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.     

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.     

Evaluation of the SCS-CN-Based Model Incorporating Antecedent Moisture

Using a large set of rainfall-runoff data from 234 small to large watersheds from USA, this paper evaluates the modified version of the [Mishra, S. K. and Singh, V. P., 2002a, SCS-CN-based hydrologic simulation package, in V. P. Singh and D. K. Frevert (eds), Mathematical Models in Small Watershed Hydrology, Water Resources Publications, Chap. 13, pp. 391–464] (MS) model which is based on the Soil Conservation Service Curve Number (SCS-CN) methodology and incorporates the antecedent moisture in direct surface runoff computations. Comparison with the existing SCS-CN method using the t-test and the ranking-based grading shows that the modified MS model performs far better than the existing SCS-CN model.     

A Rain Duration and Modified AMC-dependent SCS-CN Procedure for Long Duration Rainfall-runoff Events

This paper presents a rain duration-dependent procedure based on the popular Soil Conservation Service Curve Number (SCS-CN) methodology for computation of direct surface runoff from long duration rains. Curve numbers are derived from long-term daily rainfall-runoff data, and antecedent moisture condition (AMC) related with antecedent duration. Analysis of data from five Indian (large, in terms of area) watersheds reveals the calculated curve numbers to decrease with the considered duration, showing the existence of a characteristic value of minimum CN or maximum initial abstraction to occur in a watershed for a pre-selected AMC. The testing of the proposed procedure on the separate (measured) rainfall-runoff event data sets from the same watersheds suggests satisfactory workability of the method.     

An Improved I a S Relation Incorporating Antecedent Moisture in SCS-CN Methodology

Employing a large dataset of 84 small watersheds (area = 0.17 to 71.99 ha) of U.S.A., this paper investigates a number of initial abstraction (I _a )-potential maximum retention (S) relations incorporating antecedent moisture (M) as a function of antecedent precipitation (P ₅), and finally suggests an improved relation for use in the popular Soil Conservation Service Curve Number (SCS-CN) methodology for determination of direct runoff from given rainfall. The improved performance of the incorporated M = α and I _a = λ S ²/(S + M) relations, where λ is the initial abstraction coefficient, in the SCS-CN methodology exhibits the dependence of I _a on M, which is close to reality; the larger the M, the lesser will be I _a , and vice versa. Such incorporation obviates sudden jumps in the curve number variation with antecedent moisture condition, an unreasonable and undesirable feature of the existing SCS-CN model.     

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 modified NRCS-CN method for eliminating abrupt runoff changes induced by the categorical antecedent moisture conditions

The popular Natural Resources Conservation Service Curve Number (NRCS-CN) (earlier known as Soil Conservation Service Curve Number (SCS-CN) method of rainfall-runoff modeling has often faced the criticism of exhibiting quantum jumps in runoff computations because of the sudden jumps appearing in CN-values derived from NEH-4 tables for three antecedent moisture conditions (AMC), viz., AMC-I, AMC-II, and AMC-III valid for dry, normal, and wet conditions, respectively. The variability of antecedent soil moisture within an AMC category is responsible for the abrupt jump and other deficiencies in the CN method for runoff estimation. This paper suggests a novel procedure to account for the antecedent moisture (M), preventing quantum jumps and eliminating deficiencies in determination of CN and, in turn, estimation of direct runoff. Its validity was verified utilizing the observed rainfall (P)-runoff (Q) events from 36 US watersheds, four sub-catchments of the Godavari basin, and small agricultural plots at Roorkee, India. The performance of the proposed model (M5) for runoff prediction was compared with the existing NRCS-CN (M1), Mishra and Singh (2002) (M2), Singh et al. (2015) (M3), and Verma et al. (2021) (M4) model using various performance indices. Using the CNs derived from observed events, model M5 was seen to have performed better than M1-M4 in terms of Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Percent Bias (PBIAS) for the data of US watersheds, and CN-P correlation improved as the coefficient of determination (R²) enhanced. Similarly, using the RS & GIS-based CNs on natural watersheds of the Godavari basin and considering AMC-I, the performance of M5 was again better than M1-M4 in terms of RMSE, Mean Bias Error (mBIAS), Mean Absolute Error (MAE), and Normalized-Nash Sutcliffe Efficiency (NNSE). Interestingly, there existed a significant (p < 0.05) relationship between the in-situ water content (w) measured for the experimental plots of Roorkee and the model input variable antecedent moisture (M), offering a physical touch to the conceptual model.     

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.     

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 .     

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.     

SCS-CN Based Quantification of Potential of Rooftop Catchments and Computation of ASRC for Rainwater Harvesting

Rooftop rainwater harvesting, among other options, play a central role in addressing water security and reducing impacts on the environment. The storm or annual storm runoff coefficient (RC/ASRC) play a significant role in quantification of potential of rooftop catchments for rainwater harvesting, however, these are usually selected from generic lists available in literature. This study explores methodology/procedures based on one of the most popular and versatile hydrological model, Soil Conservation Service Curve Number (SCS-CN) (SCS 1986) and its variants, i.e., Hawkins SCS-CN (HSCS-CN) model (Hawkins et al. 2001), Michel SCS-CN (MSCS-CN) model (Michel et al. Water Resour Res 41:W02011, 2005), and Storm Water Management Model-Annual Storm Runoff Coefficient (SWMM-ASRC) (Heaney et al. 1976) and compares their performance with Central Ground Board (CGWB) (CGWB 2000) approach. It has been found that for the same amount of rainfall and same rooftop catchment area, the MSCS-CN model yields highest rooftop runoff followed by SWMM-ASRC?>?HSCS-CN?>?SCS-CN?>?CGWB. However, the SCS-CN model has close resemblance with CGWB approach followed by HSCS-CN model, SWMM-ASRC, and MSCS-CN model. ASRCs were developed using these models and it was found that MSCS-CN model has the highest value of ASRC (= 0.944) followed by SWMM-ASRC approach (=0.900), HSCS-CN model (=0.830), SCS-CN model (=0.801), and CGWB approach (=0.800). The versatility of these models lies to the fact that CN values (according to rooftop catchment characteristics) would yield rooftop runoff and therefore ASRC values based on sound hydrological perception and not just on the empiricism. The models have inherent capability to incorporate the major factors responsible for runoff production from rooftop/urban, i.e., surface characteristics, initial abstraction, and antecedent dry weather period (ADWP) for the catchments and would be better a tool for quantification rather than just using empirical runoff coefficients for the purpose.     

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 %.     

Field Applicability of the SCS-CN-Based Mishra–Singh General Model and its Variants

The general soil conservation service curve number (SCS-CN)-based Mishra and Singh (Mishra and Singh, 1999, J. Hydrologic. Eng. ASCE, 4(3), 257–264) model and its eight variants were investigated for their field applicability using a large set of rainfall-runoff events, derived from a number of U.S. watersheds varying in size from 0.3 to 30351.5 ha, grouped into five classes based on the rainfall magnitude. The analysis based on the goodness of fit criteria of root mean square error (RMSE) and error in computed and observed mean runoff revealed that the performance of the existing version of the SCS-CN method was significantly poorer than that of all the model variants on all the five data sets with rainfall 38.1 mm. The existing version showed a consistently improved performance on the data with increasing rainfall amount, but greater than 38.1 mm. The one-parameter modified SCS-CN method (a = 0.5 and = a median value) performed significantly better than the existing one on all the data sets, but far better on rainfall data less than 2 inches. Finally, the former with = 0 was recommended for routine field applications to any data set.     

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.     

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 Modified SCS-CN Method: Characterization and Testing

The Soil Conservation Service Curve Number (SCS-CN) method (SCS,1956) is modified by accounting for the static portion ofinfiltration and the antecedent moisture. A volumetric analysisshows that the ratio of the potential maximum retention (S) tothe precipitation amount versus the runoff factor relation isequivalent to the average suction pressure-moisture contentrelation for a unit rainfall amount and a given soil porosity. Asimple spreadsheet procedure is suggested for determining S withuse of a 5-day antecedent precipitation amount. The modifiedmethod is found to perform well on the same data sets as used inthe National Engineering Handbook (SCS, 1971).     

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.     

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.