和田河干流河道径流损失与植被生态需水关系研究

和田河干流植被生态需水量是多少?河道渗漏水量能否满足两岸植被的生态需水?为了回答这两个事关和田河中下游河道整治必要性的基础问题,本研究定量分离河道损耗水量,探究河道渗漏水量与天然植被生态需水之间的定量响应关系,结果表明:(1)多年平均来水条件下,和田河干流河损量约为6.46×10~8m~3,其中,蒸发量与渗漏量分别为3.5×10~8和2.65×10~8m~3,各占河损量的58.88%和41.12%;(2)和田河干流天然植被的生态需水量1.37×10~8m~3,河道渗漏水量可以满足植被生态水量需求。因此,本研究认为开展河道整治,不仅不会影响干流植被生态需水,还可以减少水面蒸发,提高和田河输水能力,更有利于开展生态水量调度,保护和恢复干流天然植被。     

Multiple Random Forests Modelling for Urban Water Consumption Forecasting

The precise forecasting of water consumption is the basis in water resources planning and management. However, predicting water consumption fluctuations is complicated, given their non-stationary and non-linear characteristics. In this paper, a multiple random forests model, integrated wavelet transform and random forests regression (W-RFR), is proposed for the prediction of daily urban water consumption in southwest of China. Raw time series were first decomposed into low- and high-frequency parts with discrete wavelet transformation (DWT). The random forests regression (RFR) method was then used for prediction using each subseries. In the process, the input and output constructions of the RFR model were proposed for each subseries on the basis of the delay times and the embedding dimension of the attractor reconstruction computed by the C-C method, respectively. The forecasting values of each subseries were summarized as the final results. Four performance criteria, i.e., correlation coefficient (R), mean absolute percentage error (MAPE), normalized root mean square error (NRMSE) and threshold static (TS), were used to evaluate the forecasting capacity of the W-RFR. The results indicated that the W-RFR can capture the basic dynamics of the daily urban water consumption. The forecasted performance of the proposed approach was also compared with those of models, i.e., the RFR and forward feed neural network (FFNN) models. The results indicated that among the models, the precision of the predictions of the proposed model was greater, which is attributed to good feature extractions from the multi-scale perspective and favorable feature learning performance using the decision trees.     

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 %.     

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.     

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.     

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.     

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.     

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.     

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.     

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 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.     

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.     

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.     

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.     

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.     

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.     

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.