ANALYTICAL SOLUTION OF GROUNDWATER FLUCTUATIONS IN ESTUARINE AQUIFER

1.INTRODUCTIONTide-induced head fluctuations are a naturalphenomenon in a coastal aquifer[1-3].For an un-confined aquifer,groundwater level changes corre-sponding to the tidal waves.As the tidal propagateinland,their amplitude is attenuated and phase-shifts occur.A typical damping distance for tidalwater table fluctuation in an unconfined aquifer isseveral hundred meters whereas the tidal influenceon a confined aquifer can extended landward byseveral thousand meters[2,4].According to thef…     

Long-Term Quantification of Stream-Aquifer Exchange in a Variably-Saturated Heterogeneous Environment

A variably-saturated finite element model HYDRUS-2D was used to simulate the spatiotemporal dynamics of stream-aquifer exchange for a perennial stream flowing through an undulating catchment and underlain by heterogeneous geology. The model was first calibrated and validated using piezometric heads measured near the stream. The model was then used a) to quantify the long-term dynamics of exchange at stream-aquifer interface and the water balance in the domain, and b) to evaluate the impact of anisotropy of geological materials, thickness (w) and hydraulic conductivity (K _s ) of the low permeability layer at the streambed, and water table fluctuations on the extent of exchange. Simulated pressure heads in the domain revealed that seasonal groundwater fluctuations were more pronounced near the stream. Daily discharge to the stream varied from 0.05 to 0.3 mm/day, annual discharge ranged from 59 to 74 mm, and the overall water balance showed a discharge (?54 mm) from the domain during 2000–2012. A five-fold increase in K _s of the low permeability layer enhanced discharge to the stream by 14% (10 mm/year) whereas an increase in the thickness of the layer by 1 m had a low impact (2.4 mm/year). A 2-m drawdown of the water table transformed a connected and gaining system into a losing, disconnected system. These results suggest that depletion of groundwater due to climate change or excessive pumping could have a pronounced impact on the availability of water resources and sustainability of the existing water-dependent ecosystem.     

Assessment of the Impact of Municipal Solid Waste on Groundwater Quality near the Sangamner City using GIS Approach

The present work aims at assessing the impact of MSW on the groundwater quality around dumping yard site, located near the Sangamner city by water quality index (WQI) and its integration in geographical information system (GIS). Groundwater samples (n?=?15) around the dumping yard were collected using Garmin GPS device in October 2013 and October 2014. Physico-chemical analysis of same samples was carried out for pH, EC, TDS, Na⁺, K⁺,Ca²⁺, Mg²⁺, TH, Cl^?, HCO₃ ^?, SO₄ ^2? and NO₃ ^? along with the heavy metals like Fe, Zn, Cd and Cr by using standard methods. Similarly, SAR, KRs, RSC and SSP were also calculated to know the groundwater quality into irrigation perspective. WQI for 15 samples were calculated using physico-chemical results/data of 12 parameters and its desirable limit of BIS standard. Generated WQI (z) for October 2013 and October 2014 were integrated with latitude (y) and longitude (x) values, collected using GPS during the field work. Integrated xyz data were then interpolated in Surfer-10 GIS software using inverse distance weight (IDW) method to estimate the groundwater quality of the study area. Study revealed that the groundwater quality around the dumping yard area does not confirm to drinking and domestic purposes as per the WQI and BIS standard. However, the groundwater quality is marginally suitable for irrigation as per SAR, KRs, RSC and SSP. The influence of leachate from MSW dumping site to surrounding groundwater is creating a serious concern and susceptible to potential health hazards. Thus, continuous monitoring of groundwater is desperately required in order to minimize the groundwater pollution for control the pollution-caused MSW.     

Improving the Pressurized Flushing Efficiency in Reservoirs: an Experimental Study

Sediment flushing in many reservoirs of the world is accomplished with low efficiency. In this study, a new configuration was proposed for reservoir bottom outlet to increase the pressurized flushing efficiency. In the new configuration, a projecting semi-circular structure was connected to the upstream edge of bottom outlet. It was observed that by employing the projecting bottom outlet, the sediment removal efficiency increased significantly compared to the flushing via typical bottom outlet. In the case of new-configuration bottom outlet with L _sc /D _outlet  = 5.26 and D _sc /D _outlet  = 1.32, the dimensionless length, width and depth of flushing cone increased 280%, 45% and 14%, respectively, compared to the reference test. The proposed structure can ensure the sustainable use of reservoirs.     

Excess Stormwater Quantification in Ungauged Watersheds Using an Event-Based Modified NRCS Model

Quantifying runoff from a storm event is a crucial part of rainfall-runoff model development. The objective of this study is to illustrate inconsistencies in the initial abstraction (I _a) and curve number (CN) in the Natural Resources Conservation Service (NRCS) model for ungauged steep slope watersheds. Five alternatives to the NRCS model were employed to estimate stormwater runoff in 39 forest-dominated mountainous watersheds. The change to the parameterization (slope-adjusted CN and I _a) leads to more efficient modified NRCS models. The model evaluations based on root mean square error (RMSE), Nash-Sutcliffe coefficient E, coefficient of determination (R ² ), and percent bias (PB) indicated that our proposed model with modified I _a, consistently performed better than the other four models and the original NRCS model, in reproducing the runoff. In addition to the quantitative statistical accuracy measures, the proposed I _a modification in the NRCS model showed very encouraging results in the scatter plots of the combined 1799 storm events, compared to other alternatives. This study’s findings support modifications to the CN and the I _a in the NRCS model for steep slope ungauged watersheds and suggest additional changes for more accurate runoff estimations.     

Modeling River Mixing Mechanism Using Data Driven Model

Modeling river mixing mechanism in terms of pollution transmission in rivers is an important subject in environmental studies. Dispersion coefficient is an important parameter in river mixing problem. In this study, to model and predict the longitudinal dispersion coefficient (D _L ) in natural streams, two soft computing techniques including multivariate adaptive regression splines (MARS) as a new approach to study hydrologic phenomena and multi-layer perceptron neural network as a common type of neural network model were prepared. To this end, related dataset were collected from literature and used for developing them. Performance of MARS model was compared with MLP and the empirical formula was proposed to calculate D _L . To define the most effective parameters on D _L structure of obtained formula from MARS model and more accurate formula was evaluated. Calculation of error indices including coefficient of determination (R²) and root mean square error (RMSE) for the results of MARS model showed that MARS model with R²?=?0.98 and RMSE?=?0.89 in testing stage has suitable performance for modeling D _L . Comparing the performance of empirical formulas, ANN and MARS showed that MARS model is more accurate compared to others. Attention to the structure of developed MARS and the most accurate empirical formulas model showed that flow velocity, depth of flow (H) and shear velocity are the most influential parameters on D _L .     

A Modified SCS-CN Method Incorporating Storm Duration and Antecedent Soil Moisture Estimation for Runoff Prediction

In one of the widely used methods to estimate surface runoff - Soil Conservation Service Curve Number (SCS-CN), the antecedent moisture condition (AMC) is categorized into three AMC levels causing irrational abrupt jumps in estimated runoff. A few improved SCS-CN methods have been developed to overcome several in-built inconsistencies in the soil moisture accounting (SMA) procedure that lies behind the SCS-CN method. However, these methods still inherit the structural inconsistency in the SMA procedure. In this study, a modified SCS-CN method was proposed based on the revised SMA procedure incorporating storm duration and a physical formulation for estimating antecedent soil moisture (V ₀ ). The proposed formulation for V ₀ estimation has shown a high degree of applicability in simulating the temporal pattern of soil moisture in the experimental plot. The modified method was calibrated and validated using a dataset of 189 storm-runoff events from two experimental watersheds in the Chinese Loess Plateau. The results indicated that the proposed method, which boosted the model efficiencies to 88% in both calibration and validation cases, performed better than the original SCS-CN and the Singh et al. (2015) method, a modified SCS-CN method based on SMA. The proposed method was then applied to a third watershed using the tabulated CN value and the parameters of the minimum infiltration rate (f _c ) and coefficient (β) derived for the first two watersheds. The root mean square error between the measured and predicted runoff values was improved from 6 mm to 1 mm. Moreover, the parameter sensitivity analysis indicated that the potential maximum retention (S) parameter is the most sensitive, followed by f _c . It can be concluded that the modified SCS-CN method, may predict surface runoff more accurately in the Chinese Loess Plateau.     

Estimation of Groundwater Recharge from the Rainfall and Irrigation in an Arid Environment Using Inverse Modeling Approach and RS

Quantifying recharge from agricultural areas is important to sustain long-term groundwater use, make intelligent groundwater allocation decisions, and develop on-farm water management strategies. The scarcity of data in many arid regions, especially in the Middle East, has necessitated the use of combined mathematical models and field observations to estimate groundwater recharge. This study was designed to assess the recharge contribution to groundwater from rainfall and irrigation return flow in the Mosian plain, west of Iran. The Inverse modeling approach and remote sensing technology (RS) were used to quantify the groundwater recharge. The recharge for steady–state conditions was estimated using the Recharge Package of MODFLOW. The land-use map for the research area was produced using remote sensing and satellite images technology. According to results, groundwater recharge from the rainfall and irrigation return flow was at the rate of 0.15 mm/day. The recharge to the groundwater from rainfall was about 0.08 mm/day (10.8 % of total rainfall). The average of groundwater recharge contribution in the study area was about 0.39 mm/day that include 15.2 % of the total water used in the irrigated fields. We can conclude that irrigation water is the most important resource of groundwater recharge in this area, consequently, it should be integrated into relevant hydrological models as the main source of groundwater recharge.     

Quantifying a Sustainable Management Space for Human Use of Coastal Groundwater under Multiple Change Pressures

In the densely populated coastal regions of the world, loss of groundwater due to seawater intrusion, driven by changes of climate, sea level, land use and water use, may critically impact many people. We analytically investigate and quantify the limits constraining a coastal aquifer’s sustainable management space, in order to avoid critical loss of the coastal groundwater resource by seawater intrusion. Limiting conditions occur when the intrusion toe reaches the pumping wells, well intrusion, or the marine-side groundwater divide, complete intrusion; in both cases the limits are functions of the seaward groundwater flow remaining after the human groundwater extractions. The study presents a screening-level approach to the quantification of the key natural and human-determined controls and sustainability limits for the human use of coastal groundwater. The physical and geometrical characteristics of the coastal aquifer along with the natural conditions for recharge and replenishment of the coastal groundwater are the key natural controls of the sustainable management space for the latter. The groundwater pumping rates and locations are the key human-determined controls of this space. The present approach to combining and accounting for both of these types of controls is simple, yet general. The approach is applicable across different scales and regions, and for historic, current and projected future conditions of changing hydro-climate, sea level, and human freshwater use. The use of this approach is also concretely demonstrated for the natural and human-determined controls and limits of the sustainable management space for two specific Mediterranean aquifers.     

Groundwater Response to Tidal Fluctuation in an Inhomogeneous Coastal Aquifer-Aquitard System

This paper investigates tide-induced groundwater fluctuation and submarine groundwater discharge (SGD) in a leaky inhomogeneous coastal aquifer system with an upper unconfined aquifer, a lower confined aquifer, and an aquitard between them. The upper left aquifer is formed due to land reclamation. The SGD defined as the groundwater flow from land into the sea is controlled mainly by the hydraulic gradient between land and sea. An analytical expression is developed to discuss and assess the effect of inhomogeneity on the groundwater head fluctuation in the leaky aquifer system. Joint effects of aquifers’ parameters such as leakage and hydraulic diffusivity on the groundwater head fluctuation and SGD are investigated. The predicted results from the analytical expression indicate that the groundwater head fluctuation in both unconfined and confined aquifers is dependent on dimensionless leakages and increases with dimensionless hydraulic diffusivity.     

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

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.     

A Two-stage Approach to Basin-scale Water Demand Prediction

Water demand prediction (WDP) is the basis for water allocation. However, traditional methods in WDP, such as statistical modeling, system dynamics modeling, and the water quota method have a critical disadvantage in that they do not consider any constraints, such as available water resources and ecological water demand. This study proposes a two-stage approach to basin-scale WDP under the constraints of total water use and ecological WD, aiming to flexibly respond to a dynamic environment. The prediction method was divided into two stages: (i) stage 1, which is the prediction of the constrained total WD of the whole basin (T _w ) under the constraints of available water resources and total water use quota released by the local government and (ii) stage 2, which is the allocation of T _w to its subregions by applying game theory. The WD of each subregion (T _s ) was predicted by calculating its weight based on selected indicators that cover regional socio-economic development and water use for different industries. The proposed approach was applied in the Dongjiang River (DjR) basin in South China. According to its constrained total water use quota and ecological WD, T _w data were 7.92, 7.3, and 5.96 billion m³ at the precipitation frequencies of 50%, 90%, and 95%, respectively (in stage 1). Industrial WDs in the domestic, primary, secondary, tertiary, and environment sectors are 1.08, 2.26, 2.02, 0.44, and 0.16 billion m³, respectively, in extreme dry years (in stage 2). T _w and T _s exhibit structures similar to that of observed water use, mainly in the upstream and midstream regions. A larger difference is observed between T _s and its total observed water use, owing to some uncertainties in calculating T _w . This study provides useful insights into adaptive basin-scale water allocation under climate change and the strict policy of water resource management.     

A Novel Approach to Modeling Wastewater Evaporation Based on Dimensional Analysis

Wastewater from municipal and industrial sources is becoming increasingly important in being reused, for example, for irrigation purposes. Wastewater is commonly stored in treatment lagoons in which evaporation is the main cause of water loss. Nonetheless, modeling wastewater evaporation (WWE) has received little attention. Driven by this knowledge gap, this study was performed to explore extent to which impurities affect water evaporation. A dimensional analysis was used to formulate WWE as a function of clear water evaporation (CWE), wastewater properties and climatic variables. We based our modeling approach on experimental data collected from the Neishaboor municipal wastewater treatment plant, Iran. As a result of this analysis, a multiplicative model to formulate WWE as a function of the influencing variables is proposed which indicated a reasonably well accuracy (RMSE?=?1.09 mm) for the WWE estimation. Clear water evaporation indicated to be the most correlated variable in the model such that a constant coefficient can also be used to estimate WWE from CWE at the cost of losing accuracy only by 4.6 %.     

Modified Affinity Laws in Hydraulic Machines towards the Best Efficiency Line

The development of hydraulic and optimization models in water networks analyses to improve the sustainability and efficiency through the installation of micro or pico hydropower is swelling. Hydraulic machines involved in these models have to operate with different rotational speed, in order that in each instant to maximize the recovered energy. When the changes of rotational speed are determined using affinity laws, the errors can be significant. Detailed analyses are developed in this research through experimental tests to validate and propose new affinity laws in different reaction turbomachines. Once the errors have been analyzed, a methodology to modify the affinity laws is applied to radial and axial turbines. An empirical method to obtain the Best Efficiency Line (BEL) in proposed (i.e., based on all the Best Efficiency Points (BEPs) for different flows). When the experimental measurements and the calculated values by the empirical method are compared, the mean errors are reduced 81.81%, 50%, and 86.67% for flow, head, and efficiency parameters, respectively. The knowledge of BEL allows managers to define the operation rules to reach the BEP for each flow, improving the energy efficiency in the optimization strategies to be adopted.     

A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic <Emphasis Type="Italic">k</Emphasis>-Nearest Neighbour Models in Daily Streamflow Forecasting

A nonlinear stochastic self-exciting threshold autoregressive (SETAR) model and a chaotic k-nearest neighbour (k-nn) model, for the first time, were compared in one and multi-step ahead daily flow forecasting for nine rivers with low, medium, and high flows in the western United States. The embedding dimension and the number of nearest neighbours of the k-nn model and the parameters of the SETAR model were identified by a trial-and-error process and a least mean square error estimation method, respectively. Employing the recursive forecasting strategy for the first time in multi-step forecasting of SETAR and k-nn, the results indicated that SETAR is superior to k-nn by means of performance indices. SETAR models were found to be more efficient in forecasting flows in one and multi-step forecasting. SETAR is less sensitive to the propagated error variances than the k-nn model, particularly for larger lead times (i.e., 5 days). The k-nn model should carefully be used in multi-step ahead forecasting where peak flow forecasting is important by considering the risk of error propagation.     

Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin,Taiwan

It is well known that sufficiently long and continuous streamflow data are required for accurate estimations and informed decisions in water-resources planning, design, and management. Although streamflow data are measured and available at most river basins, streamflow records often suffer from insufficient length or missing data. In this work, artificial neural networks (ANNs) are applied to extend daily streamflow records at Lilin station located in Gaoping River basin, southern Taiwan. Two ANNs, including feed forward back propagation (FFBP) and radial basis function (RBF) networks, associated with various time-lagged streamflow and rainfall inputs of nearby long-record stations are employed to extend short daily streamflow records. Performances of ANNs are evaluated by root-mean-square error (RMSE), coefficient of efficiency (CE), and histogram-matching dissimilarity (HMD). Inconsistency among these evaluation measures is solved by the technique for order performance by similarity to ideal solution (TOPSIS), a widely used multi-criteria decision-making approach, to find an optimal model. The results indicate that RBF-E1 (entire-year data training with Q _t and Q _t?1 inputs) has the minimum RMSE of 104.4 m³/s, second highest CE of 0.956, and third lowest HMD of 0.0096, which outperforms other ANNs and provide the most accurate reconstruction of daily streamflow records at Lilin station.     

Development of a GIS Interface for Estimation of Runoff from Watersheds

Development of accurate surface runoff estimation techniques from ungauged watersheds is relevant in Indian condition due to the non-availability of hydrologic gauging stations in majority of watersheds. Besides this, the high budgetary requirements for installation of gauging stations are another limiting factor in India, which leads to the use of surface runoff estimation techniques for ungauged watersheds. Natural Resources Conservation Services Curve Number (NRCS-CN) method is one of the most widely used methods for quick and accurate estimation of surface runoff from ungauged watershed. Also, the coupling of NRCS-CN techniques with the advanced Geographic Information System (GIS) capabilities automates the process of runoff prediction in timely and efficient manner. Keeping view of this, a GIS interface was developed using the in-built macro programming language, Visual Basic for Applications (VBA) of ArcGIS® tool to estimate the surface runoff by adopting NRCS-CN technique and its three modifications. The developed interface named as Interface for Surface Runoff Estimation using Curve Number techniques (ISRE-CN), was validated using the recorded data for the periods from 1993 to 2001 of a gauged watershed, Banha in the Upper Damodar Valley in Jharkhand, India. The observed runoff depths for different rainfall events in this study watershed was compared with the predicted values of NRCS-CN methods and its three modifications using statistical significance tests. It was revealed that using all the rainfall data for different AMC conditions, the modified CN I performed the best [R ² (coefficient of determination)?=?0.92; E (model efficiency)?=?0.89) followed by modified CN III method (R ²?=?0.88; E?=?0.87), while the modified CN II (R ²?=?0.42; E?=?0.36) failed to predict accurately the surface runoff from Banha watershed. Moreover, under AMC based estimations, the modified CN I method also performed best ( R ²?=?0.95; E?=?0.95) for AMC II condition, while the modified CN II performed the worst in all the AMC conditions. However, the developed Interface in ArcGIS® needs to be tested in other watershed systems for wider applicability of the modified CN methods.     

An Extension of the Sustainability Index Definition in Water Resources Planning and Management

The sustainability index (SI) is a relatively new concept for measuring the performance of water resource systems over long time periods. Its definition is aimed at providing an indication of the integral behaviour of the system with regards to possible undesired consequences if misbalance of available and required waters occurs. SI is initially defined as a product and later reformulated as a geometric mean of performance indicators: reliability, resilience and vulnerability. As an extension of a recently published methodology to compute and use SI, in this paper we propose introducing two more indicators of system performance: (1) reliability of annual firm (safe) water as a system yield and (2) deviation of reservoir levels from corresponding rule curves. The last indicator is of particular importance if there are multi-purpose reservoirs in the system because reservoirs are the most important and sensitive regulators of the water regime within the system. We also propose a framework for assessing system performance in a systematic manner to compute SI at various locations within the system if different operating strategies are applied and, finally, how to evaluate strategies according to the resulting SI by using multi-criteria methods. A case study example from Serbia is used to illustrate the results of measuring sustainability under alternative operating scenarios for a system with three reservoirs and two diversion structures.     

A Non-Stationary Reconnaissance Drought Index (NRDI) for Drought Monitoring in a Changing Climate

Traditionally, drought indices are calculated under stationary condition, the assumption that is not true in a changing environment. Under non-stationary conditions, it is assumed the probability distribution parameters vary linearly/non-linearly with time or other covariates. In this study, using the GAMLSS algorithm, a time-varying location parameter of lognormal distribution fitted to the initial values (α₀) of the traditional Reconnaissance Drought Index (RDI) was developed to establish a new index called the Non-Stationary RDI (NRDI), simplifying drought monitoring under non-stationarity. The fifteen meteorological stations having the longest records (1951–2014) in Iran were chose to evaluate the NRDI performances for drought monitoring. Trend analysis of the α₀ series at multiple time windows was tested by using the Mann-Kendall statistics. Although all stations detected decreasing trend in the α₀ series, eight of them were significant at the 5% probability level. The results showed that the time-dependent relationship is adequate to model the location parameter at the stations with the significant temporal trend. There were remarkable differences between the NRDI and the RDI, especially for the time windows larger than 6 months, implying monitoring droughts using the NRDI under non-stationarity. The study suggests using the NRDI where the significant time trend appears in the initial values of RDI due to changing climate.