Showing papers in "Water Resources Management in 2015"

Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition

Abstract: Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.

Multi-Criteria Analysis Framework for Potential Flood Prone Areas Mapping

Abstract: A fundamental component of the European natural disaster management policy is the detection of potential flood-prone areas, which is directly connected to the European Directive (2007/60). This study presents a framework for mapping potential flooding areas incorporating geographic information systems (GIS), fuzzy logic and clustering techniques, and multi-criteria evaluation methods. Factors are divided in different groups which do not have the same level of trade off. These groups are related to geophysical, morphological, climatological/meteorological and hydrological characteristics of the basin as well as to anthropogenic land use. GIS and numerical simulation are used for geographic data acquisition and processing. The selected factor maps are considered in order to estimate the spatial distribution of the potential flood prone areas. Using these maps, the study area is classified into five categories of flood vulnerable areas. The Multi-Criteria Analysis (MCA) techniques consist of the crisp and fuzzy analytical hierarchy processes (AHP) and are enhanced with different standardization methods. The classification is based on different clustering techniques and it is applied in two approaches. In the first approach, all criteria are normalized before the MCA process and then, the clustering techniques are applied to derive the final flood prone area maps. In the second approach, the criteria are clustered before and after the MCA process for the potential flood prone area mapping. The methodology is demonstrated in Xerias River watershed, Thessaly region, Greece. Xerias River floodplain was repeatedly flooded in the last few years. These floods had major impacts on agricultural areas, transportation networks and infrastructure. Historical flood inundation data has been used for the validation of the methodology. Results show that multiple MCA techniques should be taken into account in initial low-cost detection surveys of flood-prone areas and/or in preliminary analysis of flood hazard mapping.

A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping

Abstract: As demand for fresh groundwater in the worldwide is increasing, delineation of groundwater spring potential zones become an increasingly important tool for implementing a successful groundwater determination, protection, and management programs. Therefore, the objective of current study is to evaluate the capability of three machine learning models such as boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF), and comparison of their performance by bivariate (evidential belief function (EBF)), and multivariate (general linear model (GLM)) statistical methods in the groundwater potential mapping. This study was carried out in the Beheshtabad Watershed, Chaharmahal-e-Bakhtiari Province, Iran. In total, 1425 spring locations were detected in the study area. Seventy percent of the spring locations were used for model training, and 30 % for validation purposes. Fourteen conditioning-factors were considered in this investigation, including slope angle, slope aspect, altitude, plan curvature, profile curvature, slope length (LS), stream power index (SPI), topographic wetness index (TWI), distance from rivers, distance from faults, river density, fault density, lithology, and land use. Using the above conditioning factors and different algorithms, groundwater potential maps were generated, and the results were plotted in ArcGIS 9.3. According to the results of success rate curves (SRC), values of area under the curve (AUC) for the five models vary from 0.692 to 0.975. In contrast, the AUC for prediction rate curves (PRC) ranges from 77.26 to 86.39 %. The CART, BRT, and RF machine learning techniques showed very good performance in groundwater potential mapping with the AUC values of 86.39, 86.12, and 86.05 %, respectively. By the way, The GLM and EBF models in comparison by machine learning models showed weaker performance in spring groundwater potential mapping by the AUC values of 77.26, and 67.72 %, respectively. The proposed methods provided rapid, accurate, and cost effective results. Furthermore, the analysis may be transferable to other watersheds with similar topographic and hydro-geological characteristics.

Literature review on rebound effect of water saving measures and analysis of a Spanish case study

Abstract: The hypothesis of a rebound effect as a consequence of water saving investments is taken analogically from the Jevons paradox models in energy economics. The European Commission (EC) alert about the consequences in water stressed regions that are investing heavily in modernization of irrigation networks and systems. This paper reviews the literature, linking water savings with water diversion and water depletion, both from theoretical models and empirical evidence from the published research. In order to increase knowledge of this phenomenon, a new empirical case study is presented based on a survey of 36,000 ha of recently modernized irrigated areas in the Guadalquivir basin (southern Spain). The results of the case study illustrates the conditions that may avoid rebound effect, although the results of the available empirical evidence and the published theoretical research are diverse and lead to contradictory results. Further research is therefore needed to determine the causes and solutions of water saving investment impacts and the possible speculative rebound effect.

ANN Based Sediment Prediction Model Utilizing Different Input Scenarios

Abstract: Modeling sediment load is a significant factor in water resources engineering as it affects directly the design and management of water resources. In this study, artificial neural networks (ANNs) are employed to estimate the daily sediment load. Two different ANN algorithms, the feed forward neural network (FFNN) and radial basis function (RBF) have been used for this purpose. The neural networks are trained and tested using daily sediment and flow data from Rantau Panjang station on Johor River. The results show that combining flow data with sediment load data gives an accurate model to predict sediment load. The comparison of the results indicate that the FFNN model has superior performance than the RB model in estimating daily sediment load.

Using Multivariate Statistical Analysis, Geostatistical Techniques and Structural Equation Modeling to Identify Spatial Variability of Groundwater Quality

Abstract: Multivariate statistical analysis, geostatistical techniques and structural equation modeling were used to determine the main factors and mechanisms controlling the spatial variation of groundwater quality in the Ain Azel plain, Algeria. Cluster analysis grouped the sampling wells into two statistically significant clusters based on similarities of groundwater quality characteristics. Principal component and factor analyses (PCA/ FA) revealed that two factors explained around 85 % of the total variance, which water-rock interaction and anthropogenic impact as the dominant factors affecting the groundwater quality. The distribution of factor score one represents high loading for EC, Ca, Mg, Na, K, and SO4 in the western side and south eastern side of the plain, where water-rock interactions are dominate factors influence groundwater quality. Spatial distribution map of factor score 2 indicate that NO3, NO2, NH4, and COD show high concentration in central and southern side of the plain, where anthropogenic impact reduce groundwater quality. Further, one-way analysis of variance (one-way ANOVA) showed that the mean differences between cluster one and two show significantly differences for some water quality parameters including EC, Ca, Mg, Na, K, Cl, and SO4. Structural equation modeling (SEM) confirmed the finding of multivariate analysis. This study provides a new technique of confirming exploratory data analysis using SEM in groundwater quality.

Comparison of Different Multi Criteria Decision-Making Models in Prioritizing Flood Management Alternatives

Abstract: Recent increases in life loss, destruction and property damages caused by flood at global scale, have inevitably highlighted the pivotal considerations of sustainable development through flood risk management. Throughout the paper, a practical framework to prioritize the flood risk management alternatives for Gorganrood River in Iran was applied. Comparison between multi criteria decision making (MCDM) models with different computational mechanisms provided an opportunity to obtain the most conclusive model. Non-parametric stochastic tests, aggregation models and sensitivity analysis were employed to investigate the most suitable ranking model for the case study. The outcomes of these mentioned tools illustrated that ELimination and Et Choice Translating Reality (ELECTRE III), a non-compensatory model, stood superior to the others. Moreover, Eigen-vector’s performance for assigning weights to the criteria was proved by the application of Kendall Tau Correlation Coefficient Test. From the technical point of view, the highest priority among the criteria belonged to a social criteria named Expected Average Number of Casualties per year. Furthermore, an alternative with pre and post disaster effectiveness was determined as the top-rank measure. This alternative constituted flood insurance plus flood warning system. The present research illustrated that ELECTRE III could deal with the complexity of flood management criteria. Hence, this MCDM model would be an effective tool for dealing with complex prioritization issues.

Hydropower Potential in Water Distribution Networks: Pressure Control by PATs

Abstract: Pressure control is one of the main techniques to control leakages in Water Distribution Networks (WDNs) and to prevent pipe damage, improving the delivery standards of a water supply systems. Pressure reducing stations (PRSs) equipped by either pressure reducing valves or motor driven regulating valves are commonly used to dissipate excess hydraulic head in WDNs. An integrated new technical solution with economic and system flexibility benefits is presented which replaces PRSs with pumps used as turbines (PATs). Optimal PAT performance is obtained by a Variable Operating Strategy (VOS), recently developed for the design of small hydropower plants on the basis of valve time operation, and net return determined by both energy production and savings through minimizing leakage. The literature values of both leakages costs and energy tariffs are used to develop a buisness plan model and evaluate the economic benefit of small hydropower plants equipped with PATs. The study shows that the hydropower installation produces interesting economic benefits, even in presence of small available power, that could encourage the leakage reduction even if water savings are not economically relevant, with consequent environmental benefits.

Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions

Abstract: Evapotranspiration is a major factor that controls hydrological process and its accurate estimation provides valuable information for water resources planning and management, particularly in extremely arid regions. The objective of this research was to evaluate the use of a support vector machine (SVM) to model daily reference evapotranspiration (ET0) using limited climatic data. For the SVM, four combinations of maximum air temperature (T max ), minimum air temperature (T min ), wind speed (U 2 ) and daily solar radiation (R s ) in the extremely arid region of Ejina basin, China, were used as inputs with T max and T min as the base data set. The results of SVM models were evaluated by comparing the output with the ET0 calculated using Penman–Monteith FAO 56 equation (PMF-56). We found that the ET0 estimated using SVM with limited climatic data was in good agreement with those obtained using the conventional PMF-56 equation employing the full complement of meteorological data. In particular, three climatic parameters, T max , T min , and R s were enough to predict the daily ET0 satisfactorily. Moreover, the performance of SVM method was also compared with that of artificial neural network (ANN) and three empirical models including Priestley-Taylor, Hargreaves, and Ritchie. The results showed that the performance of SVM method was the best among these models. This offers significant potential for more accurate estimation of the ET0 with scarce data in extreme arid regions.

Is Desalination Affordable?—Regional Cost and Price Analysis

Abstract: Desalination has proven to be a reliable and efficient water supply option in many countries, especially in times of water scarcity. However, high desalination costs and high prices for desalinated water (twice or three times higher than those from traditional water sources) have been hindering an uptake and the development of desalination in many countries. Applied desalination technology, capital and operational costs, production capacity, water salinity are just a few factors determining the final cost of desalinated water that varies considerably between $1.7–9.5/kgal ($0.45–2.51/m3). The final prices for desalinated water and the related costs for local municipalities are among the most crucial determinants of the overall short- and long-term effectiveness of desalination processes. This paper provides an in-depth analysis on economics of desalination with country specific examples. It depicts a comprehensive picture of cost variability of desalinated water and points out challenges for cost-effective desalination in the future.

Assessment of the Sustainability of Water Resources Management: A Critical Review of the City Blueprint Approach

Abstract: Climate change, urbanization and water pollution cause adverse effects and rehabilitation costs that may exceed the carrying capacity of cities. Currently, there is no internationally standardized indicator framework for urban Integrated Water Resources Management (IWRM). The City Blueprint® is a first attempt and aims to enhance the transition towards water-wise cities by city-to-city learning. This paper provides a three step revision of the City Blueprint Framework (CBF) based on data of 45 municipalities and regions in 27 countries: (1) A distinction has been made between trends and pressures (on which urban IWRM has a negligible influence) and IWRM performances. Therefore, a separate trends and pressures framework has been developed; (2) Only the purely performance-oriented indicators have been selected from the CBF. Furthermore, the indicator accuracy and boundaries have been re-assessed, and new indicators have been added; (3) By analyzing correlations and variances, the performance-oriented indicators have been rearranged in order to establish a proportional contribution of all indicators and categories to the overall score, i.e., the Blue City Index®. In conclusion, six indicators have been removed because of insufficient accuracy, overlap or lack of focus on IWRM. Seven indicators have been added, i.e., secondary and tertiary wastewater treatment, operation cost recovery, green space and three indicators concerning solid waste treatment. The geometric aggregation method has been selected because it emphasizes the need to improve the lowest scoring indicators. In conclusion, the improved CBF is more performance-oriented and therefore more suitable to assist cities in their transition towards water-wise cities.

A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods

Abstract: In arid and semi-arid environments, groundwater plays a significant role in the ecosystem. In the last decades, groundwater levels have decreased due to the increasing demand for water, weak irrigation management and soil damage. For the effective management of groundwater, it is important to model and predict fluctuations in groundwater levels. In this study, several time series models were applied to predict groundwater level forecasting in Kashan plain, Isfahan province, Iran. At first, to reduce the calculation volume, the water table depths in 36 piezometric wells were clustered based on the Vard algorithm. Consequently, we categorized the 36 wells into five clusters. For each cluster, five time series models of auto-regressive (AR), moving-average (MA), auto-regressive moving-average (ARMA), auto-regressive integrated moving-average (ARIMA) and seasonal auto-regressive integrated moving-average (SARIMA) were applied. The results showed that the AR model with a two-times lag (AR(2)), shows the best forecasting of groundwater level for 60 months ahead of the five clusters, with a high accuracy of R 2 (0.89, 0.89, 0.95, 0.95 and 0.75 in clusters 1 to 5, respectively). According to the results, the average groundwater level fluctuation in 2010 and 2016 was 74.58 and 80.71 m, respectively. With these conditions, the groundwater depletion rate would be 1.02 m per year in 2016. We combined several time series models for a better performance of prediction of groundwater level. We can conclude that combining time series models have an advantage in terms of groundwater level forecasting.

Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models

Abstract: Water demand prediction is essential in any short or long-term management plans. For short-term prediction of water demand, climatic factors play an important role since they have direct influence on water consumption. In this paper, prediction of future daily water demand for Al-Khobar city in the Kingdom of Saudi Arabia is investigated. For this purpose, the combined technique of Artificial Neural Networks (ANNs) and time series models was constructed based on the available daily water consumption and climatic data. The paper covers the following: forecast daily water demand for Al-Khobar city, compare the performance of the ANNs [General Regression Neural Network (GRNN) model] technique to time series models in predicting water consumption, and study the ability of the combined technique (GRNN and time series) to forecast water consumption compared to the time series technique alone. Results indicate that combining time series models with ANNs model will give better prediction compared to the use of ANNs or time series models alone.

State of the Art Review of Ant Colony Optimization Applications in Water Resource Management

Abstract: Among the emerged metaheuristic optimization techniques, ant colony optimization (ACO) has received considerable attentions in water resources and environmental planning and management during last decade. Different versions of ACO have proved to be flexible and powerful in solving number of spatially and temporally complex water resources problems in discrete and continuous domains with single and/or multiple objectives. Reviewing large number of peer reviewed journal papers and few valuable conference papers, we intend to touch the characteristics of ant algorithms and critically review their state-of- the-art applications in water resources and environmental management problems, both in discrete and continuous domains. The paper seeks to promote Opportunities, advantages and disadvantages of the algorithm as applied to different areas of water resources problems both in research and practice. It also intends to identify and present the major and seminal contributions of ant algorithms and their findings in organized areas of reservoir operation and surface water management, water distribution systems, urban drainage and sewer systems, groundwater managements, environmental and watershed management. Current trends and challenges in ACO algorithms are discussed and called for increased attempts to carry out convergence analysis as an active area of interest.

Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites

Abstract: Reliable forecast of groundwater level is necessary for its sustainable use and for planning land and water management strategies. This paper deals with an application of artificial neural network (ANN) approach to the weekly forecasting of groundwater levels in multiple wells located over a river basin. Gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm was employed to predict groundwater levels 1 week ahead at 18 sites over the study area. Based on the domain knowledge and pertinent statistical analysis, appropriate set of inputs for the ANN model was selected. This consisted of weekly rainfall, pan evaporation, river stage, water level in the surface drain, pumping rates of 18 sites and groundwater levels of 18 sites in the previous week, which led to 40 input nodes and 18 output nodes. During training of the ANN model, the optimum number of hidden neurons was found to be 40 and the model performance was found satisfactory (RMSE = 0.2397 m, r = 0.9861, and NSE = 0.9722). During testing of the model, the values of statistical indicators RMSE, r and NSE were 0.4118 m, 0.9715 and 0.9288, respectively. Using the same inputs, the developed ANN model was further used for forecasting groundwater levels 2, 3 and 4 weeks ahead in 18 tubewells. The model performance was better while forecasting groundwater levels at shorter lead times (up to 2 weeks) than that for larger lead times.

Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements

Abstract: The AquaCrop model was used to simulate maize growth and soil water content under full and deficit irrigation managements as 1.2, 1, 0.8, and 0.6 of the potential crop water requirement. Generally, the RMSEs in simulating soil water content in calibration and validation were 0.01–0.039 and 0.012–0.037 m3 m−3, respectively, that overall corresponds to 3–14 % error. For the in-season biomass development, the RMSEs in calibration varied between 2.16 and 2.73 Mg ha−1, while they varied between 1.97 and 5.19 Mg ha−1 in validation for the four irrigation managements. The model showed poor performance for simulating biomass late in the season under deficit irrigation managements. The RMSEs of final grain yield simulation were 0.71 and 1.77 Mg ha−1 that corresponded to 7 and 18 % error in calibration and validation, respectively. Likewise, the RMSEs for simulating the final biomass in calibration and validation were 1.29 and 2.21 Mg ha−1 that equals to 6 and 10 % error, respectively. Results demonstrated that AquaCrop is a useful decision-making tool for investigating deficit irrigations and maize growth in the region. However, in agreement with the findings in earlier studies on AquaCrop, the model showed insufficient accuracy in simulating final grain yield and biomass under moderate to severe water stresses. It is suggested that AquaCrop would benefit of including some calibrating parameters about the root distribution pattern in the soil because it is a water-driven model and highly depends on the accurately simulated water uptake from the soil profile.

Regional Frequency Analysis of Droughts in China: A Multivariate Perspective

Abstract: Joint probability behavior of droughts is important for China due to the fact that China is the agricultural country with the largest population in the world and it is particularly the case in the backdrop of intensifying weather extremes in a warming climate. In this case, regionalization of droughts is done using Fuzzy C- Means (FCM) clustering technique and also multivariate L-moment method. Besides, copula is used to estimate regional joint probability in terms of drought duration and severity. Evaluation of uncertainty in the joint probability curves is done using the Bootstrap resampling technique. The results indicate that: (1) five homogenous regions of droughts are subdivided. Regionalization in this study clarified the changing properties or nature of droughts, i.e., the blurred or ambiguous boundaries of the drought-impacted regions; (2) droughts in the northwest China are characterized by longer drought duration and larger drought severity, and the occurrence of the droughts in the northwest China is subject to be higher due to longer waiting time between drought events. Adverse is found for changes of droughts in the southeast China. The droughts in the north China are moderate in terms of drought duration and severity and also waiting time between drought events when compared to those in the northwest and southeast China; (3) the regional joint frequency curves are obtained with respect to drought duration and severity using the bivariate copula functions. Then the joint probabilities of droughts can be calculated using the regional probability curves and also results of mean drought duration, drought severity and waiting time between drought events. Furthermore, droughts in the regions without meteorological data can also be estimated in terms of joint probability using index-drought method proposed in this study. This study will provides theoretical and practical grounds for development and enhancement of human mitigation to drought hazards in China, and is of great importance in terms of planning and management of water resources and agricultural activities in the backdrop of intensifying weather extremes under the influences of warming climate.

Assessing Climate Change Impact on Water Balance Components of a River Basin Using SWAT Model

Abstract: Climate change is one of the most important global environmental challenges, which affects the entire earth system in terms of negative impacts on food production, water supply, health, livelihood, energy, etc. The intent of the present study was to assess the impact of climate change on the water balance components of a data-starved Upper Baitarani River basin of Eastern India using ArcSWAT model. The ArcSWAT model was calibrated using SUFI-2 technique. The daily observed streamflow data from 1998 to 2003 were employed for calibration and those for 2004–2005 for validation. The calibration results were found to be satisfactory with the Nash-Sutcliffe efficiency (NSE) and mean absolute error (MAE) of 0.88 and 9.70 m3/s for the daily time step, respectively. Also, the model was validated successfully for simulating daily streamflow (NSE = 0.80 and MAE = 10.33 m3/s). The calibrated and validated model was then used to evaluate basin response to the anticipated climate changes by the end of the 21st century. Twelve independent as well as twenty eight combined area-specific climatic scenarios were considered in this study to evaluate the impact of climate change on the hydrology of the basin. The analysis of model results for the 12 Independent Climatic Scenarios indicated a reduction in the surface runoff ranging from 2.5 to 11 % by changing the temperature from 1 to 5 °C, whereas the increase in rainfall by 2.5 to 15 % suggested an increase in surface runoff by 6.67 to 43.42 % from the baseline condition. In case of 28 Combined Scenarios compared to the baseline condition, the changes in surface runoff would vary from −4.55 to 37.53 %, the groundwater recharge would change from −8.7 to 23.15 % and the evapotranspiration would increase from 4.05 to 11.88 %. It is concluded that future changes in the climatic condition by the end of the 21st century are most likely to produce significant impacts on the streamflow in the study area. The findings of this study and those of follow-up studies in this direction will be useful for guiding suitable adaptation measures for sustainable water management in the basin in the face of impending climate change.

Adaptive Reservoir Operation Rules Under Climatic Change

Abstract: Increases in greenhouse gases caused by human activities have raised global temperature. Global warming affects water resources systems and the hydrologic cycle and may impact the performance of water resource systems. Water resources managers face challenges balancing conflicting goals in reservoir operation given the uncertainties introduced by climatic change. The HadCM3 climate model is used in this paper to estimate temperature and precipitation for early (2025–2039), middle (2055–2069) and late (2085–2099) periods of the 21st century under the A2 greenhouse gases emission scenario. The estimated temperature and precipitation from the climate model are input to a calibrated hydrologic model (IHACRES) to simulate inflow in a river basin draining to the Karoon-4 reservoir in Iran. A meta-heuristic multi-objective optimization algorithm (NSGA-II) is used in conjunction to predicted hydrologic variables to optimize dynamic operation rules in the Karoon-4 reservoir. The Karoon4 reservoir is operated non-adaptively and adaptively under climatic change. Our results show that adaptive reservoir management increases the reliability and reduces the vulnerability associated with hydropower generation in early, middle, and late simulation periods of the 21st century. These findings establish the importance of factoring in climatic change and considering adaptive strategies in future reservoir operations.

Rapid Inundation Modelling in Large Floodplains Using LiDAR DEM

Abstract: Rapid and accurate inundation modelling in large floodplains is critical for emergency response and environmental management. This paper describes the development and implementation of a floodplain inundation model that can be used for rapid assessment of inundation in very large floodplains. The model uses high resolution DEM (such as LiDAR DEM) to derive floodplain storages and connectivity between them at different river stages. We tested the performance of the model across several large floodplains in southeast Australia for estimating floodplain inundation extent, volume, and water depth for a few recent flood events. The results are in good agreement with those obtained from high resolution satellite imageries and MIKE 21 two-dimensional hydrodynamic model. The model performed particularly well in the reaches that have confined channels with above 85 % agreement with the flood maps derived from Landsat TM imagery in cell-to-cell comparison. While the model did not performance as well in the flat and complex floodplains, the overall level of agreement of the modelled inundation maps with the satellite flood maps was still satisfactory (60–75 %). The key advantage of this model is demonstrated by its capability to simulate inundation in large floodplains (over 2000 km2) at a very high resolution of 5-m with more than 81 million cells at a reasonably low computational cost. The model is suitable for practical floodplain inundation simulation and scenario modelling under current and future climate conditions.

Assessment of Water Resources Carrying Capacity for Sustainable Development Based on a System Dynamics Model: A Case Study of Tieling City, China

Abstract: Studies of water resources carrying capacity (WRCC) can provide helpful information about how the socio-economic system is both supported and restrained by the water resources system. As such, there is a need to develop better quantitative assessment methods to determine the potential maximum socio-economic growth within a catchment subjected to a given amount of water resource. An improved WRCC assessment method based on a system dynamics model (WRCC-SDM) is proposed in this paper. WRCC-SDM is built on synthesis simulations of coupling effects and feedback mechanisms within the society-economy-water compound system. The results can integrally represent system behaviors and states, and the evaluation of WRCC is achieved using this model. Moreover, an integrated indicator (Population-GDP-GDP per capita) is proposed to express the threshold value of WRCC. Based on the natural water resources supply capacity and associated socio-economic development potential, the concepts of critical WRCC and extreme WRCC are put forward. Critical WRCC represents the socio-economic scale that will cause total water demand to reach the maximum natural water resources supply capacity, while extreme WRCC reflects the socio-economic scale when the GDP growth rate constrained by limited water resources is zero. The methodology was applied to assess the water resources situation in Tieling City, China under different scenarios. The results indicate that: (1) Given the constraints represented by water resources, projected GDP growth tends to follow an S-curve growth pattern; and (2) Rapid population growth may lead to earlier and more severe water resources constraints.

Modeling Land-Use and Land-Cover Change and Hydrological Responses under Consistent Climate Change Scenarios in the Heihe River Basin, China

Abstract: This study investigated land-use and land-cover change (LUCC) and hydrological responses under consistent climate change scenarios (A1B and B1) in the Heihe River Basin (HRB), a typical arid inland river basin in northwest China. LUCC was first projected using the Dynamic Conversion of Land-Use and its Effects (Dyna-CLUE) model. Two cases (Case 1 and Case 2) were then established to quantify the hydrological responses to single climate change and the combined responses to climate change and LUCC with the Soil and Water Assessment Tool (SWAT). The results of LUCC modeling under the A1B and B1 scenarios present distinct regional characteristics and also indicate that the projected future land-use patterns are not appreciably different than the actual map for the year 2000. In Case 1, which only considers the impacts of single climate change, overall, the streamflow at the outlet of the upper HRB is projected to decline, whereas at the outlet of the middle HRB to increase, under both climate change scenarios. Meanwhile, the frequency of occurrence of hydrological extremes is expect to increase under both scenarios. In Case 2, which considers the combined impacts of climate change and LUCC, the changes in streamflow and frequency of hydrological extremes are found to be remarkably consistent with those in Case 1. The results imply that climate change rather than LUCC are primarily responsible for the hydrological variations. The role of LUCC varies with regions in the context of climate change dominated hydrological responses.

Comparing Public Perceptions of Alternative Water Sources for Potable Use: The Case of Rainwater, Stormwater, Desalinated Water, and Recycled Water

Abstract: This research investigated how people’s perceptions of alternative water sources compare with their perceptions of other technologies, and identified significant predictors of comfort with different alternative water sources. We drew on data from four questionnaire survey studies with a total sample of more than 1200 Australian participants. Relative levels of comfort with the alternative water sources was consistent across the four studies: comfort was always highest for drinking rainwater and lowest for drinking recycled water, with comfort with drinking treated stormwater and desalinated water sitting between these two. Although comfort with drinking recycled water was always lowest of the four alternative water sources, participants were significantly more comfortable with drinking recycled water than they were with nuclear energy, or with using genetically modified plants and animals for food. In general, demographic variables were less important predictors of comfort with alternative water sources than were psychological variables; only age and gender emerged as relatively consistent predictors for recycled water, stormwater, and desalinated water, with older participants and males more comfortable with drinking these water sources. Of the psychological variables, participants’ comfort with technology in general, trust in science and trust in government emerged consistently as significant positive predictors of comfort with drinking recycled water, stormwater, and desalinated water.

Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty

Abstract: State-of-the-art applications of short-term reservoir management integrate several advanced components, namely hydrological modelling and data assimilation techniques for predicting streamflow, optimization-based techniques for decision-making on the reservoir operation and the technical framework for integrating these components with data feeds from gauging networks, remote sensing data and meteorological weather predictions. In this paper, we present such a framework for the short-term management of reservoirs operated by the Companhia Energetica de Minas Gerais S.A. (CEMIG) in the Brazilian state of Minas Gerais. Our focus is the Tres Marias hydropower reservoir in the Sao Francisco River with a drainage area of approximately 55,000 km and its operation for flood mitigation. Basis for the anticipatory short-term management of the reservoir over a forecast horizon of up to 15 days are streamflow predictions of the MGB hydrological model. The semi-distributed model is well suited to represent the watershed and shows a Nash-Sutcliffe model performance in the order of 0.83-0.90 for most streamflow gauges of the data-sparse basin. A lead time performance assessment of the deterministic and probabilistic ECMWF forecasts as model forcing indicate the superiority of the probabilistic model. The novel short-term optimization approach consists of the reduction of the ensemble forecasts into scenario trees as an input of a multi-stage stochastic optimization. We show that this approach has several advantages over commonly used deterministic methods which neglect forecast uncertainty in the short-term decision-making. First, the probabilistic forecasts have longer forecast horizons that allow an earlier and therefore better anticipation of critical flood events. Second, the stochastic optimization leads to more robust decisions than deterministic procedures which consider only a single future trajectory. Third, the stochastic optimization permits to introduce advanced chance constraints for refining the system operation.

Implications of Modelled Climate and Land Cover Changes on Runoff in the Middle Route of the South to North Water Transfer Project in China

Abstract: Sustainable management of water for human uses and maintaining river health requires reliable information about the future availability of water resources. We quantified the separate and combined impacts of climate and land cover changes on runoff for the historical record and for modelled future scenarios in the upper Han River and Luan River, supply and demand zones respectively of the middle route of the South to North Water Transfer Project in China, the world’s largest inter-basin water transfer project. We used a precipitation-runoff model, averaged multiple climate model predictions combined with three emissions scenarios, a combined CA-Markov model to predict land cover change, and a range of statistical tests. Comparing baseline with 2050: climate change would cause an average reduction in runoff of up to 15 % in the upper Han River and up to 9 % in the Luan River catchment; a scenario involving increased forest cover would reduce runoff by up to 0.19 % in the upper Han River and up to 35 % in the Luan River; a scenario involving increased grass cover would increase runoff by up to 0.42 % in the upper Han River and up to 20 % in the Luan River. In the lower Luan River, the mean annual flow after 1998 fell to only 17 % of that of the baseline period, posing a serious threat to river health. This was explained largely by extraction of surface water and groundwater, rather than climate and land use change.

Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method

Abstract: Accurate and reliable forecasting of reservoir inflow is necessary for efficient and effective water resources planning and management. The aim of this study is to develop an ensemble modeling approach based on wavelet analysis, bootstrap resampling and neural networks (BWANN) for reservoir inflow forecasting. In this study, performance of BWANN model is also compared with wavelet based ANN (WANN), wavelet based MLR (WMLR), bootstrap and wavelet analysis based multiple linear regression models (BWMLR), standard ANN, and standard multiple linear regression (MLR) models for inflow forecasting. Robust ANN and WANN models are ensured considering state of the art methodologies in the field. For development of WANN models, initially original time series data is decomposed using wavelet transformation, and wavelet sub-time series are considered to develop WANN models instead of standard data used for development of ANN model. To ensure a robust WANN model different types of wavelet functions are utilized. Further, a comparative analysis is carried out among different approaches of WANN model development using wavelet sub time series. Seven years of reservoir inflow data along with outflow data from two upstream reservoirs in the Damodar catchment along with rainfall data of 5 upstream rain gauge stations are considered in this study. Out of 7 years daily data, 5 years data are used for training the model, 1 year data are used for cross-validation and remaining 1 year data are used to evaluate the performance of the developed models. Different performance indices indicated better performance of WANN model in comparison with WMLR, ANN and MLR models for inflow forecasting. This study demonstrated the effectiveness of proper selection of wavelet functions and appropriate methodology for wavelet based model development. Moreover, performance of BWANN models is found better than BWMLR model for uncertainty assessment, and is found that instead of point predictions, range of forecast will be more reliable, accurate and can be very helpful for operational inflow forecasting.

Analyses of Drought Events in Calabria (Southern Italy) Using Standardized Precipitation Index

Abstract: A deficit in precipitation has different impacts on soil moisture, snowpack, streamflow, groundwater and reservoir storage. In this study, drought, expressed using the SPI, has been analysed in a region of southern Italy (Calabria) using a homogenised and gap-filled database for 129 monthly rainfall series in the 1916–2006 period. Both the short-term (3 and 6 months) and the long-term (12 and 24 months) SPI were estimated and, in order to identify the worst events, the percentages of the regional area falling within severe or extreme dry conditions have been evaluated. With the aim to spatially characterize the most severe drought event, the SPI data were estimated at ungauged locations and mapped using a geostatistical approach. Finally, a time series analysis of long-term SPI was performed to detect possible trends. Results showed that several heavy drought episodes have widely affected the Calabria region and, among these events, the worst one occurred between December 2001 and April 2002. The trend analysis showed a reduction in the SPI values that is a tendency towards drier conditions, although the running trend approach, carried out only for the long-term SPI, revealed that this tendency is not persistent throughout the series length, but it depends on the period examined.

Spatial Distribution and Temporal Trends in Precipitation Concentration Indices for the Southwest China

Abstract: Changes in the spatiotemporal patterns of precipitation have great impacts on drought/flood risk and utilization of water resources. In this study, we presented the results of a comparative analysis of spatial-temporal variability of precipitation in the Southwest China. The analysis investigated the trends of annual precipitation and explored the changes of two indices: the precipitation concentration index (PCI) and the concentration index (CI) which are designed for measuring seasonality and daily heterogeneity, respectively. The trends of annual precipitation and CI were tested by the Mann-Kendall method. The results show a significant seasonality of the rainfall distribution and very inhomogeneous temporal distribution of the daily rainfall in the study area. Positive trends in the CI were found at most stations, although most of them were not statistically significant. To detect the futures trends of precipitation in the study area, Hurst’s rescaled range (R/S) analysis was introduced and the corresponding Hurst Exponent was estimated. The results suggested that some drought hazards will happen in the intersection of Sichuan, Guizhou and Yunnan, and the west part of Sichuan, the north part of Chongqing and the middle part of Yunnan are under the risk of flood in the future.

Quantitative Assessment of Climate Change Vulnerability of Irrigation Demands in Mediterranean Europe

Abstract: This paper presents an analysis of water resources management under climate change in Southern European River Basin Districts. The analysis is based on the Water Availability and Adaptation Policy Analysis (WAAPA) model, which focuses on the quantitative evaluation of maximum potential water withdrawal for different types of demands. The Water Availability and Adaptation Policy Analysis model performs the simulation of water resources systems at the monthly time scale and allows the estimation of the demand-reliability curve in every subbasin of the river network. Over sixty River Basin Districts of Southern Europe have been analyzed, taking basic information from publicly available databases: basin topology from the Hydro1K database, average runoff from the University of New Hampshire Global Runoff Data Centre (GRDC) composite runoff field, population from the Global Rural–urban Mapping Project (GRUMP) and irrigation area from the Global Map of Irrigated Area dataset. Streamflow monthly time series were obtained from the results of the ENSEMBLES project in four climate scenarios for time horizon 2070–2100. Climate change vulnerability of irrigation demands is estimated from changes in maximum potential water withdrawals for irrigation in current and future scenarios. Maximum potential water withdrawal for irrigation was computed as the largest value of irrigation demand that could be supplied with a given reliability requirement once the existing urban demand is adequately satisfied. The results show significant regional disparities in vulnerability to climate change in the irrigation sector across Europe. The greatest vulnerabilities have been obtained for Southwest Europe (Iberian Peninsula) and some basins in Italy and Greece.

Hydropower Reservoir Management Under Climate Change: The Karoon Reservoir System

Abstract: This study assesses the performance of hydropower production by reservoirs with and without climate change impacts on river discharge. The case study of this research includes the Khersan 1, Karoon 4, and Karoon 3 reservoirs in Iran. The HADCM3 climate model with A2 greenhouse gas emission scenario is coupled with proportional downscaling to assess the impact of climate change on river discharge and reservoir hydropower production. The IHACRES rainfall- runoff model is implemented for calculating river discharge under climate-change conditions. Reservoir simulation and optimization models are implemented to calculate hydropower production in the base period (1986–2000), future period 1 (2025–2039), future period 2 (2055–2069), and future period 3 (2085–2099). The power production and performance criteria of the reservoirs are calculated using simulation (standard operating policy) and optimization models in the considered periods. Our results show that the largest reductions of reservoir discharge correspond, in decreasing order, to the future periods 3, 1, and 2, respectively. Moreover, the hydropower production obtained with the optimization model is found to be larger than that obtained with the simulation model. The calculated increase in power production in the base period and future periods 1, 2, and 3 is equal to 6, 19, 10, and 22 %, respectively. These results demonstrate the benefit of applying optimization modeling for hydropower production in the Khersan -Karoon reservoir system to mitigate and adapt to climate-change impacts on river discharge.