Ungaged Sites Research Articles

Model estimated baseflow for streams with endangered Atlantic Salmon in Maine,USA

AbstractWe present a regression model for estimating mean August baseflow per square kilometer of drainage area to help resource managers assess relative amounts of baseflow in Maine streams with Atlantic Salmon habitat. The model was derived from mean August baseflows computed at 31 USGS streamflow gages in Maine. We use an ordinary least squares regression model to estimate mean August baseflow per unit drainage area from two explanatory variables: percentage of the basin underlain by sand and gravel aquifers and mean July precipitation in the basin. This model provides the ability to estimate mean August baseflow in cubic meters per second per square kilometer of basin area on user‐selected, ungaged sites throughout Maine south of 46° 21′55″ N latitude. The model has an adjusted R2of 0.78 and a mean 95% prediction interval of plus or minus 0.002 cubic meters per second per square kilometer. A map of the Narraguagus watershed in eastern coastal Maine shows reaches color coded by relative amounts of baseflow predicted by the model as an example of how this method could be applied throughout Maine. The map can be used to identify reaches with relatively higher amounts of baseflow during summer low flows for habitat conservation and restoration work. These areas have the potential to be high‐quality habitat for Atlantic salmon and other cold‐water fish because baseflows are known to moderate stream temperatures in summer low‐flow periods.

Estimation of extreme quantiles at ungaged sites based on region-of-influence and weighting approaches to regional frequency analysis of maximum 24-h rainfall

Lack of adequate and reliable data for estimating the extreme values of hydrological variables at ungaged sites has always been one of the issues facing hydrologists in designing and planning water resource projects. Regionalizing the considered hydrological variable, finding an acceptable relationship for estimating its extreme values at ungaged sites using given data of other stations, and applying their available attributes are the solutions for the mentioned issue. In this study, historical data of maximum 24-h rainfall (M24-hR) covering the statistical period of 30 years (1979–2008) were collected and used from 63 rainfall gaging stations situated at Lake Urmia basin, northwestern Iran. Afterwards, using the method of region-of-influence (ROI) regionalization, the study area was regionalized through the geographic attributes of the stations (including latitude, longitude, elevation above mean sea level, and distance to the center of Lake Urmia). Then, all possible situations were considered for providing an appropriate regression relationship to estimate the extreme quantiles of M24-hR at ungaged sites by defining various scenarios of weighting to the geographic attributes and rainfall quantiles. The results showed that among different defined weighting scenarios, weighting to both stations and attributes in the at-site situation had an effective impact on forming an appropriate regression relationship for the estimation of extreme quantiles at ungaged sites. However, in the regional situation, a scenario considering no weight for both stations and attributes resulted in the most acceptable estimation of the quantiles with the lowest error (MSE = 1.29 mm). Further, the study showed that in most scenarios, the extreme quantiles estimated by means of regional regression relationships at ungaged sites (MSE = 1.29~1.75 mm) resulted in lower errors than the at-site ones (MSE = 1.35~7.64 mm).

Copula Theory as a Generalized Framework for Flow‐Duration Curve Based Streamflow Estimates in Ungaged and Partially Gaged Catchments

AbstractFlow‐duration curve (FDC) based streamflow estimation methods involve estimating an FDC at an ungaged or partially gaged location and using the time series of nonexceedance probabilities estimated from donor streamgage sites to generate estimates of streamflow. We develop a mathematical framework to illustrate the connection between copulas and prior FDC‐based approaches. The performance of copula methods is compared to several other streamflow estimation methods using a decade of daily streamflow data from 74 sites located within two river basins in the southeast United States with different climate characteristics and physiographic properties. We show that copula approaches: (1) outperform other methods in the limiting case of perfect information with regard to the rank‐based correlation structure and FDCs across the gaging network; (2) provide a hedge against poor performance when donor information becomes sparser and less informative; (3) outperform other methods when used for partially gaged sites with several years of available data; and (4) remain a competitive albeit nondominating method for ungaged sites and partially gaged sites with limited data when realistic error is introduced in the estimation of FDCs and correlations across the gaging network.

Posterior assessment of reference gages for water resources management using instantaneous flow measurements

Reference stream gages are commonly used for a variety of hydrologic studies and water resources management purposes. Reference gage selection methods have been extensively investigated in literature. However, the posterior assessment of reference gages is a long-standing challenge faced by water resources managers. This study aims to evaluate the accuracy of using reference gages in estimating low flow conditions at ungaged sites. The proposed assessment method is comprised of three fundamental components including: (1) a field campaign to obtain instantaneous flow measurements at ungaged sites during baseflow conditions; (2) streamflow correlation and streamflow ratio analyses using field measured values at ungaged sites and concurrent reference gage data; and (3) map correlation analysis to identify alternative reference gages for ungaged sites with undesirable flow correlation and flow ratio values. The method was tested using 18 systematically selected reference gages used by the Susquehanna River Basin Commission for regulating water withdrawals and ensuring compliance with passby flow requirements. Streamflow monitoring during baseflow conditions over the course of four consecutive low flow seasons resulted in the collection of ten streamflow measurements for each ungaged site. The streamflow correlation coefficients between streamflow measurements at ungaged sites and concurrent reference gage streamflow data were found to be greater than 0.7 for 17 of the 18 sites. Map correlation analysis was conducted to identify alternative reference gages for three ungaged sites which exhibited high prediction errors or low streamflow correlation. The case study demonstrates that proposed posterior assessment method for evaluating reference gage performance is easy to use with reasonable cost.

Preliminary estimate of floods discharge in Brazil using Creager envelope curves

ABSTRACT Hydrologic studies are essential for the design and safety of hydraulic structures. In the past, a number of empirical equations were developed to represent relationships between drainage areas and observed peak discharges. Although these empirical equations have not been used frequently to study peak discharges due to the use of more detailed and sophisticated analysis, they can be used to estimate reference values, especially in ungaged sites. The objective of this paper is to assess peak discharges observed in many regions in Brazil, estimate the 10,000-year discharges according the Guia para Cálculo de Cheia de Projeto de Vertedores from Eletrobrás and compare them to the Creager envelope curves. In this study we used the records of naturalized discharges for the period of 1931 to 2012, for 131 hydroelectric projects selected in the main river basins in Brazil, using the official data published by the Operador Nacional do Sistema Elétrico (ONS) for the Sistema Interligado Nacional (SIN). The results presented in this paper show that it is possible to use the Creager envelope curves as a preliminary estimate of the design floods in Brazil. Generally, values of the Greager C coefficient between 60 and 100 are recommended for a preliminary estimate of a range of maximum discharges within drainage areas bigger than 10,000 km2. The results can be used in the preliminary estimate of maximum discharges and 10,000-year discharges for sites with no data using C values obtained from areas with enough data in the same river basin and with similar physical characteristics.

A hierarchical Bayesian model for regionalized seasonal forecasts: Application to low flows in the northeastern United States

AbstractThis study presents a regional, probabilistic framework for seasonal forecasts of extreme low summer flows in the northeastern United States conditioned on antecedent climate and hydrologic conditions. The model is developed to explore three innovations in hierarchical modeling for seasonal forecasting at ungaged sites: (1) predictive climate teleconnections are inferred directly from ocean fields instead of predefined climate indices, (2) a parsimonious modeling structure is introduced to allow climate teleconnections to vary spatially across streamflow gages, and (3) climate teleconnections and antecedent hydrologic conditions are considered jointly for regional forecast development. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross‐validation framework along with five simpler nested formulations to test specific hypotheses embedded in the full model structure. Results indicate that each of the three innovations improve out‐of‐sample summer low‐flow forecasts, with the greatest benefits derived from the spatially heterogeneous effect of climate teleconnections. We conclude with a discussion of possible model improvements from a better representation of antecedent hydrologic conditions at ungaged sites.

Regional flood frequency analysis using spatial proximity and basin characteristics: Quantile regression vs. parameter regression technique

Despite wide use of regression-based regional flood frequency analysis (RFFA) methods, the majority are based on either ordinary least squares (OLS) or generalized least squares (GLS). This paper proposes ‘spatial proximity’ based RFFA methods using the spatial lagged model (SLM) and spatial error model (SEM). The proposed methods are represented by two frameworks: the quantile regression technique (QRT) and parameter regression technique (PRT). The QRT develops prediction equations for flooding quantiles in average recurrence intervals (ARIs) of 2, 5, 10, 20, and 100 years whereas the PRT provides prediction of three parameters for the selected distribution. The proposed methods are tested using data incorporating 30 basin characteristics from 237 basins in Northeastern United States. Results show that generalized extreme value (GEV) distribution properly represents flood frequencies in the study gages. Also, basin area, stream network, and precipitation seasonality are found to be the most effective explanatory variables in prediction modeling by the QRT and PRT. ‘Spatial proximity’ based RFFA methods provide reliable flood quantile estimates compared to simpler methods. Compared to the QRT, the PRT may be recommended due to its accuracy and computational simplicity. The results presented in this paper may serve as one possible guidepost for hydrologists interested in flood analysis at ungaged sites.

Quantification of uncertainties in the 100-year flow at an ungaged site near a gaged station and its application in Georgia

The Federal Emergency Management Agency has introduced the concept of the “1-percent plus” flow to incorporate various uncertainties in estimation of the 100-year or 1-percent flow. However, to the best of the authors’ knowledge, no clear directions for calculating the 1-percent plus flow have been defined in the literature. Although information about standard errors of estimation and prediction is provided along with the regression equations that are often used to estimate the 1-percent flow at ungaged sites, uncertainty estimation becomes more complicated when there is a nearby gaged station because regression flows and the peak flow estimate from a gage analysis should be weighted to compute the weighted estimate of the 1-percent flow. In this study, an equation for calculating the 1-percent plus flow at an ungaged site near a gaged station is analytically derived. Also, a detailed process is introduced for calculating the 1-percent plus flow for an ungaged site near a gaged station in Georgia as an example and a case study is performed. This study provides engineers and practitioners with a method that helps them better assess flood risks and develop mitigation plans accordingly.

Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States

AbstractUnderstanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index‐flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage‐area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern United States resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted‐variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low‐flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities.

A hierarchicalBayesian regional model for nonstationary precipitation extremes inNorthernCalifornia conditioned on tropical moisture exports

AbstractWarm, moist, and longitudinally confined tropical air masses are being linked to some of the most extreme precipitation and flooding events in the midlatitudes. The interannual frequency and intensity of such atmospheric rivers (ARs), or tropical moisture exports (TMEs), are connected to the risk of extreme precipitation events in areas where moisture convergence occurs. This study presents a nonstationary, regional frequency analysis of precipitation extremes in Northern California that is conditioned on the interannual variability of TMEs entering the region. Parameters of a multisite peaks‐over‐threshold model are allowed to vary conditional on the integrated moisture delivery from TMEs over the area. Parameters are also related to time‐invariant, local characteristics to facilitate regionalization to ungaged sites. The model is developed and calibrated in a hierarchical Bayesian framework to support partial pooling and enhance regionalization skill. The model is cross validated along with two alternative, increasingly parsimonious formulations to assess the additional skill provided by the covariates. Climate diagnostics are also used to better understand the instances where TMEs fail to explain variations in rainfall extremes to provide a path forward for further model improvement. The modeling structure is designed to link seasonal forecasting and long‐term projections of TMEs directly to regional models of extremes used for risk estimation. Results suggest that the inclusion of TME‐based information greatly improves the characterization of extremes, particularly for their frequency of occurrence. Diagnostics indicate that the model could be further improved by considering an index for frontal systems as an additional covariate.

Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change

Abstract. This study tests the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a stepwise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. To address the research questions, high-performance computing is utilized to manage the computational burden that results from high-dimensional optimization problems. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a stepwise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.

Estimating hydrologic alteration from basin characteristics in Massachusetts

Summary It is clear that humans are impacting the water cycle. There is interest in both determining where and how aquatic systems are most impacted by human development, and in determining the types and locations of basin modifications that are having the most impact. Instead of complex physical models of individual basins, we propose the use of a statistical approach to look at the relationship between human basin modifications and the resulting impacts on streamflow. We develop a set of multiple linear regression models, using principal component analysis to guide independent variable selection, to estimate current, altered streamflow from a full range of both natural and anthropogenic basin characteristics. Natural streamflow is then estimated by simulating basins with no alterations, and the difference between the natural and altered streamflow are summarized by use of the ecochange percent metric. The model suggests that dam storage, water withdrawals and discharges, and land use all impact stream flow and non-point source land use modifications such as impervious cover are potentially increasing low flows. The approach provides an opportunity to increase our understanding of the relation between human basin modifications and changes in streamflow. The model developed could potentially be used to estimate streamflow alteration at ungaged sites.

A Bayesian hierarchical approach to regional frequency analysis

Regional frequency analysis (RFA) has a long history in hydrology, and numerous distinct approaches have been proposed over the years to perform the estimation of some hydrologic quantity at a regional level. However, most of these approaches still rely on strong hypotheses that may limit their application and complicate the quantification of predictive uncertainty. The objective of this paper is to propose a general Bayesian hierarchical framework to implement RFA schemes that avoid these difficulties. The proposed framework is based on a two‐level hierarchical model. The first level of the hierarchy describes the joint distribution of observations. An arbitrary marginal distribution, whose parameters may vary in space, is assumed for at‐site series. The joint distribution is then derived by means of an elliptical copula, therefore providing an explicit description of the spatial dependence between data. The second level of the hierarchy describes the spatial variability of parameters using a regression model that links the parameter values with covariates describing site characteristics. Regression errors are modeled with a Gaussian spatial field, which may exhibit spatial dependence. This framework enables performing prediction at both gaged and ungaged sites and, importantly, rigorously quantifying the associated predictive uncertainty. A case study based on the annual maxima of daily rainfall demonstrates the applicability of this hierarchical approach. Although numerous avenues for improvement can already be identified (among which is the inclusion of temporal covariates to model time variability), the proposed model constitutes a general framework for implementing flexible RFA schemes and quantifying the associated predictive uncertainty.

Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments

Daily streamflow time series are critical to a very broad range of hydrologic problems. Whereas daily streamflow time series are readily obtained from gaged catchments, streamflow information is commonly needed at catchments for which no measured streamflow information exists. At ungaged catchments, methods to estimate daily streamflow time series typically require the use of a reference streamgage, which transfers properties of the streamflow time series at a reference streamgage to the ungaged catchment. Therefore, the selection of a reference streamgage is one of the central challenges associated with estimation of daily streamflow at ungaged basins. The reference streamgage is typically selected by choosing the nearest streamgage; however, this paper shows that selection of the nearest streamgage does not provide a consistent selection criterion. We introduce a new method, termed the map‐correlation method, which selects the reference streamgage whose daily streamflows are most correlated with an ungaged catchment. When applied to the estimation of daily streamflow at 28 streamgages across southern New England, daily streamflows estimated by a reference streamgage selected using the map‐correlation method generally provides improved estimates of daily streamflow time series over streamflows estimated by the selection and use of the nearest streamgage. The map correlation method could have potential for many other applications including identifying redundancy and uniqueness in a streamgage network, calibration of rainfall runoff models at ungaged sites, as well as for use in catchment classification.

Scaling properties of heavy rainfall at short duration: A regional analysis

The aim of this paper is to assess the scaling properties of heavy point rainfall with respect to duration. In the region of interest, the probability distribution tails of hourly to daily rainfall display log‐log linearity. The log‐log linearity of tails is a feature of fat‐tailed distributions. The conservation of this property throughout the scales will be investigated in the framework of scale‐invariant analysis. Evidence of the scaling of heavy rainfall is shown for one particularly long rainfall series through the conservation of the survival probability shape at durations in the range 1–24 h. An objective method is implemented to estimate the hyperbolic‐tail parameters of rainfall distributions. This method is automatized and detects the lower bound above which the distributions exhibit power law tails and determines the power law exponent α using a maximum likelihood estimator. The application of unbiased estimation methods and scale‐invariant properties for the estimation of the power law exponent provides a significant reduction of the intergage power law variability. This achievement is essential for a correct use of geostatistical approaches to interpolate the power law parameters at ungaged sites. The method is then applied to the rain gage network in the Cévennes‐Vivarais region, a Mediterranean mountainous region located in southern France. The maps show thicker rainfall distribution tails in the flat area between the seashore and the foothill. It is shown that in a flat region closer to the Mediterranean Sea the rainfall distribution tails are hyperbolic and the power law exponent is quasi‐constant with duration, whereas, over the mountain, the power law behavior is less defined. The physical reasons for such results and the consequences for the statistical modeling of heavy rainfall are then discussed, providing an innovative point of view for the comprehension of the rainfall extremes behavior at different temporal scales.

Regression estimates of design flows for ungaged sites using bankfull geometry and flashiness

Bankfull geometry data at 22 United States Geological Survey stream flow gaging sites in the Red River Basin of Minnesota and North Dakota were used for developing regression estimates of flows of different return periods. Regression equations were developed first by using only basin characteristics and then by using only bankfull geometry data as independent variables. Flashiness was included later as an independent variable in both cases. In all, 101 regressions were evaluated. Storage, channel slope, and bankfull width did not appear in the selected regressions. Stream flashiness was found to be an important explanatory variable in both bankfull geometry and basin characteristics regressions. The bankfull geometry data produced slightly better regression equations than the basin characteristics data. The bankfull geometry regressions will be useful to predict design flows and to compare with flow estimates developed by traditional methods for ungaged sites in the Red River Basin.

Predicting the natural flow regime: models for assessing hydrological alteration in streams

AbstractUnderstanding the extent to which natural streamflow characteristics have been altered is an important consideration for ecological assessments of streams. Assessing hydrologic condition requires that we quantify the attributes of the flow regime that would be expected in the absence of anthropogenic modifications. The objective of this study was to evaluate whether selected streamflow characteristics could be predicted at regional and national scales using geospatial data. Long‐term, gaged river basins distributed throughout the contiguous US that had streamflow characteristics representing least disturbed or near pristine conditions were identified. Thirteen metrics of the magnitude, frequency, duration, timing and rate of change of streamflow were calculated using a 20–50 year period of record for each site. We used random forests (RF), a robust statistical modelling approach, to develop models that predicted the value for each streamflow metric using natural watershed characteristics. We compared the performance (i.e. bias and precision) of national‐ and regional‐scale predictive models to that of models based on landscape classifications, including major river basins, ecoregions and hydrologic landscape regions (HLR). For all hydrologic metrics, landscape stratification models produced estimates that were less biased and more precise than a null model that accounted for no natural variability. Predictive models at the national and regional scale performed equally well, and substantially improved predictions of all hydrologic metrics relative to landscape stratification models. Prediction error rates ranged from 15 to 40%, but were ≤25% for most metrics. We selected three gaged, non‐reference sites to illustrate how predictive models could be used to assess hydrologic condition. These examples show how the models accurately estimate pre‐disturbance conditions and are sensitive to changes in streamflow variability associated with long‐term land‐use change. We also demonstrate how the models can be applied to predict expected natural flow characteristics at ungaged sites. Copyright © 2009 John Wiley & Sons, Ltd.

REGIONAL FREQUENCYANALYSIS FOR EXTREME RAINFALL IN SICILY

The study regarded the regional frequency analysis of extreme rainfall in Sicily, using rainfall annual maximum series of duration 1, 3, 6, 12 and 24 hours in 235 sites, stationed in all Sicily and for the period 1928-1998. The applied regional models, after the appropriate verifications, were: the TCEV hierarchical model (Rossi et al.), the model based on the use of the linear moments LM (Hosking et al.) and the MGs parametric model (Maione et al.). For ungaged sites, as in a flood index approach, the index value of each model was evaluated through maps derived from spatial interpolation. The three models regional estimations were compared with the at-site estimation, computed by GEV and Gumbel distribution, for the stations with record length n≥45 and return period T =200 years. The comparison was carried out taking into consideration, for the regional models, the index value estimation in the two following cases: • Case 1: Index value estimated by historical series; • Case 2: Index value estimated by interpolated maps. The results indicate that, in Case 1, the MGs regional model performs better than the other two regional models for each duration t but, in Case 2, the performance of MGs model is weakly better than the other regional models studied only for t=1÷12 hours. Furthermore, we observe the same behavior of TCEV and GEV-LM models, also detected by Brath in center-northern Italy [4].

REGIONALIZATION OF NEPALESE RIVER BASINS FOR FLOOD FREQUENCY ANALYSIS

Regional flood frequency analysis which is applicable for estimating design flood at both gauged and ungaged sites needs grouping of river basins into hydrological homogeneous regions. Specific flood, timing of flood events and spatial location are significant in reflecting physiogrpahy and climate of any river basins. These three physiographic/climatic characteristics of 46 independent Nepalese river basins have been used successfully to delineate the homogeneous regions. This study uses a clustering algorithm as a starting point for partitioning the independent catchments. The regions thus formed are subsequently revised to improve the regional homogeneity after performing the L-moment based homogeneity test. Spatial proximity consideration has been used to delineate region for mountainous dependent river basins whereas subjective considerations have been made to delineate region for river basins of alluvial plain region.

Regional flood–rainfall duration-frequency modeling at small ungaged sites

Flood frequency data for different durations of floods are required in many practical hydrologic applications. The estimation of flood frequency as an integrated function of return period and flood duration can be accomplished by flood-duration-frequency modeling. This study introduces a new approach to regional flood-duration-frequency modeling that is based on statistical properties of combined flood–rainfall events. The approach integrates flood-duration-frequency (QDF) and rainfall depth-duration-frequency (DDF) models into one regional flood–rainfall duration-frequency model (QDDF). The proposed model has only one local parameter, which accounts for site-specific physiographic characteristics. Regional parameters of the model are determined from statistical properties of regional rainfall depth-duration-frequency curves. The main advantage of the proposed approach is that it relies on rainfall data, which are spatially and temporally more abundant than streamflow data, and usually also available in hydrologically ungaged areas. The regional QDDF model was applied to a set of small catchments from a hydro-climatologically homogeneous region in south-western Ontario, Canada. The performance of the model was compared to the performance of the conventional regional converging QDF model by means of a jack-knife procedure. The results showed that the proposed approach significantly outperformed the converging QDF model, leading to quantile estimates with three-times lower average BIAS and RMSE. The proposed QDDF model seems to be a promising alternative for the regional QDF modeling of floods in the study area.