Characterising recent drought events using current reanalysis and remote-sensing soil moisture products within a common framework

<p>Climate projections indicate an increasing risk of dry and hot episodes in Central Europe, including in Switzerland. However, models display a large spread in projections of changes in summer drying, highlighting the importance of related observations to evaluate climate models and constrain projections. Land hydrological variables play an essential role for these projections. This is particularly the case for soil moisture and land evaporation, which are directly affecting the development of droughts and heatwaves in both present and future.</p><p>The recent 2020 spring as well as 2015 and 2018 summer droughts in Switzerland have highlighted the importance of monitoring and assessing changes of soil moisture and land evaporation, which are strongly related to drought impacts on agriculture, forestry, and ecosystems. The country was affected by major drought and heatwave conditions in 2015 and 2018. While the meteorological conditions started to recover at the end of the summer, the soil moisture conditions (and runoff) continued to be anomalously low for most of the fall. This illustrates the decoupling between meteorological drought and soil moisture drought conditions related to the intrinsic memory of the soil.</p><p>The only Switzerland-wide soil moisture monitoring programme currently in place is the SwissSMEX (Swiss Soil Moisture Experiment) measurement network. It was initiated in 2008 and comprises 19 soil moisture measurement profiles at 17 different sites (grassland, forest and arable land). Since 2017, seven grassland SwissSMEX sites were complemented with land evaporation measurements from mini-lysimeters.</p><p>First, a quality assessment and inter-comparison of the in-situ soil moisture and land evaporation observations at 12 grassland sites revealed substantial discrepancies between different sensor types in terms of absolute values and data availability. A standard procedure for processing and interpreting the SwissSMEX data is thus being established. Second, analyses have been carried out comparing the SwissSMEX measurements with gridded remote-sensing and reanalysis products that provide near real time soil moisture data. In particular, the European Space Agency (ESA) Climate Change Initiative (CCI) surface soil moisture product (ESA-CCI soil moisture) as well as the new ECMWF reanalysis ERA5 are considered. The seasonal evolution of the soil moisture anomalies (with respect to the long-term mean) show for 2020 two pronounced phases of dryness. These are consistently represented in SwissSMEX in-situ observations and ERA5. Also the other recent drought events of 2015 and 2018 show a similar temporal evolution in both datasets. The response of ESA-CCI surface soil moisture is less pronounced, more variable and also dependent on the measurement methodology, i.e., active or passive microwave remote sensing.</p><p>These first analyses provide useful insights in order to provide near-real time monitoring, enhance process understanding at the national scale and a better preparedness for future droughts.</p>

Estimation of root zone soil moisture using passive microwave remote sensing: A case study for rice and wheat crops for three states in the Indo-Gangetic basin

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.

Inversion of soil hydraulic properties from the DEnKF analysis of SMOS soil moisture over West Africa

High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model

Land surface models (LSMs) are limited in their ability to reproduce observed soil moisture partially due to uncertainties in model parameters. Here we conduct a variance-based sensitivity analysis to quantify the relative contribution of different model parameters and their interactions to the uncertainty in the surface and root-zone soil moisture in the Community Land Model 5.0 (CLM5). We focus on soil-texture-related parameters (porosity, saturated matric potential, saturated hydraulic conductivity, shape parameter of soil-water retention model) and organic matter fraction. A Gaussian process emulator is constructed based on CLM5 simulations and used to estimate soil moisture across the five-dimensional parameter space for sensitivity analysis. The procedure is demonstrated for four seasons across various U.S. sites of distinct soil and vegetation types. We find that the emulator captures well the CLM5 behavior across the parameter space for different soil textures and seasons. The uncertainties of surface and root-zone soil moisture are dominated by the uncertainties in porosity and shape parameter with negligible parametric interactions. However, relative importance of porosity versus shape parameter varies with soil textures (sites), depths (surface versus root zone), and seasons. At most of the sites, surface soil moisture uncertainty is attributed largely to shape parameter uncertainty, while porosity uncertainty is more important for the root-zone soil moisture uncertainty. All individual parameter and interaction effects demonstrate less variability across different soil textures and seasons for root zone than for surface soil moisture. These results provide scientific guidance to prioritize reducing the uncertainty of sensitive parameters for improving soil moisture modeling with CLM.

The NASA Soil Moisture Active Passive (SMAP) mission Level‐4 Soil Moisture (L.4_SM) product provides global, 3‐hourly, 9‐km resolution estimates of surface (0–5 cm) and root zone (0–100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. In Version 4 of the L4_SM modeling system the upward recharge of surface soil moisture from below under nonequilibrium conditions was reduced, resulting in less bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation‐minus‐forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias‐adjusted root‐mean‐square error in Version 4 is 0.039 m3/m3 for surface and 0.026 m3/m3 for root zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01–0.02 m3/m3) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm/year) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.

Distinguishing the effects of climate change and vegetation greening on soil moisture variability along aridity gradient in the drylands of northern China

Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture

High resolution root-zone soil moisture (SM) maps are important for understanding the spatial variability of water availability in agriculture, ecosystems research and water resources management. Unmanned Aerial Systems (UAS) can flexibly monitor land surfaces with thermal and optical imagery at very high spatial resolution (meter level, VHR) for most weather conditions. We modified the temperature–vegetation triangle approach to transfer it from satellite to UAS remote sensing. To consider the effects of the limited coverage of UAS mapping, theoretical dry/wet edges were introduced. The new method was tested on a bioenergy willow short rotation coppice site during growing seasons of 2016 and 2017. We demonstrated that by incorporating surface roughness parameters from the structure-from-motion in the interpretation of the measured land surface-atmosphere temperature gradients, the estimates of SM significantly improved. The correlation coefficient between estimated and measured SM increased from not significant to 0.69 and the root mean square deviation decreased from 0.045 m3∙m−3 to 0.025 m3∙m−3 when considering temporal dynamics of surface roughness in the approach. The estimated SM correlated better with in-situ root-zone SM (15–30 cm) than with surface SM (0–5 cm) which is an important advantage over alternative remote sensing methods to estimate SM. The optimal spatial resolution of the triangle approach was found to be around 1.5 m, i.e. similar to the length scale of tree-crowns. This study highlights the importance of considering the 3-D fine scale canopy structure, when addressing the links between surface temperature and SM patterns via surface energy balances. Our methodology can be applied to operationally monitor VHR root-zone SM from UAS in agricultural and natural ecosystems.

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In the present study, two currently available coarse-scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time-invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well-organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset-calibrated time-invariable coefficients to retrieve SSM, with coefficients of determination (R2) varying from 0.304 to 0.534 and root mean square errors (RMSE) ranging from 0.020 m3/m3 to 0.029 m3/m3. Further evaluation with different land use types results in acceptable debiased RMSE between 0.021 m3/m3 and 0.048 m3/m3 when comparing the estimated MSG pixel-scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time-invariable coefficients when using publicly accessed coarse-scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.

Quantification of root-zone soil moisture (RZSM) is crucial for agricultural applications and soil sciences. RZSM impacts processes such as vegetation transpiration and water percolation. Surface soil moisture (SSM) can be assessed through active and passive microwave remote sensing methods, but no current sensor enables direct RZSM retrieval. Spatial maps of RZSM can be retrieved via proxy observations (vegetation stress, water storage change, and surface soil moisture) or via land surface model predictions. In this study, we investigated the combination of surface soil moisture information with process-based inferred features involving artificial neural networks (ANNs). We considered the infiltration process through the soil water index (SWI) computed with a recursive exponential filter and the evaporation process through the evaporative efficiency computed based on a MODIS remote sensing dataset and simplified analytical model, while vegetation growth was expressed through normalized difference vegetation index (NDVI) time series. Several ANN models with different sets of features were developed. Training was conducted considering in situ stations distributed several areas worldwide characterized by different soil and climate patterns of the International Soil Moisture Network (ISMN), and testing was applied to stations of the same data hosting facility. The results indicate that the integration of process-based features into ANN models increased the overall performance over the reference model level in which only SSM features were considered. In arid and semi-arid areas, for instance, performance enhancement was observed when the evaporative efficiency was integrated into the ANN models. To assess the robustness of the approach, the trained models were applied on observation sites in Tunisia, Italy and South-India that are not part of ISMN. The results reveal that joint use of surface soil moisture, evaporative efficiency, NDVI and recursive exponential filter represented the best alternative for more accurate predictions in the case of Tunisia, where the mean correlation of the predicted RZSM based on SSM only sharply increased from 0.443 to 0.801 when process-based features were integrated into the ANN models in addition to SSM. However, process-based features have no to little added value in temperate to tropical conditions.

<p>Environmental (e.g. floods, droughts) and weather prediction systems rely on an accurate representation of soil moisture (SM). The EUMETSAT H SAF aims to provide high quality satellite-based hydrological products, including SM.<br>ECMWF is producing ASCAT root zone SM for H SAF. The production relies on an Extended Kalman filter to retrieve root zone SM from surface SM satellite data. A 10 km sampling reanalysis product (1992-2020) forced by ERA5 atmospheric fields (H141/H142) is produced for H SAF, which assimilates ERS/SCAT (1992-2006) and ASCAT-A/B/C (2007-2020) derived surface SM. The root-zone SM performance is validated using sparse in situ observations globally and generally demonstrates a positive and consistent correlation over the period. A negative trend in root-zone SM is found during summer and autumn months over much of Europe during the period (1992-2020). This is consistent with expected climate change impacts and is particularly alarming over the water-scarce Mediterranean region. The recent hot and dry summer of 2019 and dry spring of 2020 are well captured by negative root-zone SM anomalies. Plans for the future H SAF data record products will be presented, including the assimilation of high-resolution EPS-SCA-derived soil moisture data.</p>

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

Root-zone soil moisture estimation from assimilation of downscaled Soil Moisture and Ocean Salinity data