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In preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014–2018) with 1-km Sentinel-1 backscatter (ϒ°) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019–2021), the ϒ° simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 ϒ° remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil ϒ° holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors. ispartof: Remote Sensing Of Environment vol:294 status: published

This study assesses global landslide susceptibility (LSS) at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of a global LSS map with dynamic satellite-based soil moisture estimates for landslide modeling. Global LSS estimation contains uncertainty, arising from errors in the underlying data, the spatial mismatch between landslide events and predictor information, and large-scale LSS model generalizations. For a reliable uncertainty assessment, this study combines methods from the landslide community with common practices in meteorological modeling to create an ensemble of global LSS maps. The predictive LSS models are obtained from a mixed effects logistic regression, associating hydrologically triggered landslide data from the Global Landslide Catalog (GLC) with predictor variables describing the landscape. The latter are taken from the Catchment land surface modeling system (including input parameters of soil (hydrological) properties and resulting climatological statistics of water budget estimates), as well as geomorphological and lithological data. Road network density is introduced as a random effect to mitigate potential landslide inventory bias. We use a blocked random cross validation to assess the model uncertainty that propagates into the LSS maps. To account for other uncertainty sources, such as input uncertainty, we also perturb the predictor variables and obtain an ensemble of LSS maps. The perturbations are optimized so that the total predicted uncertainty fits the observed discrepancy between the ensemble average LSS and the landslide presence or absence from the GLC. We find that the most reliable total uncertainty estimates are obtained through the inclusion of a topography-dependent perturbation between 15 % and 20 % to the predictor variables. The areas with the largest LSS uncertainty coincide with moderate ensemble average LSS, because of the asymptotic nature of the LSS model. The spatial patterns of the average LSS agree well with previous global studies and yield areas under the receiver operating characteristic between 0.84 and 0.92 for independent regional to continental landslide inventories.

Soil moisture is an essential climate variable and the main driver for water exchanges between the land surface and the atmosphere. An accurate knowledge of the soil moisture conditions is also crucial to estimate the amount of water needed or used for agricultural purposes.As human demand for water is increasing along with extreme drought events, an optimal agricultural management is paramount to cope with a drier and warmer future, e.g. in Mediterranean regions. Thus, the knowledge of soil moisture is central for monitoring agricultural drought, optimizing agricultural water uses (i.e., irrigation) and improving the water cycle and land-atmosphere processes understanding. Nevertheless, the point-based nature and limited spatial coverage of in situ soil moisture observations in conjunction with the poor parameterization of human processes in earth system models (i.e., unmodelled or wrongly modelled irrigation), undermine the ability to accurately monitor and forecast drought events as well as the capacity to safely manage water resources.Remote sensing observations offer a unique opportunity to fill these gaps as they can directly observe the processes of the plant-soil continuum. Here we provide insights on the value of satellite-based soil moisture and soil moisture-related measurements (i.e, radar backscatter) for land surface models and for agricultural drought research. We will show the utility of both classical coarse-scale and new high resolution observations for a number of applications that span from irrigation estimation, crop yield analysis, improvement of water cycle processes to estimation of small scale soil moisture variability across agricultural and mountaineous European pilot sites.

Water table constitutes a master control of the general biogeochemistry in northern peatlands. The performance of peatland simulations in global ecosystem models is strongly hampered by the accuracy of the water table predictions. We examined the applicability of the Optical TRApezoid Model (OPTRAM) to monitor the temporal fluctuations in water table over 53 intact, restored, and drained northern peatlands in Finland, Estonia, Sweden, Canada, and the USA from 2018 through 2021. Various OPTRAM were computed based on Sentinel-2 data with the Google Earth Engine cloud platform. We found that (i) the choice of vegetation index utilised in OPTRAM does not significantly affect OPTRAM performance; (ii) the tree cover density is a significant factor controlling the sensitivity of OPTRAM to water table dynamics; (iii) the relationship between water table and OPTRAM often disappears for deep water tables. Based on an anomaly analysis, we further found that OPTRAM seems to be in particular suitable to monitor long-term (i.e., interannual) water table variability while the performance for short-term changes (e.g., response to individual rain events) was lower. Overall, our results support the application of OPTRAM to monitor water table dynamics in intact and restored northern peatlands with low tree cover density when the water table is shallow to moderately deep.

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.

The Fire Weather Index (FWI) is used worldwide to estimate the danger of wildfires. The FWI system integrates meteorological parameters and empirically combines them into several moisture codes, each representing a different fuel type. These moisture codes are then used in combination with wind speed to estimate a fire danger. Originally, the FWI system was developed for a standard jack pine forest, however, it is widely used by fire managers to assess the fire danger in different environments as well. Furthermore, it is often also used to assess the vulnerability of organic soils, such as peatlands, to ignition and depth of burn. The utility of which is often questioned. This research aims at improving the original FWI for northern peatlands by replacing parts of the original, purely weather-based FWI system with satellite-informed model estimates of peat moisture and water level. These come from a data assimilation output combining the NASA catchment model, including the peat modules PEATCLSM, and Soil Moisture and Ocean Salinity (SMOS) L-band brightness temperature observations. The predictive power of the new, peat-specific FWI (PEAT-FWI) is evaluated against the original FWI against fire data of the global fire atlas from 2010 through 2018 over the major northern peatlands areas. For the evaluation, the fires are split up in early and late season fires, as it is hypothesized that late fires are more hydrological driven, and the predictive power of the PEAT-FWI will thus differ between the two types of fires. Our results indeed indicate that the PEAT-FWI improves the predictive capability of estimating fire risk over northern peatlands in particular for late fires. By using a receiver operating characteristics (ROC) curve to evaluate the predictive power of the FWI against a random estimate, the area under the curve increases by up to 10% for the PEAT-FWI compared to the original FWI. The recent version 7 release of the operational Soil Moisture Active Passive (SMAP) Level-4 Soil Moisture Data Assimilation Product now includes PEATCLSM, thus, the proposed PEAT-FWI is straightforward to include in operational FWI products.

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The product is based on the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observations-based precipitation forcing. In this presentation, we describe three recent improvements in the L4_SM algorithm. First, satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) are used in two ways: (i) The climatology to which all L4_SM precipitation forcing inputs are rescaled is based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project v2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. Second, the Catchment model now includes the recently developed PEATCLSM hydrology module for peatlands and uses an updated global map of peatlands. Third, revised parameters are used in the L-band radiative transfer model that converts the simulated soil moisture and temperature estimates into Tb predictions for use in the radiance-based L4_SM analysis. Specifically, climatological parameters for the scattering albedo, soil roughness, and (seasonally-varying) vegetation opacity were derived from the SMAP Level-2 radiometer soil moisture retrieval product. The revised precipitation inputs result in considerably improved anomaly time series correlation skill of L4_SM surface soil moisture in South America, Africa, Australia, and parts of East Asia. Particularly large improvements are seen in central Australia and Myanmar, where the quality of the gauge-only precipitation product used in earlier L4_SM versions was particularly poor. In peatlands, the dynamics of water table depth, surface soil moisture and evapotranspiration are considerably improved when evaluated against in situ measurements. Moreover, the time series correlation of surface and root-zone soil moisture vs. in situ measurements is slightly improved, owing to the improved annual cycle phasing of the Level-2 derived vegetation opacity parameters. Collectively, these improvements are also manifested in smaller Tb observation-minus-forecast residuals.

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.

Vegetation is a major control on land-atmosphere fluxes of carbon and water. An improved representation of vegetation in land surface and dynamic vegetation models can therefore improve both short-term weather predictions as well as long-term climate projections. State update data assimilation (DA) of remotely sensed leaf area index (LAI) is one way to obtain vegetation state estimates consistent with physical constraints from a land surface model and observational data. Most LAI DA studies so far used bias-blind DA systems, i.e. they did not explicitly take bias between observations and model into account. However, if the observations are biased against the land surface model, this might hamper the performance of the DA system, because it can induce instabilities in the model. We therefore examined the effect of bias on an LAI DA system, and compared a bias-blind LAI DA system with bias-aware approaches. For this purpose, we assimilated the Copernicus Global Land Service (CGLS) LAI into the Noah-MP land surface model over Europe in the 2002-2019 period. We find that in areas with large LAI bias, the bias-blind LAI DA by design leads to a reduced bias between observed and modelled LAI and GPP, but it also has strong impacts on soil moisture, leading to a worse agreement with independent, satellite-derived ESA CCI soil moisture. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between update steps. This drift also propagates to short-term estimates of GPP and ET. Furthermore, internal DA diagnostics indicate suboptimal DA system performance. The bias-aware approaches avoid the negative effects of the bias-blind assimilation, and still improve anomaly estimates of LAI. Therefore, bias-aware LAI DA might be a useful method to consider in LAI DA, especially when anomalies of LAI or GPP are of interest. Our results furthermore show that LAI of CGLS and Noah-MP show strong disagreement especially in dry climates. Model calibration or DA methods that include parameter updating could be an alternative to bias-aware DA to reduce these discrepancies. Our results can guide such efforts, and highlight the need for multiple constraints.

Recently, the FAO crop model AquaCrop v7.0 has been released as open-source code along with the standard graphical user interface for single field applications, and Linux, Windows, and Mac stand-alone executables for plugin into regional or climate simulations (https://www.fao.org/aquacrop/en/). In addition, AquaCrop v7.0 has been coupled as the first crop model into NASA’s Land Information System (LIS) to support regional modeling and data assimilation (DA) using spatially complete re-analysis meteorological forcings, and to produce spatio-temporally complete geolocated NetCDF output for the first time. This presentation explores the potential of soil moisture updating for improving crop growth model estimates of AquaCrop.Our DA setup uses the one-dimensional ensemble Kalman filter to assimilate the SMAP Level-2 surface soil moisture retrieval product from April 2015 through 2021 on a quarter-degree regular model grid over Europe. Prior to assimilation, a climatological rescaling is applied to remove the observation-minus-forecast bias. A preliminary evaluation against in-situ data of the International Soil Moisture Network indicates that topsoil (0-30 cm) soil moisture estimates of AquaCrop are improved through the DA compared to the model-only estimates. Our results show that the adjusted soil moisture strongly modulates biomass accumulation during the main growing period from April to June, particularly over moisture-limited areas. The impact on biomass will be further evaluated with the Copernicus Global Land Service dry matter productivity product as the observational reference.

Improving the knowledge of agricultural water uses is in the spotlight of hydrologic sciences and water management authorities due to an increasing amount of water used for irrigation. An efficient water management system has a crucial role also considering the climate change projections scenario and the large increase in the frequency, duration, and severity of droughts, especially over the Mediterranean basin, which has been recognized as a hotspot of extreme weather events. However, simulating irrigation through large scale land surface models is not trivial, because the simplistic model parameterization do not necessarily resolve field scale conditions. In particular, the main challenge is to reproduce the amount and timing of irrigation applications by farmers, because these are often not physically-based and effectively driven by water policies instead of root zone soil moisture conditions. Some recent approaches have demonstrated the utility of remote sensing observations to either derive irrigation directly, or indirectly via their assimilation into land surface and hydrological models. Indeed, high resolution remote sensing offers an unprecedented opportunity to observe the soil/vegetation system and to consequently detect irrigation. However, although both methods seem promising, irrigation quantification and detection are still at their infancy due to limitations of both satellite data and models. In particular, recent data assimilation experiments have shown the crucial role of an accurate land surface model parameterization to optimally integrate models and satellite observations. The aim of this study is to test the benefit of directly optimizing the irrigation parameters of a sprinkler irrigation module embodied in the Noah MP land surface model running within the NASA Land Information System framework. The experiment was conducted over a highly irrigated area in the Po Valley (Italy) using synthetic irrigation benchmark data and at a spatial resolution of 0.01°. The improvement of the poorly-parameterized sprinkler irrigation scheme through a proper calibration is intended to be a valid alternative to quantify agricultural water uses.

Recent advances in gridded crop modeling and satellite observations help to improve the monitoring of crop growth and water requirements. In this contribution, we use AquaCrop v7 within the NASA Land Information System (i) to produce spatially distributed estimates of soil moisture, biomass and backscatter, and their uncertainty, and (ii) to assimilate backscatter observations from the Sentinel-1 satellite mission to improve soil moisture and biomass via state updating, at 1 km resolution over Europe. The results are evaluated against in situ observations of soil moisture and satellite-based vegetation products. We will discuss the opportunities and challenges of high-resolution gridded crop models and satellite-based active microwave data for agricultural applications.

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, www.isimip.org) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Irrigation is an important component of the terrestrial water cycle, but it is often poorly accounted for in models. When included, irrigation often relies on simplistic assumptions such as soil moisture deficit approaches. In the last years, methods have been developed to detect and quantify irrigation by making use of satellite remote sensing data. Recent developments have attempted to integrate satellite data and land surface models via data assimilation (DA) to (1) detect and quantify irrigation, and (2) better model the related land surface variables such as soil moisture, vegetation, and evapotranspiration. In this study, different synthetic DA experiments are tested to advance satellite DA for the estimation of irrigation. We assimilate synthetic Sentinel-1 backscatter observations into the Noah-MP model coupled with an irrigation scheme. When updating soil moisture, we found that the DA sets better initial conditions to trigger irrigation in the model. However, large DA updates to wetter conditions can inhibit irrigation simulation. Building on this limitation, we propose an improved DA algorithm using a buddy check approach. The method still updates the land surface, but now the irrigation trigger is not based on the evolution of soil moisture, but on an adaptive innovation outlier detection, making the trigger observation-based.The new method was tested with different levels of model and observation error. For mild model and observation errors, the DA outperforms the model-only 14-day irrigation estimates by about 30% in terms of root-mean-squared differences, when frequent (daily or every other day) observations are available. The improvements can surpass 50% for high model errors. However, with longer observation intervals (7 days), the system strongly underestimates the irrigation amounts. White noise in the signal has a milder impact on the performance, reducing the improvement by 10% compared to the assimilation of perfect observations. The method is flexible and can be expanded to other DA systems and to a real-world case.

ispartof: INTERNATIONAL JOURNAL OF WILDLAND FIRE vol:32 issue:6 pages:823-835 status: published

Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e.\\ without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in either observations or simulations, or both. We perform bias-blind and bias-aware DA of the Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–019 period, and evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. Bias-blind LAI DA can also lead to unrealistic shifts in soil moisture climatologies, for example when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between update steps. This model drift also propagates to short-term estimates of GPP and ET, and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements of GPP anomalies from the bias-blind DA, but forego improvements in the root mean square deviation (RMSD) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.

Current lightning predictions are uncertain because they rely on empirical diagnostic relationships and often use coarse-scale climate scenario simulations in which deep convection is parameterized. Previous studies demonstrated that simulations with convection-permitting resolutions improve lightning predictions compared to coarser-grid simulations using convection parameterizations for different geographical locations but not over the boreal zone. In this study, lightning simulations with the NASA Unified-Weather Research and Forecasting model are evaluated over a domain including the Great Slave Lake in Canada, for six lightning seasons. The simulations are performed at convection-parameterized (9 km) and convection-permitting (3 km) resolution using the Goddard 4ICE and the Thompson microphysics schemes. Four lightning indices are evaluated against observations from the Canadian Lightning Detection Network, in terms of spatiotemporal frequency distribution, spatial pattern, daily climatology, and an event-based overall skill assessment. The Thompson scheme is, regardless of the spatial resolution, superior to the Goddard 4ICE scheme in predicting daily climatology but worse in predicting the spatial patterns of lightning occurrence. Results indicate that lightning estimation benefits from modeling at convection-permitting resolution, in particular for the ice-based lightning indices. In contrast, the product of convective available potential energy and precipitation rate proved to be the most robust index that was largely invariant to varying spatial resolution. Finally, this study reveals issues of the models to reproduce the observed spatial pattern of lightning well, which might be related to an insufficient representation of land surface heterogeneity, including peatlands, in the study area. ispartof: Journal Of Geophysical Research-Atmospheres vol:128 issue:1 status: published

Physical properties of soils are ubiquitously heterogeneous. This spatial variability has a profound, yet still partially understood, impact on conservative transport. Moreover, molecular diffusion is often a disregarded process that can have an important counter-intuitive effect on transport: diffusion can prevent non-Fickian tailing by mobilizing mass otherwise trapped in low velocity zones. Here, we focus on macroscopically homogeneous soils presenting small scale heterogeneity, as described by the Miller-Miller method. We then analyze the dynamic control of soil heterogeneity, advection and diffusion on conservative transport. We focus especially on the importance of diffusion and of its tortuosity-dependent spatial variability on the overall transport. Our results indicate that high Peclet number systems are highly sensitive to the degree of heterogeneity, which promotes non-Fickian transport. Also, diffusion appears to have a profound impact on transport, depending on both the degree of heterogeneity and the Peclet number. For a high Peclet number and a very heterogeneous system, diffusion leads to the counter-intuitive decrease of non-Fickian macrodispersion described previously. This is not observed for a low Peclet number due to the non-trivial impact of the spatial variability in the diffusion coefficient, which appears to be a significant controlling factor of transport by promoting or preventing the accumulation of mass in low velocity zones. Globally, this work (1) highlights the complex, synergistic effect of soil heterogeneity, advective fluxes and diffusion on transport and (2), alerts on potential upscaling challenges when the spatial variability of such key processes cannot be properly described.

In recent years, the amount of water used for agricultural purposes has been rising due to an increase in food demand. However, anthropogenic water usage, such as for irrigation, is still not or poorly parameterized in regional and larger-scale land surface models (LSM). By contrast, satellite observations are directly affected by, and hence potentially able to detect, irrigation as they sense the entire integrated soil-vegetation system. By integrating satellite observations and fine-scale modelling it could thus be possible to improve estimation of irrigation amounts at the desired spatial-temporal scale. In this study we tested the potential information offered by Sentinel-1 backscatter observations to improve irrigation estimates, in the framework of a data assimilation (DA) system composed by the Noah-MP LSM, equipped with a sprinkler irrigation scheme, and a backscatter operator represented by a Water Cloud Model (WCM), as part of the NASA Land Information System (LIS). The calibrated WCM was used as an observation operator in the DA system to map model surface soil moisture and leaf area index (LAI) into backscatter predictions and, conversely, map observation-minus-forecast backscatter residuals back to updates in soil moisture and LAI through an ensemble Kalman filter (EnKF). The benefits of Sentinel-1 backscatter observation in two different polarizations (VV and VH) were tested in two separate DA experiments, performed over two irrigated sites, the first one located in the Po Valley (Italy) and the second one located in northern Germany. The results confirm that VV backscatter has a stronger link with soil moisture than VH backscatter, whereas VH backscatter observations introduce larger updates in the vegetation state variables. The backscatter DA introduced both improvements and degradations in soil moisture, evapotranspiration and irrigation estimates. The spatial and temporal scale had large impacts on the analysis, with more contradicting results obtained for the evaluation at the fine agriculture scale (i.e., field scale). Above all, this study sheds light on the limitations resulting from a poorly-parameterized sprinkler irrigation scheme which prevents improvements in the irrigation simulation due to DA, and points to future developments needed to improve the system.

The boreal zone has experienced more severe fires over the last years, often coinciding with years of anomalously high lightning frequencies. These lightning frequencies might increase even further with global warming. Current lightning predictions are however highly uncertain, either relying on empirical relationships derived from present climate, or coarse-scale climate scenario simulations in which the critical process of deep convection is parameterized, and the detailed representation of land-atmosphere interactions is lacking. In this study, we used the NASA Unified-Weather Research and Forecasting (NU-WRF) modeling framework to simulate lightning over a 550,000 km2 domain including the Great Slave Lake in Canada. Simulations were run for the six lightning seasons (June-August; 2015-2020) at both a convection-parameterized (9 km) and convection-permitting (3 km) spatial resolution. Additionally, two microphysics (MP) schemes (Goddard 4ICE and Thompson) were compared at both resolutions. From the simulation output, we derived four diagnostic lightning indices which were evaluated against observations from the Canadian Lightning Detection Network (CLDN). This evaluation was done in terms of the capability of the indices to match the observational spatial pattern (temporally averaged), spatiotemporal frequency distribution, daily and seasonal climatology (spatially averaged), and an event-based overall skill assessment. Our results show that the Thompson MP scheme better predicts the daily climatology than the Goddard 4ICE MP scheme. The Goddard 4ICE MP scheme, on the other hand, predicts the spatial pattern best. Both MP schemes predict the seasonality equally well. Concerning the spatial resolution, a clear improvement when simulating at convection-permitting resolution is only seen for the Goddard 4ICE MP scheme. Regarding the different lightning indices, no clear superior index is found as the relative performance of each index strongly depends on the evaluation criteria. Finally, the study shows that models are in particular poor in reproducing the long-term averaged observed spatial pattern of lightning occurrence. This might be related to an insufficient representation of the land surface heterogeneity in the study area.

The 16.5 million ha Cuvette Centrale peatland complex in the Congo Basin was described for the first time in 2017. However, a proper understanding of the entire hydrological functioning of this peatland complex is a challenge and large-scale land surface models (LSMs) are unlikely to accurately represent the circulation of water in this area. One of the major issues of large-scale LSMs is the quantification of the spatially- and temporally-variable lateral water input from rivers into peatlands.In this research, we applied our recently developed tropical peatland-specific module PEATCLSMTrop,Nat in a land surface modeling and assimilation scheme that uses L-band brightness temperature (Tb) data from the Soil Moisture and Ocean Salinity (SMOS) satellite mission. Despite the dense vegetation cover in tropical peatlands, preliminary results showed that the data assimilation improved the water level estimates at 4 evaluation sites over model-only simulations, with mean correlation coefficients of 0.46 for the model-only and 0.63 for the data assimilation estimates, and mean anomaly correlation coefficients of 0.02 for the model-only and 0.26 for the data assimilation estimates. To gain insight into the large-scale hydrology of the Cuvette Centrale peatland complex, we analyzed data assimilation diagnostics and found temporally autocorrelated positive and negative total water storage (tws) increments (=tws correction introduced via data assimilation) over periods of up to four months over the Cuvette Centrale peatlands. This is indicative of a temporarily suboptimal assimilation system, due to a shortcoming in the LSM. Since PEATCLSMTrop,Nat does not simulate lateral water input and the positive autocorrelated periods of tws increments coincide with anomalies in river stages measured upstream, it suggests that lateral water input (=flooding) from upstream mineral areas into the peatlands of the Cuvette Centrale is an important but unmodelled process in its hydrology. This means that land use change and a climate change-induced precipitation reduction in upstream mineral areas will influence the local hydrology of the Cuvette Centrale peatland complex, making it even more vulnerable to external disturbances.

We present a comprehensive assessment of a land surface data assimilation system, in which microwave-based satellite retrievals of surface soil moisture from the combined active-passive ESA CCI Soil Moisture product are assimilated into the Noah-MP model, using a one-dimensional Ensemble Kalman Filter (EnKF) within the NASA Land Information System (LIS). This data assimilation system produces consistent estimates of surface and root-zone soil moisture, as well as all other geophysical variables, over the European continent from January 2002 to December 2019.The aim of this study is twofold. Firstly, we explore the impact of design choices and forcing inputs on the skill of the data assimilation system, specifically: (1) the magnitude of observation errors, (2) the bias correction method, i.e., climatological or seasonal CDF matching, and (3) the choice of the meteorological reanalysis dataset used to drive the land surface model. For the latter, we compare the results obtained by forcing the Noah-MP model with the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) with those obtained by forcing the model with the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5). Secondly, we explore how the data assimilation skill is related to the quality of the satellite retrievals and environmental factors such as land cover, soil texture, and climate.For both objectives listed above, the skill of the data assimilation system is evaluated by comparing the surface and root-zone soil moisture estimates with in situ observations. Furthermore, we evaluate the behavior of internal diagnostics derived from the data assimilation innovations and increments.The results display the inevitable trade-off in choosing the observation error magnitude: a smaller observation error will cause the data assimilation to perform worse than an open loop run at some sites, whereas a larger observation error will reduce the skill at well-performing sites. We also show that the bias correction method and the choice of meteorological forcing both have a clear effect on the data assimilation diagnostics, but a negligible impact on the skill of the system that is observed over in situ reference sites. Finally, we show that the skill improvement by the data assimilation framework is strongly related to the quality of the satellite soil moisture retrievals. Acknowledgments: this work is part of the ESA CCI+ Soil Moisture CCN1 Scientific Evolution project and the FWO-FWF CONSOLIDATION project.

Streamflow forecasts suffer from errors in the initial conditions of the catchment-scale soil moisture distribution. In this research, we evaluate the potential of improving streamflow simulations through the assimilation of Sentinel-1 backscatter data into a land surface model. Our modeling setup consists of the Noah-MP land surface model coupled to the HYMAP river routing model and the 'Water Cloud Model' (WCM), which acts as backscatter observation operator, integrated into the NASA Land Information System. The system was set up at 1 km resolution for two contrasting catchments in Belgium: i) the Demer catchment dominated by agriculture and low topographic gradients, and ii) the Ourthe catchment dominated by mixed forests and high topographic gradients. Surface soil moisture and leaf area index (LAI) dynamically simulated by Noah-MP in an open-loop run were used to calibrate the parameters of the WCM using a Bayesian objective function and Sentinel-1 backscatter data processed to 1 km spatial resolution for the period 2015-2021. We present results of a suite of data assimilation experiments obtained from an ensemble Kalman filter that updates both soil moisture and LAI. We tested the use of (i) WCM parameters that were calibrated using backscatter data from all Sentinel-1 orbits simultaneously or using data from each Sentinel-1 orbit separately, (ii) backscatter observations with or without seasonal bias correction, (iii) backscatter observations in VV and VH polarization separately or combined. The different data assimilation experiments are evaluated with leaf area index from optical remote sensing, microwave-based soil moisture retrievals and streamflow measurements.Preliminary results indicate substantial differences between the different data assimilation experiments. For the Ourthe catchment, streamflow skill improvement was highest when simultaneously assimilating VV and VH observations without bias correction but using orbit-specific WCM parameters. For soil moisture and LAI, however, the highest skill was obtained by assimilating only VV observations. For the Demer catchment, assimilating observations without seasonal bias correction led to a skill degradation for streamflow while the impact of data assimilation was neutral when applying rescaled observations. Over this agriculturally dominated area, evaluation with soil moisture and LAI generally indicated the highest degradation. Difficulties in the Demer catchment might be related to crop rotation practices typical for the region that causes an interannual variability in backscatter dynamics not well accounted for by a static set of WCM parameters.

Irrigation has been applied by humans for as long as they have been cultivating plants. Nowadays, the amount of water used for agricultural purposes is rising because of an increasing food demand. However, this human influence on the water distribution on land is typically not, or poorly, parameterized in regional and larger-scale Land Surface Models (LSM). Satellite-based microwave observations indirectly observe irrigation, when they sense the entire integrated soil-vegetation system. The optimal integration of fine-scale modeling and satellite observations using data assimilation (DA) is promising to detect irrigation and possibly improve the estimation of irrigation amounts.This work was realized in the framework of the European Space Agency (ESA) Irrigation+ project. The main aim of this study was to test potential improvements in irrigation simulation due to the assimilation of 1-km Sentinel-1 backscatter data into a system composed by the Noah-MP LSM, equipped with a sprinkler irrigation scheme, and a backscatter operator represented by a Water Cloud Model (WCM), as part of the NASA Land Information System (LIS). The calibrated WCM was used as an observation operator in the DA system to map model surface soil moisture and Leaf Area Index (LAI) into backscatter predictions and, conversely, map observation-minus-forecast residuals in backscatter back to updates in soil moisture and LAI through an Ensemble Kalman Filter (EnKF). Two separate DA experiments were realized using backscatter data at VV and VH polarizations. The system was tested at two irrigated sites, located in the Po Valley (Italy) and in northern Germany.Results confirm a stronger link between the backscatter VV with soil moisture and larger updates in the vegetation state variables when using the VH polarization. The backscatter DA introduced both improvements and degradations in soil moisture, evapotranspiration and irrigation estimates. The spatial and temporal scale had a large impact on the outcomes, with more contradicting results for a detailed analysis at the plot scale. Above all, this study sheds light on the limitations resulting from a poorly-parameterized sprinkler irrigation scheme which prevents large improvements in the irrigation simulation due to the ingestion of Sentinel-1 data and points out to future developments needed to improve the system.

Tropical peatlands are among the most carbon-dense ecosystems on Earth, and their water storage dynamics strongly control these carbon stocks. The hydrological functioning of tropical peatlands differs from that of northern peatlands, which has not yet been accounted for in global land surface models (LSMs). Here, we integrated tropical peat-specific hydrology modules into a global LSM for the first time, by utilizing the peatland-specific model structure adaptation (PEATCLSM) of the NASA Catchment Land Surface Model (CLSM). We developed literature-based parameter sets for natural (PEATCLSMTrop,Nat) and drained (PEATCLSMTrop,Drain) tropical peatlands. Simulations with PEATCLSMTrop,Nat were compared against those with the default CLSM version and the northern version of PEATCLSM (PEATCLSMNorth,Nat) with tropical vegetation input. All simulations were forced with global meteorological reanalysis input data for the major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia. The evaluation against a unique and extensive data set of in situ water level and eddy covariance-derived evapotranspiration showed an overall improvement in bias and correlation compared to the default CLSM version. Over Southeast Asia, an additional simulation with PEATCLSMTrop,Drain was run to address the large fraction of drained tropical peatlands in this region. PEATCLSMTrop,Drain outperformed CLSM, PEATCLSMNorth,Nat, and PEATCLSMTrop,Nat over drained sites. Despite the overall improvements of PEATCLSMTrop,Nat over CLSM, there are strong differences in performance between the three study regions. We attribute these performance differences to regional differences in accuracy of meteorological forcing data, and differences in peatland hydrologic response that are not yet captured by our model. ispartof: Journal Of Advances In Modeling Earth Systems vol:14 issue:3 ispartof: location:United States status: Published online

Soil hydrological processes (SHP) support ecosystems, modulate the impact of climate change on terrestrial systems and control feedback mechanisms between water, energy and biogeochemical cycles. However, land-use changes and extreme events are increasingly impacting these processes. In this Review, we describe SHP across scales and examine their links with soil properties, ecosystem processes and climate. Soil structure influences SHP such as infiltration, soil water redistribution and root water uptake on small scales. On local scales, SHP are driven by root water uptake, vegetation and groundwater dynamics. Regionally, SHP are impacted by extreme events such as droughts, floods, heatwaves and land-use change; however, antecedent and current SHP partially determine the broader effects of extreme events. Emerging technologies such as wireless and automated sensing, soil moisture observation through novel synthetic aperture radars satellites, big data analysis and machine learning approaches offer unique opportunities to advance soil hydrology. These advances, in tandem with the inclusion of more key soil types and properties in models, will be pivotal in predicting the role of SHP during global change.

This study assesses global landslide susceptibility (LSS) at the coarse 36-km spatial resolution of global satellite soil moisture observations, to prepare for a subsequent combination of a global LSS map with dynamic soil moisture estimates for landslide modelling. Global LSS estimation intrinsically contains uncertainty, arising from errors in the underlying data, the spatial mismatch between landslide events and predictor information, and large-scale model generalizations. For a reliable uncertainty assessment, this study combines methods from the landslide community with common practices in meteorological modelling to create an ensemble of global LSS maps. The predictive LSS models are obtained from a mixed effects logistic regression, associating hydrologically triggered landslide data from the Global Landslide Catalog (GLC) with predictor variables from the Catchment land surface modeling system (incl. input parameters of soil (hydrological) properties and resulting climatological statistics of water budget estimates), geomorphological and lithological data. Road network density is introduced as a random effect to mitigate potential landslide inventory bias. We use a blocked random cross validation to assess the model uncertainty that propagates into the LSS maps. To account for other uncertainty sources, such as input uncertainty, we also perturb the predictor variables and obtain an ensemble of LSS maps. The perturbations are optimized so that the total predicted uncertainty fits the observed discrepancy between the ensemble average LSS and the landslide presence or absence from the GLC. We find that the most reliable total uncertainty estimates are obtained through the inclusion of a topography-dependent perturbation between 15 % and 20 % to the predictor variables. The areas with the largest LSS uncertainty coincide with moderate ensemble average LSS. The spatial patterns of the average LSS agree well with previous global studies and yield areas under the Receiver Operation Characteristic between 0.63 and 0.9 for independent regional to continental landslide inventories.

Soil hydraulic properties are frequently obtained from evaporation experiments. Different methods exist to infer soil hydraulic properties from these experiments. Commonly proposed is the ‘direct method’ (or ‘Simplified Evaporation Method’) by which soil hydraulic properties are calculated analytically. An alternative is given by inverse parameter optimization (‘inverse method’). Although soil hydraulic properties are frequently estimated by the ‘direct method’, only very few studies have focused on the question how accurate derived parameters can reproduce laboratory measurements. This can be achieved by modeling the water flow with a processed based numerical forward model, e.g. with HYDRUS-1D. Here, we applied the ‘direct method’ and ‘inverse method’ to a large dataset of evaporation experiments on 431 organic soil samples. The derived soil hydraulic parameters were used in HYDRUS-1D simulations and their performance in reproducing measured states and fluxes was compared. As an additional analysis, we tested how water contents at the permanent wilting point can aid stabilizing parameter estimation by adding information on water retention in the dry range. For all methods, soil hydraulic properties were determined with the soil hydraulic functions of van Genuchten-Mualem and Peters-Durner-Iden. The results show that parameters derived with the ‘direct method’ do often not well reproduce measured pressure heads over the complete pressure head range of the evaporation experiments when they were used for HYDRUS-1D simulations (mean objective function value 0.05831). Parameters derived by the ‘inverse method’ provided a better performance in the HYDRUS-1D simulations if the full pressure head range of the evaporation experiments was considered (mean objective function value 0.00099), but a weaker performance when focusing on wet conditions (pressure heads >−100 cm). Constraining the ‘inverse method’ by additional soil moisture measurements at permanent wilting point improved the prediction of the soil moisture at dry conditions. For the full pressure head range, the hydraulic functions of Peters-Durner-Iden performed better than the ones of van Genuchten-Mualem.

In wetlands, the water budget is traditionally quantified by measuring the hydrologic components including precipitation, evapotranspiration and surface water-groundwater inflows and outflows. However, the reliability of measurements is often questioned due to the difficulty of rigorously monitoring all components of the water budget. Quantifying the rainfall event to water table response ratio is an alternative approach with minimal need for data commonly collected in peatland studies. However, the method has been used only in a limited number of biophysical settings including different microforms, hydroclimatic and hydrogeological settings. The objectives of this study are to quantify the reactivity of the water table to precipitation for different pristine peatlands located in different hydroclimatic conditions and to provide quantitative assessments of water storage of as many peatlands as possible. To do so, site-specific hourly water table and precipitation measurements was collected from northern peatlands worldwide. In total, data from more than 30 sites were retrieved from 8 research groups. For all peatlands, water-table depths varied between 80 cm below the peat surface and 10 cm above the peat surface. The results highlight that the hydrology of all peatlands is characterized by a shift from an environment that can store water to an environment that contributes to rapid outflow, and this is a uniform feature across sites. However, for peatlands with the lowest water storage capacities, this shift occurs during relatively moderate rainfall events (40 mm) or successive small rainfall events. Blanket peat bog best embodied this type of hydrological response. For peatlands with the highest water storage capacity, this shift occurs following multiple moderate to large precipitation events (40 mm – 80 mm) and it is strongly enhanced by the shift from high to low evaporative periods. The peatlands with the highest storage capacity are raised bogs with deep water-table. These conditions are best observed in peatlands located in geographical settings with high evaporation rates. Among all the peatlands, maximum water storage capacity for given rainfall events was equal to ≈150 mm. These analyses also confirm that the water table rise caused by precipitation events contain sufficient information to constrain water storage variations around monitored wells peatlands for a wide array of biophysical settings.

Mapping the global peatland distribution is important for embedding peatland processes into Earth System Models. Peatland maps are typically compiled from nation-specific soil or ecosystem maps or based on machine learning tools trained on such data. Here, we evaluate the performance of a land surface model with two different peatland map inputs in providing critical land surface estimates (soil moisture, temperature) to a Radiative Transfer Model (RTM) for L-band brightness temperature (Tb). We hypothesize that an improved performance of the land surface model in Tb space indicates a better spatial peatland distribution input within the footprint of Tb observations (~40 km).We employ the NASA Catchment Land Surface Model (CLSM) with a recently added module for peatland hydrology (PEATCLSM modules). We run this model at a 9-km EASEv2 resolution over the Northern Hemisphere for two soil maps that differ in their peatland distributions. The applied soil distributions are: (MAP1) a combination of the Harmonized World Soil Database and the State Soil Geographic Database, also used to generate the Soil Moisture Active Passive (SMAP) Level-4 soil moisture product, and (MAP2) a hybrid of HWSD-STATSGO and the ‘PEATMAP’ product, which is mainly compiled from national peatland maps. MAP2 indicates ~30 % more peatland area over the Northern Hemisphere. For both peat distributions, CLSM is run and parameters of the RTM are calibrated with 10 years of multi-angular L-band Tb observations from the Soil Moisture and Ocean Salinity SMOS mission. Afterwards, CLSM is run together with the calibrated RTM within a data assimilation system, with and without (open-loop) assimilating SMAP Tb observations, for the period 2015-2020. Our results demonstrate that Tb misfits (in both the open-loop and assimilation runs) are reduced in the areas with the largest differences in peat distribution, thus indicating a basic validity of assuming a peatland-like hydrological dynamics for the larger peat extent of MAP2. Results will be discussed in the context of how peatlands are defined in global peatland maps and the question of what is typically modeled as a peatland in Earth System Models. We propose the evaluation of future releases of peatland maps in Tb space as a tool to evaluate their suitability for implementation into Earth System Models.

Fluctuations of water table depth (WTD) affect many processes in peatlands, such as vegetation development and emissions of greenhouse gases. Here, we present the OPtical TRApezoid Model (OPTRAM) as a new method for satellite-based monitoring of the temporal variation of WTD in peatlands. OPTRAM is based on the response of short-wave infrared reflectance to the vegetation water status. For five northern peatlands with long-term in-situ WTD records, and with diverse vegetation cover and hydrological regimes, we generate a suite of OPTRAM index time series using (a) different procedures to parametrise OPTRAM (peatland-specific manual vs. globally applicable automatic parametrisation in Google Earth Engine), and (b) different satellite input data (Landsat vs. Sentinel-2). The results based on the manual parametrisation of OPTRAM indicate a high correlation with in-situ WTD time-series for pixels with most suitable vegetation for OPTRAM application (mean Pearson correlation of 0.7 across sites), and we will present the performance differences when moving from a manual to an automatic procedure. Furthermore, for the overlap period of Landsat and Sentinel-2, which have different ranges and widths of short-wave infrared bands used for OPTRAM calculation, the impact of the satellite input data to OPTRAM will be analysed. Eventually, the challenge of merging different satellite missions in the derivation of OPTRAM time series will be explored as an important step towards a global application of OPTRAM for the monitoring of WTD dynamics in northern peatlands.