Snow droughts and the water cycle of the Central Apennines

Vegetation plays a crucial role in the land surface water and energy balance modulating the interactions and feedback with climate at the regional to global scale. The availability of unprecedented Earth observation products covering recent decades (and extended up to real-time) are therefore of paramount importance to better represent the vegetation and its time evolution in the land surface models (LSMs) used for offline analysis/initialization and for the seasonal-to-decadal predictions. Here, we integrate realistic vegetation Leaf Area Index (LAI) variability from latest generation satellite campaigns, available through Copernicus Land Monitoring Service (CLMS), in three different LSMs that conducted the same coordinated set of offline land-only simulations forced by hourly atmospheric fields derived from the ERA5 atmospheric reanalysis. The experiment implementing realistic interannually-varying LAI (SENS) is compared with simulations utilizing a climatological LAI (CTRL) to quantify the vegetation feedback and the effects on the simulation of near-surface soil moisture.The results show that the inter-annually varying LAI considerably affects the simulation of near-surface soil moisture anomalies in all three models and over the same water-limited regions, but surprisingly the effects diverge among models: compared with ESA-CCI observations, the near-surface soil moisture anomalies significantly improve in  one of the three LSMs (HTESSEL-LPJGuess) while the other two (ECLand and ISBA-CTRIP) display opposite effects with significant worsening of the anomaly correlation coefficients. It is found that the enhanced simulation of near-surface soil moisture is enabled by the positive feedback that is activated by the effective vegetation cover (EVC) parameterization, implemented only in HTESSEL-LPJGuess. The EVC parameterization works such that the effective fraction of the bare soil being covered by vegetation does vary with LAI following an exponential function constrained by available satellite observations. The increased (reduced) soil-moisture limitation during dry (wet) periods produces negative (positive) LAI and therefore EVC anomalies, which in turn generate a dominating positive feedback on the near-surface soil moisture of HTESSEL-LPJGuess by exposing more (less) bare soil to direct evaporation from the sub-surface layer. On the other hand, in the EC-Land and ISBA-CTRIP models, EVC is fixed in time as it cannot vary with LAI and so the positive feedback described cannot be activated. The only feedback on near-surface soil moisture anomalies that operates  in these two models is negative and comes from the reduced (increased) transpiration related to the negative (positive) LAI anomalies.Simply prescribing observed vegetation data into LSMs does not guarantee the introduction of the correct coupling and feedback on climate. In this respect, this multi-model comparison experiment demonstrates the fundamental role of the inclusion of the underlying vegetation processes in LSMs. Ignoring the proper representation of the vegetation processes could lead to unrealistic (and even the opposite effects compared with observations) behaviour in reanalysis and climate predictions.

Seasonal snow is an essential component of regional and global water and energy cycles, particularly in snow-dominant regions that rely on snowmelt for water resources. Land surface models (LSMs) are a common approach for developing spatially and temporally complete estimates of snow water equivalent (SWE) and hydrologic variables at a large scale. However, the accuracy of the LSM-based SWE outputs is limited and unclear by mixed factors such as uncertainties in the meteorological boundary conditions and the model physics. In this study, we assess the SWE, snowfall, precipitation, and air temperature products from a twelve-member ensemble – with four LSMs and three meteorological forcings – using automated SWE, precipitation, and temperature observations from 809 Snowpack Telemetry stations over the western U.S. Results show that the mean annual maximum LSM SWE is underestimated by 268 mm. The timing of peak SWE from the LSMs is on average 36 days earlier than that of the observations. By the date of peak SWE, winter accumulated precipitation is underestimated (forcings mean: 485 mm vs. stations: 690 mm). In addition, the precipitation partitioning physics generates different snowfall estimates by an average of 113 mm with the same forcing data. Even though there are widespread cold biases (up to 3 °C) in the temperature forcings, larger ablations and lower ratios of SWE to total precipitation are found even in the accumulation period, indicating that melting physics in LSMs drives some SWE uncertainties. Based on the principal component analysis, we find that precipitation bias has the largest contribution to the first principal component, which accounts for more than half of the total variance. The results provide insights into prioritizing strategies to improve SWE estimates from LSMs for hydrologic applications.

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 processes involved in the exchange of water, energy and carbon in terrestrial ecosystems are strongly intertwined. To accurately represent the terrestrial biosphere in land surface models (LSM), the intrinsic coupling between these processes is required. Soil moisture and leaf area index are two key variables at the nexus of water, energy and vegetation. Here, we evaluated three LSM (ISBA, ORCHIDEE and a diagnostic model, based on the LSA SAF algorithms) in their ability to simulate the latent heat flux (LE) and gross primary production (GPP) coherently, and their interactions through leaf area index (LAI) and soil moisture. The models were validated using in situ eddy covariance observations, soil moisture measurements and remote sensed LAI. It was found that the diagnostic model performed consistently well, regardless land cover, whereas important shortcomings of the prognostic models were revealed for in herbaceous/dry sites. Despite their different architecture and parametrization, ISBA and ORCHIDEE shared some key weaknesses. In both models, LE and GPP were found to be oversensitive to drought stress. Though the simulated soil water dynamics could be improved, this was not the main cause of errors in the surface fluxes. Instead, these errors were strongly correlated to errors in LAI. The simulated phenological cycle in ISBA and ORCHIDEE was delayed compared to observations, and failed to capture the observed seasonal variability. The feedback mechanism between GPP and LAI (i.e. the biomass allocation scheme) was identified as a key element to improve the intricate coupling between energy, water and vegetation in LSM.

The processes involved in the exchange of water, energy and carbon in terrestrial ecosystems are strongly intertwined. To accurately represent the terrestrial biosphere in land surface models (LSM), the intrinsic coupling between these processes is required. Soil moisture and leaf area index are two key variables at the nexus of water, energy and vegetation. Here, we evaluated three LSM (ISBA, ORCHIDEE and a diagnostic model, based on the LSA SAF algorithms) in their ability to simulate the latent heat flux (LE) and gross primary production (GPP) coherently, and their interactions through leaf area index (LAI) and soil moisture. The models were validated using in situ eddy covariance observations, soil moisture measurements and remote sensed LAI. It was found that the diagnostic model performed consistently well, regardless land cover, whereas important shortcomings of the prognostic models were revealed for in herbaceous/dry sites. Despite their different architecture and parametrization, ISBA and ORCHIDEE shared some key weaknesses. In both models, LE and GPP were found to be oversensitive to drought stress. Though the simulated soil water dynamics could be improved, this was not the main cause of errors in the surface fluxes. Instead, these errors were strongly correlated to errors in LAI. The simulated phenological cycle in ISBA and ORCHIDEE was delayed compared to observations, and failed to capture the observed seasonal variability. The feedback mechanism between GPP and LAI (i.e. the biomass allocation scheme) was identified as a key element to improve the intricate coupling between energy, water and vegetation in LSM.

A long-term high-resolution national dataset of precipitation (P), soil moisture (SM), and snow water equivalent (SWE) is necessary for predicting floods and droughts and assessing the impacts of climate change on streamflow in China. Current long-term daily or sub-daily datasets of P, SM, and SWE are limited by a coarse spatial resolution, the lack of local correction, or the lack of direct calibration. In this study, we produced a daily 0.1° dataset of P, SM, and SWE in 1981–2017 across China using global background data and local onsite data as forcing input and satellite-based data as reconstruction benchmarks. Long-term global 0.1° and local 0.25° P data are merged to reconstruct the P from the short-term 0.1° China Merged Precipitation Analysis (CMPA) using a stacking machine learning model. Long-term SM data are reconstructed by the HBV hydrological model with SM calibrated by Soil Moisture Active Passive Level 4 (SMAP-L4) data. Long-term SWE data are also reconstructed by the HBV hydrological model with SWE calibrated by the national satellite-based snow depth dataset in China (Che and Dai, 2015) and the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data. For all grids at the national and daily scale, the median Kling-Gupta Efficiency (KGE) of the reconstructed P and SM are 0.68 and 0.61 respectively. For grids in two snow-rich regions at the daily scale, the median KGEs of the reconstructed SWE are 0.55 and -2.41 in the Songhua and Liaohe Basin and the Continental Basin respectively. Generally, the reconstruction dataset performs better in southern and eastern China than in northern and western China for P and SM, and performs better in northeast China than other regions for SWE. As the first long-term 0.1° daily dataset of P, SM, and SWE that combines information from local observations and satellite-based data benchmarks, this reconstruction product is valuable for future national investigations of hydrological processes.

Comparisons between snow water equivalent (SWE) and river discharge estimates are important in evaluating the SWE fields and to our understanding of linkages in the freshwater cycle. In this study, we compared SWE drawn from land surface models and remote sensing observations with measured river discharge (Q) across 179 Arctic river basins. Over the period 1988‐2000, basin‐averaged SWE prior to snowmelt explains a relatively small (yet statistically significant) fraction of interannual variability in spring (April–June) Q, as assessed using the coefficient of determination (R2). Averaged across all basins, mean R2s vary from 0·20 to 0·28, with the best agreement noted for SWE drawn from a simulation with the Pan‐Arctic Water Balance Model (PWBM) forced with data from the European Centre for Medium‐Range Weather‐Forecasts (ECMWF) Re‐analysis (ERA‐40). Variability and magnitude in SWE derived from Special Sensor Microwave Imager (SSM/I) data are considerably lower than the variability and magnitude in SWE drawn from the land surface models, and generally poor agreement is noted between SSM/I SWE and spring Q. We find that the SWE versus Q comparisons are no better when alternate temporal integrations–using an estimate of the timing in basin thaw–are used to define pre‐melt SWE and spring Q. Thus, a majority of the variability in spring discharge must arise from factors other than basin snowpack water storage. This study demonstrates how SWE estimated from remote sensing observations, or general circulation models (GCMs), can be evaluated effectively using monthly discharge data or SWE from a hydrological model. Copyright © 2007 John Wiley & Sons, Ltd.

Hyperspectral remote sensing for growth-stage-specific water use in wheat

This paper presents the development and application of a physically based hydrological data assimilation system (HDAS) using the gridded and parallelized Soil and Water Assessment Tool (SWATGP) distributed hydrological model. This SWAT‐HDAS software integrates remotely sensed data, including the leaf area index (LAI), snow cover fraction, snow water equivalent, soil moisture, and ground‐based observational data (e.g., from discharge and ground sensor networks), with SWATGP and the Parallel Data Assimilation Framework (PDAF) to accurately characterize watershed hydrological states and fluxes. SWAT‐HDAS employs high‐performance computational technologies to address the computational challenges of high‐resolution and/or large‐area modeling. Multiple observational system simulation experiments (OSSEs), including soil moisture assimilation experiments, snow water equivalent assimilation experiments, and streamflow assimilation experiments, were designed to validate the assimilation efficiency of various types of observations within SWAT‐HDAS using an ensemble Kalman filter (EnKF) algorithm. Both the temporal and spatial correlations in the trend/pattern and the magnitudes of improvement between the simulated and “true” states (i.e., for soil moisture, snow water equivalent, and discharge) were satisfactory using the integrated assimilation, which suggests the reliability of SWAT‐HDAS for regional hydrology studies. The streamflow assimilation experiment also showed that the observation location dramatically influences the assimilation efficiency. The quantity and quality of observations have effects of varying degrees on the streamflow predictions. SWAT‐HDAS is a promising tool for hydrological studies and applications under climate and environmental change scenarios.

<p>Accurate estimation of terrestrial water storage (TWS) is crucial in the characterization of the terrestrial hydrologic cycle. The launch of GRACE and GRACE Follow-On (GRACE-FO) missions provide an unprecedented opportunity to monitor the change in TWS across the globe. However, the spatial and temporal resolutions provided by GRACE/GRACE-FO are often too coarse for many hydrologic applications. Land surface models (LSMs) provide estimates of TWS at a finer spatio-temporal resolution, but most LSMs lack complete, all-encompassing physical representations of the hydrological system such as deep groundwater storage or anthropogenic influences (e.g., groundwater pumping and surface water regulation). In recent years, geodetic measurements from the ground-based Global Positioning System (GPS) network have been increasingly used in hydrologic studies based on the elastic response of the Earth’s surface to mass redistribution. This study explores the potential of improving our knowledge in TWS change via merging the information provided by ground-based GPS, GRACE, and the NASA Catchment Land Surface Model (Catchment), especially for the TWS change during an extended drought period.</p> <p> </p> <p>Ground-based GPS observations of vertical displacement and GRACE TWS retrievals were assimilated into the Catchment LSM, respectively, using an ensemble Kalman filter (EnKF) in order to improve the estimation accuracy of TWS change. The data assimilation (DA) framework effectively downscaled TWS into its constituent components (e.g., snow and soil moisture) as well as improved estimates of hydrologic fluxes (e.g., runoff). Estimated TWS change from the open loop (OL; without assimilation) and GPS DA (i.e., using GPS-based vertical displacement during assimilation) simulations were evaluated against GRACE TWS retrievals. Results show that GPS DA improved estimation accuracy of TWS change relative to the OL, especially during an extended drought period post-2011 in the western United States (e.g., the correlation coefficient R<sub>OL</sub> = 0.46 and R<sub>GPSDA</sub> = 0.82 in the Great Basin). The performance of GPS DA and GRACE DA in estimating TWS constituent components and hydrologic fluxes were evaluated against in situ measurements. Results show that GPS DA improves snow water equivalent (SWE) estimates with improved R values found over 76% of all pixels that are collocated with in situ stations in the Great Basin. The findings in this study indicate the potential use of GPS DA and GRACE DA for TWS characterization. Both GRACE and ground-based GPS provide complementary TWS change information, which helps correct for missing physics in the LSM. Additionally, this study provides motivation for a multi-variate assimilation approach to simultaneously merge both GRACE and ground-based GPS into an LSM to further improve modeled TWS and its constituent components.</p>

Land surface-atmosphere interactions are strongly influenced by vegetation, since the latter drives the exchange of energy, water and carbon at canopy level via transpiration and photosynthesis. These biochemical processes are related to both the stomatal response to meteorological variations (linking the canopy to the deepest soil layers), and the allocation of carbon in different parts of the plant such as roots and leaves.  In recent years, the characterization of these processes has gained increasing attention in land surface models (LSMs), which are powerful tools that reproduce the soil-plant-atmosphere continuum and the mutual feedback of its components. Vegetation in LSMs is described either statically -- based on a prescribed vegetation climatology or cover -- or dynamically, that is, evolving in time its characteristics such as leaf area index and vegetation cover fraction, among the others. However, the dynamic simulation of vegetation is often simplified in LSMs with respect to state-of-the-art bio-geophysical and forest models.  In the Noah Multi-Parametrization (Noah-MP, v. 4.0.1) LSM, multiple parametrizations are available for each individual sub-process scheme such as dynamic vegetation, runoff partitioning, groundwater recharge and radiative transfer through the canopy, among others. It is thus important to identify the land cover type, soil and climate characteristics of the specific study site and tailor the parametrization to find the “optimal” combination of sub-process schemes, i.e. the one which best reproduces in-situ observations.  In this study, we evaluate point-scale simulations generated using different parametrizations of dynamic vegetation schemes within Noah-MP, run in offline mode within the NASA’s Land Information System (LIS). We compare the LSM results of gross primary productivity, soil moisture and evapotranspiration over several years between 2000 and 2023 to both ground-based estimates and remote sensing datasets derived from multiple observations and platforms such as MODIS, OCO-2, MSG and FLUXCOM. The study focuses on sites along the Italian peninsula, mostly forests, with croplands and grasslands as well, some of which are equipped with Eddy-covariance stations for carbon and water fluxes measurements and are included in the FLUXNET network.  The sites are all natural, rain-fed ecosystems mostly located in drought-prone, Mediterranean regions. This study is meant to reveal previously neglected uncertainties in dynamic vegetation simulations, especially in dry regions, and to fine-tune the combination of sub-processes schemes in Noah-MP for future data assimilation experiments.

Aimed at mapping time variations in the Earth’s gravity field, the Gravity Recovery and Climate Experiment (GRACE) satellite mission is applicable to access terrestrial water storage (TWS), which mainly includes groundwater, soil moisture (SM), and snow. In this study, SM and accumulated snow water equivalent (SWE) are simulated by the Global Land Data Assimilation System (GLDAS) land surface models (LSMs) and then used to isolate groundwater anomalies from GRACE-derived TWS in Pennsylvania and New York States of the Mid-Atlantic region of the United States. The monitoring well water-level records from the U.S. Geological Survey Ground-Water Climate Response Network from January 2005 to December 2011 are used for validation. The groundwater results from different combinations of GRACE products (from three institutions, CSR, GFZ and JPL) and GLDAS LSMs (CLM, NOAH and VIC) are compared and evaluated with in-situ measurements. The intercomparison analysis shows that the solution obtained through removing averaged simulated SM and SWE of the three LSMs from the averaged GRACE-derived TWS of the three centers would be the most robust to reduce the noises, and increase the confidence consequently. Although discrepancy exists, the GRACE-GLDAS estimated groundwater variations generally agree with in-situ observations. For monthly scales, their correlation coefficient reaches 0.70 at 95% confidence level with the RMSE of the differences of 2.6 cm. Two-tailed Mann-Kendall trend test results show that there is no significant groundwater gain or loss in this region over the study period. The GRACE time-variable field solutions and GLDAS simulations provide precise and reliable data sets in illustrating the regional groundwater storage variations, and the application will be meaningful and invaluable when applied to the data-poor regions.

To improve the prediction of crop yields at an aggregate scale, we developed a data assimilation-crop modeling framework that incorporates remotely sensed soil moisture and leaf area index (LAI) into a crop model using sequential data assimilation. The core of the framework is an Ensemble Kalman Filter (EnKF) used to control crop model runs, assimilate remote sensing (RS) data and update model state variables. We modified the Decision Support System for Agro-technology Transfer – Cropping System Model (DSSAT-CSM)-Maize model (Jones et al., 2003) to be able to stop and start simulations at any given time in the growing season, such that the EnKF can update model state variables as RS data become available. The data assimilation-crop modeling framework was evaluated against 2003–2009 maize yields in Story County, Iowa, USA, assimilating AMSR-E soil moisture and MODIS-LAI data independently and simultaneously. Assimilating LAI or soil moisture independently slightly improved the correlation of observed and simulated yields (R=0.51 and 0.50) compared to no data assimilation (open-loop; R=0.47) but prediction errors improved with reductions in MBE and RMSE by 0.5 and 0.5Mgha−1 respectively for LAI assimilation while these were reduced by 1.8 and 1.1Mgha−1 for soil moisture assimilation. Yield correlation improved more when both soil moisture and LAI were assimilated (R=0.65) suggesting a cause–effect interaction between soil moisture and LAI, prediction errors (MBE and RMSE) were also reduced by 1.7 and 1.8Mgha−1 with respect to open-loop simulations. Results suggest that assimilation of LAI independently might be preferable when conditions are extremely wet while assimilation of soil moisture+LAI might be more suitable when conditions are more nominal. AMSR-E soil moisture tends to be more biased under the presence of high vegetation (i.e., when crops are fully developed) and that updating rootzone soil moisture by near-surface soil moisture assimilation under very wet conditions could increase the modeled percolation causing excessive nitrogen (N) leaching hence reducing crop yields even with water stress reduced at a minimum due to soil moisture assimilation. However, applying the data assimilation-crop modeling framework strategically by considering a-priori information on climate condition expected during the growing season may improve yield prediction performance substantially, in our case with higher correlation (R=0.80) and more reductions in MBE and RMSE (2.5 and 3.3Mgha−1) compared to when there is no data assimilation. Scaling AMSR-E soil moisture to the climatology of the model did not improve our data assimilation results because the model is also biased. Better soil moisture products e.g., from Soil Moisture Active Passive (SMAP) mission, may solve the soil moisture data issue in the near future.

A reliable snow water equivalent (SWE) product is critical for climate and hydrology studies in Arctic regions. Passive microwave sensors aboard satellites provide a capability of observing global SWE and have produced many SWE datasets. However, these datasets have significant errors in boreal forest regions and where snowpack is deep or wet. The Gravity Recovery and Climate Experiment (GRACE) satellites are measuring changes in terrestrial water storage (TWS), of which snow mass is the primary component in winter Arctic river basins. This paper shows SWE can be derived from GRACE TWS change in regions where the ground is not covered by snow in a summer month if accurate changes in below‐ground water storage (including soil water and groundwater) can be provided by a land surface model. Based on gravity change, the GRACE‐derived SWE estimates are not affected by the boreal forest canopy and are more accurate in deep snow regions than microwave retrievals. The paper also discusses the uncertainties in the SWE retrievals.

Observed by satellites for more than a decade, surface soil moisture (SSM) is an essential component of the Earth system. Today, with the Sentinel missions, SSM can be derived at a sub-kilometer spatial resolution. In this work, aggregated 1 km × 1 km SSM observations combining Sentinel-1 (S1) and Sentinel-2 (S2) data are assimilated for the first time into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model using the global Land Data Assimilation System (LDAS-Monde) tool of Meteo-France. The ISBA simulations are driven by atmospheric variables from the Application of Research to Operations at Mesoscale (AROME) numerical weather prediction model for the period 2017–2019 for two regions in Southern France, Toulouse and Montpellier, and for the Salamanca region in Spain. The S1 SSM shows a good agreement with in situ SSM observations. The S1 SSM is assimilated either alone or together with leaf area index (LAI) observations from the PROBA-V satellite. The assimilation of S1 SSM alone has a small impact on the simulated root zone soil moisture. On the other hand, a marked impact of the assimilation is observed over agricultural areas when LAI is assimilated, and the impact is larger when S1 SSM and LAI are assimilated together.