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.     

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.     

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.     

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.     

Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods

Accurate simulation of rainfall-runoff process is of great importance in hydrology and water resources management. Rainfall–runoff modeling is a non-linear process and highly affected by the inputs to the simulation model. In this study, three kinds of soft computing methods, namely artificial neural networks (ANNs), model tree (MT) and multivariate adaptive regression splines (MARS), have been employed and compared for rainfall-runoff process simulation. Moreover, this study investigates the effect of input size, including number of input variables and number of data time series on runoff simulation by the developed models. Inputs to the simulation models for calibration and validation purposes consist two parts: I1: five variables, including daily rainfall and runoff time series (30 years) with lag times, and I2: twelve variables, including daily rainfall and runoff time series (10 years). To increase the model performances, optimal number and type for input variables are identified. The efficiency of the training and testing performances using the ANNs, MT and MARS models is then evaluated using several evaluation criteria. To implement the methodology, Tajan catchment in the northern part of Iran is selected. Based on the results, it was found that using I1 as input to the developed models results in higher simulation performance. The results also provided evidence that MT (R = 0.897, RMSE = 6.70, RSE = 0.33) with set I2 is capable of reliable model for rainfall-runoff process compared with MARS (R = 0.892, RMSE = 7.47, RSE = 0.83) and ANNs (R = 0.884, RMSE = 7.40, RSE = 0.43) models. Therefore, size (length of data time series) and type of input variables have significant effects on the modeling results.     

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.     

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.     

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

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.     

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.     

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 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.     

Regionalization of Tank Model Using Landscape Metrics of Catchments

To address the challenges inherent in accessing spatiotemporal hydrological data, water resources professionals have developed various regionalization tools. The present study examines the possibility that changes in landscape metrics including mean shape index, mean perimeter-area ratio, mean patch size and patch density of land use/ land cover could result in variations in the optimized parameters of the conceptual rainfall-runoff Tank model. Data from 30 catchments that are geographically distributed in Germany was used to develop the procedure. Regression analysis-based modeling indicated that four out of twelve model parameters (r²?≥?0.40) can be explained by changes in catchment geometrics along with a set of landscape metrics of land use/land cover. They include: coefficient of infiltration flow (r²?=?0.48, p?<?0.03), intermediate flow (r²?=?0.77, p?<?0.02), water storage level for sub-surface flow (r²?=?0.57, p?<?0.05) and water storage level for intermediate flow (r²?=?0.85, p?<?0.01). Despite developing fairly reliable regression models, uncertainty analysis also revealed that uncertainty induced unreliability of the regionalized models is of significant importance.     

An Analytical Solution of Groundwater Flow in Wedge-shaped Aquifers with Estuarine Boundary Conditions

The issue of the groundwater fluctuation due to tidal effect in a two-dimensional coastal leaky aquifer system has attracted much attention in recent years. The predictions of head fluctuation play an important role in dealing with groundwater managements and contaminant remediation problems in costal aquifers. This article presents a two-dimensional analytical model describing the groundwater flow in a coastal leaky aquifer of wedge shape affected by the tides and bounded by two estuarine rivers with an arbitrary included angle. The solution of the model is derived in the Polar coordinates by the Hankel transform and finite sine transform. The head fluctuation predicted by this new solution is compared with that by an existing solution for groundwater flow in a non-L shaped tidal aquifer. The groundwater fluctuation due to the joint effect of estuarine tides is explored based on the present solution. Moreover, the influences of the parameters such as diffusion (D_i), included angle (Ф), and tidal river coefficients (K₁, K₂) on the head fluctuation in the aquifer are also assessed and discussed. The results demonstrate that those parameters have significant effects on the head fluctuation in the leaky confined aquifer system. Moreover, the effect of D_i increases with Ф, and the effects of K₁ and K₂ on the normalized amplitude and phase lag of the groundwater fluctuation are significant when both parameter values are larger than 10^?5.     

A Species-Specific and spatially-Explicit Model for Estimating Vegetation Water Requirements in Desert Riparian Forest Zones

Balancing human demands for water with environmental requirements to maintain functioning ecosystems requires the quantification of ecological water requirements. In arid regions, high spatial variability of vegetation cover and different water consumption of plant species make it different to estimate reasonable ecological water requirements. We developed a simple and practical approach that estimates the vegetation water requirements (VWRs) of desert riparian ecosystems. This model is species-specific and spatially-explicit; it considers the water consumption characteristics required by different species and highlights the impacts that high vegetation cover spatial variability has in arid regions on evapotranspiration. The model was parameterized based on the observation of the water consumption of two typical desert riparian species, Populus euphratica and Tamarix spp., in the lower basin of the Tarim River in northwestern China. Comparisons between the modeling results and measured data for two mature Populus and Tamarix stands indicate that the model is reasonable predictive. A case study in the lower basin of the Tarim River demonstrated the model’s practicality and transferability. This model could run based on near real-time or forest weather data and spatial vegetation patterns, and provides a continuous estimation of the temporal and spatial variations of the VWR. Particularly, this model forecasts VWRs under different vegetation spatial distribution and coverage scenarios, and evaluates the impacts and consequences of different management actions. This model can serve as a useful tool for management agencies interested in improving their decisions to allocate river water between human activities and natural ecosystems in arid regions.     

A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation

Estimation of suspended sediment loads (SSL) in rivers is an important issue in water resources management and planning. This study proposes a hybrid double feedforward neural network (HDFNN) model for daily SSL estimation, by combining fuzzy pattern-recognition and continuity equation into a structure of double neural networks. A comparison is performed between HDFNN, multi-layer feedforward neural network (MFNN), double parallel feedforward neural network (DPFNN) and hybrid feedforward neural network (HFNN) models. Based on a case study on the Muddy Creek in Montana of USA, it is found that the HDFNN model is strongly superior to the other three benchmarking models in terms of root mean squared error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSEC). HDFNN model demonstrates the best generalization and estimation ability due to its configuration and capability of physically dealing with different inputs. The peak value of SSL is closely estimated by the HDFNN model as well. The performances of HDFNN model in low and medium loads are satisfactory when investigated by partitioning analysis. Thus, the HDFNN is appropriate for modeling the sediment transport process with nonlinear, fuzzy and time-varying characteristics. It explores a practical alternative for use and can be recommended as an efficient estimation model for SSL.     

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.     

Multivariate Copula-Based Joint Probability Distribution of Water Supply and Demand in Irrigation District

Based on the data series of rainfall, reference crop evapotranspiration and irrigation water from 1970 to 2013 in the Luhun irrigation district of China, the multivariate joint probability of water supply and demand are constructed with student t-copula function. The results show that student t-copula function can indicate the associated dependence structure amongst these variables well, and the constructed multivariate copula-based joint probability distribution reveal the statistical characteristics and occurrence probability of different combinations of water supply and water demand. Moreover, the trivariate joint probability distribution is more reasonable than the bivariate distribution to reflect the water shortage risk, and these joint distribution values of different combinations of water supply and demand can provide the technological support for water shortage risk evaluation in the irrigation district.     

Water Resource Use and Competition in an Evolutionary Model

Over the last few years water scarcity and pollution have been rapidly growing at both regional and global level. This has generated in many cases increasing intersectoral competition over the use of a limited amount of water resources. To examine the dynamics that such competition may generate in the economy, the present paper proposes a simple dynamic evolutionary model in which two sectors (A and B) compete for the use of water and studies the impact of water pricing on the dynamics of the two sectors in the presence of a population of interacting economic agents characterized by imitative behaviors. As it emerges from the model, when water is underpriced a self-enforcing process may be observed driving the economy towards a Pareto-dominated equilibrium. In such equilibrium the economy fully specializes in sector A, characterized by the highest negative impact on the water resource, at the expenses of sector B. The paper shows that a policy of fine tuning that increases water price through the endogenous water pricing mechanism examined in the model can inhibit the convergence of the economy to such an equilibrium point and can progressively shift the system towards the less water-consuming sector. Finally, assuming a Leontief production function and performing numerical simulations, it is shown how a change in water price can affect the dynamics of the model, and that the same results hold also in a more general, three-sector context.     

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.