The Land surface Data Toolkit (LDTv7.2) – a data fusion environment for land data assimilation systems

The 16.8 million ha of peatlands in the Cuvette Centrale wetland complex in the Congo Basin is one of the largest peatland regions on Earth but still highly understudied. Understanding the hydrological functioning of these peatlands and the effects of external disturbances thereon remains a major challenge. Recent research suggested fundamental hydrological differences between the Congo peatlands and the well-studied Southeast Asian peatlands. The Congo peatlands have a doming gradient that is up to ten times smaller, and they are influenced by river hydrology to some extent.In this study, we explore the Congo peatland hydrology through land surface modeling and data assimilation. We build upon our recently developed tropical PEATCLSM module (Apers et al., 2022) that was parameterized based on data from Southeast Asian peatlands due to the lack of field data from other tropical peatland regions. In a first step, we derive Congo-specific peat hydraulic and discharge function parameters from a scalar parametrization of water level dynamics in the Congo peatlands, using observed water level data at two locations. These Congo-specific parameters differ considerably from the original literature-based parameters from Southeast Asian peatlands. In a second step, we apply our original and Congo-specific parameters in an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture and Ocean Salinity (SMOS) mission. The data assimilation results are used in two ways. First, the effect of these parameters on the simulated peatland hydrology and the observation-minus-forecast Tb residuals is evaluated. It is hypothesized that the new parameters reduce the previously reported modeling errors over the Congo peatlands and reduce the residuals in Tb as well. Second, we analyze the data assimilation diagnostics to learn about other model improvement possibilities. In preliminary results, we found long periods of temporally autocorrelated total water storage increments (difference of pre- and post-update) that coincided with anomalies in river stages measured upstream of the peatlands. Since PEATLCSM neglects possible river influence, this concurrence suggests that the typically used grid-based approach of land surface models should be combined with a river routing scheme over the Congo peatlands.Apers, S., De Lannoy, G. J. M., Baird, A. J., Cobb, A. R., Dargie, G. C., del Aguila Pasquel, J., ... & Bechtold, M. (2022). Tropical peatland hydrology simulated with a global land surface model. Journal of advances in modeling earth systems, 14(3), e2021MS002784.

This study investigates some of the benefits and drawbacks of assimilating Terrestrial Water Storage (TWS) observations from the Gravity Recovery and Climate Experiment (GRACE) into a land surface model over India. GRACE observes TWS depletion associated with anthropogenic groundwater extraction in northwest India. The model, however, does not represent anthropogenic groundwater withdrawals and is not skillful in reproducing the interannual variability of groundwater. Assimilation of GRACE TWS introduces long-term trends and improves the interannual variability in groundwater. But the assimilation also introduces a negative trend in simulated evapotranspiration whereas in reality evapotranspiration is likely enhanced by irrigation, which is also unmodeled. Moreover, in situ measurements of shallow groundwater show no trend, suggesting that the trends are erroneously introduced by the assimilation into the modeled shallow groundwater, when in reality the groundwater is depleted in deeper aquifers. The results emphasize the importance of representing anthropogenic processes in land surface modeling and data assimilation systems.

The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.

Soil moisture is a vital parameter for a variety of applications including hydrological modelling and climate change studies, particularly in permafrost regions where freeze–thaw processes and complex terrain pose significant monitoring challenges. This study evaluates the accuracy of seven surface soil moisture (SSM) products (SMOS‐IC, ESA CCI, AMSR2 LPRM, SMAP‐L3, SMAP‐L4, ERA5‐Land, GLDAS‐Noah) and three root‐zone soil moisture (RZSM) products (SMAP‐L4, ERA5‐Land, GLDAS‐Noah) using in situ observations from 19 stations in the permafrost region of the Heihe River Basin, China, from 2012 to 2020. Focusing on the thawing season (July–October), the analysis employs statistical metrics including Pearson correlation coefficient (R), unbiased root mean square error (ubRMSE), bias, and slope. Results indicate that SMAP‐L3 and SMAP‐L4 exhibit the highest SSM accuracy ( R = 0.24 and 0.23, respectively) with low ubRMSE (0.037–0.038), while ERA5‐Land shows the best RZSM correlation ( R = 0.43) but may indicate the presence of systematic biases, nonlinear responses, or limitations in dynamic range, among other issues (slope = 0.01). Environmental factors such as precipitation, land surface temperature, and normalised difference vegetation index significantly influence accuracy. Spatial variability and scale mismatches highlight the need for improved land surface models and data assimilation. This study provides critical insights for selecting and refining soil moisture products to enhance hydrological and climate research in permafrost regions.

Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve the simulation of land-atmosphere water and energy exchanges. In this study, two different types of LST assimilation techniques are implemented and the benefits from the techniques are compared. One of the techniques is to directly assimilate LST using ensemble Kalman filter (EnKF) data assimilation (DA) utilities. The other is to use the Atmosphere-Land Exchange Inversion model (ALEXI) as an “observation operator” that converts LST retrievals into the soil moisture (SM) proxy based on the ratio of actual to potential evapotranspiration (fPET), which is then assimilated into an LSM. While most current studies have shown some success in both directly the assimilating LST and assimilating ALEXI SM proxy into offline LSMs, the potential impact of the assimilation of TIR information through coupled numerical weather prediction (NWP) models is unclear. In this study, a semi-coupled Land Information System (LIS) and Weather Research and Forecast (WRF) system is employed to assess the impact of the two different techniques for assimilating the TIR observations from NOAA GOES satellites on WRF model forecasts. The NASA LIS, equipped with a variety of LSMs and advanced data assimilation tools (e.g., the ensemble Kalman Filter (EnKF)), takes atmospheric forcing data from the WRF model run, generates updated initial land surface conditions with the assimilation of either LST- or TIR-based SM and returns them to WRF for initializing the forecasts. The WRF forecasts using the daily updated initializations with the TIR data assimilation are evaluated against ground weather observations and re-analysis products. It is found that WRF forecasts with the LST-based SM assimilation have better agreement with the ground weather observations than those with the direct LST assimilation or without the land TIR data assimilation.

Satellite remotely sensed (RS) soil moisture (SM) is commonly assimilated to leverage land surface modeling. However, the improvement in deep soil simulation remains challenging as limited by the relative shallow penetration depth (< 5 cm). With advancements in satellite RS technologies like P ‐band sensors, retrieving deeper SM has become increasingly feasible. Here, we demonstrate the potentially added value of enriched deep soil moisture information in land surface data assimilation (DA) by using ground‐based SM observations from a dense monitoring network in the central Tibetan Plateau. Specifically, a cost function‐based multi‐layer SM DA framework is developed to optimize soil texture profiles and organic matter content. A series of DA experiments were conducted to explore the optimal assimilation and optimization depths. The results suggest that assimilation of top 20 cm SM is adequate to reasonably optimize key soil parameters for all soil layers, and thereby improves SM estimates to the depth of 40 cm. This improvement can further propagate into soil temperature profiles and surface flux (e.g., evapotranspiration) estimates. Besides, the performance of deep SM DA is found insensitive to assimilation frequency varying up to 5 days, highlighting the promise and feasibility of regional land DA with 0–20 cm SM products, which are readily accessible from future satellite missions.

Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).

Recent decades have seen tremendous increases in the quantity of empirical ecological data collected by individual investigators, as well as through research networks such as FLUXNET (Baldocchi et al., 2001). At the same time, advances in computer technology have facilitated the development and implementation of large and complex land surface and ecological process models. Separately, each of these information streams provides useful, but imperfect information about ecosystems. To develop the best scientific understanding of ecological processes, and most accurately predict how ecosystems may cope with global change, integration of empirical and modeling approaches is necessary. However, true integration - in which models inform empirical research, which in turn informs models (Fig. 1) - is not yet common in ecological research (Luo et al., 2011). The goal of this workshop, sponsored by the Department of Energy, Office of Science, Biological and Environmental Research (BER) program, was to bring together members of the empirical and modeling communities to exchange ideas and discuss scientific practices for increasing empirical - model integration, and to explore infrastructure and/or virtual network needs for institutionalizing empirical - model integration (Yiqi Luo, University of Oklahoma, Norman, OK, USA). The workshop included presentations and small group discussions that coveredmore » topics ranging from model-assisted experimental design to data driven modeling (e.g. benchmarking and data assimilation) to infrastructure needs for empirical - model integration. Ultimately, three central questions emerged. How can models be used to inform experiments and observations? How can experimental and observational results be used to inform models? What are effective strategies to promote empirical - model integration?« less

As soil moisture and vegetation water content both affect the emissivity from the land surface, each of them can be derived from satellite-based passive microwave measurements. In this study, we use soil moisture retrievals from the 36 km SMAP L2 product and X-band vegetation optical depth (VOD) from AMSR2 LPRM version 6. VOD is a proxy for vegetation water content, linked to the leaf area index (LAI). We developed a machine learning-based observation operator to map LAI to VOD.We assimilate the SMAP and AMSR2 products into the Noah-MP land surface model (LSM) with dynamic vegetation. This is done by means of a one-dimensional Ensemble Kalman Filter (EnKF) within the NASA Land Information System (LIS). SMAP soil moisture retrievals update soil moisture in each of the four soil layers of the LSM, while AMSR2 VOD retrievals update the LAI. A cumulative distribution function (CDF) matching approach rescales the soil moisture retrievals to the model climatology. Model LAI is mapped to VOD by means of the above-mentioned observation operator. The resulting data assimilation (DA) system produces consistent estimates of all land surface variables on a quarter-degree regular grid over the European continent from 1 April 2015 through 31 March 2022.This joint SMAP and AMSR2 DA system is validated by assessing a number of geophysical variables. The surface and root-zone soil moisture estimates are evaluated using in situ observations from the ISMN. Gross primary production (GPP) and evapotranspiration are evaluated using FLUXNET data. Estimates for LAI are compared with optical satellite data from MODIS. The results are compared with open loop (model-only), and SMAP- and AMSR2-only DA experiments.SMAP-only DA primarily improves soil moisture estimates, while AMSR2-only DA mainly improves estimates of GPP and ET. Preliminary results indicate that the joint DA has the potential to combine the improvements of both individual assimilation systems.

Soil moisture is a significant environmental factor that influences both the water and energy balance at the land-atmosphere interface. Therefore, proper assessment of the spatial and temporal distribution of soil moisture is crucial for many hydrological applications such as weather forecasting, agricultural water resource management and drought monitoring. This study involves the assimilation of Soil Moisture Active Passive (SMAP) soil moisture dataset within a land surface model and the evaluation of its performance in precise estimation of soil moisture by comparing the statistics with respect to standard European Space Agency’s Climate Change Initiative (ESA-CCI) soil moisture dataset. The Ensemble Kalman Filter technique has been used for assimilating SMAP soil moisture data using Noah-MP land surface model within NASA Land Information System (LIS) framework. The data assimilation (DA) framework includes Cumulative Distribution Function (CDF) matching for bias correction and twenty ensembles per tile. Meteorological forcings for the simulations have been taken from MERRA2 and IMD. Improvement or degradation due to DA has been analyzed in terms of the difference in anomaly correlation between open loop (OL) and DA soil moisture outputs with respect to the ESA-CCI soil moisture dataset over the entire Indian domain. The DA result shows improvement over larger areas in the case of MERRA2 forced simulations than IMD+MERRA2. The seasonal impact of DA in terms of the differences in DA and OL simulated soil moisture shows less variability in summer than winter. The results are validated with in-situ soil moisture datasets. Overall, the study shows that data assimilation is giving better results than open loop LSM simulation, which can be used for improved estimation of other water balance components.

Hydrological modelling plays a crucial role in Earth System Models at many scales, affecting results in numerical weather predictions through surface fluxes and storages. Data assimilation is used in weather models to ascertain accurate initial conditions for weather forecasts based on observations. At the land surface, the land data assimilation system (LDAS) exploits observational data to update selected fields of the state vector, such as soil moisture or snow cover. In turn, soil moisture and snow cover affect the terrestrial water cycle through the partitioning of runoff components, and consequently streamflow.Whereas LDAS configuration choices are usually driven by improvements in meteorology, here we aim to investigate how they impact streamflow. To this end, several LDAS configuration are used to run ECMWF’s Land Surface Modelling System (ecLand), which generates grid-wise runoff routed as streamflow in rivers by the Catchment-based Macro-scale Floodplain (CaMa-Flood) hydrological model. Two experiments, one with snow analysis and the other with soil moisture analysis, are compared against the baseline without any data assimilation over 1990-2023 using meteorological forcing from ERA5. In addition, a baseline experiment without any data assimilation is run. Therefore, differences in the model output of these experiments can be attributed to the data assimilation procedure. Snow cover analysis uses ESA-CCI data from 1990 to 2010, and IMS snow cover data from 2010 to 2023. The soil moisture analysis assimilates ERS-SCAT products from 1992 to 2006 and ASCAT soil moisture products from 2007 to 2023, as well as gridded SYNOP observations of 2-m temperature and relative humidity.Streamflow is the main diagnostic variable to quantify the impact of LDAS on river hydrology, as observational data is available. A filtering procedure was applied to the Global Runoff Data Centre (GRDC) dataset to ensure sufficient observations to represent local climatology: at least 25 daily values per month, for seasonal representation, and a minimum of 19 years over the 33-year period. Monthly climatologies of simulated discharge and surface fluxes are calculated to identify catchment-scale patterns. Surface fluxes, such as evapotranspiration and (sub-)surface runoff, and streamflow output of the respective experiments are compared for 342 gauged catchments, across 209 distinct river systems in the northern hemisphere.The Kling–Gupta Efficiency and its components are computed for each catchment, allowing the assessment of bias, correlation, and variability separately. Assessments show a widespread impact of LDAS configuration on the hydrological skill of ecLand-Cama-Flood system: for 48% of the stations the baseline experiment has a higher hydrological skill, while 20% of the stations benefit from snow cover analysis and 32% from soil moisture analysis. Then, to assess hydrological processes across horizontal and vertical spatial scales, performance of data assimilation is analysed as function of catchment characteristics such as upstream drainage area, orography, and spatial variability of orography.This study pinpoints catchments where river hydrology benefits or is negatively impacted by land data assimilation and directly supports further development of ecLand.

<p>Worldwide, the amount of water used for agricultural purposes is rising because of an increasing food demand. In this context, the detection and quantification of irrigation is crucial, but the availability of ground observations is limited. Therefore, an increasing number of studies are focusing on the use of models and satellite data to detect and quantify irrigation. For instance, the parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still characterized by simplifying assumptions, such as the lack of dynamic crop information, the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining models and satellite information through data assimilation can offer a viable way to quantify the water used for irrigation.</p><p>The aim of this study is to test how well modelled soil moisture and vegetation estimates from the Noah-MP LSM, with or without irrigation parameterization in the NASA Land Information System (LIS), are able to mimic in situ observations or to capture the signal of high-resolution Sentinel-1 backscatter observations in an irrigated area. The experiments were carried out over select sites in the Po river Valley, an important agricultural area in Northern Italy. To prepare for a data assimilation system, Level-1 Sentinel-1 backscatter observations, aggregated and sampled onto the 1 km EASE-v2 grid, were used to calibrate a Water Cloud Model (WCM) using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP. Results demonstrate that the use of the irrigation scheme provides the optimal calibration of the WCM, confirming the ability of Sentinel-1 to track the impact of human activities on the water cycle. Additionally, a first data assimilation experiment demonstrates the potential of Sentinel-1 backscatter observations to correct errors in Land Surface Model (LSM) simulations that are caused by unmodelled or wrongly modelled irrigation.</p>

Given the increasing significance of urban hydro-thermal research, including the evaluation of green-blue spaces and hydro-meteorological risk assessment, it is crucial to explore advanced simulation tools for urban hydro-meteorology. Typically, urban thermal environments and water resource management are treated as separate fields, studied independently using urban land surface models (ULSMs) and hydrology models (UHMs). This critical review addresses three main questions: 1) What are the similarities and differences between various urban land surface and hydrology models? 2) What gaps exist between these models? 3) What are the current capabilities of simulation tools for urban hydrometeorology?Canyon concept ULSMs are the most widely used simulation tools and have seen rapid development. In contrast, distributed UHMs, which show greater potential for the hydrometeorology interface, are less commonly adopted due to the requirement for high-resolution input data. Our comparison of the complexity of physical processes and schematic methods reveals that ULSMs often inadequately represent water surfaces, while UHMs lack explicit surface energy balance solutions and detailed building representations. ULSMs focus on vertical water transfer within unsaturated soil layers, whereas UHMs address saturated soil layers and lateral runoff. Most ULSMs lack a comprehensive hydraulic system, often assuming unlimited pipe capacities for surface runoff.The review highlights the importance of implementing and coupling processes across the atmosphere, land surface, and subsurface to bridge the gap in simulating urban hydrometeorology. Enhanced interdisciplinary communication between urban climate and urban water management is encouraged.

Hydrometeological forcing data with high spatio-temperal resolution,such as wind,temperature,pressure,humidity,radiation,precipitation,is needed for driving regional-scale and watershed-scale hydrological,ecological,land models or data simulations system.In this paper,the importance of regional-scal and watershed-scale forcing data is clarified;methods of producing forcing data,such as interpolation based on observation data,reanalysis data,dynamical downscaling method,statistical downscaling method and multi-source information fusion method,are described in detail;some cases about precipitation,radiation,temperature and other variables are given;some case studies on producing forcing data for land data assimilation systems are introduced,such as Global Land Data Assimilation System(GLDAS),European Land Data Assimilation System to predict Floods and Droughts(ELDAS),North American Land Data Assimilation System(NLDAS),Chinese Land Data Assimilation System(CLDAS) and Land Information System(LIS).Finally,existing problems of producing high spatio-temperal forcing data for regional-scale and watershed-scale hydrological,ecological,land models or data simulations system are pointed out in detail,and four proposals are put forward: ①it is difficult to satisfy all forcing variables by one simple method;②it is urgent to provide proper land surface initial condition for dynamical downscaling model;③it is a major method to combine with Doppler radar data,drop size meter and rain gauge observations to improve the accuracy of rainfall;④it is a trend to input remote sensing images into dynamical downscaling model by data assimilation method to generate high spatio-temperal forcing data.Some ideas on preparing forcing data for land surface model of Western China are proposed.

<p>Over the last 40 years remote sensing has significantly changed the way we observe and predict the Earth system, particularly in the oceanographic and meteorological sciences. Today, every General Circulation model (GCM) relies upon advanced and well-established data assimilation (DA) techniques, and Land Surface Models (LSMs) – which are integral components of GCM – have been increasingly using DA to constrain the LSM model predictions with available remote sensing data of hydrological, carbon and energy cycles.</p> <p>Despite this, the use of DA into hydrological models (HMs) is still operationally limited and the reasons for that lie in 1) the considerable variability between different HMs, with much uncertainty in their respective representations of processes (often conceptual) and their sensitivity to changes in key variables, 2) the contrast between the scale of application of HMs (often smaller than LSMs) and the coarse-scale information provided by remote sensing along with their associated accuracy and, 3) the variety of the data assimilation setups, specificity of the study areas, and pre-processing used by a plethora of studies on the topic which provide a blurred picture on the real benefit of DA of relevant hydrological variables into HMs (e.g. soil moisture, precipitation, snow, terrestrial water storage anomalies).</p> <p>The recent exponential grown of high-resolution remote sensing data (the European Union's Earth observation Programme Copernicus with the constellation of the Sentinel satellites is a notable example) has potentially opened new opportunities for improving our HMs also for small scale applications.  However, their usefulness is still limited by our ability to analyse and integrate efficiently a large volume of observations with current hydrologic models. In other words, most of the issues mentioned above have been not overcome with a consequent under-exploitation of potentially useful information to constrain HMs.</p> <p>This contribution aims to summarize the main challenges and opportunities of DA into HMs from a hydrological perspective in light of the availaiblity of new and more skillful Earth observations. It identifies and explains critical challenges by using published literature by the author on European catchments as well as on-going studies, and offers insights for a productive research based on new available models and observations as to build a comprehensive hydrologic data assimilation framework that is a critical component of future hydrologic observation and modelling systems.</p>