Assimilation of Remotely Sensed Soil Moisture in the MESH Model

Soil moisture information is critically important to weather, climate, and hydrology forecasts since the wetness of the land strongly affects the partitioning of energy and water at the land surface. Spatially distributed soil moisture information, especially at regional, continental, and global scales, is difficult to obtain from ground-based (in situ) measurements, which are typically based upon sparse point sources in practice. Satellite microwave remote sensing can provide large-scale monitoring of surface soil moisture because microwave measurements respond to changes in the surface soil’s dielectric properties, which are strongly controlled by soil water content. With recent advances in satellite microwave soil moisture estimation, in particular the launch of the Soil Moisture and Ocean Salinity (SMOS) satellite and the Soil Moisture Active Passive (SMAP) mission, there is an increased demand for exploiting the potential of satellite microwave soil moisture observations to improve the predictive capability of hydrologic and land surface models. In this work, an Ensemble Kalman Filter (EnKF) scheme is designed for assimilating satellite soil moisture into a land surface-hydrological model, Environment Canada’s standalone MESH to improve simulations of soil moisture. After validating the established assimilation scheme through an observing system simulation experiment (synthetic experiment), this study explores for the first time the assimilation of soil moisture retrievals, derived from SMOS, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), in the MESH model over the Great Lakes basin. A priori rescaling on satellite retrievals (separately for each sensor) is performed by matching their cumulative distribution function (CDF) to the model surface soil moisture’s CDF, in order to reduce the satellite-model bias (systematic error) in the assimilation system that is based upon the hypothesis of unbiased errors in model and observation. The satellite retrievals, the open-loop model soil moisture (no assimilation) and the assimilation estimates are, respectively, validated against point-scale in situ soil moisture measurements in terms of the daily-spaced time series correlation coefficient (skill R). Results show that assimilating either L-band retrievals (SMOS) or X-band retrievals (AMSR-E/AMSR2) can favorably influence the model soil moisture skill for both surface and root zone soil layers except for the cases with a small observation (retrieval) skill and a large open-loop skill. The skill improvement ΔRA-M, defined as the skill for the assimilation soil moisture product minus the skill for the open-loop estimates, typically increases with the retrieval skill and decreases with increased open-loop skill, showing a strong dependence upon ΔRS-M, defined as the retrieval skill minus the model (open-loop) surface soil moisture skill. The SMOS assimilation reveals that the cropped areas typically experience large ΔRA-M, consistent with a high satellite observation skill and a low open-loop skill, while ΔRA-M is usually weak or even negative for the forest-dominated grids due to the presence of a low retrieval skill and a high open-loop skill. The assimilation of L-band retrievals (SMOS) typically results in greater ΔRA-M than the assimilation of X-band products (AMSR-E/AMSR2), although the sensitivity of the assimilation to the satellite retrieval capability may become progressively weaker as the open-loop skill increases. The joint assimilation of L-band and X-band retrievals does not necessarily yield the best skill improvement. As compared to previous studies, the primary contributions of this thesis are as follows. (i) This work examined the potential of latest satellite soil moisture products (SMOS and AMSR2), through data assimilation, to improve soil moisture model estimates. (ii) This work, by taking advantage of the ability of SMOS to estimate surface soil moisture underneath different vegetation types, revealed the vegetation cover modulation of satellite soil moisture assimilation. (iii) The assimilation of L-band retrievals (SMOS) was compared with the assimilation of X-band retrievals (AMSR-E/AMSR2), providing new insight into the dependence of the assimilation upon satellite retrieval capability. (iv) The influence of satellite-model skill difference ΔRS-M on skill improvement ΔRA-M was consistently demonstrated through assimilating soil moisture retrievals derived from radiometers operating at different microwave frequencies, different vegetation cover types, and different retrieval algorithms

Application of a Hillslope-Scale Soil Moisture Data Assimilation System to Military Trafficability Assessment

Soil moisture is an important environmental variable that impacts military operations and weapons systems. Accurate and timely forecasts of soil moisture at appropriate spatial scales, therefore, are important for mission planning. We present an application of a soil moisture data assimilation system to military trafficability assessment. The data assimilation system combines hillslope-scale (e.g., 10s to 100s of m) estimates of soil moisture from a hydrologic model with synthetic L-band microwave radar observations broadly consistent with the planned NASA Soil Moisture Active–Passive (SMAP) mission. Soil moisture outputs from the data assimilation system are input to a simple index-based model for vehicle trafficability. Since the data assimilation system uses the ensemble Kalman Filter, the risks of impaired trafficability due to uncertainties in the observations and model inputs can be quantified. Assimilating the remote sensing observations leads to significantly different predictions of trafficability conditions and associated risk of impaired trafficability, compared to an approach that propagates forward uncertainties in model inputs without assimilation. Specifically, assimilating the observations is associated with an increase in the risk of “slow go” conditions in approximately two-thirds of the watershed, and an increase in the risk of “no go” conditions in approximately 40% of the watershed. Despite the simplicity of the trafficability assessment tool, results suggest that ensemble-based data assimilation can potentially improve trafficability assessment by constraining predictions to observations and facilitating quantitative assessment of the risk of impaired trafficability

Assimilation des données GRACE dans le modèle MESH pour l’amélioration de l'estimation de l'équivalent en eau de la neige

Abstract: Water storage changes over space and time play a major rule in the Earth’s climate system through the exchange of water and energy fluxes among the Earth’s water storage compartments and between atmosphere, continents, and oceans. In many parts of northern-latitude areas spring meltwater controls the availability of freshwater resources. With respect to terrestrial hydrologic process, snow water equivalent (SWE) is the most critical snow characteristic to hydrologists and water resource managers. The first objective of this study examined the spatiotemporal variations of terrestrial water storages and their linkages with SWE variabilities over Canada. Terrestrial water storage anomaly (TWSA) from the Gravity Recovery and Climate Experiment (GRACE), the WaterGAP Global Hydrology Model (WGHM), and the Global Land Data Assimilation System (GLDAS) were employed. SWE anomaly (SWEA) products were provided by the Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR-E), and Canadian Meteorological Centre (CMC). The grid cell (1°×1°) and basin-averaged analyses were applied to find any possible relationship between TWSA and SWEA over the Canadian territory, from December 2002 to March 2011. Results showed that GRACE versus CMC provided the highest percentage of significant positive correlation (62.4% of the 1128 grid cells), with an average significant positive correlation coefficient of 0.5, and a maximum of 0.9. In western Canada, GRACE correlated better with multiple SWE data sets than GLDAS. Yet, over eastern Canada, mainly in the northern Québec area (~ 55ºN), GRACE provided weak or insignificant correlations with all snow products, while GLDAS appeared to be significantly correlated. For the TWSA-SWEA analysis at the basin-averaged scale, significant relationships were observed between TWSA and SWEA for most of the fifteen basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products, compared to satellite-based SWE data. Stronger relationships were found in snow-dominated basins (Rs >= 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in northern Québec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behavior was observed in the western Hudson Bay Basin. In both regions, it was found that the contribution of non-SWE compartments, including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the WGHM simulations and then subtracted from GRACE observations. The GRACE-derived SWEA correlation results showed improved relationships with three SWEA products (CMC, GlobSnow2, AMSR-E). The improvement is particularly important in the sub-basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE-derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). In general, results revealed the importance of SWE changes in association with the terrestrial water storage (TWS) variations. The second objective of this thesis investigates whether integration of remotely sensed terrestrial water storage (TWS) information, which is derived from GRACE, can improve SWE and streamflow simulations within a semi-distributed hydrology land surface model. A data assimilation (DA) framework was developed to combine TWS observations with the MESH (Modélisation Environnementale Communautaire – Surface Hydrology) model using an ensemble Kalman smoother (EnKS). This study examined the incorporation and development of the ensemble-based GRACE data assimilation framework into the MESH modeling framework for the first time. The snow-dominated Liard Basin was selected as a case study. The proposed assimilation methodology reduced bias of monthly SWE simulations at the basin scale by 17.5 % and improved unbiased root-mean-square difference (ubRMSD) by 23 %. At the grid scale, the DA method improved ubRMSD values and correlation coefficients of SWE estimates for 85 % and 97 % of the grid cells, respectively. Effects of GRACE DA on streamflow simulations were evaluated against observations from three river gauges, where it could effectively improve the simulation of high flows during snowmelt season from April to June. The influence of GRACE DA on the total flow volume and low flows was found to be variable. In general, the use of GRACE observations in the assimilation framework not only improved the simulation of SWE, but also effectively influenced the simulation of streamflow estimates.Les variations dans l'espace et le temps du stock d'eau à travers jouent un rôle important dans le système climatique de la Terre à travers l'échange des flux d'eau et d'énergie entre les compartiments du stock d’eau de la Terre, et entre l'atmosphère, les continents et les océans. Dans les régions nordiques, la fonte de la neige contrôle la disponibilité des ressources en eau. Concernant le processus hydrologique terrestre, l'équivalent en eau de la neige (SWE) est la caractéristique de neige la plus importante pour les hydrologues et les gestionnaires des ressources en eau. Le premier objectif de cette étude a examiné les variations spatio-temporelles des réservoirs terrestres d'eau et leurs liens avec les variabilités de SWE au Canada. Des anomalies de stockage d'eau terrestre (TWSA) provenant de GRACE (Gravity Recovery and Climate Experiment), du modèle hydrologique mondial WaterGAP (WGHM) et du modèle GLDAS (Global Land Data Assimilation System) ont été utilisées. Les produits du SWEA (Snow Water Equiavalent Anomaly) sont fournis par le GlobSnow2 (Global Snow Monitoring for Climate Research version 2), le AMSR-E (Advanced Microwave Scanning Radiometer‐Earth Observing System) et le Centre météorologique canadien (CMC). L'analyse par cellule de grille (1°×1°) a été appliquée pour trouver toute relation possible entre TWSA et SWEA sur le territoire canadien, de décembre 2002 à mars 2011. Les résultats montrent que GRACE par rapport à CMC a fourni le pourcentage le plus élevé de corrélation positive significative (62,4% des 1128 cellules de la grille), avec un coefficient de corrélation positif significatif moyen de 0,5 et un maximum de 0,9. Dans la partie ouest du pays, GRACE a montré un meilleur accord avec plusieurs produits SWE que GLDAS. Pourtant, dans l'est du Canada, principalement dans le nord du Québec (~ 55° N), GRACE a fourni des corrélations faibles ou insignifiantes avec tous les produits SWE, contrairement à GLDAS qui semblait être significativement corrélé. Dans le cas de l’analyse à l'échelle du bassin versant, les relations significatives ont été observées entre TWSA et SWEA pour la plupart des quinze bassins considérés (53% à 80% des bassins, selon les produits SWE considérés). Les meilleurs résultats ont été obtenus avec les produits CMC SWE, par rapport aux données SWE satellitaires. Des relations plus fortes ont été trouvées dans les bassins dominés par la neige (Rs> = 0,7), tels que le bassin versant de Liard [erreur quadratique moyenne (RMSE) = 21,4 mm] et le bassin versant de Peace (RMSE = 26,76 mm). Cependant, malgré une forte accumulation de neige dans le nord du Québec, GRACE a montré des corrélations faibles ou insignifiantes avec SWEA, peu importent les sources de données. Le même comportement a été observé dans le bassin versant ouest de la Baie d’Hudson. Dans les deux régions, il a été constaté que la contribution des compartiments non-SWE, y compris les zones humides, les eaux de surface, ainsi que les stocks d'eau du sol a un effet significatif sur les variations du stock total. Ces composantes ont été estimées à l'aide des simulations du modèle WGHM, puis soustraites des observations GRACE. Ces résultats de corrélation SWEA dérivés de GRACE ont montré une amélioration des relations avec les trois produits SWE (CMC, GlobSnow2, AMSR-E). L'amélioration est particulièrement importante dans les sous-bassins de la Baie d’Hudson, où des résultats très faibles et insignifiants avaient été précédemment trouvés avec les données GRACE TWSA. La SWEA dérivée de GRACE a montré une relation significative avec les données CMC dans 93% des bassins (13% de plus que GRACE TWSA). En somme, les résultats obtenus dans ce premier objectif ont montré le rôle important du SWE dans les variations du stock terrestre de l'eau dans la région d’étude. Le deuxième objectif de cette thèse examine si l'intégration des informations de TWS (terrestrial water storage) dérivées de GRACE (Gravity Recovery and Climate Experiment), peut améliorer les simulations du SWE et du débit d’eau dans un modèle hydrologique semi-distribué de schéma de surface. Un cadre d'assimilation de données (DA) a été développé pour combiner les observations TWS avec le modèle MESH (Modélisation Environnementale Communautaire - Hydrologie de Surface) en utilisant un ensemble Kalman Smoother (EnKS). Cette étude était la première du genre à tenter une assimilation des données GRACE dans le modèle MESH pour améliorer l’estimation du SWE. Le bassin versant de la Liard dominé par la neige a été choisi pour le site d’étude. À l’échelle du bassin versant, la méthodologie d'assimilation proposée a réduit le biais des simulations mensuelles de SWE à 17,5% et amélioré le ubRMSD (unbiased root-mean-square difference) de 23%. À l'échelle de la grille, la méthode DA a amélioré l’estimation du SWE pour les valeurs ubRMSD et les coefficients de corrélation pour 85% et 97% des cellules de la grille, respectivement. Les effets de GRACE DA sur les simulations de débit ont été évalués par rapport aux observations de trois stations des débits, où il pourrait effectivement améliorer la simulation des débits élevés pendant la saison de fonte de la neige d'avril à juin. L'influence de GRACE DA sur le volume total et les faibles débits d’eau a été trouvée variable. En général, l'utilisation des observations GRACE dans le cadre d'assimilation non seulement a amélioré la simulation de SWE, mais a également influencé efficacement la simulation des estimations de débit

Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope

Data assimilation has recently been the focus of much attention for integrated surface–subsurface hydrological models, whereby joint assimilation of water table, soil moisture, and river discharge measurements with the ensemble Kalman filter (EnKF) has been extensively applied. Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of the EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant, as well as to quantify possible tradeoffs associated with assimilation of multi-source data. Here, the CATHY (CATchment HYdrology) model is applied to reproduce the hydrological dynamics observed in an experimental two-layered hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. Pressure head, soil moisture, and subsurface outflow are assimilated with the EnKF in a number of scenarios and the challenges and issues arising from the assimilation of multi-source data in this real-world test case are discussed. Our results demonstrate that the EnKF is able to effectively correct states and parameters even in a real application characterized by strong nonlinearities. However, multi-source data assimilation may lead to significant tradeoffs: the assimilation of additional variables can lead to degradation of model predictions for other variables that are otherwise well reproduced. Furthermore, we show that integrated observations such as outflow discharge cannot compensate for the lack of well-distributed data in heterogeneous hillslopes.</p

Assimilating high resolution remotely sensed soil moisture into a distributed hydrologic model to improve runoff prediction

The susceptibility of a catchment to flooding during an extreme rainfall event is affected by its soil moisture condition prior to the event. A study to improve the state vector of a distributed hydrologic model by assimilating high resolution remotely sensed soil moisture is described. The launch of Sentinel-1 has stimulated interest in measuring soil moisture at high resolution suitable for hydrological studies using Synthetic Aperture Radars (SARs). The advantages of using SAR soil moisture in conjunction with land cover data are considered. These include the ability to reduce contamination of the surface soil signal due to vegetation, radar artefacts, mixed pixels and land cover classes not providing meaningful soil moistures. Results for 2008 using ASAR data showed that the assimilation of ASAR soil moisture values improved the predicted flows for all images. The improvement was less marked for 2007, probably because the antecedent soil moisture conditions were of reduced importance during the extreme flooding that occurred then. Particularly for 2008, the higher resolution of ASAR data improved predicted flows compared to low resolution ASCAT data that were not disaggregated and limited to the temporal frequency of ASAR. The method is likely to give better results with Sentinel-1 rather than ASAR data due to its higher temporal resolution

Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable

Parameter-state ensemble thinning for short-term hydrological prediction

The main sources of uncertainty in hydrological modelling can be summarized as structural errors, parameter errors, and data errors. Operational modellers are generally more concerned with predictive ability than model errors, and this paper presents a new, simple method to improve predictive ability. The method is called parameter-state ensemble thinning (P-SET). P-SET takes a large ensemble of continuous model runs and applies screening criteria to reduce the size of the ensemble. The goal is to find the most promising parameter-state combinations for analysis during the prediction period. Each prediction period begins with the same large ensemble, but the screening criteria are free to select a different sub-set of simulations for each separate prediction period. The case study is from June to October 2014 for a small (1324&thinsp;km2) watershed just north of Lake Superior in Ontario, Canada, using a Canadian semi-distributed hydrologic land-surface scheme. The study examines how well the approach works given various levels of certainty in the data, beginning with certainty in the streamflow and precipitation, followed by uncertainty in the streamflow and certainty in the precipitation, and finally uncertainty in both the streamflow and precipitation. The approach is found to work in this case when streamflow and precipitation are fairly certain, while being more challenging to implement in a forecasting scenario where future streamflow and precipitation are much less certain. The main challenge is determined to be related to parametric uncertainty and ideas for overcoming this challenge are discussed. The approach also highlights model structural errors, which are also discussed.</p

Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System

A new version of the AWI Coupled Prediction System is developed based on the Alfred Wegener Institute Climate Model v3.0. Both the ocean and the atmosphere models are upgraded or replaced, reducing the computation time by a factor of 5 at a given resolution. This allowed us to increase the ensemble size from 12 to 30, maintaining a similar resolution in both model components. The online coupled data assimilation scheme now additionally utilizes sea-surface salinity and sea-level anomaly as well as temperature and salinity profile observations. Results from the data assimilation demonstrate that the sea-ice and ocean states are reasonably constrained. In particular, the temperature and salinity profile assimilation has mitigated systematic errors in the deeper ocean, although issues remain over polar regions where strong atmosphere-ocean-ice interaction occurs. One-year-long sea-ice forecasts initialized on 1 January, 1 April, 1 July and 1 October from 2003 to 2019 are described. To correct systematic forecast errors, sea-ice concentration from 2011 to 2019 is calibrated by trend-adjusted quantile mapping using the preceding forecasts from 2003 to 2010. The sea-ice edge raw forecast skill is within the range of operational global subseasonal-to-seasonal forecast systems, outperforming a climatological benchmark for about 2 weeks in the Arctic and about 3 weeks in the Antarctic. The calibration is much more effective in the Arctic: Calibrated sea-ice edge forecasts outperform climatology for about 45 days in the Arctic but only 27 days in the Antarctic. Both the raw and the calibrated forecast skill exhibit strong seasonal variations