Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented into operational forecast systems to improve the skill of forecasts to better inform real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. <br><br> The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, The Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical considerations in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modelling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters

This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model. The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of the hourly HBV-96 model. Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km²), a quickly responding catchment in the Belgian Ardennes. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF scheme is mainly changing the pdf's of the two routing model storages. This also holds for situations, where the uncertainty in the discharge simulations is smaller than the defined observation uncertainty

Abstract. Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data.

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Abstract. Land surface and hydrologic models (LSM/HM) are used at diverse spatial resolutions ranging from 1–10 km in catchment-scale applications to over 50 km in global-scale applications. Application of the same model structure at different spatial scales requires that the model estimates similar fluxes independent of the model resolution and fulfills a flux-matching condition across scales. An analysis of state-of-the-art LSMs and HMs reveals that most do not have consistent and realistic parameter fields for land surface geophysical properties. Multiple experiments with the mHM, Noah-MP, PCR-GLOBWB and WaterGAP models are conducted to demonstrate the pitfalls of poor parameterization practices currently used in most operational models, which are insufficient to satisfy the flux-matching condition. These examples demonstrate that J. Dooge's 1982 statement on the unsolved problem of parameterization in these models remains true. We provide a short review of existing parameter regionalization techniques and discuss a method for obtaining seamless hydrological predictions of water fluxes and states across multiple spatial resolutions. The multiscale parameter regionalization (MPR) technique is a practical and robust method that provides consistent (seamless) parameter and flux fields across scales. A general model protocol is presented to describe how MPR can be applied to a specific model, with an example of this application using the PCR-GLOBWB model. Applying MPR to PCR-GLOBWB substantially improves the flux-matching condition. Estimation of evapotranspiration without MPR at 5 arcmin and 30 arcmin spatial resolutions for the Rhine river basin results in a difference of approximately 29 %. Applying MPR reduce this difference to 9 %. For total soil water, the differences without and with MPR are 25 % and 7 %, respectively.

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Sound spatially distributed rainfall fields including a proper spatial and temporal error structure are of key interest for hydrologists to force hydrological models and to identify uncertainties in the simulated and forecasted catchment response. The current paper presents a temporal coherent error identification method based on time-dependent multivariate spatial conditional simulations, which are made further conditional on preceding simulations. <br><br> Synthetic and real world experiments are carried out within the hilly region of the Belgian Ardennes. Precipitation fields are simulated for pixels of 10 × 10 km² resolution. Uncertainty analyses in the simulated fields focus on (1) the number of previous simulation hours on which the new simulation is conditioned, (2) the advection speed of the rainfall event, (3) the size of the catchment considered, and (4) the rain gauge density within the catchment. The results for a synthetic experiment show for typical advection speeds of >20 km h⁻¹, no uncertainty is added in terms of across ensemble spread when conditioned on more than one or two previous simulations. However, for the real world experiment, additional uncertainty can be still added when conditioning on a higher number of previous simulations. This is, because for actual precipitation fields, the dynamics exhibit a larger spatial and temporal variability. Moreover, by thinning the observation network with 50%, the added uncertainty increases only slightly. Finally, the first order autocorrelation coefficients show clear temporal coherence in the time series of the areal precipitation using the time-dependent multivariate conditional simulations, which was not the case using the time-independent univariate conditional simulations

&lt;p&gt;This study synthesizes the advancements made in the setup of the mesoscale Hydrologic Model (mHM; [1,2,3]) at the global scale. Underlying vegetation and geophysical characteristics are provided at &amp;#8776;200m, while the mHM simulates water fluxes and states between 10 km and 100 km spatial resolution. The meteorologic forcing data are derived from the readily available, near-real time ERA-5 dataset [4]. The total of 50 global parameters of the Multiscale Parameter Regionalization (MPR) are constrained in two modes: (1) streamflow only across 3054 gauges, and (2) streamflow across 3054 gauges and simultaneously with FLUXNET ET and GRACE TWSA across 258 domains consisting of &amp;#8776;10&amp;#176; x 10&amp;#176; blocks. Model performance is finally evaluated against a range of observed and reference data since 1985.&amp;#160;&lt;/p&gt;&lt;p&gt;The single best parameter set evaluated across 3054 GRDC global streamflow station yield median performance of 0.47 daily KGE (0.55 monthly KGE). This performance varies strongly between continents. For example, median daily KGE across Europe is around 0.55 (N basins=972) and across northern America around 0.5 (N basins=1264). So far, the worst model performance is observed across Africa, with median KGE of 0 (N basins=202), using the same globally constrained parameter set. The deterioration of model performance based on seamless parameterization can be explained by the quality of the underlying data, which corresponds to areas, where water balance closure error is the biggest. Additionally, missed model processes play an important role as well. Finally, there remains a large gap between the onsite calibrations and global calibrations and ongoing research is being done to narrow down these differences. This work is the fundament for building skillful global seasonal forecasting system ULYSSES [6], which provides hindcasts and operational seasonal forecasts of hydrologic variables using four state of the art hydrologic/land surface models with lead time of 6 months.&lt;/p&gt;&lt;ul&gt;&lt;li&gt;[1] https://www.ufz.de/mhm&lt;/li&gt; &lt;li&gt;[2] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008WR007327&lt;/li&gt; &lt;li&gt;[3] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012WR012195&lt;/li&gt; &lt;li&gt;[4] https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803&lt;/li&gt; &lt;li&gt;[5] https://www.ufz.de/ulysses&lt;/li&gt; &lt;/ul&gt;