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.     

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 %.     

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.     

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.     

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.     

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 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.     

Gene-Expression Programming for Short-Term Forecasting of Daily Reference Evapotranspiration Using Public Weather Forecast Information

This study aimed to forecast the daily reference evapotranspiration (ETo) using a gene-expression programming (GEP) algorithm with limited public weather forecast information over Gaoyou station, located in Jiangsu province, China. To calibrate and validate the gene-expression code, important meteorological data and weather forecast information were collected from the local meteorological station and public weather media, respectively. The GEP algebraic formulation was successfully constructed based only on daily minimum and maximum air temperature using the true FAO56 Penman-Monteith (PM) set as reference values. The performance of the models was then assessed using the correlation coefficient (R), root mean squared error (RMSE), root relative squared error (RRSE) and mean absolute error (MAE). The study demonstrated that GEP is able to calibrate ETo (all errors ≤0.990 mm/day, R = 0.832–0.866) and forecast the daily ETo with good accuracy (RMSE = 1.207 mm/day, MAE = 0.902 mm/day, RRSE = 0.629 mm/day, R = 0.777). The model accuracies slightly decreased over a 7-day forecast lead-time. These results suggest that the GEP algorithm can be considered as a deployable tool for ETo forecast to anticipate decision on short-term irrigation schedule in the study zone.     

Seasonality of Roughness - the Indicator of Annual River Flow Resistance Condition in a Lowland Catchment

Accurate estimation of flow resistance restricts the quality of the hydraulic model performance. In this study, we try to investigate the seasonal dynamic of the Manning’s roughness coefficient (n) based on the one-dimensional hydraulic model HEC-RAS in a German lowland area. We set up four river section models based on the 1 m digital elevation model and field measurements, in which the seasonal roughness factors were calibrated and validated with the gauge record. The results revealed that: 1) the Manning’s n varied from 46% to 135% from the base value in autumn; 2) adopting the seasonal roughness factor improved the quality of the model output; 3) the vegetation condition and water elevation dominated the Manning’s n in summer (April–September) and winter (October–March) half year respectively. Water temperature increased the flow resistence in winter half year; 4) the peak value of Manning’s n appeared in late summer due to the highest biomass, while the minimum roughness occurred in early-spring because of the combined influence of low biomass, high water level and relatively higher temperature. The involvement of seasonal roughness factor improved the model performance and the results are comparable to the previous research of the same area.     

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.     

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.     

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.     

Simulating Microbial Water Quality in Data-Scarce Catchments: an Update of the WQSAM Model to Simulate the Fate of <Emphasis Type="BoldItalic">Escherichia coli</Emphasis>

Faecal-derived microbial pollution of fresh surface waters is a global problem. Water quality models can play an important role in the management of microbial pollution; however, most existing models are too complex and require a large amount of observed data for calibration, thereby excluding their use in data-scarce catchments. The Water Quality Systems Assessment Model (WQSAM) is a water quality water model structured on the concept of requisite simplicity, thereby limiting the complexity and data requirements of the model. Here, microbial water quality simulation functionality was added to WQSAM, with the aim of assessing whether a simplified representation of processes affecting microbial water quality is sufficiently accurate for purposes of water resource management. Simulations of microbial water quality were based on the inputs and fate of an indicator organism, Escherichia coli. Non-point source inputs were modelled by assigning microbial water quality ‘signatures’ to incremental flow components, whereas a similar signature was assigned to point source inputs. The instream fate of E. coli was based on a first-order rate equation, moderated by salinity and water temperature. The model was validated by application to the upper to middle Crocodile River Catchment, Mpumalanga, South Africa, for historical conditions. Model simulations were obtained that were representative of the variability of observed temperature, salinity and microbial water quality data. The simulations of E. coli were found to be most sensitive to the decay rate k ₀. It is argued here that the uncertainty in model results due to the use of a relatively simple model structure would be no more, or even less that that due to the application of a complex model to a catchment with insufficient observed data for adequate model calibration.     

Assessments of Composite and Discrete Sampling Approaches for Water Quality Monitoring

Achieving an operational compromise between spatial coverage and temporal resolution in national scale river water quality monitoring is a major challenge for regulatory authorities, particularly where chemical concentrations are hydrologically dependent. The efficacy of flow-weighted composite sampling (FWCS) approaches for total phosphorus (TP) sampling (n?=?26–52 analysed samples per year), previously applied in monitoring programmes in Norway, Sweden and Denmark, and which account for low to high flow discharges, was assessed by repeated simulated sampling on high resolution TP data. These data were collected in three research catchments in Ireland over the period 2010–13 covering a base-flow index range of 0.38 to 0.69. Comparisons of load estimates were also made with discrete (set time interval) daily and sub-daily sampling approaches (n?=?365 to >1200 analysed samples per year). For all years and all sites a proxy of the Norwegian sampling approach, which is based on re-forecasting discharge for each 2-week deployment, proved most stable (median TP load estimates of 87–98%). Danish and Swedish approaches, using long-term flow records to set a flow constant, were only slightly less effective (median load estimates of 64–102% and 80–96%, respectively). Though TP load estimates over repeated iterations were more accurate using the discrete approaches, particularly the 24/7 approach (one sample every 7 h in a 24 bottle sampler - median % load estimates of 93–100%), composite load estimates were more stable, due to the integration of multiple small samples (n?=?100–588) over a deployment.     

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.     

Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing

Information of Soil Moisture Content (SMC) at different depths i.e. vertical Soil Moisture (SM) profile is important as it influences several hydrological processes. In the era of microwave remote sensing, spatial distribution of soil moisture information can be retrieved from satellite data for large basins. However, satellite data can provide only the surface (~0–10?cm) soil moisture information. In this study, a methodological framework is proposed to estimate the vertical SM profile knowing the information of SMC at surface layer. The approach is developed by coupling the memory component of SMC within a layer and the forcing component from soil layer lying above by an Auto-Regressive model with an exogenous input (ARX) where forcing component is the exogenous input. The study highlights the mutual reliance between SMC at different depths at a given location assuming the ground water table is much below the study domain. The methodology is demonstrated for three depths: 25, 50 and 80?cm using SMC values of 10?cm depth. Model performance is promising for all three depths. It is further observed that forcing is predominant than memory for near surface layers than deeper layers. With increase in depth, contribution of SM memory increases and forcing dissipates. Potential of the proposed methodology shows some promise to integrate satellite estimated surface soil moisture maps to prepare a fine resolution, 3-dimensional soil moisture profile for large areas, which is kept as future scope of this study.