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 water chemistry of selected rivers in Kota Marudu, Sabah was studied based on the major ion chemistry and its suitability for drinking and irrigation purposes. Ten sampling stations were selected and water samples were collected from each station to assess its chemical properties. The physico-chemical variables including temperature, electrical conductivity (EC), total dissolved solids (TDS), salinity, dissolved oxygen (DO), pH, turbidity, ammoniacal-nitrogen (NH3-N), biological oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solid (TSS) were measured. The cations (K, Mg, Ca, Na) were analyzed by ICP-MS. Most of the variables were within the drinking water quality standards stipulated by the World Health Organization (WHO) and the Ministry of Health (MOH), Malaysia except for turbidity. Sodium adsorption ratio (SAR) and salinity hazard were calculated to identify the suitability of the water as irrigation water. The Wilcox diagram classifies that only 10% of samples are not suitable for the purpose of irrigation. The overall results showed that most of the rivers in Kota Marudu are still in a clean condition and suitable for drinking and irrigation purposes except for Sumbilingan River, which is considered as slightly polluted. The results are supported by the hierarchical cluster analysis as the stations were grouped into two groups; low and high pollution intensities. This preliminary result can update the baseline data of selected water quality parameters in the Kota Marudu and could serve as tool for assisting relevant government bodies in regulating the water resources policies in the future.
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.
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.
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.
Managers of water quality and water monitoring programs are often faced with constraints in terms of budget, time, and laboratory capacity for sample analysis. In such situation, the ideal solution is to reduce the number of sampling sites and/or monitored variables. In this case, selecting appropriate monitoring sites is a challenge. To overcome this problem, this study was conducted to statistically assess and identify the appropriate sampling stations of monitoring network under the monitored parameters. To achieve this goal, two sets of water quality data acquired from two different monitoring networks were used. The hierarchical agglomerative cluster analysis (HACA) were used to group stations with similar characteristics in the networks, the time series analysis was then performed to observe the temporal variation of water quality within the station clusters, and the geo-statistical analysis associated Kendall's coefficient of concordance were finally applied to identify the most appropriate and least appropriate sampling stations. Based on the overall result, five stations were identified in the networks that contribute the most to the knowledge of water quality status of the entire river. In addition, five stations deemed less important were identified and could therefore be considered as redundant in the network. This result demonstrated that geo-statistical technique coupled with Kendall's coefficient of concordance can be a reliable method for water resource managers to identify appropriate sampling sites in a river monitoring network.
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.
This paper presents the application of TOPMODEL in the Pinang catchment of Malaysia for stream flow simulation. An attempt has been made to use remote-sensing data (ASTER DEM of 30 m resolution) as a primary input for TOPMODEL in order to simulate the stream flow pattern of this tropical catchment. A calibration period was executed based on 2007-2008 hydro-meteorological dataset which gave a satisfactory Nash-Sutcliffe model (NS) model efficiency of 0.749 and a relative volume error (RVE) of -19.2. The recession curve parameter (m) and soil transmissivity at saturation zone (To), were established as the most sensitive parameters through a sensitivity analysis processes. Hydro-meteorological datasets for the period between 2009 and 2010 were used to validate the model which resulted in satisfactory efficiencies of 0.774 (NS) and -19.84 (RVE), respectively. This study demonstrated the ability ASTER DEM acquired from remote sensing to generate the required TOPMODEL parameters for stream flow simulation which gives insights into better management of available water resources.
Rainfall-runoff information is critical for water resource and river basin management. Runoff can be estimated by using two methods; gauged method (direct measurement) and ungauged method (indirect formula and equation). The in-situ measurement provides real-time and accurate yet required time-consuming operation and inaccessibility topography. Therefore, the runoff estimation modelling and equation was developed to overcome the limitation of in-situ measurement. SCS-CN is a simple model of ungauged method, where runoff volume (Q) resulting from rainfall (P) is formulated using equation of (Q= (P-Ia) 2 / (P-Ia + S). It was known as the best technique to be adopted for large basin study where time and manpower also accessibility are limited. SCS-CN method also is widely use in prediction software as it taken into consideration of the effects of soil, properties, land cover and antecedent moisture. Curve Number is well developed in USA for the agriculture purpose with many investigations to validate and calibrate the values of curve number. It was applied in numerous river basins in temperate and other regions e.g. US, Argentina, India, China, South Korea, Palestine and Malaysia. However, the reliability of the CN in the tropics is doubtable due to different land use characteristics, soil type, climate, geological features and rainfall pattern and variability. Based on the reviewed conceptual and applications of SCS-CN in temperate and tropics, numerous studies found the SCS-CN method is reliable and practical for runoff estimation in tropics region.
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*
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.
Climate change-induced spatial and temporal variability of stremflow has significant implications for hydrological processes and water supplies at basin scale. This study investigated the impacts of climate change on streamflow of the Kurau River Basin in Malaysia using a Climate-Smart Decision Support System (CSDSS) to predict future climate sequences. For this, we used 25 reliazations consisting from 10 Global Climate Models (GCMs) and three IPCC Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5). The generated climate sequences were used as input to Soil and Water Assessment Tool (SWAT) to simulate projected changes in hydrological processes in the basin over the period 2021-2080. The model performed fairly well for the Kurau River Basin, with coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) of 0.65, 0.65 and -3.0, respectively for calibration period (1981-1998) and 0.60, 0.59 and -4.6, respectively for validation period (1996-2005). Future projections over 2021-2080 period show an increase in rainfall during August to January (relatively wet season, called the main irrigation season) but a decrease in rainfall during February to July (relatively dry season, called the off season). Temperature projections show increase in both the maximum and minimum temperatures under the three RCP scenarios, with a maximum increase of 2.5 °C by 2021-2080 relative to baseline period of 1976-2005 under RCP8.5 scenario. The model predicted reduced streamflow under all RCP scenarios compared to the baseline period. Compared to 2021-2050 period, the projected streamflow will be higher during 2051-2080 period by 1.5 m3/s except in February for RCP8.5. The highest streamflow is predicted during August to December for both future periods under RCP8.5. The seasonal changes in streamflow range between -2.8% and -4.3% during the off season, and between 0% (nil) and -3.8% during the main season. The assessment of the impacts of climatic variabilities on the available water resources is necessary to identify adaptation strategies. It is supposed that such assessment on the Kurau River Basin under changing climate would improve operation policy for the Bukit Merah reservoir located at downstream of the basin. Thus, the predicted streamflow of the basin would be of importance to quantify potential impacts of climate change on the Bukit Merah reservoir and to determine the best possible operational strategies for irrigation release.
A catchment-scale investigation of the prevalence of Cryptosporidium and Giardia in the Kuang River Basin was carried out during the dry and rainy seasons. Water samples were collected from the Kuang River and its tributaries as well as a major irrigation canal at the study site. We also investigated the prevalence of gastrointestinal parasitic infection among dairy and beef cattle hosts. Cryptosporidium and/or Giardia were detected in all the rivers considered for this study, reflecting their ubiquity within the Kuang River Basin. The high prevalence of Cryptosporidium/Giardia in the upper Kuang River and Lai River is of a particular concern as both drain into the Mae Kuang Reservoir, a vital source of drinking-water to many local towns and villages at the research area. We did not, however, detected neither Cryptosporidium nor Giardia were in the irrigation canal. The frequency of Cryptosporidium/Giardia detection nearly doubled during the rainy season compared to the dry season, highlighting the importance of water as an agent of transport. In addition to the overland transport of these protozoa from their land sources (e.g. cattle manure, cess pits), Cryptosporidium/Giardia may also be re-suspended from the streambeds (a potentially important repository) into the water column of rivers during storm events. Faecal samples from dairy and beef cattle showed high infection rates from various intestinal parasites - 97% and 94%, respectively. However, Cryptosporidium and Giardia were only detected in beef cattle. The difference in management style between beef (freeranging) and dairy cattle (confined) may account for this disparity. Finally, phylogenetic analyses revealed that the Cryptosporidium/Giardia-positive samples contained C. ryanae (non-zoonotic) as well as Giardia intestinalis assemblages B (zoonotic) and E (non-zoonotic). With only basic water treatment facilities afforded to them, the communities of the rural area relying on these water supplies are highly at risk to Cryptosporidium/Giardia infections.
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.
Drought is considered one of the costliest natural disasters that result in water scarcity and crop damage almost every year. Drought monitoring and forecasting are essential for the efficient management of water resources and sustainability in agriculture. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index with its antecedent values remains a challenging task. In the present research, the SVR (support vector regression) model was hybridized with two different optimization algorithms namely; Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) for reliable prediction of effective drought index (EDI) 1 month ahead, at different locations of Uttarakhand State of India. The inputs of the models were selected through partial autocorrelation function (PACF) analysis. The output produced by the SVR-HHO and SVR-PSO models was compared with the EDI estimated from observed data using five statistical indicators, i.e., RMSE (Root Mean Square Error), MAE (Mean Absolute Error), COC (Coefficient of Correlation), NSE (Nash-Sutcliffe Efficiency), WI (Willmott Index), and graphical inspection of radar-chart, time-variation plot, box-whisker plot, and Taylor diagram. Appraisal of results indicates that the SVR-HHO model (RMSE = 0.535-0.965, MAE = 0.363-0.622, NSE = 0.558-0.860, COC = 0.760-0.930, and WI = 0.862-0.959) outperformed the SVR-PSO model (RMSE = 0.546-0.967, MAE = 0.372-0.625, NSE = 0.556-0.855, COC = 0.758-0.929, and WI = 0.861-0.956) in predicting EDI. Visual inspection of model performances also showed a better performance of SVR-HHO compared to SVR-PSO in replicating the median, inter-quartile range, spread, and pattern of the EDI estimated from observed rainfall. The results indicate that the hybrid SVR-HHO approach can be utilized for reliable EDI predictions in the study area.
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.
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.
The objective of the study is to examine the impact of natural disasters on external migration, price level, poverty incidence, health expenditures, energy and environmental resources, water demand, financial development, and economic growth in a panel of selected Asian countries for a period of 2005-2017. The results confirm that natural disasters in the form of storm and flood largely increase migration, price level, and poverty incidence, which negatively influenced country's economic resources, including enlarge healthcare expenditures, high energy demand, and low economic growth. The study further presented the following results: i) natural resource depletion increases external migration, ii) FDI inflows increase price level, iii) increase healthcare spending and energy demand decreases poverty headcount, iv) poverty incidence and mortality rate negatively influenced healthcare expenditures, v) industrialization increases energy demand, and vi) agriculture value added, fertilizer, and cereal yields required more water supply to produce greater yield. The study emphasized the need to magnify the intensity of natural disasters and create natural disaster mitigation unit to access the human and infrastructure cost and attempt quick recovery for global prosperity.
Matched MeSH terms: Water Resources/supply & distribution*
Conventional hydrogeologic framework models used to compute ocean island sustainable yields and aquifer storage neglect the complexity of the nearshore and offshore submarine environment. However, the onshore aquifer at the island of Hawai'i exhibits a notable volumetric discrepancy between high-elevation freshwater recharge and coastal discharge. In this study, we present a novel transport mechanism of freshwater moving from onshore to offshore through a multilayer formation of water-saturated layered basalts with interbedded low-permeability layers of ash/soil. Marine electromagnetic imaging reveals ∼35 km of laterally continuous resistive layers that extend to at least 4 km from west of Hawai'i's coastline, containing about 3.5 km3 of freshened water. We propose that this newly found transport mechanism of fresh groundwater may be the governing mechanism in other volcanic islands. In such a scenario, volcanic islands worldwide can use these renewable offshore reservoirs, considered more resilient to climate change-driven droughts, as new water resources.
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.