Streamflow Estimates Research Articles

Spatial and temporal distribution of blue water in the Limpopo River Basin, Southern Africa: A case study

Water is vital for human survival and ecosystem health. In arid and semi-arid areas like the Limpopo River Basin (LRB) in Southern Africa, water is a precious commodity. The study of spatial and temporal distribution of freshwater components is an important step toward sustainable planning and management of water resources. This case study applied the Soil and Water Assessment Tool (SWAT) to characterize freshwater availability and scarcity in the LRB. SWAT predictions of hydrologic fluxes in the basin are generally satisfactory when compared to measured streamflow records, although uncertainties persist in model estimates. Estimates of streamflow, herein referred to as blue water, varied from 0.2 to 570mm/year between 1984 and 2013 over the basin. The analysis revealed alternating cycles of one to two years of water surplus periods and three to five years of dry periods during the study period. The results also indicated that, considering the natural water yield, 20% of the basin (mostly east) has enough water, while the remaining 80% continues to experience dryness and water stress.

Impacts of land use/land cover change on stream flow and sediment yield of Gojeb watershed, Omo-Gibe basin, Ethiopia

Land use/land cover (LULC) change is an important landscape process capable of altering the fluxes of water, sediment, contaminants and energy. In this study, Soil and Water Assessment Tool (SWAT) model was used to examine the effects of LULC on the hydrological process of Gojeb Watershed in the Omo-Gibe basin. The performance of the model was evaluated through sensitivity, uncertainty analysis, calibration and validation. The LULC change analyses for three periods (1989, 2000 and 2013) were performed using ERDAS Imagine 2014 with a maximum likelihood classifier. The time series LULC changes results were used for estimation of stream flow and sediment yield. The result has shown that during the study period most parts of the forest land were converted into cultivated land with an increase of cultivated land by 14.97% over the study periods which resulted in an increase of stream flow and sediment yield by 8.6 m3/s and 41.07ton/km2 respectively. The Nash sutcliff efficiency (NSE), coefficient of determination (R2) and percent of bias (PBIAS) were used for evaluating the model performance. The model results have shown a good agreement with the observed values with NSE > 0.75, R2 > 0.78, and PBIAS < 0.5 values. Based on the validated sediment yield results of 2013 land use, high potential source of spatial variability of sediment was found at middle and downstream of the watershed. Hence, for the critical sub-watersheds, the design and development of three best management practices (BMPs) were performed. These best management scenarios include: S1 (filter strip), S2 (terrace/ bund) and S3 (reforestation). Based on these scenarios, the findings have shown a sediment yield reductions by 22.9%, 29.4% and 32.3% with the implementation of S1, S2 and S3 respectively. Therefore, based on the findings, implementing S3 is the best management strategy as compared to other options and hence, such intervention should be encouraged at a wider scale for efficient sediment reductions of Gojeb watershed.

Hydrologic and thermal conditions occupied by a species within a single watershed predict the geographic extent of occurrence of freshwater fishes

AbstractHydrologic regimes and water temperatures are primary predictors of freshwater species occurrence. Although these variables have been demonstrated to be important in regulating species diversity at particular locations, whether species occurrences across lotic habitats within a single, relatively small watershed can predict the full geographic extent of a species is unclear. We use river reach estimates of streamflow and water temperature derived from a watershed‐scale hydrologic model, coupled with body size measures, to investigate whether the type and variability of thermal and hydrologic habitat used by fish species within the Mobile River Basin (MRB) can predict the overall geographic extent of occurrence (GEO) for these taxa. Locality data for 108 species of fishes within MRB, one of the most ecologically diverse watersheds in the United States, were intersected with streamflow and water temperature estimates to characterize minimum and maximum streamflow and water temperature conditions and thermal breadth (range of thermal conditions) occupied by each species. Among all species, variation in GEO was associated with variation in thermal breadth and body size. Thermal variables were also important predictors of variation in GEO among Cyprinidae. Flow variables were important predictors of variation in GEO for Centrarchidae, Ictaluridae, and Percidae and within Etheostoma and Percina. Results generally indicate that species with large body size, relatively broad thermal tolerances, or preference for relatively high discharge environments tend to exhibit broader distributions across North America, yet these relationships vary among taxonomic groups.

Exploring Uncertainty in Streamflow Estimates

A review of streamflow uncertainty estimation methods reveals that one method does not fit all situations and provides recommendations for how to improve streamflow estimates.

Efficiency of the groundwater artificial recharge from dam water release in arid area

This paper describes a methodology to estimate the groundwater artificial recharge efficiency. The water table fluctuation method (WTF), stream-flow (SF) estimation method and Hydrus-1D model are used to estimate the effective water quantity reaching the aquifer. The efficiency of the stream flooding on groundwater level trend was discussed based on groundwater table fluctuation. The comparison between the three methods is found to be valuable for determining the range of plausible recharge amounts and for highlighting the uncertainty of the estimates. SF method yields the largest recharge estimate whereas the WTF method provides actual recharge reaching the aquifer. SF method and Hydrus-1D model show the best fit between estimated recharge volume and water volume released with a coefficient-of-determination (R2) of 0.9 and 0.8, respectively. The combination of the stream flow with water-table fluctuations methods is recommended to increase the constancy of recharge estimates and preliminary assessment.

A Framework for Diagnosing Factors Degrading the Streamflow Performance of a Soil Moisture Data Assimilation System

Abstract Data assimilation (DA) techniques have been widely applied to assimilate satellite-based soil moisture (SM) measurements into hydrologic models to improve streamflow simulations. However, past studies have reached mixed conclusions regarding the degree of runoff improvement achieved via SM state updating. In this study, a synthetic diagnostic framework is designed to 1) decompose the random error components in a hydrologic simulation, 2) quantify the error terms that originate from SM states, and 3) assess the effectiveness of SM DA to correct these random errors. The general framework is illustrated through a case study in which surface Soil Moisture Active Passive (SMAP) data are assimilated into a large-scale land surface model in the Arkansas–Red River basin. The case study includes systematic error in the simulated streamflow that imposes a first-order limit on DA performance. In addition, about 60% of the random runoff error originates directly from rainfall and cannot be corrected by SM DA. In particular, fast-response runoff dominates in much of the basin but is relatively unresponsive to state updating. Slow-response runoff is strongly controlled by the bottom-layer SM and therefore only modestly improved via the assimilation of surface measurements. Combined, the total runoff improvement in the synthetic analysis is small (&amp;lt;10% over the basin). Improvements in the real SMAP-assimilated case are further limited due to systematic error and other factors such as inaccurate error assumptions and SMAP rescaling. Findings from the diagnostic framework suggest that SM DA alone is insufficient to substantially improve streamflow estimates in large basins.

Two calibration methods for modeling streamflow and suspended sediment with the swat model

The proper estimation of streamflow (Q) and suspended sediment (SS) have important implications for sustainable water management. The Soil and Water Assessment Tool (SWAT) is a distributed, physically-based and dynamic model that integrates water quantity and quality routines. SWAT is considered an effective tool for assessing water and soil resources problems in a worldwide range of environmental conditions; however every hydrological model application is limited by the choices made in the calibration process. The aim of this paper was to assess the differences between the sequential and simultaneous calibration methods when simulating streamflow and suspended sediment with SWAT. We used the Sequential Uncertainty Fitting (SUFI-2) combined with SWAT for: (i) sensitivity analysis; (ii) parameter calibration using Q and SS data with a combined multi-site and multi-objective approaches; and (iii) evaluation of the differences in the estimated uncertainty of both calibration methods. Our results suggest that the simultaneous calibration was more accurate, especially for SS values with King-Gupta Efficiency (KGE) objective function. The simultaneous calibration was less time consuming and the sensitivity analysis indicated that it needed less parameters in the calibration process. The multi-site calibration method improved the model results in 3 out of 4 simulations of suspended sediment in the basin outlet.

Hydrologic responses to climate warming for a snow‐dominated watershed and a transient snow watershed in the California Sierra

AbstractClimate warming will have substantial impacts on hydrological fluxes in the California Sierra. A commonly used approach for assessing these impacts, particularly in mountain watersheds, is to substitute space for time. This conceptual model assumes that with warming, the hydrologic behaviour of higher elevation snow dominated watersheds (SDWs) will converge to the hydrologic behaviour of lower elevation transient snow watersheds (TSWs). To investigate the efficacy of this conceptual model, a process‐based model (RHESSys) was applied to a TSW and a SDW with a mean annual temperature 2 °C lower than the TSW in the Sierra National forest, California. This study investigated the effect of climate warming (2 and 4 °C) on the model estimates of snow water equivalent (SWE), streamflow, evapotranspiration (ET), and moisture deficit in the two watersheds. Modelling results show that SDW under 2 °C warming scenarios generates monthly SWE similar in magnitude and timing as TSW under historic conditions. However, SDW under 2 °C warming scenarios generates higher annual and summer streamflow due to shallower groundwater storage and experiences less water limitation due to lower ET, compared with TSW under historical climate conditions. In both watersheds, leaf area index and wetness index are primary factors controlling spatial patterns of seasonal ET under both historical climate conditions and warming scenarios. Climate warming increases the spatial variability in monthly ET, especially in the summer period. These modelling results suggest that vegetation structure and subsurface properties may be as important as climate in explaining hydrologic response to climate warming in small Sierra Nevada watersheds.

Top-down seasonal streamflow model with spatiotemporal forest sapwood area

Forest growth dynamics affect streamflow with changes in actual evapotranspiration (AET) during forest regeneration. Using a “top-down” model approach, we present a seasonal forest hydrology model that estimates streamflow in forested catchments containing sparse soil and climate data. Across a 1423 km2 remote study area, a rainfall interpolation procedure using available rainfall data and information on terrain was integrated into a new seasonal streamflow model, called ABCF, which uses catchment sapwood area (SA) as the emergent property that equilibrates with potential evapotranspiration (PET), and a soil storage threshold that reduces AET below PET when soil water is limiting. We produce seasonal estimates of streamflow with Nash Sutcliffe efficiencies of 0.85, 0.87, and 0.91 for three major catchments within the study area. A fundamental feature of the “top-down” model approach is the use of LiDAR data and forest inventory data to model forest structural properties that relate strongly with SA. Building on our previous work with this modelling framework, our representation of eco-hydrological properties of the forest has been refined with a more accurate procedure for estimating stand mean sapwood thickness, and hence SA, and a remotely sensed tree stocking density (N) of old-growth forests to correct the temporal evolution of N as a means to improve SA estimates. Regional consistency of model parameters shows that the “top-down” modelling framework may be used to estimate streamflow in ungauged catchments using a forest growth model. The seasonal model generalised for both water-limited and water-unlimited forest conditions has significant potential for application in water supply planning and drought security.

Probabilistic Snow Cover and Ensemble Streamflow Estimations in the Upper Euphrates Basin

Abstract Predicting snow cover dynamics and relevant streamflow due to snowmelt is a challenging issue in mountainous basins. Spatio-temporal variations of snow extent can be analyzed using probabilistic snow cover maps derived from satellite images within a relatively long period. In this study, Probabilistic Snow Depletion Curves (P-SDCs) and Probabilistic Snow Lines (P-SLs) are acquired from Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-filtered daily snow cover images. Analyses of P-SDCs show a strong correlation with average daily runoff (R2 = 0.90) and temperature (R2 = 0.96). On the other hand, the challenge lies in developing noteworthy methods to use P-SDCs in streamflow estimations. Therefore, the main objective is to explore the feasibility of producing probabilistic runoff forecasts with P-SDC forcing in a snow dominated basin. Upper Euphrates Basin in Turkey has large snow extent and high snowmelt contribution during spring and summer periods. The melting characteristics are defined by P-SDCs using MODIS imagery for 2001-2012. The value of snow probability maps on ensemble runoff predictions is shown with Snowmelt Runoff Model (SRM) during 2013-2015 where the estimated runoff values indicate good consistency (NSE: 0.47-0.93) with forecasts based on the derived P-SDCs. Therefore, the probabilistic approach distinguishes the snow cover characteristics for a region and promotes a useful methodology on the application of probabilistic runoff predictions especially for snow dominated areas.

Considerations for using turbidity as a surrogate for suspended sediment in small, ungaged streams: Time‐series selection, streamflow estimation, and regional transferability

AbstractSuspended sediment (SS) transport is an important and natural process in aquatic ecosystems. However, accelerated erosion due to anthropogenic land uses causes excessive sedimentation, which makes SS pollution one of the most prevalent stressors causing biological impairments of freshwater. Despite the ubiquitous nature of sedimentation and the ways in which it negatively impacts streams, inherent obstacles exist to effective measurement and characterization of fluvial SS loading. SS concentrations (SSCs) can vary widely during similar hydrologic conditions, making discharge an unreliable predictor variable. Alternatively, turbidity has been shown to be an effective surrogate for SSC, and methods using in situ optical turbidity sensors to indirectly measure SSCs have been developed. We present data and results from research using turbidity as a surrogate for SSC in five small, ungaged streams in the Chesapeake Bay watershed. Objectives of this study include presenting practical considerations and strategies for applying the turbidity–SSC surrogate method in small headwater streams. Empirical evidence is presented illustrating the potential correlation between land use and sediment loading. Additionally, accurate estimates of sediment loading require utilization of high frequency time‐series data and on‐site monitoring to capture timing and magnitude of streamflow, especially during intense, short‐duration runoff events. Similar turbidity–SSC relationships across watersheds suggest that regional regression curves could be established to allow more efficient and cost‐effective estimation of sediment loading. The broad application of this method would also benefit water resource management, by enabling prioritization of sites for stream restoration, standardizing techniques for monitoring stream recovery, or documenting impacts of discrete land‐use changes.

Value of uncertain streamflow observations for hydrological modelling

Abstract. Previous studies have shown that hydrological models can be parameterised using a limited number of streamflow measurements. Citizen science projects can collect such data for otherwise ungauged catchments but an important question is whether these observations are informative given that these streamflow estimates will be uncertain. We assess the value of inaccurate streamflow estimates for calibration of a simple bucket-type runoff model for six Swiss catchments. We pretended that only a few observations were available and that these were affected by different levels of inaccuracy. The level of inaccuracy was based on a log-normal error distribution that was fitted to streamflow estimates of 136 citizens for medium-sized streams. Two additional levels of inaccuracy, for which the standard deviation of the error distribution was divided by 2 and 4, were used as well. Based on these error distributions, random errors were added to the measured hourly streamflow data. New time series with different temporal resolutions were created from these synthetic streamflow time series. These included scenarios with one observation each week or month, as well as scenarios that are more realistic for crowdsourced data that generally have an irregular distribution of data points throughout the year, or focus on a particular season. The model was then calibrated for the six catchments using the synthetic time series for a dry, an average and a wet year. The performance of the calibrated models was evaluated based on the measured hourly streamflow time series. The results indicate that streamflow estimates from untrained citizens are not informative for model calibration. However, if the errors can be reduced, the estimates are informative and useful for model calibration. As expected, the model performance increased when the number of observations used for calibration increased. The model performance was also better when the observations were more evenly distributed throughout the year. This study indicates that uncertain streamflow estimates can be useful for model calibration but that the estimates by citizen scientists need to be improved by training or more advanced data filtering before they are useful for model calibration.

Future Changes in Flood Hazards across Canada under a Changing Climate

Climate change has induced considerable changes in the dynamics of key hydro-climatic variables across Canada, including floods. In this study, runoff projections made by 21 General Climate Models (GCMs) under four Representative Concentration Pathways (RCPs) are used to generate 25 km resolution streamflow estimates across Canada for historical (1961–2005) and future (2061–2100) time-periods. These estimates are used to calculate future projected changes in flood magnitudes and timings across Canada. Results obtained indicate that flood frequencies in the northernmost regions of Canada, and south-western Ontario can be expected to increase in the future. As an example, the historical 100-year return period events in these regions are expected to become 10–60 year return period events. On the other hand, northern prairies and north-central Ontario can be expected to experience decreases in flooding frequencies in future. The historical 100-year return period flood events in these regions are expected to become 160–200 year return period events in future. Furthermore, prairies, parts of Quebec, Ontario, Nunavut, and Yukon territories can be expected to experience earlier snowmelt-driven floods in the future. The results from this study will help decision-makers to effectively manage and design municipal and civil infrastructure in Canada under a changing climate.

Uncertainties of 3D soil hydraulic parameters in streamflow simulations using a distributed hydrological model system

Parameter calibration and uncertainty estimation are crucial for hydrological simulations in the distributed land surface-hydrological model. To investigate soil properties impacting hydrological processes, five conventional pedo-transfer functions (PTFs) are applied to create a 3D soil hydraulic parameter (SHP) ensemble in the Weather Research and Forecasting-Hydrological extension (WRF-Hydro), a distributed, multi-physics land surface hydrological model. The SHPs are generated, based on a high-resolution Chinese soil property dataset, over the heterogeneous Upper Huaihe River basin. The results show that the SHPs can influence the streamflow in WRF-Hydro, which is similar to the impact of the scaling parameters on the streamflow over the study basin. Analyses of the uncertainty in the SHP ensemble reveal that SHPs mainly constrain the peak flow during the flood rise and impact the baseflow during the flood recession. A hydrological Bayesian model average (BMA) method is constructed to postprocess the streamflow ensemble based on the 3D SHPs. Probabilistic streamflow estimations by the BMA method are more skillful than the simulations using the individual 3D SHP ensemble members for all five studied hydrological stations, especially for high flows. Therefore, improved estimation of the uncertainty in the 3D SHPs may enhance the spatial representation of flood processes, resulting in more accurate estimates of the streamflow in the main streams in a heterogeneous basin.

Toward continental hydrologic–hydrodynamic modeling in South America

Abstract. Providing reliable estimates of streamflow and hydrological fluxes is a major challenge for water resources management over national and transnational basins in South America. Global hydrological models and land surface models are a possible solution to simulate the terrestrial water cycle at the continental scale, but issues about parameterization and limitations in representing lowland river systems can place constraints on these models to meet local needs. In an attempt to overcome such limitations, we extended a regional, fully coupled hydrologic–hydrodynamic model (MGB; Modelo hidrológico de Grandes Bacias) to the continental domain of South America and assessed its performance using daily river discharge, water levels from independent sources (in situ, satellite altimetry), estimates of terrestrial water storage (TWS) and evapotranspiration (ET) from remote sensing and other available global datasets. In addition, river discharge was compared with outputs from global models acquired through the eartH2Observe project (HTESSEL/CaMa-Flood, LISFLOOD and WaterGAP3), providing the first cross-scale assessment (regional/continental × global models) that makes use of spatially distributed, daily discharge data. A satisfactory representation of discharge and water levels was obtained (Nash–Sutcliffe efficiency, NSE &gt; 0.6 in 55 % of the cases) and the continental model was able to capture patterns of seasonality and magnitude of TWS and ET, especially over the largest basins of South America. After the comparison with global models, we found that it is possible to obtain considerable improvement on daily river discharge, even by using current global forcing data, just by combining parameterization and better routing physics based on regional experience. Issues about the potential sources of errors related to both global- and continental-scale modeling are discussed, as well as future directions for improving large-scale model applications in this continent. We hope that our study provides important insights to reduce the gap between global and regional hydrological modeling communities.

A Database of Natural Monthly Streamflow Estimates from 1950 to 2015 for the Conterminous United States

AbstractQuantifying and understanding the natural streamflow regime, defined as expected streamflow that would occur in the absence of anthropogenic modification to the hydrologic system, is critically important for the development of management strategies aimed at protecting aquatic ecosystems. Water balance models have been applied frequently to estimate natural flows, but are limited in the number of predictor variables that can be included. Here, a statistical machine learning technique — random forest modeling — was applied to estimate natural flows at a monthly time‐step from 1950 to 2015 for &gt;2.5 million stream reaches in the conterminous United States (U.S.) using 200 potential predictor variables. We describe the development and documentation of this dataset and assess model performance. Model fit statistics (mean Nash–Sutcliffe efficiency = 0.85; observed/expected ratio = 0.94) indicate good correspondence between predicted and observed flows at nearly 2,000 streamgages. As an example application of the dataset, the observed streamflow record at a site prior to and after the construction of an upstream reservoir was compared with estimated natural flows to demonstrate the magnitude of seasonal depletions in streamflow due to the reservoir. This dataset can be applied to quantify natural and anthropogenic processes contributing to streamflow depletion or augmentation, and assess associated ecological effects.

Spatial Scale Resolution of Prognostic Hydrological Models: Simulation Performance and Application in Climate Change Impact Assessment

In this paper, long-term hydrological response of a watershed to climate change was investigated taking into account the spatial scale effect on the performance of hydrological models. A water balance model was used in which variations of soil moisture, snow budget, deep infiltration and interactions with groundwater resources were modeled. Four various combinations of sub-catchment delineation, altitudinal discretization and division into square-shaped grids were tested for semi-distributed water balance modeling of a basin located in southwest of Iran, namely Roodzard Basin, with arid and semiarid climate based on Koppen-Geiger climate classification. The results showed improvement in the model performances when spatial variations of the meteorological data and topographic characteristics of the basin were incorporated in the modeling process. The effects of spatial scale resolution dependency were evaluated in projecting streamflow for various climate change scenarios. The results showed that finer resolution of grid cells in the semi-distributed model does not necessarily result in more accurate estimation of monthly streamflows and altitudinal discretization provides almost same accuracy as the results of grid-based models. Moreover, probability distribution of projections obtained from water balance models for A2 and B2 of Special Report on Emissions Scenarios (SRES) scenarios presented less coefficient of variation and skewness compared with historical observations.

High-spatial-resolution streamflow estimation at ungauged river sites or gauged sites with missing data using the National Hydrography Dataset (NHD) and U.S. Geological Survey (USGS) streamflow data

The need for a method of estimating streamflow with both high-quality and high spatial resolution, particularly for ungauged sites, is critical under current circumstances: intensifying competition over water resources and limited streamgages. A source of accurate and timely computations of streamflow, particularly at high spatial resolutions, has been the National Hydrograph Dataset (NHD), a comprehensive geographic information system (GIS) dataset of vector-based surface water features such as streams, lakes, and streamgages in fine detail. Utilizing the NHD with streamflow data from the U.S. Geological Survey and usage records of local water utilities, this paper proposes a simple streamflow estimation method that computes streamflow at any point on river reaches of the NHD with valid hydrograph data and tests the method in a local 613 square mile watershed. The method assumes that linearly connecting the timings of peak or bottom-base flows from upstream to downstream hydrographs leads to retracing upstream lateral inflows that have eventually arrived at a user-selected site between the upstream and downstream gauge stations of the hydrographs at any given time in a common period of record between the two hydrographs. Subsequently, the lateral inflows are added up to become the streamflow for a given site at a given time. NHD river networks and ArcGIS tools identify the locations of lateral inflows and the necessary parameters of the method. This paper assesses the results of the method by comparing them to measured streamflows through recommended evaluation statistics and visual inspection. The method has yielded promising results, including values of the Nash-Sutcliffe efficiency within 0.83 and 0.99 for daily streamflow estimates over seven years. It also credibly reconstructs missing daily streamflow over a considerable number of years.

How good are hydrological models for gap-filling streamflow data?

Abstract. Gap-filling streamflow data is a critical step for most hydrological studies, such as streamflow trend, flood, and drought analysis and hydrological response variable estimates and predictions. However, there is a lack of quantitative evaluation of the gap-filled data accuracy in most hydrological studies. Here we show that when the missing data rate is less than 10 %, the gap-filled streamflow data obtained using calibrated hydrological models perform almost the same as the benchmark data (less than 1 % missing) when estimating annual trends for 217 unregulated catchments widely spread across Australia. Furthermore, the relative streamflow trend bias caused by the gap filling is not very large in very dry catchments where the hydrological model calibration is normally poor. Our results clearly demonstrate that the gap filling using hydrological modelling has little impact on the estimation of annual streamflow and its trends.

A probabilistic appraisal of rainfall-runoff modeling approaches within SWAT in mixed land use watersheds

A probabilistic approach is presented to assess the performance validity of the empirical Curve Number (CN) and physically-based Green and Ampt (G&A) rainfall-runoff methods in the SWAT model. Specifically, the effects of modeling uncertainties on characterization of the hydrologic budgets and streamflow regimes at various spatial scales and upstream land use conditions are investigated. A Bayesian total uncertainty assessment framework, which explicitly accounts for uncertainties from model parameters, inputs, structure, and measurement data, was employed to explore uncertainties in streamflow simulation using SWAT with different rainfall-runoff methods in a mixed-land use watershed. While the models were trained for streamflow estimation only at the watershed outlet, the performances of the models were compared at different stream locations within the watershed. At the watershed outlet, the CN method had a slightly better, but not significant, performance in terms of streamflow error statistics. Similar results were obtained for the predominantly forested and agricultural tributaries. However, in tributaries with higher percentage of developed land, G&A outperformed the CN method in simulating streamflow based on various performance metrics. In general, the 95% prediction intervals from the models with G&A method covered a higher percentage of observed streamflow especially during the high flow events. However, they were approximately 20–45% wider than the corresponding 95% prediction intervals from the CN methods. Using 95% prediction interval for estimated flow duration curves, results indicated that the models with CN methods underestimated high flow events especially in tributaries with highly developed land use. However, the CN methods generated higher water yields to streams than the G&A method. The results of this study have important implications for the selection and application of appropriate rainfall-runoff methods within complex distributed hydrologic models particularly when simulating hydrologic responses in mixed-land use watersheds. In the present study, while CN and G&A methods in the SWAT model performed similarly at the outlet of a mixed-land use watershed, G&A captured the internal processes more realistically. The subsequent effects on the representation of internal hydrological processes and budgets are discussed.