Prediction Of Flow Duration Curves Research Articles

Regional Analysis of Flow Duration Curves through Support Vector Regression

A flow-duration curve (FDC) shows the relationship between magnitude and frequency of daily streamflows over a specific time period. Artificial intelligence methods e.g. Support Vector Machines for Regression (SVR) and Artificial Neural Network (ANN) are useful techniques in the prediction of FDCs in ungagged basins. Regional analysis of FDCs were performed through SVR, ANN and Nonlinear Regression (NLR) using streamflow with durations of 0.02, 0.10, 0.20, 0.50 and 0.90% as dependent variables and six watershed characteristics chosen as effective independent variables on 33 selected watersheds in the Namak-Lake basin located in central zone of Iran. The results shows that the most important watershed characteristics are weighted average height, area, rangeland area, drainage density, permeable formation, and average stream slope. SVR has higher accuracy with relative root mean squared error (RMSEr) of 9.37 to 1.45 and Nash-Sutcliff criterion (NSE) of 0.54 to 0.91 than ANN with RMSEr with 9.42 to 3.79 and NSE of 0.39 to 0.86 and NLR with RMSEr with 18.04 to 3.38 and NSE of 0.53 to 0.79. In general, SVR is proposed to be used to estimate FDCs.

Prediction of flow duration curves for ungauged basins

This study presents novel models for prediction of flow Duration Curves (FDCs) at ungauged basins using artificial neural networks (ANN) and Gene Expression Programming (GEP) trained and tested using historical flow records from 171 unregulated and 89 regulated basins across North America. For the 89 regulated basins, FDCs were generated for both before and after flow regulation. Topographic, climatic, and land use characteristics are used to develop relationships between these basin characteristics and FDC statistical distribution parameters: mean (m) and variance (ν). The two main hypotheses that flow regulation has negligible effect on the mean (m) while it the variance (ν) were confirmed. The novel GEP model that predicts the mean (GEP-m) performed very well with high R2 (0.9) and D (0.95) values and low RAE value of 0.25. The simple regression model that predicts the variance (REG-v) was developed as a function of the mean (m) and a flow regulation index (R). The measured performance and uncertainty analysis indicated that the ANN-m was the best performing model with R2 (0.97), RAE (0.21), D (0.93) and the lowest 95% confidence prediction error interval (+0.22 to +3.49). Both GEP and ANN models were most sensitive to drainage area followed by mean annual precipitation, apportionment entropy disorder index, and shape factor.

Model swapping: A comparative performance signature for the prediction of flow duration curves in ungauged basins

The issue of lack of donor basins for prediction of flow duration curves (FDCs) in ungauged basins (PUB) is an important area of research that is not resolved in the literature. We present a distance based approach to predict FDCs at ungauged basins by quantifying the dissimilarity between FDCs and characteristics data of basins. This enables us to bracket hydrologically similar basins and thus allowing us to estimate FDCs at ungauged basins. Generally, a single regression model is selected to make hydrological estimates at an ungauged basin. Based on established laws and theories of hydrology, we work to devise a method to improve the output of selected model for an ungauged basin by swapping it with another model in case the latter gives better coverage and statistical estimates of the nearest neighbors of an ungauged basin. We report two examples to demonstrate the effectiveness of model swapping. Out of 124 basins used in analysis, 34 basins in example 1 and 41 basins in example 2 fulfill the set criteria of model swapping and subsequently their estimates are improved significantly.

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

Abstract. The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash–Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

Prediction of Flow Duration Curve in Ungauged Catchments Using Genetic Expression Programming

A set of multivariate equations have been developed using gene expression programming (GEP) based symbolic regression technique to generate the flow quantiles of flow duration curve (FDC) in the ungauged catchments in the East Coast of Peninsular Malaysia. The equations were derived from four to seven candidate explanatory variables prepared from climatic, geomorphologic, geographic characteristics, soil properties, and land use and land cover information. Support vector machine (SVM) was used to optimize the best combinations for calibration and validation of GEP models from the data available in thirteen gauged catchments in the study area. Seven flow percentiles namely 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95 as well as extreme, maximum, minimum and mean annual flows were identified to develop a framework for predicting various flow metrics. Obtained results revealed that nonlinear regression equations developed using GEP can generate FDCs in ungauged catchments of East Coast of Peninsular Malaysia with an efficiency of up to 0.92.

Comparing Spatial Interpolation Schemes for Constructing a Flow Duration Curve in an Ungauged Basin

This study aims to investigate the applicability of proposed ensemble interpolation technique based on both geographical and physiographical spaces, for prediction of flow duration curve (FDC) at ungauged site. The interpolation schemes were applied for selected 15 different percentiles of the FDC. Firstly, different FDC parametric models were tested to approximate FDCs at gauged sites. It was found that the suggested reliable ensemble averaging (REA) model outperformed the traditional deterministic models (linear, exponential, and logarithmic models). The REA model approximated the FDC with high consistency and small deviations from the observed percentiles. The results obtained by the REA model were further utilized to define homogeneity in the selected region. Secondly, based on the inverse distance weighting (IDW) concept, both geographical and physiographical spaces estimations were ensemble (using proposed estimated true value (ETV) method) to predict the hydrological information (FDC) at ungauged sites. It was noted that ETV based interpolation provided more efficient, reliable and consistent efficiency. Finally, to identify physical conditions that could favor the high predictability of the proposed methodology, we investigated the relationship between Nash-Sutcliffe efficiency (NASH) and catchments attributes. The results indicated that significance correlations were observed between NASH and catchments attributes even though no clear tends exist.

Prediction of Flow Duration Curve for Seasonal Rivers in Iraq

This paper presents a new method to estimate the flow duration curves [FDCs] for ungauged river basins whose natural and meterological characteristics are known. This study highlights the modeling of the lower three-quarters of the section of the flow duration curves [FDCs]. Eight sub-catchments were used to develop and evaluate the proposed flow duration model in the north of Iraq. The logarithmic type function has been found appropriate for the lower three-quarters of the section of [FDCs] for all river sub-catchments located in the study area. Parameter values of the logarithmic function model were calculated using topographic, hydrological and climatic characteristics of the basins under study by two regional regression models: first CA-MAP (catchment area-mean annual precipitation) model and second MAF-PE (mean annual flowpotential evapotranspiration) model. Generally, it is found that both models used were predicting a good estimate at the end of the flow duration curve (low-flows). In most cases, the statistics and graphical results showed that the agreement between observed and estimated FDCs is very good by using MAF-PE model as compared to CA-MAP model.

Prediction of Flow Duration Curves for Ungauged Basins with Quasi-Newton Method

Prediction of flow-duration-curves (FDC) is an important task for water resources planning, management and hydraulic energy production. Classification of the basins as carstic and non-carstic may be used to estimate parameters of the FDC with predictive tools for catchments with/without observed stream flow. There is a need for obtaining FDC for ungauged stations for efficient water resource planning. Thus, study proposes a quite new approach, called the EREFDC model, for estimating the parameters of the FDC for which the parameters of the FDC are obtained with quasi-Newton method. Estimation are made for using the bv gauged stations at first than the FDC parameters are estimated for ungauged stations based on drainage area, annual mean precipitation, mean permeability, mean slope, latitude, longitude, and elevation from the mean sea level are used. The EREFDC model consists of various type of linear- and nonlinear mathematical equations, is able to predict a wide range of the FDC parameters for gauged and ungauged basins. The method is applied to 72 unimpaired catchments studied are about for 50 years average daily measured stream flow. Results showed that the EREFDC model may be used for estimating. FDC parameters for ungauged hydrological basins in order to find FDC for ungauged stations. Results also showed that the EREFDC model performs better in carstic regions than non-carstic regions. In addition, parameters of FDC for tributaries at the basins with insufficient flow data or without flow data may be determined by using basin characteristics.