Conceptual Rainfall-runoff Model Research Articles

Runoff predictions in ungauged catchments in southeast Tibetan Plateau

The Tibetan Plateau (TP) plays a key role on both hydrology and climate for southern and eastern Asia. Improving runoff predictions in ungauged catchments in the TP is critical for surface water hydrology and water resources management in this region. However, a detailed runoff prediction study in this region has not been reported yet. To fill the gap, this study evaluates two regionalization approaches, spatial proximity and physical similarity, for predicting runoff using two rainfall-runoff models (SIMHYD and GR4J). These models are driven by meteorological inputs from eight large non-nested catchments (4000-50,000 km(2)) in the Yarlung Tsangpo River basin located in southeast TP. For each catchment, the two models are calibrated using data from the first two-thirds of the observation period and validated over the remaining period. The calibrated and validated Nash-Sutcliffe Efficiency of monthly runoff (NSE) varies from 0.73 to 0.93 for the SIMHYD model, and are similar to or slightly better than those obtained for the GR4J model. The incorporation of snowfall-snowmelt processes into the rainfall-runoff models does not noticeably improve the runoff predictions in the study area. The main reason is that monthly runoff is dominated by summer precipitation and snowfall in winter accounts for a small percentage (less than 14%). The results from both models show that the spatial proximity approach marginally outperforms the physical similarity approach and both approaches are better than random selection of a donor catchment. This is consistent with recent regionalization studies carried out in Europe and Australia. The study suggests that conceptual rainfall-runoff models are powerful and simple tools for monthly runoff predictions in large catchments in southeast TP, and incorporation of more catchments into regionalization can further improve prediction skills. Crown Copyright (c) 2014 Published by Elsevier B.V. All rights reserved.

Calibrating the Rainfall-Runoff Model GR4J and GR2M on the Koulountou River Basin, a Tributary of the Gambia River

Rainfall runoff modelling is the first step in water resources management. It is the only way to simulate the hydrological behavior of the basin for a good evaluation of the potentiality of this in term of water production. Many approaches are actually in use. In physically distributed models, deterministic relations issued from conservation laws of physics (mass conservation, moment momentum conservation) are solved to describe the hydrological processes generating flow and their interaction. A DEM that should be as complete as possible is associed. Complexity of the equations to be solved and the huge amount of required data, uncertainty in these data make these models of limited use. Conceptual rainfall-runoff models are often preferred by hydrologists. These models are based on equations relating in a realistic manner the different terms of the hydrological cycle. They are simpler than determistic models and more flexible, Conceptual models are generally global. According to the way hydrological cycle terms are taken into account, conceptual model can be classified as empirical or not. The aim of this paper is to evaluate the availability of water in the Koulountou river basin, a tributary of Gambia River. This river basin should reinforce the water resource in a neighboring Kayanga river basin. Two empirical models at daily and monthly scale, the GR4J and GR2M have been used to describe the hydrological behavior of this basin. These models have been realized by the CEMAGREF, a French research Office. They use as inputs daily or monthly rainfall and potential evapotranspiration and river basin area, and give as output daily or monthly runoff. The first step before applying a hydrological model is to calibrate it that is to estimate the best parameters that fit the outputs in a given period. The Nash criterion has been used as goodness-of-fit criterion. Model performs satisfactory when this criterion is greater than 0.70 according to available data. A period from 1971 to 1994 has been selected. This period have been divided into three parts: one for calibration (1971-1978), one for validation (1978-1986), and the last for application (1987-1994). The results we obtain shows that GR4J and GR2M performs well in the Koulountou river basin since the Nash criterion is greater than 0.8.

The effects of climate change on the water resources of the Geumho River Basin, Republic of Korea

Assessments of the variation and vulnerability of water resources due to climate change are essential for future planning in agriculture. In this study, the impacts and uncertainty associated with climate change on water resources in the Geumho River Basin were measured based on the relative change in the mean annual runoff and the aridity index. Statistically adjusted and downscaled multi-ensemble General Circulation Model (GCM) predicted rainfall and temperature data for three representative concentration pathways (RCPs) (RCP2.6, 4.5 and 8.5) were applied to two lumped parameter conceptual rainfall runoff models. The results revealed considerable uncertainty in the projected temperature, rainfall, potential evapotranspiration (PET), runoff and aridity index (AI). Additionally, temperature and rainfall were predicted to increase significantly in the future. The PET was projected to increase by a mean (range) of 9% (7–12%), 18% (9–30%) and 25% (8–49%), while the mean annual runoff was projected to change by a mean (range) of 1% (−33 to 40%), −9% (−47 to 27%) and −4% (−44 to 35%), in the 2030s, 2060s and 2090s, respectively. The AI was projected to decrease in the future, particularly for the RCP8.5. Overall, the results of this study indicate that climate change will most likely lead to lower water resource levels than are currently present in the Geumho River Basin.

Assimilation of Passive Microwave Streamflow Signals for Improving Flood Forecasting: A First Study in Cubango River Basin, Africa

Floods are among the most frequently occurring and disastrous natural hazards in the world. The overarching goal of this study is to investigate the utility of passive microwave AMSR-E signal and TRMM based precipitation estimates in improving flood prediction at the sparsely gauged Cubango River Basin, Africa. This is accomplished by coupling a widely used conceptual rainfall-runoff hydrological model with Ensemble Square Root Filter (EnSRF) to account for uncertainty in both forcing data and model initial conditions. Three experiments were designed to quantify the contributions of the AMSR-E signal to the flood prediction accuracy, in comparison to the benchmark assimilation of in-situ streamflow observations, for both “Open Loop” and “Assimilation” modules. In general, the EnSRF assimilation of both in-situ observations and AMSR-E signal-converted-streamflow effectively improved streamflow modeling performance in terms of three statistical measures. In order to further investigate AMSR-E signals' contribution to extreme events prediction skill, the upper 10th percentile daily streamflow was taken as the threshold. Results show significantly improved skill and detectability of floods as well as reduced false alarm rates. Given the global availability of satellite-based precipitation from current TRMM and future GPM, together with soil moisture information from the current AMSR-E and future SMAP mission at near real-time, this “first attempt” study at a sparsely gauged African basin shows that opportunities exist for an integrated application of a suite of satellite data in improving flood forecasting worldwide by careful fusion of remote sensing and in-situ observations.

Impact of potential and (scintillometer-based) actual evapotranspiration estimates on the performance of a lumped rainfall–runoff model

Abstract. Evapotranspiration (ET) plays a key role in hydrological impact studies and operational flood forecasting models as ET represents a loss of water from a catchment. Although ET is a major component of the catchment water balance, the evapotranspiration input for rainfall–runoff models is often simplified in contrast to the detailed estimates of catchment averaged precipitation. In this study, an existing conceptual rainfall–runoff model calibrated for and operational in the Bellebeek catchment in Belgium firstly has been validated and its sensitivity to different available potential ET input has been studied. It has been shown that when applying a calibrated rainfall–runoff model, the model input should be consistent with the input used for the calibration process, not only on the volume of ET, but also on the seasonal pattern. Secondly, estimates of the actual evapotranspiration based on measurements of a large aperture scintillometer (LAS) have been used as model forcing in the rainfall–runoff model. From this analysis, it has been shown that the actual evapotranspiration is a crucial factor in simulating the catchment water balance and the resulting stream flow. Regarding the actual evapotranspiration estimates from the LAS, it has been concluded that they can be considered realistic in summer months. In the months where stable conditions prevail (autumn, winter and (early) spring), an underestimation of the actual evapotranspiration is made, which has an important impact on the catchment's water balance.

Development of an Automatic Calibration Tool Using Genetic Algorithm for the ARNO Conceptual Rainfall-Runoff Model

Rainfall-runoff simulation is one of the key steps in hydrology. Conceptual models are frequently used in rainfall-runoff simulation. However, a major difficulty in practice remains on how to optimize the parameters of the model. This is often a time-consuming and labor-intensive task for the modeler when manual calibration is adopted together with employing the knowledge of the model structure and parameters. In this study, an automatic calibration tool was developed to calibrate the ARNO conceptual rainfall-runoff model using the simple genetic algorithm (SGA). SGA is a simple, powerful, and popular optimization method, which explores the search space for the global optimum and has been successfully employed in many optimizations problems. The ARNO model was calibrated automatically for rainfall-runoff simulation of the Pataveh basin, which is a sub-basin of Karun River basin in Iran. The simulation performance of the model was evaluated on the basis of various performance criteria. Efficiency coefficient and coefficient of determination reached values higher than 0.80 during calibration and validation. The values of the remaining performance statistics were acceptable. The results show that this model with employed automatic calibration tool can successfully be used for continuous rainfall-runoff simulation.

A new approach to visualizing time-varying sensitivity indices for environmental model diagnostics across evaluation time-scales

Assessing the time-varying sensitivity of environmental models has become a common approach to understand both the value of different data periods for estimating specific parameters, and as part of a diagnostic analysis of the model structure itself (i.e. whether dominant processes are emerging in the model at the right times and over the appropriate time periods). It is not straightforward to visualize these results though, given that the window size over which the time-varying sensitivity is best integrated generally varies for different parameters. In this short communication we present a new approach to visualizing such time-varying sensitivity across time scales of integration. As a case study, we estimate first order sensitivity indices with the FAST (Fourier Amplitude Sensitivity Test) method for a typical conceptual rainfall–runoff model. The resulting plots can guide data selection for model calibration, support diagnostic model evaluation and help to define the timing and length of spot gauging campaigns in places where long-term calibration data are not yet available.

Urban drainage models – simplifying uncertainty analysis for practitioners

There is increasing awareness about uncertainties in the modelling of urban drainage systems and, as such, many new methods for uncertainty analyses have been developed. Despite this, all available methods have limitations which restrict their widespread application among practitioners. Here, a modified Monte-Carlo based method is presented that reduces the subjectivity inherent in typical uncertainty approaches (e.g. cut-off thresholds), while using tangible concepts and providing practical outcomes for practitioners. The method compares the model's uncertainty bands to the uncertainty inherent in each measured/observed datapoint; an issue that is commonly overlooked in the uncertainty analysis of urban drainage models. This comparison allows the user to intuitively estimate the optimum number of simulations required to conduct uncertainty analyses. The output of the method includes parameter probability distributions (often used for sensitivity analyses) and prediction intervals. To demonstrate the new method, it is applied to a conceptual rainfall-runoff model (MOPUS) using a dataset collected from Melbourne, Australia.

Analysis of the effect of climate change on the Nakdong river stream flow using indicators of hydrological alteration

This study models the effect of climate change on runoff in southeast Korea using the TANK conceptual rainfall-runoff model. The results are assessed using the indicators of hydrological alteration (IHA) developed by U.S. Nature Conservancy. Future climate time series are obtained by scaling historical series, provided by four global climate models (GCMs, IPCC, 2007) and three greenhouse gas (GHG) emissions scenarios (IPCC, 2000), to reflect a maximum increase of 3.6 °C in the average surface air temperature and 33% in the annual precipitation. To this end, the spatio-temporal change factor method is used, which considers changes in the future mean seasonal rainfall and potential evapotranspiration as well as the daily rainfall distribution. In this study, the variance range for precipitation is from +3.55% to +33.44% compared to the present for years between 2071 and 2100. The variance range for the daily mean temperature is estimated between +1.59 °C and +3.58 °C. Although the simulation results from different GCMs and GHG emissions scenarios indicate different responses of the flows to the climate change, the majority of modeling results show that there will be more runoff in southeast Korea in the future. According to the analysis results, the predicted impacts of hydrological alteration caused by climate change on the aquatic ecosystem are as follows: 1) an increase in the availability of aquatic ecosystem habitats in Nakdong River in future summers and winters, 2) an increase in stress on the aquatic ecosystem due to extremely high stream flow, 3) an increase in the stress duration of flood events for the Nakdong River downstream and 4) an increase in aquatic ecosystem stress caused by rapid increases or decreases in stream flow.

Addressing ten questions about conceptual rainfall–runoff models with global sensitivity analyses in R

Summary Sensitivity analysis (SA) is generally recognized as a worthwhile step to diagnose and remedy difficulties in identifying model parameters, and indeed in discriminating between model structures. An analysis of papers in three journals indicates that SA is a standard omission in hydrological modeling exercises. We provide some answers to ten reasonably generic questions using the Morris and Sobol SA methods, including to what extent sensitivities are dependent on parameter ranges selected, length of data period, catchment response type, model structures assumed and climatic forcing. Results presented demonstrate the sensitivity of four target functions to parameter variations of four rainfall–runoff models of varying complexity (4–13 parameters). Daily rainfall, streamflow and pan evaporation data are used from four 10-year data sets and from five catchments in the Australian Capital Territory (ACT) region. Similar results are obtained using the Morris and Sobol methods. It is shown how modelers can easily identify parameters that are insensitive, and how they might improve identifiability. Using a more complex objective function, however, may not result in all parameters becoming sensitive. Crucially, the results of the SA can be influenced by the parameter ranges selected. The length of data period required to characterize the sensitivities assuredly is a minimum of five years. The results confirm that only the simpler models have well-identified parameters, but parameter sensitivities vary between catchments. Answering these ten questions in other case studies is relatively easy using freely available software with the Hydromad and Sensitivity packages in R.

Effective Optimization Technique for a Nonlinear Rainfall-Runoff Model

AbstractThe purpose of this paper is to propose an effective optimization technique that can overcome the local minimum problem resulting from the nonlinear construction of the rainfall-runoff model. First, this technique attempts to approximate the nonlinear function by using a finite number of terms of linear functions, referred to as the linearization process. Then, these linear functions would be optimized by the sum of squared errors (MSSE). Finally, the optimized parameters of a nonlinear function using the step-iterative algorithm were obtained. Its features and capabilities are demonstrated by means of a binary function and a conceptual rainfall-runoff model (Xinanjiang model). To optimize the model parameters, 10 years of historical data in the Changzhao basin are applied, whereas the most recent three years are utilized to verify these parameters. With reference to the binary function, the proposed linearlized search technique (LST) converge quickly from any starting point. With regard to the hy...

Modeling the current and future capacity of water resources to meet water demands in the Ebro basin

Summary Worldwide studies have shown that the Mediterranean region is one of the most vulnerable areas to water crisis. The region is characterized by limited and unequally distributed water resources and increasing water demands. The Ebro catchment (85,000 km 2 , Spain) is representative of this context. Since the late 1970s, a negative trend in river discharge has been observed, attributed to a decrease in mean precipitation, and a rise in mean temperature and in water consumption. Finally, over 230 storage dams regulate river discharge. In this context, an integrated water resources modeling framework was developed to evaluate the current and future capacity of water resources to meet domestic and agricultural water demands as well as environmental flow requirements. The approach was driven by a conceptual rainfall-runoff model generating water supplies and by a demand driven storage dam model. The approach defines current pressures on water resources and evaluates future changes in water allocation in the medium term under climatic and water use scenarios, considering changes in population and in irrigated areas. Currently, water demands in the Ebro catchment are satisfied. In 2050, water resources are projected to decrease by 15–35% during spring and summer, leading to growing competition among users and severe water shortages for irrigated agriculture. This study provides an original approach to identify the most vulnerable regions to water use conflicts. It also highlights the interest of integrated modeling for complete analysis of the ability of water resources to meet water demands in complex change scenarios as a support for decision making.

The impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchments

Abstract. Although regenerating forests make up an increasingly large portion of humid tropical landscapes, little is known of their water use and effects on streamflow (Q). Since the 1950s the island of Puerto Rico has experienced widespread abandonment of pastures and agricultural lands, followed by forest regeneration. This paper examines the possible impacts of these secondary forests on several Q characteristics for 12 mesoscale catchments (23–346 km2; mean precipitation 1720–3422 mm yr−1) with long (33–51 yr) and simultaneous records for Q, precipitation (P), potential evaporation (PET), and land cover. A simple spatially-lumped, conceptual rainfall–runoff model that uses daily P and PET time series as inputs (HBV-light) was used to simulate Q for each catchment. Annual time series of observed and simulated values of four Q characteristics were calculated. A least-squares trend was fitted through annual time series of the residual difference between observed and simulated time series of each Q characteristic. From this the total cumulative change (Â) was calculated, representing the change in each Q characteristic after controlling for climate variability and water storage carry-over effects between years. Negative values of  were found for most catchments and Q characteristics, suggesting enhanced actual evaporation overall following forest regeneration. However, correlations between changes in urban or forest area and values of  were insignificant (p ≥ 0.389) for all Q characteristics. This suggests there is no convincing evidence that changes in the chosen Q characteristics in these Puerto Rican catchments can be ascribed to changes in urban or forest area. The present results are in line with previous studies of meso- and macro-scale (sub-)tropical catchments, which generally found no significant change in Q that can be attributed to changes in forest cover. Possible explanations for the lack of a clear signal may include errors in the land cover, climate, Q, and/or catchment boundary data; changes in forest area occurring mainly in the less rainy lowlands; and heterogeneity in catchment response. Different results were obtained for different catchments, and using a smaller subset of catchments could have led to very different conclusions. This highlights the importance of including multiple catchments in land-cover impact analysis at the mesoscale.

Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting

The objective of the study is to evaluate the potential of a data assimilation system for real-time flash flood forecasting over small watersheds by updating model states. To this end, the Ensemble Square-Root-Filter (EnSRF) based on the Ensemble Kalman Filter (EnKF) technique was coupled to a widely used conceptual rainfall-runoff model called HyMOD. Two small watersheds susceptible to flash flooding from America and China were selected in this study. The modeling and observational errors were considered in the framework of data assimilation, followed by an ensemble size sensitivity experiment. Once the appropriate model error and ensemble size was determined, a simulation study focused on the performance of a data assimilation system, based on the correlation between streamflow observation and model states, was conducted. The EnSRF method was implemented within HyMOD and results for flash flood forecasting were analyzed, where the calibrated streamflow simulation without state updating was treated as the benchmark or nature run. Results for twenty-four flash-flood events in total from the two watersheds indicated that the data assimilation approach effectively improved the predictions of peak flows and the hydrographs in general. This study demonstrated the benefit and efficiency of implementing data assimilation into a hydrological model to improve flash flood forecasting over small, instrumented basins with potential application to real-time alert systems.

Hydrological change detection using modeling: Half a century of runoff from four rivers in the Blue Nile Basin

[1] Land cover changes can have significant impacts on hydrological regime. The objective of this study was to detect possible hydrological changes of four watersheds in the Blue Nile Basin using a model-based method for hydrological change detection. The four watersheds, Birr, Upper-Didesa, Gilgel Abbay, and Koga range in size from 260 to 1800 km2. The changes were assessed based on model parameters, model residuals, and in the overall function of the watersheds in transferring rainfall into runoff. The entire time series (1960–2004) was divided into three periods based on political and land management policy changes. A conceptual rainfall-runoff model, the HBV (Hydrologiska Byrans Vattenbalansavdelning) model, was used for the analysis, and suitable parameter sets for each period were found based on a Monte Carlo approach. The values of six out of nine parameters changed significantly between the periods. Model residuals also showed significant changes between the three periods in three of the four watersheds. On the other hand, the overall functioning of the watersheds in processing rainfall to runoff changed little. So even though the individual parameters and model residuals were changing, the integrated functioning of the watersheds showed minimal changes. This study demonstrated the value of using different approaches for detecting hydrological change and highlighted the sensitivity of the outcome to the applied modeling and statistical methods.

Improving flow forecasting by error correction modelling in altered catchment conditions

AbstractDespite human is an increasingly significant component of the hydrologic cycle in many river basins, most hydrologic models are still developed to accurately reproduce the natural processes and ignore the effect of human activities on the watershed response. This results in non‐stationary model forecast errors and poor predicting performance every time these models are used in non‐pristine watersheds. In the last decade, the representation of human activities in hydrological models has been extensively studied. However, mathematical models integrating the human and the natural dimension are not very common in hydrological applications and nearly unknown in the day‐to‐day practice. In this paper, we propose a new simple data‐driven flow forecast correction method that can be used to simultaneously tackle forecast errors from structural, parameter and input uncertainty, and errors that arise from neglecting human‐induced alterations in conceptual rainfall–runoff models. The correction system is composed of two layers: (i) a classification system that, based on the current flow condition, detects whether the source of error is natural or human induced and (ii) a set of error correction models that are alternatively activated, each tailored to the specific source of errors. As a case study, we consider the highly anthropized Aniene river basin in Italy, where a flow forecasting system is being established to support the operation of a hydropower dam. Results show that, even by using very basic methods, namely if‐then classification rules and linear correction models, the proposed methodology considerably improves the forecasting capability of the original hydrological model. Copyright © 2013 John Wiley & Sons, Ltd.

Flood-initiating catchment conditions: a spatio-temporal analysis of large-scale soil moisture patterns in the Elbe River basin

Abstract. Floods are the result of a complex interaction between meteorological event characteristics and pre-event catchment conditions. While the large-scale meteorological conditions have been classified and successfully linked to floods, this is lacking for the large-scale pre-event catchment conditions. Therefore, we propose classifying soil moisture as a key variable of pre-event catchment conditions and investigating the link between soil moisture patterns and flood occurrence in the Elbe River basin. Soil moisture is simulated using a semi-distributed conceptual rainfall-runoff model over the period 1951–2003. Principal component analysis (PCA) and cluster analysis are applied successively to identify days of similar soil moisture patterns. The results show that PCA considerably reduced the dimensionality of the soil moisture data. The first principal component (PC) explains 75.71% of the soil moisture variability and represents the large-scale seasonal wetting and drying. The successive PCs express spatially heterogeneous catchment processes. By clustering the leading PCs, we identify large-scale soil moisture patterns which frequently occur before the onset of floods. In winter, floods are initiated by overall high soil moisture content, whereas in summer the flood-initiating soil moisture patterns are diverse and less stable in time.

A Different Light in Predicting Ungauged Basins: Regionalization Approach Based on Eastern USA Catchments

This paper reviewed the existing methods in regionalization studies to predict ungauged catchments and considering all the aspects a different methodology is developed, which is named as RDS method. ROPE (robust parameter estimation)-D (data depth)-S (spatial proximity) together gets this name RDS. Catchment properties and hydrological model parameters are used consistently to predict ungauged basin. This study explores the potential of the regionalization process to predict ungauged basins using the data of the Eastern USA catchments. Two conceptual rainfall-runoff hydrological models: HYMOD and HBV are used in this study. Analysis shows 95% success in predicting ungauged basins with HBV and 90% success with HYMOD. It is undoubtedly perceptible that RDS method is very effective in predicting ungauged basin and regionalization is independent of the conceptual rainfall-runoff model.

Assessment of a seasonal calibration technique using multiple objectives in rainfall–runoff analysis

AbstractThe hydrological response characteristics for the catchments in the Republic of Korea are related to a strong seasonality in the rainfall and streamflow distributions with distinct wet and dry seasons. This study aims to improve a model's ability to predict streamflows by minimizing information loss from the available data during the calibration processes. This study assesses calibration techniques incorporating a multi‐objective approach and seasonal calibration. The lumped conceptual rainfall–runoff model IHACRES was applied to selected catchments in Korea. The model was calibrated based on three different methods: the classical approach using a single performance statistic (the single‐objective method), the multi‐objective approach (the multi‐objective method (I)) and the combined approach incorporating multi‐objective and seasonal calibrations (the multi‐objective method (II)). In the multi‐objective approach, the ‘best fit’ models in the calibration period were selected by considering the trade‐offs among multiple statistics. During seasonal calibration, the calibration period was divided into four seasons to investigate whether these calibrated models can improve the model performance with regards to seasonal climate, rainfall and streamflow distributions. The adequacy of the three different calibration methods was assessed through comparison of the variability of model performance in high and low flows and water balance for the entire period and for each seasonal period. The multi‐objective methods yielded more accurate and consistent predictions for high and low flows and water balance simultaneously, compared to the single‐objective method. In particular, the multi‐objective method (II) produces the best modelling capacity to capture the non‐stationary nature of the hydrological response under different climate conditions. The pattern of improvement with the multi‐objective method (II) was generally consistent through the seasons, with the exception of the winter period in the regions partially affected by snow. This exception is due to a potential limitation of the IHACRES model in reflecting the impact of snow on the catchment hydrology. Copyright © 2013 John Wiley & Sons, Ltd.

Determination of standard target water quality in the Nakdong River basin for the total maximum daily load management system in Korea

This study suggests methods to improve the total maximum daily loads management system currently being regulated in Korea. The TANK model which is a conceptual rainfall-runoff model including the river routing function and the evapotranspiration process is adapted to simulate daily stream flows, and the 7-parameter log-linear model combined with the minimum variance unbiased estimator is used to calculate daily stream contaminant loads. Based on these methods, Load Duration Curves (LDCs) for each unit watershed are constructed. Using LDCs of all unit watersheds, representative specific LDC of Nakdong River is derived to indicate the average contaminant status of the whole river basin in terms of water quality items. Using representative specific LDCs, appropriate target water qualities with respect to reference flow conditions are assigned to all of unit watersheds, and point sources or non-point sources priority management areas of 40 unit watersheds of the Nakdong River basin are identified.