Soil Moisture Data Assimilation Research Articles

Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation

This study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.

Improving weather forecast skill of the Korean Integrated Model by assimilating Soil Moisture Active Passive soil moisture anomalies

AbstractThis study investigates the potential benefit of assimilating soil moisture (SM) data retrieved from Soil Moisture Active Passive (SMAP) in improving global SM estimates and enhancing the weather forecast skill of the Korean Integrated Model (KIM). The 36‐km SMAP L2 SM retrievals are assimilated into the Noah land surface model (LSM) using the ensemble Kalman filter scheme through the National Aeronautics and Space Administration Land Information System (LIS) that is weakly coupled to KIM. A suite of cycling experiments of the KIM–LIS system that includes land and atmospheric data assimilation (DA) and five‐day weather forecasts are conducted over the global domain from March to July 2022. In the SMAP SM DA, two different SM bias correction methods, namely the cumulative distribution function matching and anomaly‐based bias correction, are applied to correct systematic biases between SMAP and Noah‐LSM before assimilation. The global triple collocation analysis reveals that compared to the control case (without SM DA), significant improvements in the global SM estimates are achieved by assimilating the SMAP data, especially by employing the anomaly‐based bias correction. Notably, the improved SM initial conditions lead to an improved screen‐level specific humidity and air temperature analysis and forecasts when the results are compared against the European Centre for Medium Range Weather Forecasts‐Integrated Forecasting System analysis. The beneficial impacts of the SMAP DA on the atmospheric variables extend up to an atmospheric level of 700 hPa. Prominent improvements in the KIM forecast skill by the SMAP DA applying the anomaly‐based bias correction are observed in the northern part of Africa and West and Central Asia with stronger impacts for longer forecast lead time. This paper demonstrates the feasibility of assimilating the SMAP data within KIM–LIS to enhance the KIM weather forecasts in the lower atmosphere when a proper SM bias correction method is applied.

Contributions of Irrigation Modeling, Soil Moisture and Snow Data Assimilation to High‐Resolution Water Budget Estimates Over the Po Basin: Progress Towards Digital Replicas

AbstractHigh‐resolution water budget estimates benefit from modeling of human water management and satellite data assimilation (DA) in river basins with a large human footprint. Utilizing the Noah‐MP land surface model with dynamic vegetation growth and river routing, in combination with an irrigation module, Sentinel‐1 backscatter and snow depth retrievals, we produce a set of 0.7‐km2 water budget estimates of the Po river basin (Italy) for 2015–2023. The results demonstrate that irrigation modeling improves the seasonal soil moisture variation and summer streamflow at all gauges in the valley after withdrawal of irrigation water from the streamflow in postprocessing (12% error reduction relative to observed low summer streamflow), even if the basin‐wide irrigation amount is underestimated. Sentinel‐1 backscatter DA for soil moisture updating strongly interacts with irrigation modeling: when both are activated, the soil moisture updates are limited, and the simulated irrigation amounts are reduced. Backscatter DA systematically reduces soil moisture in the spring, which improves downstream spring streamflow. Assimilating Sentinel‐1 snow depth retrievals over the surrounding Alps and Apennines further improves spring streamflow in a complementary way (2% error reduction relative to observed high spring streamflow). Despite the seasonal improvements, irrigation modeling and Sentinel‐1 backscatter DA cannot significantly improve short‐term or interannual variations in soil moisture, irrigation modeling causes a systematically prolonged high vegetation productivity, and snow depth DA only impacts the deep snowpacks. This study helps advancing the design of digital water budget replicas for river basins.

Improved Representation of Vegetation Soil Moisture Coupling Enhances Soil Moisture Data Assimilation in Water‐Limited Regimes: A Case Study Over Texas

AbstractAssimilating remotely sensed surface soil moisture (SSM) into land surface models (LSM) is widely used to improve model representations of soil moisture (SM). However, the efficacy of SSM data assimilation (DA) has been found to be limited, particularly in resolving root‐zone soil moisture (RZSM). This study investigates how the representation of vegetation phenology, modulates the efficacy of SSM DA in enhancing the realism of RZSM simulations. To this end, two sets of climatological leaf area index (LAI) are implemented in Noah‐MP LSM over the state of Texas: (a) Noah‐MP default based on long‐term MODIS observations, (b) an alternative LAI adapted from AVHRR products. The former are found to exhibit conspicuous phase errors whereas the latter are more consistent with observed seasonal cycle. Two sets of DA experiments were performed accordingly, wherein SMAP L3 SSM is assimilated into Noah‐MP equipped with each LAI product from 2015 to 2019, Validation of the resulting products against in‐situ data reveals that (a) using the AVHRR‐based LAI, the Noah‐MP outperforms the baseline in reproducing the dynamics of RZSM, and the outperformance is particularly evident over the warm season and water‐stressed western Texas; (b) using the alternative LAI enhances the ability of DA to improve the accuracy of Noah‐MP RZSM, and to a lesser extent, SSM; and (c) gains in SM attained through improvement of LAI and application of DA is most pronounced over regions featuring tight vertical SM coupling. Additional model mechanistic limitations that need to be overcome to improve efficacy of DA are discussed.

Comparison of Met Office regional model soil moisture with COSMOS‐UK field‐scale in situ observations

AbstractThe UK Met Office state‐of‐the‐art, deterministic, convection‐permitting, coupled land‐atmosphere, regional weather forecasting system, known as the UKV or UK Variable resolution model (Tang et al. Meteorological Applications, 2013; 20:417–426), has been operational since 2015. Science updates are regularly made to the UKV land surface data assimilation scheme when those updates improve predictions of screen temperature and humidity, since these quantities have a direct impact on atmospheric states and weather forecasts. Less attention has been paid to whether UKV soil moisture analyses are close to independent, in‐situ soil moisture observations, partly because it is difficult to make meaningful comparisons between 1.5 km2 gridded model outputs and traditional point sensor measurements. Soil moisture is recognized to be important when hydrological forecasts for runoff and rivers are required. This is because soil moisture controls the extent to which rainfall can infiltrate the soil, and the amount of surface runoff affects the timing of peak river flows (Ward & Robinson, Principles of Hydrology. McGraw‐Hill Publishing Company; 2000; Singh et al. Water Resources Research, 2021, 57, e2020WR028827). Gómez et al. (Remote Sensing, 2020; 12:3691) report benefits to river flow forecasts when using soil moisture data assimilation in the UKV system instead of a daily downscaled product from the Met Office global model. The Met Office measures soil temperature and soil moisture at Cardington (Osborne & Weedon, Journal of Hydrometeorology, 2021, 22:279–295); there is no other UK Met Office site at which soil moisture is measured. In this study, we use field‐scale (~200 m radius) soil moisture measurements from the UK Centre for Ecology and Hydrology's (UKCEH's) COSMOS‐UK network to provide independent verification and analysis of UKV soil moisture during summer 2018, an unusually dry period in the United Kingdom. We find that the match to COSMOS‐UK soil moisture observations is generally good, and that changes made to the land data assimilation approach during a recent operational upgrade had a generally beneficial impact on UKV soil moisture analyses under very dry conditions.

The Impact of Satellite Soil Moisture Data Assimilation on the Hydrological Modeling of SWAT in a Highly Disturbed Catchment

The potential of satellite soil moisture (SM) in improving hydrological modeling has been addressed in synthetic experiments, but it is less explored in real data cases. Here, we investigate the added value of Soil Moisture and Passive (SMAP) and Advanced Scatterometer (ASCAT) SM data to distributed hydrological modeling with the soil and water assessment tool (SWAT) in a highly human disturbed catchment (126, 486 km2) featuring a network of SM and streamflow observations. The investigation is based on the ensemble Kalman filter (EnKF) considering SM errors from satellite data using the triple collocation. The assimilation of SMAP and ASCAT SM improved the surface (0–10 cm) and rootzone (10–30 cm) SM at >70% and > 50% stations of the basin, respectively. However, the assimilation effects on distributed streamflow simulation of the basin are un-significant and not robust. SM assimilation improved the simulated streamflow at two upstream stations, while it deteriorated the streamflow at the remaining stations. This can be largely attributed to the poor vertical soil water coupling of SWAT, suboptimal model parameters, satellite SM data quality, humid climate, and human disturbance to rainfall-runoff processes. This study offers strong evidence of integrating satellite SM into hydrological modeling in improving SM estimation and provides implications for achieving the added value of remotely sensed SM in streamflow improvement.

Data assimilation of soil moisture and leaf area index effectively improves the simulation accuracy of water and carbon fluxes in coupled farmland hydrological model

Real time status of farmland hydrology and crop growth is essential for agricultural management. Data assimilation is a common method to improve the prediction accuracy of the model by fusing observed and simulated data. For the farmland hydrological processes, evapotranspiration (ET) and net ecosystem productivity (NEP) are widely concerned and strongly affected by crop growth and soil moisture. In this study, data assimilation for soil water content (SWC) and leaf area index (LAI) was combined with a coupled farmland hydrological model, and the potential of Kalman filter (KF) and ensemble Kalman filter (EnKF) methods to enhance model accuracy were explored. Furthermore, the impact of observation density of assimilated data and different assimilation strategies (single-factor or dual-factor assimilation) were analyzed. The findings revealed that both KF and EnKF methods effectively improved the simulation ability of SWC and LAI. When assimilation was performed daily, KF could obtain results comparable to EnKF with assimilation efficiency coefficient (Eff) exceeded 70%. However, with a reduced assimilation frequency for LAI to ten-day interval, EnKF exhibited superior applicability, demonstrating a 13% increase in Eff. The assimilation of soil moisture could positively affect the simulation results of ET with Eff close to 10%, and the assimilation of LAI could improve the simulation accuracy of NEP with Eff close to 15%. Overall, dual-factor assimilation proved to have a more substantial impact than single-factor, even reducing the frequency to ten-day interval. The sensitivity analysis showed that the coupling model could resist the influence of the preset observation error in the filter, with data assimilation effectively mitigating the influence of parameter errors in coupling model. These analyses supply an effective basis to deepen the understanding of improve real time simulation accuracy of farmland hydrological model with data assimilation.

Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model

Abstract. This study explores coupled land–atmosphere data assimilation (DA) for improving weather and hydrological forecasts by assimilating soil moisture (SM) data. This study integrates a land DA component into a global atmospheric DA system of the Nonhydrostatic ICosahedral Atmospheric Model and the local ensemble transform Kalman filter (NICAM-LETKF) and performs both strongly and weakly coupled land–atmosphere DA experiments. We explore various types of coupled DA experiments by assimilating atmospheric observations and SM data simultaneously. The results show that analyzing atmospheric variables by assimilating SM data improves the SM analysis and forecasts and mitigates a warm bias in the lower troposphere where a dry SM bias exists. On the other hand, updating SM by assimilating atmospheric observations has detrimental impacts due to spurious error correlations between the atmospheric observations and land model variables. We also find that assimilating SM by strongly coupled DA is beneficial in the Sahel and equatorial Africa from May to October. These regions are characterized by seasonal variations in the precipitation patterns and benefit from updates in the atmospheric variables through SM DA during periods of increased precipitation. Additionally, these regions coincide with those identified in the previous studies, where a global initialization of SM would enhance the prediction skill of seasonal precipitation.

Soil Moisture and Streamflow Data Assimilation for Streamflow Prediction in the Narmada River Basin

Abstract An accurate streamflow forecast is vital for flood prediction and early warning systems. Notwithstanding the rising frequency and intensity of floods during the summer monsoon season in India, efforts to examine the utility of data assimilation (DA) for streamflow prediction remain limited. We examine soil moisture and streamflow DA to improve streamflow simulations in the Narmada River basin, considered a testbed. Data assimilation was performed using the Variable Infiltration Capacity (VIC) model at four gauge stations in the basin. First, we used the ensemble Kalman filter (EnKF) to assimilate the satellite soil moisture from the European Space Agency Climate Change Initiative (ESA-CCI) to the initial state of the VIC model. We examined the usefulness of observed streamflow from the India Water Resources Information System (India-WRIS) to improve the initial hydrological conditions of the VIC model in the streamflow DA during the summer monsoon (June–September) season from 1980 to 2018. The assimilation of ESA-CCI soil moisture showed less improvement in percent error reduction (PER) and efficiency index (EFF) (less than 2%) than the streamflow DA at all of the four gauge locations in the Narmada basin. On the other hand, the streamflow DA showed a significant improvement in PER and EFF (more than 10%) at all the gauge stations for both mean and high-flow conditions. Streamflow data assimilation improved errors in the magnitude and timing for the major floods in 1994 and 2013. Significance Statement India is considerably affected by floods during the summer monsoon. Floods have increased and are projected to become more frequent under the warming climate. Flood prediction and early warning systems are essential in reducing their impacts and exposure. In the present study, we are interested in examining the role of soil moisture and streamflow data assimilation on flood prediction in the Narmada River basin. Our results show that streamflow data assimilation is more effective in reducing errors and bias in streamflow prediction than soil moisture data assimilation. The hydrologic framework used for the Narmada basin can be extended and evaluated to other flood-prone basins in India.

Impact of Design Factors for ESA CCI Satellite Soil Moisture Data Assimilation over Europe

Abstract In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.

Non-Gaussian Ensemble Filtering and Adaptive Inflation for Soil Moisture Data Assimilation

Abstract The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.

Joint assimilation of satellite soil moisture and streamflow data for the hydrological application of a two-dimensional shallow water model

Data assimilation (DA) in physically-based hydrodynamic models is conditioned by the difference in temporal and spatial scales of the observed data and the resolution of the model itself. In order to use remote sensing data in small-scale hydrodynamic modelling, it is necessary to explore innovative DA methods that can lead to a more plausible representation of the spatial variability of the parameters and processes involved. In the present study, satellite-derived soil moisture and in situ-observed streamflow data were jointly assimilated into a high-resolution hydrological-hydrodynamic model based on the Iber software, using the Tempered Particle Filter (TPF) for the dual estimation of model state variables and parameters. Twelve storm events occurring in a 199 km2 catchment located in NW Spain were used for testing the proposed approach. A 3-step procedure was followed: (1) sensitivity analysis of the model parameters; (2) joint assimilation of soil moisture and discharge data to estimate correlations between observations and model parameters; (3) joint assimilation of soil moisture and discharge data using an initial set of particles and parameter standard deviations derived from prior information. The numerical model correctly reproduces the observed data, with an average Nash-Sutcliffe efficiency (NSE) value of 0.74 over the 12 events when the prior information is used. The approach described is shown to be most efficient with storm events that produce isolated peak discharges.

A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest

Abstract. Today, the most popular approaches in agricultural forecasting leverage process-based crop models, crop monitoring data, and/or remote sensing imagery. Individually, each of these tools has its own unique advantages but is, nonetheless, limited in prediction accuracy, precision, or both. In this study we integrate in situ and remote sensing (RS) soil moisture observations with APSIM model through sequential data assimilation to evaluate the improvement in model predictions of downstream state variables across five experimental sites in the US Midwest. Four RS data products and in situ observations spanning 19 site years were used through two data assimilation approaches, namely ensemble Kalman filter (EnKF) and generalized ensemble filter (GEF), to constrain model states at observed time steps and estimate joint background and observation error matrices. Then, the assimilation's impact on estimates of soil moisture, yield, normalized difference vegetation index (NDVI), tile drainage, and nitrate leaching was assessed across all site years. When assimilating in situ observations, the accuracy of soil moisture forecasts in the assimilation layers was improved by reducing RMSE by an average of 17 % for 10 cm and ∼28 % for 20 cm depth soil layer across all site years. These changes also led to improved simulation of soil moisture in deeper soil layers by an average of 12 %. Although crop yield was improved by an average of 23 %, the greatest improvement in yield accuracy was demonstrated in site years with higher water stress, where assimilation served to increase available soil water for crop uptake. Alternatively, estimates of annual tile drainage and nitrate leaching were not well constrained across the study sites. Trends in drainage constraint suggest the importance of including additional data constraint such as evapotranspiration. The assimilation of RS soil moisture showed a weaker constraint of downstream model state variables when compared to the assimilation of in situ soil moisture. The median reduction in soil moisture RMSE for observed soil layers was lower, on average, by a factor of 5. However, crop yield estimates were still improved overall with a median RMSE reduction of 17.2 %. Crop yield prediction was improved when assimilating both in situ and remote sensing soil moisture observations, and there is strong evidence that yield improvement was higher when under water-stressed conditions. Comparisons of system performance across different combinations of remote sensing data products indicated the importance of high temporal resolution and accurate observation uncertainty estimates when assimilating surface soil moisture observations.

SMAP soil moisture data assimilation impacts on water quality and crop yield predictions in watershed modeling

Model soil moisture predictions are routinely updated with independent observations using data assimilation techniques. While the impacts of these updates on modeled hydrology are well studied, impacts on predicted water quality and crop yield are unresolved. In this study, we evaluated data assimilation effects for a range of predictions in the Soil and Water Assessment Tool (SWAT). We used satellite surface soil moisture data products from the Soil Moisture Active/Passive (SMAP) mission to update SWAT soil moisture for two U.S. experimental watersheds. We addressed possible limitations to the vertical transfer of surface soil moisture updates to deeper layers in the model by additionally testing a modified soil percolation approach that relies on relative saturation rather than the original thresholding behavior of SWAT. Results at both watersheds demonstrated that data assimilation greatly impacted water quality and crop yield predictions. Modifying the soil percolation algorithm, however, did not improve assimilation results. Assimilation increased median soil moisture (+2% to +6 %) which in turn increased total streamflow (+0.43 to +1.70 m3/s daily). Critically, streamflow alone was not a sufficient predictor for the assimilation changes to water quality predictions as flow component contributions and seasonality differed between sites. A varied response in annual water quality predictions to soil moisture updates was identified (+1.70 to +123 kg/ha total nitrogen; −0.11 to −0.57 kg/ha total phosphorous; +8.1 to +50.0 kg/ha sediment) that was dependent upon the timing of the updates, site characteristics, and change to specific transport and transformation processes. Crop yield predictions were similarly impacted by data assimilation from changes to both water and nutrient availability that varied by crop type. The overall strong and diverse response of water quality and crop yield predictions to soil moisture updates provides evidence that assimilation can impact a range of model predictions. Efforts to improve soil moisture simulations, therefore, have potential for meaningful improvements in targeted, non-hydrologic predictions.

Application of Soil Moisture Data Assimilation in Flood Forecasting of Xun River in Hanjiang River Basin

Accurate projection of floods is of great significance to safeguard economic and social development as well as people’s life and property. The development of hydrological models can improve the level of flood projection, however, the numerous uncertainties in the models limit the projection accuracy. By adding observations to correct the operation of prediction models, the accuracy can be improved to some extent. In this paper, taking the Xun River, of the Hanjiang River Basin in China, as the research object, combined with the soil moisture satellite data obtained by the soil moisture active and passive satellite (SMAP), the ensemble Kalman filter (EnKF) algorithm was used to assimilate the upper soil water content (WU) of the Xinanjiang model. In addition, based on the simultaneous assimilation of state variables and parameters, two improved assimilation schemes were proposed here, namely, the augmented ensemble Kalman filter (AEnKF) scheme and the dual ensemble Kalman filter (DEnKF) scheme. The results showed that compared with the WU assimilation scheme, the simultaneous assimilation of parameters and WU improved the prediction ability of the Xinanjiang model to a greater extent. The two improved schemes had similar effects on flood prediction accuracy, and improved the overall Nash–Sutcliffe efficiency coefficient (NSE) from 0.725 for non-assimilated, and 0.758 for assimilated WU, to 0.781. Among them, AEnKF and DEnKF schemes, respectively, improved the NSE by 10.1% and 11% at maximum. This study demonstrated that the application of data assimilation for the Xun River effectively improved the flood forecast accuracy of the Xinanjiang model, which will provide a reference basis and technical support for future flood prevention and mitigation and flood projection in this basin.

Flash Drought Onset and Development Mechanisms Captured With Soil Moisture and Vegetation Data Assimilation

AbstractFlash droughts evolve and intensify rapidly under the influence of anomalous atmospheric conditions. In this study, we investigate the role of assimilating remotely sensed soil moisture (SM) and vegetation properties in capturing the evolution and impacts of two flash droughts in the Northern Great Plains. We find that during 2016 drought triggered by anomalously high temperatures and excessive evaporative demands, multivariate data assimilation (DA) of MODIS‐derived leaf area index (LAI) and Soil Moisture Active Passive SM within Noah‐Multiparameterization model helps capture elevated transpiration at onset. Assimilation of LAI particularly helped model the resulting rapid decline in SM during onset with as high as 10.0% steeper rate of decline compared to the simulation without any assimilation. Modeled‐SM anomalies exhibit a 7.5% and 11.7% increase in similarity with Evaporative Stress Index (ESI) data and U.S. Drought Monitor (USDM) maps, respectively. In contrast, during 2017 flash drought driven by record‐low precipitation during summers, SM assimilation resulted in largest rates of decline in rootzone SM, as large as 48.4% compared to results from no assimilation. Multivariate DA of SM and LAI results in 6.7% and 14.3% higher spatial similarity with ESI and USDM, respectively, and is necessary to model rapid intensification caused by anomalous precipitation deficits. This study elucidates the need to incorporate multiple observational constraints from remote sensing to effectively capture rapid onset rates, intensification, and severity of flash drought following different propagation mechanisms. This is fundamental for drought early detection to provide a wider window of response and implement efficient mitigation strategies.

Assimilation of Backscatter Observations into a Hydrological Model: A Case Study in Belgium Using ASCAT Data

We investigated the possibilities of improving hydrological simulations by assimilating radar backscatter observations from the advanced scatterometer (ASCAT) in the hydrological model SCHEME using a calibrated water cloud model (WCM) as an observation operator. The WCM simulates backscatter based on soil moisture and vegetation data and can therefore be used to generate observation predictions for data assimilation. The study was conducted over two Belgian catchments with different hydrological regimes: the Demer and the Ourthe catchment. The main differences between the two catchments can be summarized in precipitation and streamflow levels, which are higher in the Ourthe. The data assimilation method adopted here was the ensemble Kalman filter (EnKF), whereby the uncertainty of the state estimate was described via the ensemble statistics. The focus was on the optimization of the EnKF, and possible solutions to address biases introduced by ensemble perturbations were investigated. The latter issue contributes to the fact that backscatter data assimilation only marginally improves the overall scores of the discharge simulations over the deterministic reference run, and only for the Ourthe catchment. These performances, however, considerably depend on the period considered within the 5 years of analysis. Future lines of research on bias correction, the data assimilation of soil moisture and backscatter data are also outlined.

Soil Moisture Data Assimilation in MISDc for Improved Hydrological Simulation in Upper Huai River Basin, China

In recent years, flash floods have become increasingly serious. Improving the runoff simulation and forecasting ability of hydrological models is urgent. Therefore, data assimilation (DA) methods have become an important tool. Many studies have shown that the assimilation of remotely sensed soil moisture (SM) data could help improve the simulation and forecasting capability of hydrological models. Still, very few studies have attempted to assimilate SM data from land surface process models into hydrological models to improve model simulation and forecasting accuracy. Therefore, in this study, we used the ensemble Kalman filter (EnKF) to assimilate the China Land Data Assimilation System (CLDAS) SM product into the MISDc model. We also corrected the CLDAS SM and assimilated the corrected SM data into the hydrological model. In addition, the effects of the 5th and 95th percentiles of flow were evaluated to see how SM DA affected low and high flows, respectively. Additionally, we tried to find an appropriate size for the number of ensemble members of the EnKF for this study. The results showed that the EnKF SM DA improved the runoff simulation ability of the hydrological model, especially for the high flows of the model; however, the simulation for the low flows deteriorated. In general, SM DA positively affected the ability of the MISDc model runoff simulation.

Spatiotemporal estimation of model error to improve soil moisture analysis in ensemble Kalman filter data assimilation

As a key variable in the surface hydrological cycle and energy balance, soil moisture is often analyzed using data assimilation to improve precise and high spatiotemporal resolution from multisource data. However, accurate estimation of model error in soil moisture data assimilation is not sufficient in spatial and temporal dimensions. This paper proposes a spatiotemporal error estimation method that integrates the spatial and temporal dimensions of the study area into model error estimation. The in-situ data was regarded as the “true value” for the temporal dimensions model error estimation. The triple collocation (TC) technique was used to estimate the spatial error of the model. Based on the aforementioned estimation, a linear method to construct the relationship between spatial and temporal information was proposed to fuse the spatiotemporal model error. Result from the proposed method was compared against the overall model error estimation and spatial estimation methods in ensemble Kalman filter (EnKF) data assimilation. The ERA-Interim reanalysis data and in-situ soil moisture data from Washington State in 2016 were used to evaluate the performance of EnKF data assimilation. Using statistical metrics, including the root mean square error (RMSE), the mean bias error (MBE), and the Pearson correlation coefficient (i.e., R-value), the results show that our proposed approach, which uses spatiotemporal approach to model error estimation in EnKF data assimilation, yielded more accurate soil moisture estimates. When ERA-Interim reanalysis soil moisture data was used as the reference data, compared with the overall model error estimation EnKF Data Assimilation Experiment, RMSE and MBE of spatiotemporal model error estimation EnKF Data Assimilation Experiment were reduced by 0.0062 and 0.0061 m3 / m3, respectively, and R remained basically unchanged. When using in-situ measurements as reference data, RMSE and MBE decreased by 0.0034 and 0.0038 m3 / m3, respectively, and R increased by 0.0365. Compared with the other estimation approaches in the experiments, the spatiotemporal error estimation approach greatly improved the estimation accuracy of soil moisture analysis, yielding the closest values and variation trends with the in-situ measurements. Fully considering the temporal and spatial variation of model error and improving the estimation accuracy of model error can provide more accurate model information for data assimilation. The proposed method and framework in this paper can be used in model error estimation and data assimilation, particularly in the fields of numerical weather forecasting, natural environment monitoring, and geographical environment research.

Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information

To minimize systematic differences between soil moisture (SM) time series derived from remote sensing (RS) and land surface model (LSM), RS-based SM climatology information is typically discarded during land data assimilation (DA). However, recent studies have demonstrated that SM climatology estimates provided by L-band microwave RS retrievals can significantly outperform comparable estimates derived from LSMs. Consequently, neglecting a RS-based SM climatology may lead to degraded SM spatial patterns generated by land DA. Here, we propose a climatology-optimized SM DA framework, which first calibrates LSM parameters to leverage the RS SM climatology information. Next, multi-sensor RS SM retrievals are assimilated into the calibrated LSM using Ensemble Kalman Filter (EnKF). This framework is demonstrated using the Variable Infiltration Capacity (VIC) model and RS SM retrievals derived from the L-band Soil Moisture Active Passive (SMAP) and C-band Advanced SCATterometer (ASCAT) sensors. Here, both SMAP and ASCAT SM retrievals are assimilated into the VIC model after calibrating VIC to match spatial variations captured in the SMAP SM climatology. The DA SM results are validated using in-situ SM observations derived from 820 stations. Results show that SMAP-based SM climatology calibration directly improves the quality of SM spatial patterns estimated by VIC. In addition, the SMAP-based calibration also benefits our model-error representation and thereby yields better Kalman gains that improve temporal SM DA accuracy. Relative to typical DA approaches, this newly proposed DA framework improves both spatial (0.26 versus 0.51 (−)) and temporal correlations (0.48 versus 0.52 (−)) versus in-situ SM observations. In addition, SM improvements are effectively propagated into improved streamflow estimates – leading to an average increase of Nash and Sutcliffe coefficient from 0.74 to 0.76 (−). Overall, we demonstrate that RS SM climatology information is valuable for land DA and our climatology-optimized framework successfully retains such information to the benefit of land DA performance.