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Lake heatwaves—prolonged periods of hot surface water temperature in lakes—have recently been shown to increase in intensity and duration, with numerous potential implications for aquatic ecosystems. However, an important physical attribute of lake heatwaves that has not yet been investigated is their spatial extent, and how it varies within a warming world. Here, we show that the spatial extent of lake heatwaves, defined as contiguous regions within a lake that simultaneously experience extreme warm conditions, is increasing in the largest group of freshwater lakes on Earth, The Laurentian Great Lakes. We show that the maximum spatial extent of lake heatwaves is sensitive to inter-annual variations in winter ice cover and the timing of stratification onset in spring. Notably, we find that a lengthening of the warm summer season and, in turn, an overall increase in surface water temperature, stimulates the development of larger lake heatwaves. On average, our results suggest that the mean spatial extent of lake heatwaves has increased two-fold since 1995. We anticipate this rapid expansion of lake heatwaves to have widespread implications for heat-related impacts on aquatic species.

An emerging concern for lake ecosystems is the occurrence of compound extreme events i.e. situations where multiple within-lake extremes occur simultaneously. Of particular concern are the co-occurrence of lake heatwaves (anomalously warm temperatures) and high chlorophyll-a extremes, two important variables that influence the functioning of aquatic ecosystems. Here, using satellite observations, we provide the first assessment of univariate and compound extreme events in lakes worldwide. Our analysis suggests that the intensity of lake heatwaves and high chlorophyll-a extremes differ across lakes and are influenced primarily by the annual range in surface water temperature and chlorophyll-a concentrations. The intensity of lake heatwaves is even greater in smaller lakes and in those that are shallow and experience cooler average temperatures. Our analysis also suggests that, in most of the studied lakes, compound extremes occur more often than would be assumed from the product of their independent probabilities. We anticipate compound extreme events to have more severe impacts on lake ecosystems than those previously reported due to the occurrence of univariate extremes.

High-Mountain Asia (HMA) exhibits one of the highest increases in vegetation greenness on Earth, subsequently influencing the exchange of water and energy between the land surface and the atmosphere. Given the strong interactions between the hydrosphere, the biosphere, and the cryosphere, understanding the drivers of greening in this highly complex region with significant land cover heterogeneity is essential to assess the changes in the regional water budget. Here we perform a holistic multivariate remote sensing analysis to simultaneously examine the primary components of the terrestrial water cycle from 2003 to 2020 and decipher the principal drivers of greening in HMA. We identified three drivers of greening: (1) precipitation drives greening in mid and low elevation areas covered by evergreen and mixed forests (e.g., Irrawaddy basin), (2) decreases in snow enhance greening in most of the hydrologic basins, and (3) irrigation induces greening in irrigated lands (Ganges-Brahmaputra and Indus).

The present study aims to investigate the potential of multi-configuration Sentinel-1 (S-1) synthetic aperture radar (SAR) images for characterizing four wheat parameters: total fresh mass (TFM), total dry mass (TDM), plant heights (He), and water content (WC). Because they are almost independent on the weather conditions, we have chosen to use only SAR. Samples of wheat parameters were collected over seven fields (three irrigated and four rainfed fields) in Southwestern France. We first analyzed the temporal behaviors of wheat parameters (He, TDM, TFM and WC) between February and June 2016. Then, the temporal profiles of the S-1 backscattering coefficients (VV, VH), the difference (VH − VV), the sum of the polarizations (VH + VV) and their cumulative values are analyzed for two orbits (30 and 132) during the wheat-growing season (from January to July 2016). After that, S-1 signals were statistically compared with all crop parameters considering the impact of pass orbit, irrigation and two vegetative periods in order to identify the best S-1 configuration for estimating crop parameters. Interesting S-1 backscattering behaviors were observed with the various wheat parameters after separating irrigation impacts and vegetative periods. For the orbit 30 (mean incidence angle of 33.6°); results show that the best S-1 configurations (with coefficient of determination (R2) > 0.7) were obtained using the VV and VH + VV as a function of the He, TDM and WC, over irrigated fields and during the second vegetative period. For the orbit 132 (mean incidence angle of 43.4°), the highest dynamic sensitivities (R2 > 0.8) were observed for the VV and VH + VV configurations with He, TDM and TFM over irrigated fields during the first vegetative period. Overall, the sensitivity of S-1 data to wheat variables depended on the radar configuration (orbits and polarizations), the vegetative periods and was often better over irrigated fields in comparison with rainfed ones. Significant improvements of the determination coefficients were obtained when the cumulative (VH + VV) index was considered for He (R² > 0.9), TDM (R² > 0.9) and TFM (R² > 0.75) for irrigated fields, all along the crop cycle. The estimate of WC was more limited (R² > 0.6) and remained limited to the second period of the vegetation cycle (from flowering onwards). Whatever parameters were considered, the relative errors never exceeded 23%. This study has shown the importance of considering the agricultural practices (irrigation) and vegetative periods to effectively monitor some wheat parameters with S-1 data.

We present the application of a generic, semi-empirical first-order radiative transfer modelling approach for the retrieval of soil- and vegetation related parameters from coarse-resolution space-borne scatterometer measurements ( σ 0 ). It is shown that both angular- and temporal variabilities of ASCAT σ 0 measurements can be sufficiently represented by modelling the scattering characteristics of the soil-surface and the covering vegetation-layer via linear combinations of idealized distribution-functions. The temporal variations are modelled using only two dynamic variables, the vegetation optical depth ( τ ) and the nadir hemispherical reflectance (N) of the chosen soil-bidirectional reflectance distribution function ( B R D F ). The remaining spatial variabilities of the soil- and vegetation composition are accounted for via temporally constant parameters. The model was applied to series of 158 selected test-sites within France. Parameter estimates are obtained by using ASCAT σ 0 measurements together with auxiliary Leaf Area Index ( L A I ) and soil-moisture ( S M ) datasets provided by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land-surface model within the SURFEX modelling platform for a time-period from 2007–2009. The resulting parametrization was then used used to perform S M and τ retrievals both with and without the incorporation of auxiliary L A I and S M datasets for a subsequent time-period from 2010 to 2012.

Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of < 0.05 m3 m−3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m−3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

The land data assimilation system, LDAS-Monde, developed by the research department of the French meteorological service (Centre National de Recherches Météorologiques – CNRM) is capable of well representing land surface variables (LSVs) from regional to global scales. It jointly assimilates satellite-derived observations of leaf area index (LAI) and surface soil moisture (SSM) into the interactions between soil–biosphere–atmosphere (ISBA) land surface model (LSM), increasing the accuracy of the model simulations of the LSVs. The assimilation of vegetation variables directly impacts root zone soil moisture (RZSM) through seven control variables consisting in soil moisture of seven soil layers from the soil surface to 1 m depth. This positive impact is particularly useful in dry conditions, where SSM and RZSM are decoupled to a large extent. However, this positive impact does not reach its full potential due to the low temporal availability of optical-based LAI observations, which is, at best, every 10 d, and can suffer from months of missing data over regions and seasons with heavy cloud cover such as winter or in monsoon conditions. In that context, this study investigates the assimilation of low-frequency passive microwave vegetation optical depth (VOD), available in almost all weather conditions, as a proxy for LAI. The Vegetation Optical Depth Climate Archive (VODCA) dataset provides near-daily observations of vegetation conditions, which is far more frequent than optical-based products such as LAI. This study's goal is to convert the more frequent X-band VOD observations into proxy-LAI observations through linear seasonal re-scaling and to assimilate them in place of direct LAI observations. Seven assimilation experiments are run from 2003 to 2018 over the contiguous United States (CONUS), with (1) no assimilation and the assimilation of (2) SSM, (3) LAI, (4) re-scaled X-band VOD (VODX), (5) re-scaled VODX only when LAI observations are available, (6) LAI + SSM, and (7) re-scaled VODX + SSM. This study analyzes these assimilation experiments by comparing them to satellite-derived observations and in situ measurements and is focused on the variables of LAI, SSM, gross primary production (GPP), and evapotranspiration (ET). Each experiment is driven by atmospheric forcing reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5. Results show improved representation of GPP and ET by assimilating re-scaled VOD in place of LAI. Additionally, the joint assimilation of vegetation-related variables (i.e., LAI or re-scaled VOD) and SSM demonstrates a small improvement in the representation of soil moisture over the assimilation of any dataset by itself.

In particular, synthetic aperture radar missions have made substantial progress, with the arrival of the Sentinel-1 constellation from the European Copernicus program, the development of various spaceborne missions based on the use of Global Navigation Satellite System Reflectometry (GNSS-R) and, increasingly, the development of long time series observations relying on low-resolution sensors (ASCAT (Advanced SCAT terometer), SMAP(Advanced SCATterometer), SMOS (Soil Moisture and Ocean Salinity), etc.). [...]they show that the Water Cloud Model (WCM), relying on surface soil moisture and vegetation information produced by a land surface model, could be used as an observation operator for the assimilation of ASCAT σ° observations. [10] investigated the adjustment of vegetation characteristics derived from global parameters, as a function of regional conditions, and tested the validity of this approach in terms of improvements to the seasonal representation of soil moisture and VOD. In particular, SAR, GNSS-R and scatterometer data are being used to retrieve soil moisture content, whereas physical and semi-empirical models, satellite time series, the synergetic combination of multi-sensor data and multi-resolution observations are all key components that contribute to our improved understanding of the complex spatio-temporal patterns of surface soil moisture distributions.

Within a global Land Data Assimilation System (LDAS-Monde), satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) products are jointly assimilated with a focus on the Euro-Mediterranean region at 0.5∘ resolution between 2007 and 2015 to improve the monitoring quality of land surface variables. These products are assimilated in the CO2 responsive version of ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model, which is able to represent the vegetation processes including the functional relationship between stomatal aperture and photosynthesis, plant growth and mortality (ISBA-A-gs). This study shows the positive impact on SSM and LAI simulations through assimilating their satellite-derived counterparts into the model. Using independent flux estimates related to vegetation dynamics (evapotranspiration, Sun-Induced Fluorescence (SIF) and Gross Primary Productivity (GPP)), it is also shown that simulated water and CO2 fluxes are improved with the assimilation. These vegetation products tend to have higher root-mean-square deviations in summer when their values are also at their highest, representing 20–35% of their absolute values. Moreover, the connection between SIF and GPP is investigated, showing a linear relationship depending on the vegetation type with correlation coefficient values larger than 0.8, which is further improved by the assimilation.

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable.