Showing papers in "Water Resources Management in 2018"

Environmental Benefits and Control of Pollution to Surface Water and Groundwater by Agroforestry Systems: a Review

Abstract: Agricultural pollution consists a serious concern for environmental protection managers. Among the pollutants, nitrates, phosphoric compounds and organic pesticides from agricultural activities are the most common and hazardous to the environment and human health. Several mitigation techniques have been proposed to control these pollutants from entering aquatic systems. Agroforestry, which is the common cultivation of crops and trees, is one such mitigation technique. In the present study, the efficiency of agroforestry systems in pollutant reduction is reviewed. A search of relevant international literature was conducted using Scopus, Science Direct and Google Scholar search engines, using relevant keyword combinations for agrochemical pollution abatement with trees. More than 2000 results were found and the most relevant were selected and extensively studied, and are summarized here. From the current knowledge, it can be generally seen that tree roots in agroforestry systems are able to reduce nitrogen and phosphorus residues in soils from 20% up to 100%, have the potential to reduce pesticides leaching and runoff in considerable amounts (up to 90% for runoff), and simultaneously they provide additional benefits to the ecosystems including erosion control, improvement of soil quality and positive effects on biodiversity.

Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

Abstract: In this study, a new hybrid model integrated adaptive neuro fuzzy inference system with Firefly Optimization algorithm (ANFIS-FFA), is proposed for forecasting monthly rainfall with one-month lead time. The proposed ANFIS-FFA model is compared with standard ANFIS model, achieved using predictor-predictand data from the Pahang river catchment located in the Malaysian Peninsular. To develop the predictive models, a total of fifteen years of data were selected, split into nine years for training and six years for testing the accuracy of the proposed ANFIS-FFA model. To attain optimal models, several input combinations of antecedents’ rainfall data were used as predictor variables with sixteen different model combination considered for rainfall prediction. The performances of ANFIS-FFA models were evaluated using five statistical indices: the coefficient of determination (R 2 ), Nash-Sutcliffe efficiency (NSE), Willmott’s Index (WI), root mean square error (RMSE) and mean absolute error (MAE). The results attained show that, the ANFIS-FFA model performed better than the standard ANFIS model, with high values of R 2 , NSE and WI and low values of RMSE and MAE. In test phase, the monthly rainfall predictions using ANFIS-FFA yielded R 2 , NSE and WI of about 0.999, 0.998 and 0.999, respectively, while the RMSE and MAE values were found to be about 0.272 mm and 0.133 mm, respectively. It was also evident that the performances of the ANFIS-FFA and ANFIS models were very much governed by the input data size where the ANFIS-FFA model resulted in an increase in the value of R 2 , NSE and WI from 0.463, 0.207 and 0.548, using only one antecedent month of data as an input (t-1), to almost 0.999, 0.998 and 0.999, respectively, using five antecedent months of predictor data (t-1, t-2, t-3, t-6, t-12, t-24). We ascertain that the ANFIS-FFA is a prudent modelling approach that could be adopted for the simulation of monthly rainfall in the present study region.

Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Abstract: Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.

Changing Pattern of Droughts during Cropping Seasons of Bangladesh

Abstract: There has been a growing concern on temporal variations on drought characteristics due to climate change. This study compares meteorological drought characteristics for two different periods to quantify the temporal changes in seasonal droughts of 18 weather stations of the country. Fifty-five years rainfall and temperature data are divided into two different thirty-year periods, 1961–1990 and 1985–2014 and standardized precipitation evapotranspiration index (SPEI) for those periods are calculated to assess the changes. Four seasons in this study are selected as two major crop growing seasons namely, Rabi (November to April) and Kharif (May to October) and two critical periods for crop growth in term of water supply namely critical Rabi (March–April) and critical Kharif (May). Results show that moderate, extreme, and severe Rabi droughts has increased in 11, 9, and 4 stations out of 18 stations, respectively, and Kharif severe and extreme droughts has increased in 8 and 9 stations, respectively, In addition, the frequency analysis shows that the return periods have decreased during 1985–2014 at the stations where it was high during 1961–1990 and vice versa. This has made the spatial distribution of return periods of droughts more uniform over the country for most of the seasons. Increased return period of droughts in highly drought prone north and northwest Bangladesh has caused decrease in average frequency of droughts. Consequently, this result corresponds that Bangladesh experiences fewer droughts in recent years. Trend analysis of rainfall and temperature data reveals that significant increase of mean temperature and no significant change in rainfall in almost all months have increased the frequency of droughts in the regions where droughts were less frequent.

Streamflow and Hydrological Drought Trend Analysis and Forecasting in Cyprus

Abstract: The persistent water shortage in Cyprus has been alleviated by importing freshwater from neighbouring countries, and severe droughts have been met with financial reimbursement from the EU at least twice. The goal of this research is to investigate and perform short-term forecasting of both streamflow and hydrological drought trends over the island. Eleven hydrometric stations with a 34-year common record length of the mean daily discharge from 10/1979 to 09/2013 are used for this purpose, with the relevant upstream catchments considered to represent pristine conditions. The Streamflow Drought Index (SDI) successfully captures the hydrological drought conditions over the island, and the performance of the index is validated based on both the historic drought archives and results from other drought indices for the island. The Mann–Kendall (M-K) test reveals that the annual and seasonal time series of the discharge volumes always illustrate a decreasing but insignificant trend at a significance level of a = 0.05; additionally, the decrease per decade in the average annual streamflow volume based on Sen’s slope statistic is approximately −9.4%. The M-K test on the SDI reveals that drought conditions intensified with time. Ten autoregressive integrated moving average (ARIMA) models are built and used to forecast the mean monthly streamflow values with moderate accuracy; the best ARIMA forecast model in each catchment is derived by comparing two model-performance statistical measures for the different (p,d,q) model parameters. The predicted discharge values are processed by the SDI-3 index, revealing that non-drought conditions are expected in most catchments in the upcoming three months, although mild-drought conditions are anticipated for catchments 7, 8 and 9.

Investigation Anthropogenic Impacts and Climate Factors on Drying up of Urmia Lake using Water Budget and Drought Analysis

Abstract: The water level of Urmia Lake, the largest inland lake in Iran with maximum water surface area of about 6000 km2, has been shrinking for the last two decades. Although a number of study have been performed to determine drought condition and coastline changes of Urmia Lake, there has not been a detailed study to distinguish anthropogenic effects from climate impacts on the drying of Urmia Lake. In this study, water budget of Urmia Lake and the intensity of drought in the basin were analyzed in the period from 1985 to 2010 and a new hypothesis is proposed to quantify anthropogenic and climate impacts in reducing the volume of Urmia Lake. The results of this study indicate that human impacts on the Lake and its basin are more important than climate factors. Though previous studies assumed that ground water output from Urmia Lake is negligible, the results of this study show the presence of significant groundwater seepage from Urmia Lake. Major changes in the variables that reduced the water level of Urmia Lake were observed since 1998. Anthropogenic impacts and climate factors have roughly 80% and 20% effects on the drying up of Urmia Lake, respectively. Hence, the first step to recover Urmia Lake could be the revision of management surface water, operation of dams and groundwater resources. The second step could be the review and classification of agricultural products grown in the region in terms of water consumption and teach local people the best practice methods for irrigation.

Application of the Innovative Trend Analysis Method for the Trend Analysis of Rainfall Anomalies in Southern Italy

Abstract: In this paper, an investigation of the temporal rainfall variability, in a large area of southern Italy, has been carried out using a homogeneous monthly rainfall dataset of 559 rain gauges with more than 50 years of observation. The area under investigation is a large portion of the Italian peninsula, ranging from the Campania and the Apulia regions in the North, to Sicily in the South, and covering an area of about 85,000 km2. Possible trends in seasonal and annual rainfall values have been detected by means of a new graphical technique, Sen’s method, which allows the trend identification of the low, medium and high values of a series. Moreover, the Mann–Kendall test has been also applied. As a result, different values and tendencies of the highest and of the lowest rainfall data have emerged among the five regions considered in the analysis. In particular, at seasonal scale, a negative trend has been detected especially in winter and in autumn in the whole study area, whereas not well defined trend signals have been identified in summer and spring.

A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Abstract: Monthly forecasting of streamflow is of particular importance in water resources management especially in the provision of rule curves for dams. In this paper, the performance of four data-driven models with different structures including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN), Least Square-Support Vector Regression (LS-SVR), and K-Nearest Neighbor Regression (KNN) are evaluated in order to forecast monthly inflow to Karkheh dam, Iran, in linear and non-linear conditions while the optimized values of the model parameters are determined in the same condition via the Leave-One-Out Cross Validation (LOOCV) method. Results show that the performance of the models is different in linear and nonlinear conditions; the cumulative ranking of the models according to the three assessment criteria including NSE, RMSE and R2 indicates that ANN performs best in linear conditions while LS-SVR, GRNN and KNN are in the next ranks, respectively. But in nonlinear conditions, the best performance belongs to LS-SVR, followed by KNN, ANN, and GRNN models.

Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons

Abstract: Streamflow forecasting is paramount process in water and flood management, determination of river water flow potentials, environmental flow analysis, agricultural practices and hydro-power generation. However, the dynamicity, stochasticity and inherent complexities present in the temporal evolution of streamflow could hinder the accurate and reliable forecasting of this important hydrological parameter. In this study, the uncertainty and nonstationary characteristics of streamflow data has been treated using a set of coupled data pre-processing methods before being considered as input for an artificial neural network algorithm namely; rolling mechanism (RM) and grey models (GM). The rolling mechanism method is applied to smooth out the dataset based on the antecedent values of the model inputs before being applied to the GM algorithm. The optimization of the input datasets selection was performed using auto-correlation (ACF) and partial auto-correlation (PACF) functions. The pre-processed data was then integrated with two artificial neural network models, the back propagation (RMGM-BP) and Elman Recurrent Neural Network (RMGM-ERNN). The development, training, testing and evaluation of the proposed hybrid models were undertaken using streamflow data for two tropical hydrological basins (Johor and Kelantan Rivers). The hybrid RMGM-ERNN was found to provide better results than the hybrid RMGM-BP model. Relatively good performance of the proposed hybrid models with a data pre-processing approach provides a successful alternative to achieve better accuracy in streamflow forecasting compared to the traditional artificial neural network approach without a data pre-processing scheme.

Development and Comparison of Two Fast Surrogate Models for Urban Pluvial Flood Simulations

Abstract: Detailed full hydrodynamic 1D-2D dual drainage models are a well-established approach to simulate urban pluvial floods. However, despite modelling advances and increasing computational power, this approach remains unsuitable for many real time applications. We propose and test two computationally efficient surrogate models. The first approach links a detailed 1D sewer model to a GIS-based overland flood network. For the second approach, we developed a conceptual sewer and flood model using data-driven and physically based structures, and coupled the model to pre-simulated flood maps. The city of Ghent (Belgium) is used as a test case. Both surrogate models can provide comparable results to the original model in terms of peak surface flood volumes and maximum flood extent and depth maps, with a significant reduction in computing time.

Forecasting of Multi-Step Ahead Reference Evapotranspiration Using Wavelet- Gaussian Process Regression Model

Abstract: Evapotranspiration is one of the most important components in the optimization of water use in agriculture and water resources management. In recent years, artificial intelligence methods and wavelet based hybrid model have been used for forecasting of hydrological parameters. In present study the application of the Gaussian Process Regression (GPR) and Wavelet-GPR models to forecast multi step ahead daily (1–30 days ahead) reference evapotranspiration at the synoptic station of Zanjan (Iran) were investigated. For this purpose a 10-year statistical period (2000–2009) was considered, 7 years (2000–2006) for training and the final three years (2007–2009) for testing the various models. Various combinations of input data (various lag times) and different kinds of mother wavelets were evaluated. Results showed that, compared to the GPR model, the hybrid model Wavelet-GPR had greater ability and accuracy in forecasting of daily evapotranspiration. Moreover, the use of yearly lag times in the GPR and wavelet-GPR model increased its accuracy. Investigation of various kinds of mother wavelets also indicated that the Meyer wavelet was the most suitable mother wavelet for forecasting of daily reference evapotranspiration. The results showed that by increasing the forecasting time period from 1 to 30 days, the accuracy of the models is reduced (RMSE = 0.068 mm/day for one day ahead and RMSE = 0.816 mm/day for 30 days ahead). Application of the proposed model to summer season showed that the performance of the model at summer season is better than its performance throughout the year.

Multi-criteria Approach for Selection of Green and Grey Infrastructure to Reduce Flood Risk and Increase CO-benefits

Abstract: Continuous changes in climate conditions combined with urban population growth pose cities as one of the most vulnerable areas to increasing flood risk. In such an atmosphere of growing uncertainty, a more effective flood risk management is becoming crucial. Nevertheless, decision-making and selection of adequate systems is a difficult task due to complex interactions between natural, social and built environments. The combination of green (or sustainable) and grey (or traditional) options has been proposed as a way forward to ensure resilience in advance of extreme events, and at the same time to obtain co-benefits for society and the environment. The present paper describes a novel method for selection of urban flood measures, based on a multi-criteria analysis that includes flood risk reduction, cost minimization and enhancement of co-benefits. The aim of this method is to assist decision makers in selecting and planning measures, which afterwards can be part of either high level scoping analysis or more complex studies, such as model based assessment. The proposed method is implemented within a tool which operates as a standalone application. Through this tool, the method has been applied in three study cases. The findings obtained indicate promising potential of the method here introduced.

A Selective Literature Review on Leak Management Techniques for Water Distribution System

Abstract: Water Distribution System suffers from leakages causing social and economic costs. There is need of platform to manage water distribution system more efficiently by detecting, localizing and controlling the leakages even before or as soon as they occur, ensuring quality water services to the consumers. Since last two decades, high efforts have been made by researchers for the development of efficient leakage management techniques for reduction of water losses in distribution system. This paper provides a comprehensive analysis on leakage management techniques covering three aspects: leakage assessment, leakage detection and leakage control, with an objective to identify present challenges and future scope in their respected field. Role of smart water technologies for efficient leakages management in pipeline network is also examined and discussed. Conclusion is drawn regarding current leakage management techniques and proposals for future work and existing challenges are also outlined.

Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting

Abstract: In recent years, the data-driven modeling techniques have gained more attention in hydrology and water resources studies. River runoff estimation and forecasting are one of the research fields that these techniques have several applications in them. In the current study, four common data-driven modeling techniques including multiple linear regression, K-nearest neighbors, artificial neural networks and adaptive neuro-fuzzy inference systems have been used to form runoff forecasting models and then their results have been evaluated. Also, effects of using of some different scenarios for selecting predictor variables have been studied. It is evident from the results that using flow data of one or two month ago in the predictor variables dataset can improve accuracy of results. In addition, comparison of general performances of the modeling techniques shows superiority of results of KNN models among the studied models. Among selected models of the different techniques, the selected KNN model presented best performance with a linear correlation coefficient equal to 0.84 between observed flow data and predicted values and a RMSE equal to 2.64.

Emotional ANN (EANN) and Wavelet-ANN (WANN) Approaches for Markovian and Seasonal Based Modeling of Rainfall-Runoff Process

Abstract: The hydrological time series have three principle components (autoregressive, seasonality and trend) and the performance of the models is strongly related to the nature of these components. The current research examines the accuracy of two Artificial Neural Network (ANN) based approaches for rainfall-runoff (r-r) modeling of two catchments with different geomorphological conditions at monthly and daily time scales. The techniques proposed here are hybrid wavelet-ANN (WANN) model, as a multi-resolution forecasting tool and Emotional Artificial Neural Network (EANN) (a new generation of ANN based models) which serves artificial emotional factors as well as classic bias and weights parameters. The obtained results for monthly modeling show that WANN could perform better than the simple feed forward neural network (FFNN) model up to 40% and 35% in terms of verification and training efficiency criteria due to significant seasonality involved in the monthly time series of the process. On the other hand, the obtained results for daily modeling via FFNN and EANN, both as Markovian models, indicates the superiority of EANN over FFNN because of EANN capability to better learning of extraordinary and extreme conditions of the process in the training phase.

Short-Term Urban Water Demand Prediction Considering Weather Factors

Abstract: Accurate and reliable forecasting plays a key role in the planning and designing of municipal water supply infrastructures. Recent studies related to water demand prediction have shown that water demand is driven by weather variables, but the results do not clearly show to what extent. The principal aim of this research was to better understand the effects of weather variables on water demand. Additionally, it aimed to offer an appropriate and reliable technique to predict municipal water demand by using the Gravitational Search Algorithm (GSA) and Backtracking Search Algorithm (BSA) with Artificial Neural Network (ANN). Moreover, eight weather factors were adopted to evaluate their impact on the water demand. The principal findings of this research are that the hybrid GSA-ANN (Agent = 40) model is superior in terms of fitness function (based on RMSE) for yearly and seasonal phases. In addition, it is evidently clear from the findings that the GSA-ANN model has the ability to simulate both seasonal and yearly patterns for daily data water consumption.

Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows

Abstract: An investigation is presented in this paper to study the performance of Artificial Intelligence running Multiple Models (AIMM) using time series of river flows. This is a modelling strategy, which is formed by first running two Artificial Intelligence (AI) models: Support Vector Machine (SVM) and its hybrid with the Fire-Fly Algorithm (FFA) and they both form supervised learning at Level 1. The outputs of Level 1 models serve as inputs to another AI Model at Level 2. The AIMM strategy at Level 2 is run by Artificial Neural Network (MM-ANN) and this is compared with the Simple Averaging (MM-SA) of both inputs. The study of the performances of these models (SVM, SVM-FFA, MM-SA and MM-ANN) in the paper shows that the ability of SVM-FFA in matching observed values is significantly better than that of SVM and that of MM-ANN is considerably better than each SVM and/or SVM-FFA but the performances are deteriorated by using the MM-SA strategy. The results also show that the residuals of MM-ANN are less noisy than those shown by the models at Level 1 and those at Level 2 do not display any trend.

Intelligent Water Management: a Triangular Type-2 Intuitionistic Fuzzy Matrix Games Approach

Abstract: Matrix games with fuzzy payoffs have spread itself nowadays in diverse fields. Fuzzy game theory with triangular type-1 fuzzy numbers are visited more by researchers. In this paper, we consider matrix games with payoffs as triangular type-2 intuitionistic fuzzy numbers, i.e., Triangular Type-2 Intuitionistic Fuzzy Matrix Game (TT2IFMG) as a new and rare concept. A new ranking function is used to get relevant solutions of TT2IFMG. We are living in times of unprecedented scientific-technical advancement, yet facing several critical global problems that threaten human welfare and our ecosystem. Water management, a burning problem of the Earth now-a-days, is treated here under the scanner of TT2IFMG environment where we discuss some policy-management toward the free and fair accession of water against its limited resources.

Accuracy and Sensitivity Evaluation of TFR Method for Leak Detection in Multiple-Pipeline Water Supply Systems

Abstract: The transient frequency response (TFR) based pipe leak detection method has been developed and applied to water pipeline systems with different connection complexities such as branched and looped pipe networks. Previous development and preliminary applications have demonstrated the advantages of high efficiency and non-intrusion for this TFR method. Despite of the successful validations through extensive numerical applications in the literature, this type of method has not yet been examined systematically for its inherent characteristics and application accuracy under different system and flow conditions. This paper investigates the influences of the analytical approximations and assumptions originated from the method development process and the impacts of different uncertainty factors in practical application systems on the accuracy and applicability of the TFR method. The influence factors considered for the analysis contain system properties, derivation approximations and data measurement, and the pipeline systems used for the investigation include simple branched and looped multi-pipe networks. The methods of analytical analysis and numerical simulations are adopted for the investigation. The accuracy and sensitivity of the TFR method is evaluated for different factors and system conditions in this study. The results and findings are useful to understand the validity range and sensitivity of the TFR-based method, so as to better apply this efficient and non-intrusive method in practical pipeline systems.

Future Scenarios of Surface Water Resources Availability in North African Dams

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

Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT

Abstract: This study applied a time series evapotranspiration (ET) data derived from the remote sensing to evaluate Soil and Water Assessment Tool (SWAT) model calibration, which is a unique method. The SWAT hydrologic model utilized monthly stream flow data from two US Geological Survey (USGS) stations within the Big Sunflower River Watershed (BSRW) in Northwestern, Mississippi. Surface energy balance algorithm for land (SEBAL), which utilized MODerate Resolution Imaging Spectro-radiometer (MODIS) to generate monthly ET time series data images were evaluated with the SWAT model. The SWAT hydrological model was calibrated and validated using monthly stream flow data with the default, flow only, ET only, and flow-ET modeling scenarios. The flow only and ET only modeling scenarios showed equally good model performances with the coefficient of determination (R2) and Nash Sutcliffe Efficiency (NSE) from 0.71 to 0.86 followed by flow-ET only scenario with the R2 and NSE from 0.66 to 0.83, and default scenario with R2 and NSE from 0.39 to 0.78 during model calibration and validation at Merigold and Sunflower gage stations within the watershed. The SWAT model over-predicted ET when compared with the Modis-based ET. The ET-based ET had the closest ET prediction (~8% over-prediction) as followed by flow-ET-based ET (~16%), default-based ET (~27%) and flow-based ET (~47%). The ET-based modeling scenario demonstrated consistently good model performance on streamflow and ET simulation in this study. The results of this study demonstrated use of Modis-based remote sensing data to evaluate the SWAT model streamflow and ET calibration and validation, which can be applied in watersheds with the lack of meteorological data.

GIS-based Methodology for Identification of Suitable Locations for Rainwater Harvesting Structures

Abstract: Presently, the water resources across the world are being continuously depleted. It is essential to find sustainable solutions for this shortage of water. Rainwater harvesting is one such promising solution to this problem. This paper presents a new GIS-based methodology to identify suitable locations for rainwater harvesting structures using only freely available imageries/remote sensing data and data from other sources. The methodology has been developed for the semi-arid environment of Khushkhera-Bhiwadi-Neemrana Investment Region (KBNIR) in Alwar district of Rajasthan. For identifying locations suitable for rainwater harvesting structures, the layers of surface elevation (ASTER-DEM), landuse/landcover, soil map, drainage map and depression map are used and further analyzed for their depression volume, and availability of surface runoff using Soil Conservation Service - Curve Number (SCS-CN) method. Based on the proposed criteria total seven locations were identified, out of which two locations are excellent; three locations are good, (if provisions of overflow structure are made for them) and two locations are not suitable for rain water harvesting. The total rainwater harvesting potential of the study area is 54.49 million cubic meters which is sufficient to meet the water requirements if harvested and conserved properly. This methodology is time-saving and cost-effective. It can minimize cost of earthwork and can be utilized for the planning of cost effective water resource management.

Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning Algorithms: a Multiple-Case Study from Greece

Abstract: We provide contingent empirical evidence on the solutions to three problems associated with univariate time series forecasting using machine learning (ML) algorithms by conducting an extensive multiple-case study. These problems are: (a) lagged variable selection, (b) hyperparameter handling, and (c) comparison between ML and classical algorithms. The multiple-case study is composed by 50 single-case studies, which use time series of mean monthly temperature and total monthly precipitation observed in Greece. We focus on two ML algorithms, i.e. neural networks and support vector machines, while we also include four classical algorithms and a naive benchmark in the comparisons. We apply a fixed methodology to each individual case and, subsequently, we perform a cross-case synthesis to facilitate the detection of systematic patterns. We fit the models to the deseasonalized time series. We compare the one- and multi-step ahead forecasting performance of the algorithms. Regarding the one-step ahead forecasting performance, the assessment is based on the absolute error of the forecast of the last monthly observation. For the quantification of the multi-step ahead forecasting performance we compute five metrics on the test set (last year’s monthly observations), i.e. the root mean square error, the Nash-Sutcliffe efficiency, the ratio of standard deviations, the coefficient of correlation and the index of agreement. The evidence derived by the experiments can be summarized as follows: (a) the results mostly favour using less recent lagged variables, (b) hyperparameter optimization does not necessarily lead to better forecasts, (c) the ML and classical algorithms seem to be equally competitive.

Development and Implementation of Support Vector Machine Regression Surrogate Models for Predicting Groundwater Pumping-Induced Saltwater Intrusion into Coastal Aquifers

Abstract: Predicting the extent of saltwater intrusion (SWI) into coastal aquifers in response to changing pumping patterns is a prerequisite of any groundwater management framework. This study investigates the feasibility of using support vector machine regression (SVMr), an innovative artificial intelligence-based machine learning algorithm for predicting salinity concentrations at selected monitoring wells in an illustrative aquifer under variable groundwater pumping conditions. For evaluation purpose, the prediction results of SVMr are compared with well-established genetic programming (GP) based surrogate models. SVMr and GP models are trained and validated using identical sets of input (pumping) and output (salinity concentration) datasets. The trained and validated models are then used to predict salinity concentrations at specified monitoring wells in response to new pumping datasets. Prediction capabilities of the two learning machines are evaluated using different proficiency measures to ensure their practicality and generalisation ability. The performance evaluation results suggest that the prediction capability of SVMr is superior to GP models. Also, a sensitivity analysis methodology is proposed for assessing the impact of pumping rates on salt concentrations at monitoring locations. This sensitivity analysis provides a subset of most influential pumping rates, which is used to construct new SVMr surrogate models with improved predictive capabilities. The improved prediction capability and the generalisation ability of the SVMr models together with the ability to improve the accuracy of prediction by refining the input set for training makes the use of proposed SVMr models more attractive. Prediction models with more accurate prediction capability makes it potentially very useful for designing large scale coastal aquifer management strategies.

Analysis of Impacts of Climate Change and Human Activities on Hydrological Drought: a Case Study in the Wei River Basin, China

Abstract: Climate change and human activity are the two major drivers that can alter hydrological cycle processes and influence the characteristics of hydrological drought in river basins. The present study selects the Wei River Basin (WRB) as a case study region in which to assess the impacts of climate change and human activity on hydrological drought based on the Standardized Runoff Index (SRI) on different time scales. The Generalized Additive Models in Location, Scale and Shape (GAMLSS) are used to construct a time-dependent SRI (SRIvar) considering the non-stationarity of runoff series under changing environmental conditions. The results indicate that the SRIvar is more robust and reliable than the traditional SRI. We also determine that different driving factors can influence the hydrological drought evolution on different time scales. On shorter time scales, the effects of human activity on hydrological drought are stronger than those of climate change; on longer time scales, climate change is considered to be the dominant factor. The results presented in this study are beneficial for providing a reference for hydrological drought analysis by considering non-stationarity as well as investigating how hydrological drought responds to climate change and human activity on various time scales, thereby providing scientific information for drought forecasting and water resources management over different time scales under non-stationary conditions.

Impact of Normalization and Input on ARMAX-ANN Model Performance in Suspended Sediment Load Prediction

Abstract: The suspended sediment load in rivers is an important parameter in watershed planning and management. Since daily suspended sediment time series contain linear and nonlinear components, existing prediction models are associated with limitations. Therefore, this study introduces a new hybrid model comprising two commonly used stochastic and nonlinear models. The sediment load is first modeled by an autoregressive-moving average with exogenous terms (ARMAX) model. Subsequently, the ARMAX residuals are modeled with an artificial neural network (ANN). For this purpose, discharge (Q) and sediment (S) are considered as model input parameters. Three modeling scenarios are defined to investigate the impact of data normalization on the hybrid model. The exponential and Box-Cox transformation methods are combined into a new data normalization method called mixed transformation. The performance of these methods is then compared. In addition, the impact of the type and number of input combinations on ARMAX-ANN model accuracy is evaluated. To this end, 12 input combinations and 1331 ARMAX and ANN models are verified. The ARMAX model inputs include S, Q and the white noise disturbance term (e), while the ANN model inputs include the ARMAX model results and residuals. Moreover, the hybrid model’s accuracy is compared with the ARMAX and ANN models.

Optimization of Reservoir Operation with a New Approach in Evolutionary Computation Using TLBO Algorithm and Jaya Algorithm

Abstract: Reservoir operation and management are complex engineering problems, due to the stochastic nature of inflow, various demands and as well as tailwater in the downstream. The complexity increases when the number of reservoirs gets increased such as multi-reservoir system or chain system. To obtain optimal operation in such condition become more difficult. It requires powerful optimization algorithm to solve aforesaid problems. Teaching Learning Based Optimization (TLBO) algorithm and Jaya Algorithm (JA) are recently developed advanced optimization techniques a novel approach comparatively simple, easy, and robust. The main advantages of these algorithms are it only requires the common control parameters such as number of iterations and population size. In the present study, three different benchmark problems were evaluated to check the applicability and performance of TLBO and JA in multi-reservoir operation problems. The benchmark problems are the discrete time four-reservoir operation (DFRO), the continuous time four-reservoir operation (CFRO), and the ten-reservoir operation (TRO). The results from the TLBO and JA are compared with different approaches from the literature. The optimal net benefits obtained from JA for DFRO, CFRO and TRO problems are 401.44, 308.40 and 1194.59, respectively, and that of TLBO algorithm are 401.33, 308.30 and 1194.44, respectively. It is found that both JA and TLBO algorithms provided a satisfactory solution as other optimization techniques, from literature. In conclusion, JA outperformed over TLBO.

Copulas-Based Drought Characteristics Analysis and Risk Assessment across the Loess Plateau of China

Abstract: The Loess Plateau (LP) of China is famous with soil erosion and water shortage problems. Droughts were frequently occurred in this region, which becomes a critical limiting factor to the socioeconomic development, ecology and food production. Therefore, the major motivation of the present study is to investigate the drought characteristics and assess the potential drought risk in this area, which is crucial for drought resistance, water resource management as well as agricultural production. This study analyzes the variations of meteorological drought, characterized by the Standardized Precipitation Evapotranspiration Index (SPEI), and assesses the drought hazards in the LP during 1950–2014. The results show that the northwest of LP is more likely to experience long duration and large severity droughts than the southeast of LP. From the perspective of statistical probability models, the exponential distribution and Gamma distribution can well fit the drought duration and severity, respectively. Compared to Frank and Clayton copula, the Gumbel copula can better model the dependence structure between the drought variables in our study area. Moreover, the estimation of the upper tail dependence coefficient between drought duration and severity also demonstrate that Gumbel copula can provide the best description of the upper tail. The spatial distribution of joint return period under different cases indicates that drought risk in northwestern LP is relatively higher than that in other areas of LP. The results presented in this study can provide some scientific basis for the strategic planning of drought resistance and water resource management in the LP.

Tempo-Spatial Analysis of Water Quality in the Three Gorges Reservoir, China, after its 175-m Experimental Impoundment

Abstract: The Three Gorges Reservoir (TGR) has brought great socio-economic benefits and has had huge effects on the aquatic environment. The large scale and diversity of the TGR result in the variations of the water quality among the mainstream and its tributaries. Comprehensive understanding of the water quality status is crucial for water management and regional development of the TGR. Monthly data of 8 water quality parameters, including potential of hydrogen, biochemical indexes and nutrients indexes, were collected from 14 sampling sites distributed in the Yangtze River and four tributaries. The temporal and spatial distributions of each water quality parameter were presented, and the underlying causes were disclosed. The cluster analysis (CA) and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) were adopted to analyze and assess the water quality statuses and trend. The results showed that most of the water quality parameters presented significant seasonal patterns due to the seasonality of hydrologic/hydraulic variables. Water quality status and pollution type varied among the mainstream and its tributaries, due to the spatial heterogeneity of geology, geomorphology and anthropogenic activities. NO3-N, TN and TP were identified as the key pollution indexes, presenting the enriched nutrients in the water body. A large proportion of NO3-N in the TN (over 80%) was linked to the abuse of chemical fertilizers. The water quality in the TGR cannot always reach natural or desirable levels at several of the sampling sites where development and urbanization are relatively high, such as those near the main urban area of Chongqing or the inflow section of the Wu River. This study is expected to have major implications for water quality analysis and assessment approaches and water environment protection and management at large scales.

Impact of Changes in Groundwater Extractions and Climate Change on Groundwater-Dependent Ecosystems in a Complex Hydrogeological Setting

Abstract: Climate change and other future developments can influence the availability of groundwater resources for drinking water. The uncertainty of the projected impact is a challenge given the urgency to decide on adaptation measures to secure the drinking water supply. Improved understanding on how climate change affects the groundwater system is necessary to develop adaptation strategies. AZURE is used, a detailed, well-calibrated hydrological model to study the projected impact of climate change scenarios on the large Veluwe aquifer in the Netherlands. The Veluwe area is an important source of drinking water. However, some existing groundwater extractions in the area affect nearby groundwater-dependent ecosystems. Redistribution of the licensed extraction volumes of these sites is considered to reduce the impact on these ecosystems. The projected impact of climate change and redistribution to groundwater levels is studied. The research shows that in a slowly responding large aquifer the projected climate change may cause rising groundwater levels despite the projected increase in summer dryness. The results indicate that this impact may exceed the impact of redistribution of extraction volumes. In addition, it is shown that the combined effect strongly depends on local conditions, thus highlighting the need for high-resolution modelling.