Streamflow Assimilation Research Articles

Assimilation of Sentinel‐Based Leaf Area Index for Modeling Surface‐Ground Water Interactions in Irrigation Districts

AbstractVegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield.

SWAT_DA: Sequential Multivariate Data Assimilation‐Oriented Modification of SWAT

AbstractMultivariate data assimilation (DA), a novel way to couple big data with land surface models, was extensively employed in forecasting‐reanalyzing systems (FRSs), for example, ECMWF and GLDAS. Meanwhile, most (distributed) hydrological models, like soil and water assessment tool (SWAT), have not been equipped with straightforward ways to link to DA algorithms. Therefore, it is one of the main barriers to utilizing such hydrological models in FRSs. This paper deals with multivariate DA into SWAT (DA‐SWAT), which is complicated since the original model does not provide full access to the models' initial conditions (ICs) at the hydrologic response unit (HRU) scale. The preceding DA‐SWAT works commonly used an integrated approach in which the DA and SWAT codes were implemented in the same programming environment. We discuss how this approach complicates and prevents the application of DA‐SWAT in multivariate, multimodel, and multisensor systems. Accordingly, we proposed a new approach for DA‐SWAT by which SWAT can be perfectly linked with any DA algorithm of interest coded in any desired programming environment. Our framework utilizes input/output text files to access ICs and to link DA with SWAT. Moreover, we designed some univariate and multivariate scenarios for assimilating in situ streamflow measurement and MODIS's snow cover fraction (SCF) data, which has not yet been focused on in the SWAT calibration context. Results show that compared to the univariate assimilation of streamflow (SCF), the multivariate assimilation mitigates the equifinality problem and more accurately estimates SCF (streamflow) by improving NS and PBIAS measures with the differences of 0.4 (0.86), 12% (64%), respectively.

Comparison of sequential and variational assimilation methods to improve hydrological predictions in snow dominated mountainous catchments

• Assimilation of streamflow and snow data using variational and sequential techniques in a hydrological model. • Satellite snow coverage products are assimilated together with streamflow data. • Variational techniques outperform sequential techniques in two mountainous basins. • Average relative gains in variational assimilation are 49% for streamflow and 28% for snow data. Snowmelt modeling is vital issue for proper operational flow forecasting systems but still challenging in upstream mountainous basins due to limited observations and associated uncertainties. Data assimilation is a valuable tool to improve model state estimates to improve hydrological forecast. While assimilation of snow has been increasingly implemented in research, particularly using sequential data assimilation methods, less attention has been dedicated to compare different types of assimilation techniques. This study aims at comparing the well-known Ensemble Kalman Filter (EnKF) sequential data assimilation and the less known Moving Horizon Estimation (MHE) variational assimilation. Initial conditions of the hydrological model states are improved by assimilating in-situ streamflow and remotely sensed snow covered area data into the Hydrologiska Byråns Vattenbalansavdelning (HBV) conceptual rainfall-runoff model. The study is applied to improve runoff predictions over two mountainous basins: one in the uppermost catchment of the Karasu river, in Eastern Turkey, and the second in an alpine basin located in the headwaters of the Genil River in the northern flank of the Sierra Nevada Mountain in Southern Spain. The forecast models are tested in a hindcasting condition, i.e. closed loop environment, which simulates real-time forecasting systems. The results are analyzed using forecast verification metrics. Results showed that streamflow and snow state predictions using MHE outperform the commonly used EnKF based on the continuous ranked probability score (CRPS).

Multivariate assimilation of satellite-based leaf area index and ground-based river streamflow for hydrological modelling of irrigated watersheds using SWAT+

• Performance of the SWAT + model with and without data assimilation (DA) is assessed. • Some modules of SWAT + source code are modified to be connected to the DA routine. • Multivariate and univariate assimilations of discharge and LAI data are compared. • High-resolution crop mapping is essential for LAI assimilation in irrigated watersheds. • Joint assimilation of streamflow and Sentinel-2′s LAI results in better simulation of streamflow, vegetation, and irrigation. Vegetation dynamics have different effects on the terrestrial water cycle and therefore play an important role in catchment-scale biophysical and hydrological modeling. Meanwhile, validation of hydrological models of irrigated watersheds concerning vegetation dynamics has rarely been studied. In this study, we propose a combinatorial approach for modelling irrigated watersheds; at first, based on Sentinel-2 (S2), we provide crop/land-use mapping; then we retrieve S2-based leaf area index (LAI) through the neural network algorithm and the PROSAIL model; finally, we employ sequential particle filter (PF) technique to explore the benefits of jointly assimilating high-resolution S2-LAI as well as in-situ river discharge observations for improving the predictive accuracy of SWAT + model. Moreover, we explain how SWAT + source code must be modified to implement the assimilation procedure. The developed methodology is applied to an irrigated watershed in Iran; we provide 66 raster LAI maps with a spatial resolution of 20 m for the study area related to January to December of 2019 as well as crop/land-use mapping of 2019 with a spatial resolution of 10 m. Results show that improvements in the hydrological simulation of LAI, evapotranspiration, and river discharge are achieved when we apply multivariate assimilation of S2-LAI and streamflow observations, compared to univariate assimilation scenarios. Results also reveal that LAI assimilation has a significant influence on the estimation of irrigation volume and timing.

Development of a stochastic hydrological modeling system for improving ensemble streamflow prediction

Streamflow prediction plays a crucial role in water resources systems planning and the mitigation of hydrological extremes such as floods and droughts. Since a variety of uncertainties exist in streamflow prediction, it is necessary to enhance our efforts to robustly address uncertainties and their interactions for improving the reliability of streamflow prediction. This paper presents a stochastic hydrological modeling system (SHMS) for improving daily streamflow prediction by explicitly addressing uncertainties in error and model parameters as well as in forcing data and model outputs. Specifically, the SHMS merges the strengths of the ensemble Kalman filter and the particle filter algorithms for improving the effectiveness and robustness of daily streamflow assimilation. Factorial analysis of variance and variance-based global sensitivity analysis are performed to reveal parameter interactions affecting predictive performance and temporal dynamics of parameter sensitivities, maximizing the accuracy of streamflow prediction. The SHMS has been applied to the Guadalupe River basin located in Texas of the United States to demonstrate feasibility and applicability. Our findings indicate that the SHMS improves upon the well-known ensemble Kalman filter for sequential estimation of hydrological model parameters through a more rapid and accurate convergence of model parameters in streamflow simulation. The SHMS also demonstrates a higher level of skill in streamflow prediction compared to the conditional vine copula model. The proposed SHMS can be applied straightforwardly to other river basins for probabilistic hydrological prediction.

Featured Collection Introduction: National Water Model III

Featured Collection Introduction: National Water Model III

Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models

When there exist catchment-wide biases in the distributed hydrologic model states, state updating based on streamflow assimilation at the catchment outlet tends to over- and under-adjust model states close to and away from the outlet, respectively. This is due to the greater sensitivity of the simulated outlet flow to the model states that are located more closely to the outlet in the hydraulic sense, and the subsequent overcompensation of the states in the more influential grid boxes to make up for the larger scale bias. In this work, we describe Mean Field Bias (MFB)-aware variational (VAR) assimilation, or MVAR, to address the above. MVAR performs bi-scale state updating of the distributed hydrologic model using streamflow observations in which MFB in the model states are first corrected at the catchment scale before the resulting states are adjusted at the grid box scale. We comparatively evaluate MVAR with conventional VAR based on streamflow assimilation into the distributed Sacramento Soil Moisture Accounting model for a headwater catchment. Compared to VAR, MVAR adjusts model states at remote cells by larger margins and reduces the Mean Squared Error of streamflow analysis by 2–8% at the outlet Tiff City, and by 1–10% at the interior location Lanagan.

Ensemble Streamflow Forecasting Using an Energy Balance Snowmelt Model Coupled to a Distributed Hydrologic Model with Assimilation of Snow and Streamflow Observations

AbstractIn many river basins across the world, snowmelt is an important source of streamflow. However, detailed snowmelt modeling is hampered by limited input data and uncertainty arising from inadequate model structure and parametrization. Data assimilation that updates model states based on observations, reduces uncertainty and improves streamflow forecasts. In this study, we evaluated the Utah Energy Balance (UEB) snowmelt model coupled to the Sacramento Soil Moisture Accounting (SAC‐SMA) and rutpix7 stream routing models, integrated within the Research Distributed Hydrologic Model (RDHM) framework for streamflow forecasting. We implemented an ensemble Kalman filter for assimilation of snow water equivalent (SWE) observations in UEB and a particle filter for assimilation of streamflow to update the SAC‐SMA and rutpix7 states. Using leave one out validation, it was shown that the modeled SWE at a location where observations were excluded from data assimilation was improved through assimilation of data from other stations, suggesting that assimilation of sparse observations of SWE has the potential to improve the distributed modeling of SWE over watershed grid cells. In addition, the spatially distributed snow data assimilation improved streamflow forecasts and the forecast volume error was reduced. On the other hand, the assimilation of streamflow observations did not provide additional forecast improvement over that achieved by the SWE assimilation for seasonal forecast volume likely due to there being little information content in streamflow at the forecast date prior to its rising during the melt period and this application of particle filter being better suited for shorter timescales.

Identifying time-varying hydrological model parameters to improve simulation efficiency by the ensemble Kalman filter: A joint assimilation of streamflow and actual evapotranspiration

Hydrological model parameters are essential for model simulation, which may vary with time owing to climatic variations and human activities. As a result, the implementation of stationary parameters may lead to inaccurate streamflow simulation. Actual evapotranspiration (ET) is a crucial component of hydrological cycles with an important influence on regional climate characteristics. Our aim was to investigate an appropriate way to estimate time-varying parameters using streamflow and ET observations, and to explore the how the simulation efficiency would change when ET data were added into assimilation. Data assimilation techniques can automatically adjust hydrological model parameters according to changing conditions. The ensemble Kalman filter (EnKF) technique was used to assimilate observations into a two-parameter monthly water balance model. Four data assimilation schemes were designed and applied in a synthetic experiment and 173 catchments in USA, including: (1) sole assimilation of streamflow to update both parameters, (2) joint assimilation of streamflow and ET to simultaneously update parameters, (3) sole assimilation of ET first to update parameter C (measuring evapotranspiration), and then subsequently joint assimilation of streamflow and ET to update parameter SC (measuring water storage capacity), and (4) sole assimilation of ET to update parameter C, and then sole assimilation of streamflow to update parameter SC. The four schemes were compared in terms of deterministic and probabilistic model performances, and model parameter correlations. The results indicated that: (1) the estimation of parameter C can be improved by assimilating ET observations into the model, and the time-varying model parameters obtained by adding ET data into assimilation led to a significant enhancement in deterministic streamflow and ET prediction, (2) among the three joint assimilation schemes, the deterministic model simulation performances were similar, while the ensemble predictions were more reliable in scheme 2. The joint assimilation of streamflow and ET to update both parameters in one step, i.e., scheme 2, outperformed the other two schemes in parameter SC estimation and model performance, (3) the ability to enhance simulation accuracy and to weaken correlations between parameters was more significant in humid catchments. The joint assimilation was helpful for the application of multiple sources of information into a hydrological simulation.

SWAT‐Based Hydrological Data Assimilation System (SWAT‐HDAS): Description and Case Application to River Basin‐Scale Hydrological Predictions

AbstractThis paper presents the development and application of a physically based hydrological data assimilation system (HDAS) using the gridded and parallelized Soil and Water Assessment Tool (SWATGP) distributed hydrological model. This SWAT‐HDAS software integrates remotely sensed data, including the leaf area index (LAI), snow cover fraction, snow water equivalent, soil moisture, and ground‐based observational data (e.g., from discharge and ground sensor networks), with SWATGP and the Parallel Data Assimilation Framework (PDAF) to accurately characterize watershed hydrological states and fluxes. SWAT‐HDAS employs high‐performance computational technologies to address the computational challenges of high‐resolution and/or large‐area modeling. Multiple observational system simulation experiments (OSSEs), including soil moisture assimilation experiments, snow water equivalent assimilation experiments, and streamflow assimilation experiments, were designed to validate the assimilation efficiency of various types of observations within SWAT‐HDAS using an ensemble Kalman filter (EnKF) algorithm. Both the temporal and spatial correlations in the trend/pattern and the magnitudes of improvement between the simulated and “true” states (i.e., for soil moisture, snow water equivalent, and discharge) were satisfactory using the integrated assimilation, which suggests the reliability of SWAT‐HDAS for regional hydrology studies. The streamflow assimilation experiment also showed that the observation location dramatically influences the assimilation efficiency. The quantity and quality of observations have effects of varying degrees on the streamflow predictions. SWAT‐HDAS is a promising tool for hydrological studies and applications under climate and environmental change scenarios.

Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags

Assimilation of either soil moisture or streamflow has been well demonstrated to improve flood forecasting. However, it is difficult to assimilate two different types of observations into a rainfall–runoff model simultaneously because there is a time lag between soil moisture and streamflow owing to the runoff routing process. In this study, we developed an effective data assimilation scheme based on the ensemble Kalman filter and smoother (named as EnKF-S) to exploit the benefits of the two observation types while accounting for the runoff routing lag. To prove the importance of accounting for the time lag, a scheme named Dual-EnKF was used to compare. To demonstrate the schemes, we designed synthetic cases regarding two typical flood patterns, i.e., flash flood and gradual flood. The results show that EnKF-S can effectively improve flood forecasting compared with Dual-EnKF, particularly when the runoff routing has distinct time lags. For the synthetic cases, EnKF-S reduced root–mean–square error (RMSE) by more than 70% relative to the data assimilation scheme without considering runoff routing lags. Therefore, this effective data assimilation scheme holds great potential for short-term flood forecasting by merging observations from ground measurement and remote sensing retrievals.

Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion

The particle filtering techniques have been receiving increasing attention from the hydrologic community due to its ability to properly estimate model parameters and states of nonlinear and non-Gaussian systems. To facilitate a robust quantification of uncertainty in hydrologic predictions, it is necessary to explicitly examine the forward propagation and evolution of parameter uncertainties and their interactions that affect the predictive performance. This paper presents a unified probabilistic framework that merges the strengths of particle Markov chain Monte Carlo (PMCMC) and factorial polynomial chaos expansion (FPCE) algorithms to robustly quantify and reduce uncertainties in hydrologic predictions. A Gaussian anamorphosis technique is used to establish a seamless bridge between the data assimilation using the PMCMC and the uncertainty propagation using the FPCE through a straightforward transformation of posterior distributions of model parameters. The unified probabilistic framework is applied to the Xiangxi River watershed of the Three Gorges Reservoir (TGR) region in China to demonstrate its validity and applicability. Results reveal that the degree of spatial variability of soil moisture capacity is the most identifiable model parameter with the fastest convergence through the streamflow assimilation process. The potential interaction between the spatial variability in soil moisture conditions and the maximum soil moisture capacity has the most significant effect on the performance of streamflow predictions. In addition, parameter sensitivities and interactions vary in magnitude and direction over time due to temporal and spatial dynamics of hydrologic processes.

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Abstract. The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the ensemble Kalman filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British Columbia, Canada. Synthetic data include daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector due to the limitations imposed by the EnKF. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than their individual counterparts, offering improvements over all forecast horizons considered and throughout the whole year, including the critical period of snowmelt. This highlights the potential benefit of using multivariate data assimilation for streamflow predictions in snow-dominated regions.

Simultaneous assimilation of in situ soil moisture and streamflow in the SWAT model using the Extended Kalman Filter

The Extended Kalman Filter (EKF) is used to assimilate in situ surface soil moisture and streamflow observation at the outlet of an experimental watershed outlet into a semi-distributed SWAT (Soil and Water Assessment Tool) model. Watershed scale, instead of HRU scale soil moisture was used in state vector to reduce computational burden. Numerical experiments were designed to select the best state vector which consists of streamflow and soil moisture in all vertical soil layers. Compared to open-loop model and direct-insert method, the estimate of both soil moisture and streamflow has been improved by EKF assimilation. The combined assimilation of surface soil moisture and streamflow outperforms the assimilation with only surface soil moisture or streamflow especially in the estimate of full profile soil moisture. The NSC has been improved to 0.63 from −4.45 and the RMSE has been reduced to 12.34mm from 47.44mm in open-loop. Such improvement is also reflected in the short term forecast of soil moisture. The improvement of streamflow prediction is relatively moderate in both simulation and forecast mode compared to quality of the soil moisture prediction. The quantification of the model error, especially the error covariance between different state variables, was found to be critical to the estimate of the state variable corresponding to the error covariance.

Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations

Abstract. The coarse spatial resolution of global hydrological models (typically > 0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (downscaled from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). Downscaled satellite-derived soil moisture (downscaled from ∼ 0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse-resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently made to move to global hyper-resolution modelling and can help to advance this research.

Combined assimilation of streamflow and satellite soil moisture with the particle filter and geostatistical modeling

Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years. This study provides a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. Performance is assessed over the Salt River Watershed in Arizona, which is one of the watersheds without anthropogenic effects in Model Parameter Estimation Experiment (MOPEX). A total of five data assimilation (DA) scenarios are designed and the effects of the locations of streamflow gauges and the ASCAT soil moisture on the predictions of soil moisture and streamflow are assessed. In addition, a geostatistical model is introduced to overcome the significantly biased satellite soil moisture and also discontinuity issue. The results indicate that: (1) solely assimilating outlet streamflow can lead to biased soil moisture estimation; (2) when the study area can only be partially covered by the satellite data, the geostatistical approach can estimate the soil moisture for those uncovered grid cells; (3) joint assimilation of streamflow and soil moisture from geostatistical modeling can further improve the surface soil moisture prediction. This study recommends that the geostatistical model is a helpful tool to aid the remote sensing technique and the hydrologic DA study.

Assessing the Performance of Two Hydrologic Models for Forecasting Daily Streamflows in the Cazones River Basin (Mexico)

Floods have caused significant human and economic losses in the Cazones River Basin, located on the Gulf of Mexico. Despite this knowledge, steps towards the design and implementation of an early warning system for the Cazones are still a pending task. In this study we contributed by establishing a hydrological scheme for forecasting mean daily discharges in the Cazones Basin. For these purposes, we calibrated, validated and compared the HyMod model (HM) which is physics-based, and an autoregressive-based model coupled with the Discrete Kalman Filter (ARX-DKF). The ability of both models to accurately predict discharges proved satisfactory results during the validation period with RMSEHYMOD = 2.77 [mm/day]; and RMSEARX-DKF = [2.38 mm/day]. Further analysis based on a Streamflow Assimilation Ratio (SAR) revealed that both models underestimate the discharges in a similar proportion. This evaluation also showed that, under the most common conditions, the simpler stochastic model (ARX-DKF) performs better; however, under extreme hydrological conditions the deterministic HM model reveals a better performance. These results are discussed under the context of future applications and additional requirements needed to implement an early warning hydrologic system for the Cazones Basin.

Streamflow data assimilation in SWAT model using Extended Kalman Filter

Summary The Extended Kalman Filter (EKF) is coupled with the Soil and Water Assessment Tools (SWAT) model in the streamflow assimilation of the upstream Senegal River in West Africa. Given the large number of distributed variables in SWAT, only the average watershed scale variables are included in the state vector and the Hydrological Response Unit (HRU) scale variables are updated with the a posteriori / a priori ratio of their watershed scale counterparts. The Jacobian matrix is calculated numerically by perturbing the state variables. Both the soil moisture and CN2 are significantly updated in the wet season, yet they have opposite update patterns. A case study for a large flood forecast shows that for up to seven days, the streamflow forecast is moderately improved using the EKF-subsequent open loop scheme but significantly improved with a newly designed quasi-error update scheme. The former has better performances in the flood rising period while the latter has better performances in the recession period. For both schemes, the streamflow forecast is improved more significantly when the lead time is shorter.

Exploration of sequential streamflow assimilation in snow dominated watersheds

This paper evaluates Ensemble Kalman filter (EnKF) sequential data assimilation on a semi-distributed hydrological model implementation on two snow-dominated watersheds, focussing strictly on snow accumulation and melt periods while assimilating streamflow for the updating of various state variables combinations. Three scenarios are explored in depth: (1) updating the three state variables that were previously identified pertinent for snow-free hydrological processes: soil moisture in the intermediate layer, soil moisture in the deep layer, and the overland routing reservoir, (2) updating the snow water equivalent, and (3) updating all of the above state variables. Inputs (precipitation and temperature) and output (streamflow) perturbation factors are first identified for each scenario, based on their performance and reliability for simulation with assimilation. The three EnKF implementations are next compared to one another and to an open-loop run, in an ensemble forecasting context. The third scenario outperforms the others in most situations and provides the largest gain in reliability. The ensemble size may also be reduced, from 1000 to 50 members, without much loss in performance or reliability.

Exploration of sequential streamflow assimilation in snow dominated watersheds

This paper evaluates Ensemble Kalman filter (EnKF) sequential data assimilation on a semi-distributed hydrological model implementation on two snow-dominated watersheds, focussing strictly on snow accumulation and melt periods while assimilating streamflow for the updating of various state variables combinations. Three scenarios are explored in depth: (1) updating the three state variables that were previously identified pertinent for snow-free hydrological processes: soil moisture in the intermediate layer, soil moisture in the deep layer, and the overland routing reservoir, (2) updating the snow water equivalent, and (3) updating all of the above state variables. Inputs (precipitation and temperature) and output (streamflow) perturbation factors are first identified for each scenario, based on their performance and reliability for simulation with assimilation. The three EnKF implementations are next compared to one another and to an open-loop run, in an ensemble forecasting context. The third scenario outperforms the others in most situations and provides the largest gain in reliability. The ensemble size may also be reduced, from 1000 to 50 members, without much loss in performance or reliability.