runoff from some rivers in the world, especially in the arid and semi-arid regions, has significantly decreased. To reveal the varying characteristics leading to the change in runoff, detecting the influencing factors has been important in recent scientific discussions for water resources management in drainage basins. In this paper, an investigation into attributing the runoff response to climate change and human activities were conducted in two catchments (Wushan and Shetang), situated in the upper reaches of Weihe River in China. Prior to the identification of the factors that influenced runoff changes, the Mann-Kendall test was adopted to identify the trends in hydro-climate series. Also, change-points in the annual runoff were detected through Pettitt's test and the precipitation-runoff double cumulative curve method. It is found that both catchments presented significant negative trend in annual runoff and the detected change-point in runoff occurs in 1993. Hence, the pre-change period and post-change period are defined before and after 1993, respectively. Then, runoff response to climate change and human activities was quantitatively evaluated on the basis of hydrologic sensitivity analysis and hydrologic model simulation. They provided similar estimates of the percentage change in mean annual runoff for the post-change period over the considered catchments. It is found that the decline in annual runoff over both catchments can be mainly attributed to the human activities, the reduction percentages due to human activities range from 59 to 77 %. The results of this study can provide a reference for the development, utilization and management of the regional water resources and ecological environment protection. [ABSTRACT FROM AUTHOR]

This paper explores our ability to map the extent of large floods in near real time using coarse resolution C-band radar remote sensing. The European Space Agency’s advanced synthetic aperture radar aboard the Envisat satellite, operating in global monitoring mode (GM), is considered for Australia due to its high temporal frequency, comprehensive coverage and ease of acquisition. Challenges are identified which relate both to the use of radar generally, and also in particular to GM data, in the demarcation of water and land. In Australia, the need for a better understanding of the expected backscatter response from inundated areas in tropical savanna, which covers one third of its landmass, is targeted. The backscatter responses to two large flood events in the tropical savanna of northern Australia are investigated, showing markedly different results. One flood allows the accurate classification of inundated extents, while the other is almost completely indistinguishable from the surrounding wet vegetation. Data from water height loggers established in the neighbouring Mitchell floodplain over a dry/wet season period provide an insight into the interaction of these particular vegetation conditions under flood. Results concur with the work of others, that backscatter response is a complex combination of effects depending on relative water height, vegetation spatial density, biomass, and verticality, or enmeshment, of super-surface grasses. Opportunities are also identified that relate to future space missions, the synoptic use with optical data, and better knowledge of the processes that govern the applicability of radar data for mapping large flood events. [ABSTRACT FROM AUTHOR]

In water resources there is an increasing demand for interoperable data and information systems to provide people and software agents seamless access to multiple relevant data sources. Information and data modelling, with associated standards, controlled vocabularies and ontologies provide the key components in overcoming the syntactic and semantic challenges of interoperability. When combined with metadata registries and data services these components allow users to search and find data and information through structured approaches, and to access and obtain these for a range of end uses. This paper describes the concepts and fundamental components required for successful use of information modelling, including the work in Australia led by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and Bureau of Meteorology to develop and apply information modelling to the problems of interoperability for Australian water resources information systems. These initiatives are delivering tools and methods that support the long term maintenance of information models as they evolve to meet new needs and expanded requirements. The investigation reveals that we have a strong conceptual framework along with rapidly maturing methods and tools, suggesting that we are at the forefront of a new generation of water resources data and information access and interoperability. [ABSTRACT FROM AUTHOR]

Reservoir operation is one of the challenging problems for water resources planners and managers. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. This paper discusses an overview of simulation and optimization modeling methods utilized in resolving critical issues with regard to reservoir systems. In designing a highly efficient as well as effective dam and reservoir operational system, reservoir simulation constitutes one of the most important steps to be considered. Reservoirs with well-functional and reliable optimization models require very accurate simulations. However, the nonlinearity of natural physical processes causes a major problem in determining the simulation of the reservoir's parameters (elevation, surface-area, storage). Optimization techniques have shown high efficiency when used with simulation modeling and the combination of the two methods had given the best results in the reservoir management. The principal concern of this review study is to critically evaluate and analyze simulation, optimization and combined simulation-optimization modeling approach and present an overview of their utility in previous studies. Inferences and suggestions which may assist in improving quality of this overview in the future are provided. These will also enable future researchers, system analysts and managers to achieve more precise optimal operational system. [ABSTRACT FROM AUTHOR]

This paper presents the development and the first application of an inexact quadratic programming (IQP) approach for sustainable water supply under multiple uncertainties. The developed IQP improves conventional nonlinear programming by tackling multiple uncertainties within an individual parameter; IQP is also superior to existing inexact methods due to its reflection of economies of scale and reduction of computational requirements. An interactive solution algorithm with high computational efficiency was also proposed. The application of IQP to long-term planning of a multi-source multi-sector water supply system demonstrated its applicability. The close reflection of system complexities, such as multiple uncertainties, scale economies and dynamic parameters, could enhance the robustness of the optimization process as well as the acceptability of obtained results. Corresponding to varied system conditions and decision priorities, the interval solutions from IQP could help generate a series of long-term water supply strategies under a number of economic, environmental, ecological, and water-security targets. [ABSTRACT FROM AUTHOR]

Society benefits from rivers in many aspects. To the extent of water resources management, one of the salient issues is that the social benefit of in-stream water quality improvements is often difficult to be quantified for possible cost justification in many water pollution control programs. The difficulties arise from that many service flows of water quality are not channelled through the market system to consumers and producers. With different socio-economic structures, such valuation could be even more challenging when taking river basins with low-income level into account. Recent advances in fuzzy set theory provide a germain insight to viewing the in-stream water quality as a kind of fuzzy resource due to varying awareness of the quality of life. This paper provides a technical analysis using the fuzzy contingent valuation mothod (FCVM) to value in-stream water quality improvements in terms of three fuzzy resources from aesthetic to recreational, and to ecological aspects. Traditional CVM may allow interest groups or affected parties to join and present a more flexible asset assessment with respect to the prescribed environmental features in the river corridor. Yet the FCVM provides a mechanism that lies in providing a mapping (via fuzzy set theory) from a survey of respondents’ valuation of subjective assessments of water quality into objective economic measures in terms of water quality parameters that management can more directly manipulate. With this new tool, the traditional CVM assessment outputs in a well-developed river basin may even lead to derive a simular valuaton function in a form of a regression equation in a developing river basin where the incme level is relatively low. As part of the sustainablity analysis basin wide, a case study in Taiwan showed that such effort may provide supportive information for cost benefit analysis in many water pollution control programs corresponding to different temporal and spatial scales. [ABSTRACT FROM AUTHOR]

Planning and management of water resources systems under drought conditions often require the estimation of return periods of drought events characterized by high severities. Among the several methods proposed for describing droughts, the run method is the most suitable to provide an objective identification and characterization of drought events. According to such a method, droughts are identified as consecutive intervals where the investigated hydrological variable is continuously below a fixed threshold, and may be described by means of two characteristics, namely, drought duration and drought severity. Since both characteristics are necessary to estimate water deficit risks, frequency analysis of drought events cannot be based on the same approach generally used for flood analysis, such as maximum annual series or partial duration series of a single characteristic. In particular, the evaluation of return period for drought events needs to consider both duration and severity in order to take into account the pluriannual duration of several droughts. Very often a reliable analysis of the probabilistic structure of droughts based on the observed samples, using an inferential approach, cannot be properly carried out due to the limited number of drought events which can be identified even on quite long historical series. This problem has been faced by Shiau and Shen (2001), who have determined the conditional distribution of drought severity given a drought duration on the basis of generated hydrological series. In this paper their approach is extended by deriving analytically the parameters of the probability distribution of drought severity based on the stochastic process describing the underlying hydrological variable. More specifically, a gamma distribution is adopted to model drought severity and its parameter are theoretically determined as a function of the threshold level and the coefficient of variation of annual precipitation series assumed independent and lognormal distributed. Then, the return period of drought events with severity greater than or equal to a fixed value is computed as the mean interarrival time of drought events with a certain severity or greater. Such procedure has been applied on 88 annual series of data recorded in Sicilian rainfall stations, by computing for each series the return period corresponding to drought of fixed severity and by plotting the spatial distribution in order to provide a description of drought spatial variability at a regional level. [ABSTRACT FROM AUTHOR]