Watershed modelling of hydrology and water quality in the Sacramento River watershed,California

Agricultural pollutant runoff is a major source of water contamination in California's Sacramento River watershed where 8500 km² of agricultural land influences water quality. The Soil and Water Assessment Tool (SWAT) hydrology, sediment, nitrate and pesticide transport components were assessed for the Sacramento River watershed. To represent flood conveyance in the area, the model was improved by implementing a flood routing algorithm. Sensitivity/uncertainty analyses and multi‐objective calibration were incorporated into the model application for predicting streamflow, sediment, nitrate and pesticides (chlorpyrifos and diazinon) at multiple watershed sites from 1992 to 2008. Most of the observed data were within the 95% uncertainty interval, indicating that the SWAT simulations were capturing the uncertainties that existed, such as model simplification, observed data errors and lack of agricultural management data. The monthly Nash–Sutcliffe coefficients at the watershed outlet ranged from 0.48 to 0.82, indicating that the model was able to successfully predict streamflow and agricultural pollutant transport after calibration. Predicted sediment loads were highly correlated to streamflow, whereas nitrate, chlorpyrifos and diazinon were moderately correlated to streamflow. This indicates that timing of agricultural management operations plays a role in agricultural pollutant runoff. Best management practices, such as pesticide use limits during wet seasons, could improve water quality in the Sacramento River watershed. The calibrated model establishes a modelling framework for further studies of hydrology, water quality and ecosystem protection in the study area. Copyright © 2012 John Wiley & Sons, Ltd.     

Development and application of a physically based landscape water balance in the SWAT model

Watershed scale hydrological and biogeochemical models rely on the correct spatial‐temporal prediction of processes governing water and contaminant movement. The Soil and Water Assessment Tool (SWAT) model, one of the most commonly used watershed scale models, uses the popular curve number (CN) method to determine the respective amounts of infiltration and surface runoff. Although appropriate for flood forecasting in temperate climates, the CN method has been shown to be less than ideal in many situations (e.g. monsoonal climates and areas dominated by variable source area hydrology). The CN model is based on the assumption that there is a unique relationship between the average moisture content and the CN for all hydrologic response units (HRUs), and that the moisture content distribution is similar for each runoff event, which is not the case in many regions. Presented here is a physically based water balance that was coded in the SWAT model to replace the CN method of runoff generation. To compare this new water balance SWAT (SWAT‐WB) to the original CN‐based SWAT (SWAT‐CN), two watersheds were initialized; one in the headwaters of the Blue Nile in Ethiopia and one in the Catskill Mountains of New York. In the Ethiopian watershed, streamflow predictions were better using SWAT‐WB than SWAT‐CN [Nash–Sutcliffe efficiencies (NSE) of 0·79 and 0·67, respectively]. In the temperate Catskills, SWAT‐WB and SWAT‐CN predictions were approximately equivalent (NSE > 0·70). The spatial distribution of runoff‐generating areas differed greatly between the two models, with SWAT‐WB reflecting the topographical controls imposed on the model. Results show that a water balance provides results equal to or better than the CN, but with a more physically based approach. Copyright © 2010 John Wiley & Sons, Ltd.     

Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging

In this paper, the Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) were used to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this combined method, several SWAT models with different structures are first selected; next GA is used to calibrate each model using observed streamflow data; finally, BMA is applied to combine the ensemble predictions and provide uncertainty interval estimation. This method was tested in two contrasting basins, the Little River Experimental Basin in Georgia, USA, and the Yellow River Headwater Basin in China. The results obtained in the two case studies show that this combined method can provide deterministic predictions better than or comparable to the best calibrated model using GA. The 66.7% and 90% uncertainty intervals estimated by this method were analyzed. The differences between the percentage of coverage of observations and the corresponding expected coverage percentage are within 10% for both calibration and validation periods in these two test basins. This combined methodology provides a practical and flexible tool to attain reliable deterministic simulation and uncertainty analysis of SWAT.     

Parameter identification and uncertainty analysis for simulating streamflow in a river basin of Eastern India

Uncertainty is inherent in modelling studies. However, the quantification of uncertainties associated with a model is a challenging task, and hence, such studies are somewhat limited. As distributed or semi‐distributed hydrological models are being increasingly used these days to simulate hydrological processes, it is vital that these models should be equipped with robust calibration and uncertainty analysis techniques. The goal of the present study was to calibrate and validate the Soil and Water Assessment Tool (SWAT) model for simulating streamflow in a river basin of Eastern India, and to evaluate the performance of salient optimization techniques in quantifying uncertainties. The SWAT model for the study basin was developed and calibrated using Parameter Solution (ParaSol), Sequential Uncertainty Fitting Algorithm (SUFI‐2) and Generalized Likelihood Uncertainty Estimation (GLUE) optimization techniques. The daily observed streamflow data from 1998 to 2003 were used for model calibration, and those for 2004–2005 were used for model validation. Modelling results indicated that all the three techniques invariably yield better results for the monthly time step than for the daily time step during both calibration and validation. The model performances for the daily streamflow simulation using ParaSol and SUFI‐2 during calibration are reasonably good with a Nash–Sutcliffe efficiency and mean absolute error (MAE) of 0.88 and 9.70 m³/s for ParaSol, and 0.86 and 10.07 m³/s for SUFI‐2, respectively. The simulation results of GLUE revealed that the model simulates daily streamflow during calibration with the highest accuracy in the case of GLUE (R² = 0.88, MAE = 9.56 m³/s and root mean square error = 19.70 m³/s). The results of uncertainty analyses by SUFI‐2 and GLUE were compared in terms of parameter uncertainty. It was found that SUFI‐2 is capable of estimating uncertainties in complex hydrological models like SWAT, but it warrants sound knowledge of the parameters and their effects on the model output. On the other hand, GLUE predicts more reliable uncertainty ranges (R‐factor = 0.52 for daily calibration and 0.48 for validation) compared to SUFI‐2 (R‐factor = 0.59 for daily calibration and 0.55 for validation), though it is computationally demanding. Although both SUFI‐2 and GLUE appear to be promising techniques for the uncertainty analysis of modelling results, more and more studies in this direction are required under varying agro‐climatic conditions for assessing their generic capability. Copyright © 2015 John Wiley & Sons, Ltd.     

Incorporating landscape depression heterogeneity into the Soil and Water Assessment Tool (SWAT) using a probability distribution

Modelling the hydrology of North American Prairie watersheds is complicated because of the existence of numerous landscape depressions that vary in storage capacity. The Soil and Water Assessment Tool (SWAT) is a widely applied model for long‐term hydrological simulations in watersheds dominated by agricultural land uses. However, several studies show that the SWAT model has had limited success in handling prairie watersheds. In past works using SWAT, landscape depression storage heterogeneity has largely been neglected or lumped. In this study, a probability distributed model of depression storage is introduced into the SWAT model to better handle landscape storage heterogeneity. The work utilizes a probability density function to describe the spatial heterogeneity of the landscape depression storages that was developed from topographic characteristics. The integrated SWAT–PDLD model is tested using datasets for two prairie depression dominated watersheds in Canada: the Moose Jaw River watershed, Saskatchewan; and the Assiniboine River watershed, Saskatchewan. Simulation results were compared to observed streamflow using graphical and multiple statistical criterions. Representation of landscape depressions within SWAT using a probability distribution (SWAT–PDLD) provides improved estimations of streamflow for large prairie watersheds in comparison to results using a lumped, single storage approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Parameter estimation and uncertainty analysis of SWAT model in upper reaches of the Heihe river basin

Heihe river basin, the second largest inland river basin in China, has attracted more attention in China due to the ever increasing water resources and eco‐environmental problems. In this article, SWAT (Soil and Water Assessment Tool; http://www.brc.tamus.edu/swat/ ) model was applied to upper reaches of the basin for better understanding of the hydrological process over the watershed. Parameter uncertainty and its contribution on model simulation are the main foci. In model calibration, the aggregate parameters instead of the original parameters in SWAT model were used to reduce the computing effort. The Bayesian approach was employed for parameter estimation and uncertainty analysis because its posterior distribution provides not only parameter estimation but also uncertainty analysis without normality assumption. The results indicated that: (1) SWAT model performs satisfactorily in this watershed as a whole, although some low and high flows were under‐ or overestimated, particularly in dry (e.g. 1991) and wet (e.g. 1996) years; (2) all calibrated parameters were not normally distributed (essentially positively or negatively skewed) and the parameter uncertainties were relatively small; and (3) the contributions of parameter uncertainty on model simulation uncertainty were relatively small. Copyright © 2009 John Wiley & Sons, Ltd.     

Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference

Calibration of hydrologic models is very difficult because of measurement errors in input and response, errors in model structure, and the large number of non-identifiable parameters of distributed models. The difficulties even increase in arid regions with high seasonal variation of precipitation, where the modelled residuals often exhibit high heteroscedasticity and autocorrelation. On the other hand, support of water management by hydrologic models is important in arid regions, particularly if there is increasing water demand due to urbanization. The use and assessment of model results for this purpose require a careful calibration and uncertainty analysis. Extending earlier work in this field, we developed a procedure to overcome (i) the problem of non-identifiability of distributed parameters by introducing aggregate parameters and using Bayesian inference, (ii) the problem of heteroscedasticity of errors by combining a Box–Cox transformation of results and data with seasonally dependent error variances, (iii) the problems of autocorrelated errors, missing data and outlier omission with a continuous-time autoregressive error model, and (iv) the problem of the seasonal variation of error correlations with seasonally dependent characteristic correlation times. The technique was tested with the calibration of the hydrologic sub-model of the Soil and Water Assessment Tool (SWAT) in the Chaohe Basin in North China. The results demonstrated the good performance of this approach to uncertainty analysis, particularly with respect to the fulfilment of statistical assumptions of the error model. A comparison with an independent error model and with error models that only considered a subset of the suggested techniques clearly showed the superiority of the approach based on all the features (i)–(iv) mentioned above.     

Assessing effects of model complexity and structure on predictions of hydrological responses using serial and parallel model design

By utilizing functional relationships based on observations at plot or field scales, water quality models first compute surface runoff and then use it as the primary governing variable to estimate sediment and nutrient transport. When these models are applied at watershed scales, this serial model structure, coupling a surface runoff sub-model with a water quality sub-model, may be inappropriate because dominant hydrological processes differ among scales. A parallel modeling approach is proposed to evaluate how best to combine dominant hydrological processes for predicting water quality at watershed scales. In the parallel scheme, dominant variables of water quality models are identified based entirely on their statistical significance using time series analysis. Four surface runoff models of different model complexity were assessed using both the serial and parallel approaches to quantify the uncertainty on forcing variables used to predict water quality. The eight alternative model structures were tested against a 25-year high-resolution data set of streamflow, suspended sediment discharge, and phosphorous discharge at weekly time steps. Models using the parallel approach consistently performed better than serial-based models, by having less error in predictions of watershed scale streamflow, sediment and phosphorus, which suggests model structures of water quantity and quality models at watershed scales should be reformulated by incorporating the dominant variables. The implication is that hydrological models should be constructed in a way that avoids stacking one sub-model with one set of scale assumptions onto the front end of another sub-model with a different set of scale assumptions.     

Hydrologic comparison between a forested and a wetland/lake dominated watershed using SWAT

The Soil and Water Assessment Tool (SWAT) is a physically‐based hydrologic model developed for agricultural watersheds, which has been infrequently validated for forested watersheds, particularly those with deep overwinter snow accumulation and abundant lakes and wetlands. The goal of this study was to determine the applicability of SWAT for modelling streamflow in two watersheds of the Ontonagon River basin of northern Michigan which differ in proportion of wetland and lake area. The forest‐dominated East Branch watershed contains 17% wetland and lake area, whereas the wetland/lake‐dominated Middle Branch watershed contains 26% wetland and lake area. The specific objectives were to: (1) calibrate and validate SWAT models for the East Branch and Middle Branch watersheds to simulate monthly stream flow, and (2) compare the effects of wetland and lake abundance on the magnitude and timing of streamflow. Model calibration and validation was satisfactory, as determined by deviation of discharge D and Nash and Sutcliffe coefficient values E that compared simulated monthly mean discharge versus measured monthly mean discharge. Streamflow simulation discrepancies occurred during summer and fall months and dry years. Several snow melting parameters were found to be critical for the SWAT simulation: TIMP (snow temperature lag factor) and SMFMX and SMFMN (melting factors). Snow melting parameters were not transferable between adjacent watersheds. Differences in seasonal pattern of long‐term monthly streamflow were found, with the forest‐dominated watershed having a higher peak flow during April but a lower flow during the remainder of the year in comparison to the wetland and lake‐dominated watershed. The results suggested that a greater proportion of wetland and lake area increases the capacity of a watershed to impound surface runoff and to delay storm and snow melting events. Representation of wetlands and lakes in a watershed model is required to simulate monthly stream flow in a wetland/lake‐dominated watershed. Copyright © 2007 John Wiley & Sons, Ltd.     

A simple concept for calibrating runoff thresholds in quasi‐distributed variable source area watershed models

Most semi‐distributed watershed water quality models divide the watershed into hydrologic response units (HRU) with no flow among them. This is problematic when watersheds are delineated to include variable source areas (VSAs) because it is the lateral flows from upslope areas to downslope areas that generate VSAs. Although hydrologic modellers have often successfully calibrated these types of models, there can still be considerable uncertainty in model results. In this paper, a topographic‐index‐based method is described and tested to distribute effective soil water holding capacity among HRUs, which can be subsequently adjusted using the watershed baseflow coefficient. The method is tested using a version of the Soil and Water Assessment Tool (SWAT) model that simulates VSA runoff and is applied to two watersheds: a New York State (NYS) watershed, and one in the head waters of the Blue Nile Basin (BNB) in Ethiopia. Daily streamflow predicted using effective soil water storage capacities based only on the topographic index were reassuringly accurate in both the NYS watershed (daily Nash Sutcliffe (E) = 0·73) and in the BNB (E = 0·70). Using the baseflow coefficient to adjust the effective soil water storage capacity only slightly improved streamflow predictions in NYS (E = 0·75) but substantially improved the BNB predictions (E = 0·80). By comparison, the standard SWAT model, which uses the traditional look‐up tables to determine a runoff curve number, performed considerably less accurately in un‐calibrated form (E = 0·51 for NYS and E = 0·45 for BNB), but improved substantially when explicitly calibrated to streamflow measurements (E = 0·76 for NYS and E = 0·67 for the BNB). The calibration method presented here provides a parsimonious, systematic approach to using established models in VSA watersheds that reduces the ambiguity inherent in model calibration. Copyright © 2011 John Wiley & Sons, Ltd.     

Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China

Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily streamflow simulation in the Upper Huai River Basin. Study results have demonstrated that the SWAT model with hourly rainfall inputs performed better than the model with daily rainfall inputs in daily streamflow simulation, primarily due to its better capability of simulating peak flows during the flood season. The sub-daily SWAT model estimated that 58 % of streamflow was contributed by baseflow compared to 34 % estimated by the daily model. Using the future daily and 3-h precipitation projections under the RCP (Representative Concentration Pathways) 4.5 scenario as inputs, the sub-daily SWAT model predicted a larger amount of monthly maximum daily flow during the wet years than the daily model. The differences between the daily and sub-daily SWAT model simulation results indicated that temporal rainfall resolution could have much impact on the simulation of hydrological process, streamflow, and consequently pollutant transport by SWAT models. There is an imperative need for more studies to examine the effects of temporal rainfall resolution on the simulation of hydrological and water pollutant transport processes by SWAT in river basins of different environmental conditions.     

Reducing uncertainty in stochastic streamflow generation and reservoir sizing by combining observed,reconstructed and projected streamflow

Reservoir sizing is one of the most important aspects of water resources engineering as the storage in a reservoir must be sufficient to supply water during extended droughts. Typically, observed streamflow is used to stochastically generate multiple realizations of streamflow to estimate the required storage based on the Sequent Peak Algorithm (SQP). The main limitation in this approach is that the parameters of the stochastic model are purely derived from the observed record (limited to less than 80 years of data) which does not have information related to prehistoric droughts. Further, reservoir sizing is typically estimated to meet future increase in water demand, and there is no guarantee that future streamflow over the planning period will be representative of past streamflow records. In this context, reconstructed streamflow records, usually estimated based on tree ring chronologies, provide better estimates of prehistoric droughts, and future streamflow records over the planning period could be obtained from general circulation models (GCMs) which provide 30 year near-term climate change projections. In this study, we developed paleo streamflow records and future streamflow records for 30 years are obtained by forcing the projected precipitation and temperature from the GCMs over a lumped watershed model. We propose combining observed, reconstructed and projected streamflows to generate synthetic streamflow records using a Bayesian framework that provides the posterior distribution of reservoir storage estimates. The performance of the Bayesian framework is compared to a traditional stochastic streamflow generation approach. Findings based on the split-sample validation show that the Bayesian approach yielded generated streamflow traces more representative of future streamflow conditions than the traditional stochastic approach thereby, reducing uncertainty on storage estimates corresponding to higher reliabilities. Potential strategies for improving future streamflow projections and its utility in reservoir sizing and capacity expansion projects are also discussed.     

Simultaneous calibration of surface flow and baseflow simulations: a revisit of the SWAT model calibration framework

Accurate analysis of water flow pathways from rainfall to streams is critical for simulating water use, climate change impact, and contaminants transport. In this study, we developed a new scheme to simultaneously calibrate surface flow (SF) and baseflow (BF) simulations of soil and water assessment tool (SWAT) by combing evolutionary multi‐objective optimization (EMO) and BF separation techniques. The application of this scheme demonstrated pronounced trade‐off of SWAT's performance on SF and BF simulations. The simulated major water fluxes and storages variables (e.g. soil moisture, evapotranspiration, and groundwater) using the multiple parameters from EMO span wide ranges. Uncertainty analysis was conducted by Bayesian model averaging of the Pareto optimal solutions. The 90% confidence interval (CI) estimated using all streamflows substantially overestimate the uncertainty of low flows on BF days while underestimating the uncertainty of high flows on SF days. Despite using statistical criteria calculated based on streamflow for model selection, it is important to conduct diagnostic analysis of the agreement of SWAT behaviour and actual watershed dynamics. The new calibration technique can serve as a useful tool to explore the trade‐off between SF and BF simulations and provide candidates for further diagnostic assessment and model identification. Copyright © 2011 John Wiley & Sons, Ltd.     

Understanding hydrological processes with scarce data in a mountain environment

Performance of process‐based hydrological models is usually assessed through comparison between simulated and measured streamflow. Although necessary, this analysis is not sufficient to estimate the quality and realism of the modelling since streamflow integrates all processes of the water cycle, including intermediate production or redistribution processes such as snowmelt or groundwater flow. Assessing the performance of hydrological models in simulating accurately intermediate processes is often difficult and requires heavy experimental investments. In this study, conceptual hydrological modelling (using SWAT) of a semi‐arid mountainous watershed in the High Atlas in Morocco is attempted. Our objective is to analyse whether good intermediate processes simulation is reached when global‐satisfying streamflow simulation is possible. First, parameters presenting intercorrelation issues are identified: from the soil, the groundwater and, to a lesser extent, from the snow. Second, methodologies are developed to retrieve information from accessible intermediate hydrological processes. A geochemical method is used to quantify the contribution of a superficial and a deep reservoir to streamflow. It is shown that, for this specific process, the model formalism is not adapted to our study area and thus leads to poor simulation results. A remote‐sensing methodology is proposed to retrieve the snow surfaces. Comparison with the simulation shows that this process can be satisfyingly simulated by the model. The multidisciplinary approach adopted in this study, although supported by the hydrological community, is still uncommon. Copyright © 2007 John Wiley & Sons, Ltd.     

Simulation of streamflow with statistically downscaled daily rainfall using a hybrid of wavelet and GAMLSS models

ABSTRACT

A rainfall–streamflow model is proposed, in which a downscaled rainfall series and its wavelet-based decomposed sub-series at optimum lags were used as covariates in GAMLSS (Generalized Additive Model in Location, Scale and Shape). GAMLSS is applied in climate change impact assessment using CMIP5 general climate model to simulate daily streamflow in three sub-catchments of the Onkaparinga catchment, South Australia. The Spearman correlation and Nash-Sutcliffe efficiency between the observed and median simulated streamflow values were high and comparable for both the calibration and validation periods for each sub-catchment. We show that the GAMLSS has the capability to capture non-stationarity in the rainfall–streamflow process. It was also observed that the use of wavelet-based decomposed rainfall sub-series with optimum lags as covariates in the GAMLSS model captures the underlying physics of the rainfall–streamflow process. The development and application of an empirical rainfall–streamflow model that can be used to assess the impact of catchment-scale climate change on streamflow is demonstrated.     

Response to comment by Keith Beven on “Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?”

In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchments.     

Global sensitivity analysis of hydrologic processes in major snow‐dominated mountainous river basins in Colorado

The performance of watershed models in simulating stream discharge depends on the adequate representation of important watershed processes. In snow‐dominated systems, snow, surface and subsurface hydrologic processes comprise a complex network of nonlinear interactions that influence the magnitude and timing of discharge. This study aims to identify critical processes and interactions that control discharge hydrographs in five major mountainous snow‐dominated river basins in Colorado, USA. A comprehensive watershed model (Soil and Water Assessment Tool) and a variance‐based global sensitivity analysis technique (Fourier Amplitude Sensitivity Test) were used in conjunction to identify critical models parameters and processes that they represent. Average monthly streamflow and streamflow root mean square error over a period of 20 years were used as two separate objective functions in this analysis. Examination of the sensitivity of monthly streamflow revealed the influence of parameters on flow volume, whereas the sensitivity of streamflow root mean square error also exposed the influence of parameters on the timing of the hydrographs. A stability analysis was performed to investigate the computational requirements for a robust sensitivity analysis. Results show that streamflow volume is mostly influenced by shallow subsurface processes, whereas interactions between groundwater and snow processes were the key in the timing of streamflows. A large majority of important parameters were common among all study watersheds, which underlies the prospect for regionalization of process‐based hydrologic modelling in headwater river basins in Colorado. Copyright © 2013 John Wiley & Sons, Ltd.     

Evolutionary assimilation of streamflow in distributed hydrologic modeling using in-situ soil moisture data

This study has applied evolutionary algorithm to address the data assimilation problem in a distributed hydrological model. The evolutionary data assimilation (EDA) method uses multi-objective evolutionary strategy to continuously evolve ensemble of model states and parameter sets where it adaptively determines the model error and the penalty function for different assimilation time steps. The assimilation was determined by applying the penalty function to merge background information (i.e., model forecast) with perturbed observation data. The assimilation was based on updated estimates of the model state and its parameterizations, and was complemented by a continuous evolution of competitive solutions.The EDA was illustrated in an integrated assimilation approach to estimate model state using soil moisture, which in turn was incorporated into the soil and water assessment tool (SWAT) to assimilate streamflow. Soil moisture was independently assimilated to allow estimation of its model error, where the estimated model state was integrated into SWAT to determine background streamflow information before they are merged with perturbed observation data. Application of the EDA in Spencer Creek watershed in southern Ontario, Canada generates a time series of soil moisture and streamflow. Evaluation of soil moisture and streamflow assimilation results demonstrates the capability of the EDA to simultaneously estimate model state and parameterizations for real-time forecasting operations. The results show improvement in both streamflow and soil moisture estimates when compared to open-loop simulation, and a close matching between the background and the assimilation illustrates the forecasting performance of the EDA approach.