In recent years, water resources management has become more complicated and controversial due to the impacts of various factors affecting hydrological systems. System Dynamics (SD) has in turn become increasingly popular due to its advantages as a tool for dealing with such complex systems. However, SD also has some limitations. This review contains a comprehensive survey of the existing literature on SD as a potential method to deal with the complexity of system integrated modeling, with a particular focus on the application of SD to the integrated modeling of water resources systems. It discusses the limitations of SD in these contexts, and highlights a number of studies which have applied a combination of SD and other methods to overcome these limitations. Finally, our study makes a number of recommendations for future modifications in the application of SD methods in order to enhance their performance.
This paper reviewed the impacts of climate change on the management of the water sector in Malaysia discussing the current status of water resources, water service, and water-related disasters. The implementation of engineering practices was discussed to provide the detailed assessment of climate change impacts, risks, and adaptation for sustainable development. The narrative methods of reviewing the literatures were used to get an understanding on the engineering practices of water infrastructures, implication of the government policies, and several models as the main motivation behind the concept of integrated water resource management to contribute as part of the sustainable development goals to achieve a better and more sustainable future for all. The findings of this review highlighted the impacts of climate change on the rivers, sea, lakes, dams, and groundwater affecting the availability of water for domestic and industrial water supplies, irrigation, hydropower, and fisheries. The impacts of climate change on the water-related disasters have been indicated affecting drought-flood abrupt alternation and water pollution. Challenges of water management practices facing climate change should be aware of the updated intensity-duration-frequency curves, alternative sources of water, effective water demand management, efficiency of irrigation water, inter-basin water transfer, and nonrevenue water. The transferability of this review findings contribute to an engagement with the society and policy makers to mobilize for climate change adaptation in the water sector.
The current study explores the relationship between water resources and tourism in South Asia for the period of 1995-2017. The study employs the CIPS unit root test for stationarity of the variables and the CD test for cross-sectional dependence among cross-sectional units. As for the long-run parameters, a novel technique, known as dynamic common correlated effect (DCCE) model, is used which was recently developed by Chudik and Pesaran (J Econ 188:393-420, 2015b). The outcomes from the DCCE method suggest that water resources have a positive impact on tourism in South Asia. It is also proven that ignoring cross-sectional dependence among the cross-sectional units may bring about misleading outcomes. The findings of the study can be helpful for policymakers to understand the role of water resources in boosting tourism and contributing to the economic prosperity of South Asian countries.
The objective of the study is to examine the relationship between air pollution, fossil fuel energy consumption, water resources, and natural resource rents in the panel of selected Asia-Pacific countries, over a period of 1975-2012. The study includes number of variables in the model for robust analysis. The results of cross-sectional analysis show that there is a significant relationship between air pollution, energy consumption, and water productivity in the individual countries of Asia-Pacific. However, the results of each country vary according to the time invariant shocks. For this purpose, the study employed the panel least square technique which includes the panel least square regression, panel fixed effect regression, and panel two-stage least square regression. In general, all the panel tests indicate that there is a significant and positive relationship between air pollution, energy consumption, and water resources in the region. The fossil fuel energy consumption has a major dominating impact on the changes in the air pollution in the region.
We present precipitation isotope data (δ2H and δ18O values) from 19 stations across the tropics collected from 2012 to 2017 under the Coordinated Research Project F31004 sponsored by the International Atomic Energy Agency. Rainfall samples were collected daily and analysed for stable isotopic ratios of oxygen and hydrogen by participating laboratories following a common analytical framework. We also calculated daily mean stratiform rainfall area fractions around each station over an area of 5° x 5° longitude/latitude based on TRMM/GPM satellite data. Isotope time series, along with information on rainfall amount and stratiform/convective proportions provide a valuable tool for rainfall characterisation and to improve the ability of isotope-enabled Global Circulation Models to predict variability and availability of inputs to fresh water resources across the tropics.
In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.
Proper integrated management of a dam reservoir requires that all components of the water resource system be known. One of these components is the daily reservoir inflow which is the subject matter of this study, i.e. to establish predictions of what is coming in the next rainfall-runoff process over a catchment. The transformation of rainfall into runoff is an extremely complex, dynamic, and more of a non-linear process. The available six-year average daily rainfall data across the Sembrong dam catchment were computed using the well-known Theissen’s polygon method. Daily reservoir inflow data were extracted by applying the water balance model to the Sembrong dam reservoir. Modelling of relationship between rainfall and reservoir inflow data was done using feed-forward back-propagation neural networks. The final selected model has one hidden layer with 11 neurons in the hidden layer. The selected model was applied for an independent data series testing. Results in relation to specific climatic and hydrologic properties of a small tropical catchment suggested that the model is suitable to be used in forecasting the next day’s reservoir inflow. The efficiencies of the model Abtained indicated the validity of using the neural network for modelling reservoir inflow series.
Eshtehard aquifer located in southwest of Tehran province, Iran, provides a large amount of water requirement for inhabitants of Eshtehard district. Monitoring and analyzing of groundwater quality are important for protecting groundwater as sustainable water resource. One of the most advanced techniques for groundwater quality interpolation and mapping is geostatistics methods. The purposes of this study are (1) to investigate major ions concentration and their relative abundance to provide an overview of present groundwater chemistry and (2) to map the groundwater quality in the study area using geostatistics techniques. In this investigation, ArcGIS 9.2 was used for predicting spatial distribution of some groundwater characteristics such as: Chloride, Sulfate, pH, and Conductivity. These methods are applied for data from 44 wells within the study area. The final maps show that the south parts of the Eshtehard aquifer have suitable groundwater quality for human consumption and in general, the groundwater quality degrades south to north and west to east of the Eshtehard plain along the groundwater flow path.
Groundwater is the main source of water in the Kingdom of Saudi Arabia (KSA). A larger part of groundwater is founded in alluvial (unconfined) aquifers. Prediction of water table elevations in
unconfined aquifers is very useful in water resources planning and management. During the last two
decades, many aquifers in different regions of the KSA experienced significant groundwater decline.
The declines in these aquifers raised concerns over the quantity and quality of groundwater, as well
as concerns over the planning and management policies used in KSA. The main objective of this study was to predict water table fluctuations and to estimate the annual change in water table at an alluvial aquifer at wadi Hada Al Sham near Makkah, KSA. The methodology was achieved using numerical groundwater model (MODFLOW). The model was calibrated and then used to predict water table elevations due to pumping for a period of 5 years. The output of the model was found to be in agreement with the previous records. Moreover, the simulation results also show reasonable declination of water table elevations in the study area during the study period.
Estimation and forecast of groundwater recharge and capacity of aquifer are essential issues in water resources investigation. In the current research, groundwater recharge, recharge coefficient and effective rainfall were determined through a case study using empirical methods applicable to the tropical zones. The related climatological data between January 2000 and December 2010 were collected in Selangor, Malaysia. The results showed that groundwater recharge was326.39 mm per year, effective precipitation was 1807.97 mm per year and recharge coefficient was 18% for the study area. In summary, the precipitation converted to recharge, surface runoff and evapotranspiration are 12, 32 and 56% of rainfall, respectively. Correlation between climatic parameters and groundwater recharge showed positive and negative relationships. The highest correlation was found between precipitation and recharge. Linear multiple regressions between
recharge and measured climatologic data proved significant relationship between recharge and rainfall and wind speed. It was also proven that the proposed model provided an accurate estimation for similar projects.
Understanding rainfall trend can be a first step in the planning and management of water resources
especially at the basin scale. In this study, standard tests are used to examine rainfall trends based on monthly, seasonal and mean annual series at the Niger-South Basin, Nigeria, between 1948 and 2008. Rainfall variability index showed that the decade 2000s was the driest (-2.1), while 1950s was the wettest (+0.8), with the decade 1980s being the driest in the second half of the last century, whereas the year 1983 was the driest throughout the series. Over the entire basin, rainfall variability was generally low, but higher intra-monthly than inter-annually. Annual rainfall was dominated by August, contributing about 15%, while December contributed the least (0.7%). On a seasonal scale, July-August-September (JJA) contributed over 40% of the annual rainfall, while rainfall was lowest during December-January-February (DJF) (4.5%). The entire basin displayed negative trends but only 15% indicated significant changes (α ‹ 0.1), while the magnitudes of change varied between -3.75 and -0.25 mm/yr. Similarly, only JJA exhibited insignificant upward trend, while the rest showed negative trends. About eight months of the year showed reducing trends, but only January trend was significant. Annual downward trend was generally observed in the series. The trend during 1948–1977 was negative, but it was positive for the 1978–2008 period. Hence, water resources management planning may require construction of water storage facilities to reduce summer flooding and prevent possible future water scarcity in the basin.
The accuracy level for reservoir evaporation prediction is an important issue for decision making in the water resources field. The traditional methods for evaporation prediction could encounter numerous obstacles owing to the effect of several parameters on the shape of the evaporation pattern. The current research presented modern model called the Coactive Neuro-Fuzzy Inference System (CANFIS). Modification for such model has been achieved for enhancing the evaporation prediction accuracy. Genetic algorithm was utilized to select the effective input combination. The efficiency of the proposed model has been compared with popular artificial intelligence models according to several statistical indicators. Two different case studies Aswan High Dam (AHD) and Timah Tasoh Dam (TTD) have been considered to explore the performance of the proposed models. It is concluded that the modified GA-CANFIS model is better than GA-ANFIS, GA-SVR, and GA-RBFNN for evaporation prediction for both case studies. GA-CANFIS attained minimum RMSE (15.22 mm month-1 for AHD, 8.78 mm month-1 for TTD), minimum MAE (12.48 mm month-1 for AHD, 5.11 mm month-1 for TTD), and maximum determination coefficient (0.98 for AHD, 0.95 for TTD).
Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations.
Internal erosion is known as the most important cause of dam failure after overtopping. It is important to improve the erosion resistance of the erodible soil by selecting an effective technique along with the reasonable costs. To prevent internal erosion of embankment dams the use of chemical stabilizers that reduce the soil erodibility potential is highly recommended. In the present study, a lignin-based chemical, known as lignosulfonate, is used to improve the erodibility of clayey sand specimen. The clayey sand was tested in various hydraulic heads in terms of internal erosion in its natural state as well as when it is mixed with the different percentages of lignosulfonate. The results show that erodibility of collected clayey sand is very high and is dramatically reduced by adding lignosulfonate. Adding 3% of lignosulfonate to clayey sand can reduce the coefficient of soil erosion from 0.01020 to 0.000017. It is also found that the qualitative erodibility of stabilized soil with 3% lignosulfonate is altered from the group of extremely rapid to the group of moderately slow.
The worsening water scarcity has imposed a significant stress on food production in many parts of the world. This stress becomes more critical when countries seek self-sufficiency. A literature review shows that food self-sufficiency has not been assessed as the main factor in determining the optimal cultivation patterns. However, food self-sufficiency is one of the main policies of these countries and requires the most attention and concentration. Previous works have focused on the virtual water trade to meet regional food demand and to calculate trade flows. The potential of the trade network can be exploited to improve the cropping pattern to ensure food and water security. To this end, and based on the research gaps mentioned, this study develops a method to link intra-country trade networks, food security, and total water footprints (WFs) to improve food security. The method is applied in Iran, a water-scarce country. The study shows that 781 × 106 m3 of water could be saved by creating a trade network. Results of the balanced trade network are input to a multi-objective optimization model to improve cropping patterns based on the objectives of achieving food security and preventing water crises. The method provides 400 management scenarios to improve cropping patterns considering 51 main crops in Iran. Results show a range of improvements in food security (19-45%) and a decrease in WFs (2-3%). The selected scenario for Iran would reduce the blue water footprint by 1207 × 106 m3, and reduce the cropland area by 19 × 103 ha. This methodology allows decision makers to develop policies that achieve food security under limited water resources in arid and semi-arid regions.
Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.
Matched MeSH terms: Water Resources/supply & distribution*
This article provides an analytical solute transport model to investigate the potential of groundwater contamination by polluted surface water in a two dimensional domain. The clogging of streambed which makes the aquifer partially penetrated by the stream, is considered in the model. The impacts of pumping process, hydraulic conductivity and clogging layer on the quality of water produced from nearby drinking water wells are evaluated. It is found that results are consistent with numerical simulation conducted by MODFLOW software. Moreover, the model is applied using data of contamination occurrence in Malaysia, where high contaminants concentrations are found close to streams. Results show that the pumping activities (rate and time period) are crucial factors when evaluating the risk of groundwater contamination from surface water. Additionally, this study illustrates that the increase in either hydraulic conductivity or leakance coefficient parameters due to the clogging layer will enlarge the area of contamination. The model is able to determine the suitable pumping rate and location of the well so that the contamination plume never reaches the extraction well, which is useful in constructing riverbank filtration sites.
Droughts are recurring events in Australia and cause a severe effect on agricultural and water resources. However, the studies about agricultural drought risk mapping are very limited in Australia. Therefore, a comprehensive agricultural drought risk assessment approach that incorporates all the risk components with their influencing criteria is essential to generate detailed drought risk information for operational drought management. A comprehensive agricultural drought risk assessment approach was prepared in this work incorporating all components of risk (hazard, vulnerability, exposure, and mitigation capacity) with their relevant criteria using geospatial techniques. The prepared approach is then applied to identify the spatial pattern of agricultural drought risk for Northern New South Wales region of Australia. A total of 16 relevant criteria under each risk component were considered, and fuzzy logic aided geospatial techniques were used to prepare vulnerability, exposure, hazard, and mitigation capacity indices. These indices were then incorporated to quantify agricultural drought risk comprehensively in the study area. The outputs depicted that about 19.2% and 41.7% areas are under very-high and moderate to high risk to agricultural droughts, respectively. The efficiency of the results is successfully evaluated using a drought inventory map. The generated spatial drought risk information produced by this study can assist relevant authorities in formulating proactive agricultural drought mitigation strategies.
Forecasting of groundwater level variations is a significantly needed in groundwater resource management. Precise water level prediction assists in practical and optimal usage of water resources. The main objective of using an artificial neural network (ANN) was to investigate the feasibility of feed-forward, Elman and Cascade forward neural networks with different algorithms to estimate groundwater levels in the Langat Basin from 2007 to 2013. In order to examine the accuracy of monthly water level forecasts, effectiveness of the steepness coefficient in the sigmoid function of a developed ANN model was evaluated in this research. The performance of the models was evaluated using the mean squared error (MSE) and the correlation coefficient (R). The results indicated that the ANN technique was well suited for forecasting groundwater levels. All models developed had shown acceptable results. Based on the observation, the feed-forward neural network model optimized with the Levenberg-Marquardt algorithms showed the most beneficial results with the minimum MSE value of (0.048) and maximum R value of (0.839), obtained for simulation of groundwater levels. The present research conclusively showed the capability of ANNs to provide excellent estimation accuracy and valuable sensitivity analyses.
The hydrological effects of climate variation and land use conversion can occur at various spatial scales, but the most important sources of these changes are at the regional or watershed scale. In addition, the managerial and technical measures are primarily implemented at local and watershed scales in order to mitigate adverse impacts of human activities on the renewable resources of the watershed. Therefore, quantitative estimation of the possible hydrological consequences of potential land use and climate changes on hydrological regime at watershed scale is of tremendous importance. This paper focuses on the impacts of climate change as well as land use change on the hydrological processes of river basin based on pertinent published literature which were precisely scrutinized. The various causes, forms, and consequences of such impacts were discussed to synthesize the key findings of literature in reputable sources and to identify gaps in the knowledge where further research is required. Results indicate that the watershed-scale studies were found as a gap in tropical regions. Also, these studies are important to facilitate the application of results to real environment. Watershed scale studies are essential to measure the extent of influences made to the hydrological conditions and understanding of causes and effects of climate variation and land use conversion on hydrological cycle and water resources.