Abstract. Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

The below-ground component of the Earth’s critical zone is crucial to human activities and underpins numerous chemical, physical and biological processes. However, previous studies primarily concentrated on below-ground temperatures (BGT) until depths up to 3 m and periods shorter than 70 years; few studies have globally analyzed the historical spatiotemporal variability of BGT beyond those ranges. The objective of this study was to investigate BGT anomalies (ΔBGT) between depths of 0–42 m during 1850–2100 using model outputs from CMIP6. The results show a three-stage accelerating warming pattern (1850–2014): weak pre-1943 warming (0.02 °C decade⁻¹, depth-average), mid-century stagnation, and post-1984 acceleration (0.33 °C decade⁻¹, depth-average) for depth mean of 0.05∼1.75 m. Future mean warming rises ∼1.7 times from SSP1‑2.6 (2.08 °C) to SSP5‑8.5 (3.45 °C), with maximum of warming mean expanding 2.6 times. Asymmetric BGT extremes drive elevated subsurface heat risk under high emissions. A robust seasonal hierarchy reversal occurs (DJF‑ to JJA‑dominated), with winter BGT most sensitive to radiative forcing. ΔBGT amplifies strongly from 60°N, and enhances in high‑altitude/coastal regions under high emissions. Heterogeneous bottom boundary condition placement (BBCP) is an important structured uncertainty source in multi-model BGT analysis, introducing non-physical sampling artifacts in ensemble-mean vertical profiles. Despite inter‑model heterogeneity, the multi‑model ensemble yields physically consistent depth‑attenuated warming, providing an ensemble-constrained reference for subsurface thermal change investigation. By 2100, low-moderate emission scenarios (e.g., SSP1‑2.6, SSP2‑4.5) will slow BGT warming. This study can provide insightful understanding of the overlooked BGT and inform future model intercomparison projects and ensemble mean analysis.

The Seasonal Tropical Dry Forest (STDF) known as Caatinga occupies approx. 10% of the Brazilian territory. Its vegetation exhibits rapid phenological responses to rainfall resulting in corresponding increases in gross primary productivity and biomass production. Determining the timing of the start and end of the growing season is very important to ecosystem studies and to precisely quantify the carbon balance. Satellite-derived vegetation indices have been widely used to capture the vegetation dynamics in response to fluctuating environmental conditions. However, the spatial and temporal resolution of these indices cannot capture fine vegetation features and phenology metrics in a highly biodiverse and heterogeneous environment such as the Caatinga. On the other hand, phenocameras have been successfully used for this particular purpose for tropical and dry ecosystems. Complementarily, proximal spectral response sensors (SRS) have been used to allow computation of vegetation indices as phenology proxies. Due to their ability to capture high spatial resolution imagery, Unmanned Aerial Systems (UAS) or drones, can deliver an excellent spatial and a very good temporal resolution for diverse detailed vegetation studies. In this context, the objective of this study was to verify whether multi-sensor and multi-platform technologies provide an enhanced assessment of spectral indices and phenological dynamics of the Caatinga. The field campaign occurred in a pristine area of caatinga vegetation, located at the Legal Reserve of Caatinga, Embrapa Semi-Arid, Petrolina, Brazil. Indices for detecting phenology dynamics were obtained using multi-spectral cameras installed on unmanned aerial vehicles (UAV), field spectral response sensors (SRS), phenocameras (digital RGB cameras) and MODIS satellite data (visible and near infrared) from 2020 to 2023. Environmental driving data were measured via instrumentation installed on a flux tower. Standard statistical measures, including correlation coefficients were employed to verify the relationship observed on Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), and Green Chromatic Coordinate (Gcc) determined by different sensors and platforms. We observed a substantial and fast increase in Gcc, NDVI and PRI immediately after rainfall events. The sensitivity of NDVI and PRI to changes in vegetation can vary depending on factors such as vegetation greenness, overall plant health, and stress responses according to the environmental conditions of the study area. Particularly during the dry season, indices derived from higher spatial resolution sensors consistently showed lower NDVI values compared to those obtained from proximal spectral response sensors (SRS) and drones. Our observations indicate that the representation of vegetation captured by satellites and drones aligns well with the data obtained from phenocamera and proximal SRS platforms. The combination of high temporal resolution provided by SRS and phenocameras resulted in improved and more reliable indices that will be indispensable for evaluating the response of Caatinga vegetation to current and future conditions. Funding: This study was supported by the São Paulo Research Foundation-FAPESP (grants ##2015/50488-5, #2019/11835-2; #2021/10639-5; #2022/07735-5), the Coordination for the Improvement of Higher Education Personnel - CAPES (Finance Code 001), the National Council for Scientific and Technological Development - CNPq (306563/2022-3).

From heightened canopy dieback to tree die-off, many forest ecosystems are showing signs of poorly coping with more severe, more frequent, or hotter droughts. Understanding forest resilience to drought has become paramount, and eco‐physiological optimisation approaches that test behavioural hypotheses have been proposed as a means to build this understanding in global terrestrial models. Here, we used a land-surface model that considers competing optimality principles to simulate canopy gas exchange and leaf nitrogen investments into the photosynthetic apparatus, whilst also accounting for sustained hydraulic impairment (Sabot et al., 2022). We applied this model to a pristine observational site of the Caatinga, Brazil’s drought-hardy, seasonally deciduous, and exceptionally diverse dry tropical forest. Six woody species dominate 80% of the study area whilst displaying contrasting functional strategies – for example, their respective P50s (the water potential at which 50% of a plant’s hydraulic conductivity is lost) range between -1 MPa and -5 MPa. Model predictions were assessed against species-specific leaf-level observations of stomatal conductance and photosynthetic uptake, as well as eddy covariance measurements of ecosystem carbon and water fluxes spanning a period with high interannual rainfall variability (and including a severe multi-year regional drought). We found that none of the six species could, in isolation, explain the magnitude and dynamics of the observed surface fluxes. However, taken together and accounting for their relative contribution to total ecosystem fluxes, they did. Further, our analysis emphasises the vital role of phenology in mitigating seasonal and inter-annual hydraulic risks, with foliage reductions triggered by a 10 to 20% loss of hydraulic conductivity in the canopy. On the whole, accounting for diverging species-level responses and their relative influence at the ecosystem-scale appears key to improving model predictions in functional diverse forests. Reference: Sabot, M.E.B., De Kauwe, M.G., Pitman, A.J., Ellsworth, D.S., Medlyn, B.E., Caldararu, S. et al. (2022) Predicting resilience through the lens of competing adjustments to vegetation function. Plant, Cell & Environment, 45, 2744–2761.

The soil health assessment has evolved from focusing primarily on agricultural productivity to an integrated evaluation of soil biota and biotic processes that impact soil properties. Consequently, soil health assessment has shifted from a predominantly physicochemical approach to incorporating ecological, biological and molecular microbiology indicators. This shift enables a comprehensive exploration of soil microbial community properties and their responses to environmental changes arising from climate change and anthropogenic disturbances. Despite the increasing availability of soil health indicators (physical, chemical, and biological) and data, a holistic mechanistic linkage has not yet been fully established between indicators and soil functions across multiple spatiotemporal scales. This article reviews the state-of-the-art of soil health monitoring, focusing on understanding how soil-microbiome-plant processes contribute to feedback mechanisms and causes of changes in soil properties, as well as the impact these changes have on soil functions. Furthermore, we survey the opportunities afforded by the soil-plant digital twin approach, an integrative framework that amalgamates process-based models, Earth Observation data, data assimilation, and physics-informed machine learning, to achieve a nuanced comprehension of soil health. This review delineates the prospective trajectory for monitoring soil health by embracing a digital twin approach to systematically observe and model the soil-plant system. We further identify gaps and opportunities, and provide perspectives for future research for an enhanced understanding of the intricate interplay between soil properties, soil hydrological processes, soil-plant hydraulics, soil microbiome, and landscape genomics.

Soil properties are key drivers of vegetation structure, yet their influence on above-ground woody biomass (AGBW) in seasonally dry tropical forests (SDTFs) remains underexplored, particularly at larger scales. This gap is evident in the Caatinga, Latin America’s largest SDTF, known for its biodiversity and carbon storage potential. We investigated relationships among soil, climate, and vegetation properties to understand accumulation patterns of AGBW in SDTFs. We used standardised soil and vegetation data from 29 research plots spanning diverse geological and floristic conditions. Linear mixed models and multi-model inference were applied to analyse relationships between AGBW and environmental variables, including soil texture, fertility, plant-available soil water, mean annual precipitation (MAP), temperature, and climatic water deficit (CWD). Structural equation modelling (SEM) was utilised to assess how environmental variables influenced community-weighted maximum stem diameter, wood density, functional richness, and their combined effects on AGBW. AGBW was influenced by MAP, soil fertility, maximum plant-available soil water, and CWD. SEM indicated that soil nutrient availability shaped community functional traits, reflecting trade-offs between growth and water-use strategies. In turn, species’ maximum stem diameter and, to a lesser extent, functional richness positively influenced AGBW, underscoring the role of soil-mediated functional traits in shaping biomass. AGBW in the Caatinga is shaped by soil, climate, and their interactions, with soil properties exerting strong effects on community functional diversity and composition. Our findings highlight patterns of functional trait variability and biomass storage, offering insights for biodiversity conservation and carbon sequestration in SDTFs under global environmental change.

Various studies have investigated the effects of grazing on soil hydraulic properties (SHPs) under different soil and environmental conditions, and grazing management practices, across different regions of the world. However, despite a relatively large body of research on this topic, the overall influence of grazing on SHPs across diverse contexts remains ambiguous due to the complex interplay of factors that moderate these effects. This study adopts a multi-level meta-analytic model to systematically collate and analyse global field data, obtained from the literature (comprising 74 papers), to investigate the magnitude of changes in SHP as influenced by grazing, moderated by 17 factors relating to management (grazing intensity, duration, strategy, livestock type, rooting depth), climate, and intrinsic soil physical properties (texture, clay content, clay type fraction and related mechanical properties). The moderating factors were obtained from details reported in the publications, as well as from independent globally distributed databases (the clay property database by Ito and Wagai (2017), with clay mechanical properties derived from equations provided in Lehmann et al. (2021)); the WorldClim 2.1 dataset (Fick and Hijmans, 2017) for mean annual rainfall and temperatures; germplasm databases for individual species listed in the publications to obtain rooting depth). Our findings showed that grazing significantly affects soil structure, causing decreased saturated hydraulic conductivity, Ksat (56%), mean infiltration rates, MIR (38%), and macroporosity, MP (10%), and an increase in bulk density, BD (28%). The meta-analysis reveals that the impact of grazing on SHPs is significantly greater under heavy grazing (for MIR, BD), long-term grazing (Ksat, BD), in areas dominated by shallow-rooted pasture compared to mixed or deep-rooted systems (BD, MP), and in cattle dominated grazing systems as opposed to sheep or mixed grazing systems (Ksat, BD, MP). Additionally, the negative effects of grazing increase with increases in mean annual precipitation (all SHP) and temperature (all, but not BD). It is also notable that clay type properties, specifically derived mechanical properties, also showed significant relationships with grazing effects, across all SHPs. The findings suggest that future research should be focused on the long-term effects of cattle grazing on soils with large fractions of active to moderately active clay types in climates with high precipitation to help develop grazing management and planting strategies that support sustainable grazing while mitigating negative soil hydrological impacts.Fick and Hijmans (2017), DOI: 10.1002/joc.5086; Ito and Wagai (2017), DOI: 10.1038/sdata.2017.103; Lehmann et al. (2021), DOI: 10.1029/2021GL0953

Reliable soil property maps are essential for environmental modeling, yet conventional mapping methods remain costly and time-consuming. We developed a machine learning framework that integrates the Soil-Landscape Estimation and Evaluation Program (SLEEP) with gradient boosting to predict soil properties at regional scales and multiple depths. Our approach addresses multicollinearity through a recursive feature selection algorithm. We applied this framework to a tropical region characterized by a ∼700-km longitudinal gradient of contrasting topography, climate, and vegetation (∼98,000 km²; NE Brazil), where scarce soil physicochemical data limit environmental modeling. We used six topographical, ten climate, and two vegetation covariates, along with data from 223 soil profiles (∼1 profile per 440 km²). Training and testing of our framework demonstrated strong spatial performance (r² = 0.79–0.98 and percent bias = −1.39–1.14 %). Topographic and climatic factors held greater weight than other variables in predicting soil layers, texture, and sum of bases. Moreover, we used our soil parameters combined with multiple pedotransfer functions (PTFs) to derive soil hydraulic properties. Our PTFs-derived estimates of hydraulic conductivity were considerably lower than high-resolution global predictions available for our study areadue to differences in clay fraction and mineralogy. Therefore, we recommend the use of region-specific PTFs for hydraulic properties based on multi-covariate soil property maps. This cost-effective framework accurately integrates diverse environmental covariates, adapts to varying soil data availability, and scales across spatial resolutions, making it highly transferable to other data-scarce regions.

Domestic gardens comprise up to 30% of urban area in the UK, providing many ecosystem services (ES), such as flood risk mitigation and temperature regulation, through vegetation present. Current estimates of ES provisioning using urban land surface models often focus on green space in an entire town/city, rather than specific greenspace types (Zawadzka et al., 2021), and often omit domestic gardens entirely, which may lead to unreliable recommendations. We chose the Urban Tethys-Chloris model (UT&C; Meili et al., 2020) to estimate ES delivery by domestic gardens because it considers both the energy and water balance, at the local scale, and allows for configuration and simulation of both vegetated and man-made surfaces. UT&C is a fully coupled energy and water balance model, that calculates 2m air temperature and skin temperatures of urban areas, accounting for biophysical and ecophysiological characteristics of ground vegetation and urban trees. Input meteorological data over the course of 2024 was obtained from the University of Reading Atmospheric Observatory (Reading, UK). Model garden plant species were specified as Lolium perenne (Perennial ryegrass; for ground vegetation) and Pyrus calleryana (Callery pear; for urban trees), and urban geometry values (such as house height and width) were specified as UK averages. We have found that UT&C realistically estimates seasonal and diurnal urban surface energy fluxes within a typical UK garden. Specifically, in summer, a garden made up of 100% vegetation (short lawn and 2 trees) had a peak surface temperature 13°C cooler, and a 2m air temperature 1°C cooler, than a garden made entirely of concrete. This is largely because vegetated ground loses heat through latent heat flux throughout the growing period, while impermeable surfaces can only do so after heavy rainfall (when water ponds on the surface). Gardens with 100% granite, concrete and slate surfaces had a surface temperature up to 6°C lower and a 2m air temperature 0.5°C lower than asphalt and wood decking as a result of their high thermal conductivity and heat capacity, suggesting these materials would be marginally better at maintaining a lower air temperature within an entirely impermeable garden, particularly in urban summers. Air and surface temperatures over semi-permeable materials, such as artificial turf and wood chips, were often higher than those found for impermeable surfaces, suggesting that they may not be an appropriate method of reducing air temperatures in gardens. Further work will focus on modelling the role of vegetation and garden surface choices on the surface water balance, and on translating the mechanistic model outputs into human comfort and flood mitigation indices. Additional models, such as SUEWS (Järvi et al., 2011), will also be used to estimate ES delivery, and to allow for model intercomparison. Following these simulations, we hope to provide recommendations to UK gardeners about the best hard landscaping materials, plant species, and garden configuration (e.g. proportion of trees versus lawn and bedding plants) to help reduce air temperatures and flooding within their neighbourhoods.

Observations of drought-driven damage to vegetation are widespread but, until recently, large-scale terrestrial models used to study climate-vegetation interactions did not capture the contrasting sensitivities of plants to drought. This is changing with the advent of a generation of models that consider plant hydraulics. Plant hydraulics link plant water status to pedoclimatic conditions; as such, explicit consideration of plant hydraulics should make models more mechanistic and predictive. Models, however, diverge in how they represent the plant water transport pathway and relate it to other plant functions (e.g., photosynthesis), so they further diverge in their parameterisation approach for hydraulic processes. Only at the most basic level do plant hydraulic implementations converge on a common set of measurable traits or parameters: two that describe a hydraulic vulnerability curve (e.g., P12 and P50, the water potentials at which 12% and 50% of a plant’s hydraulic conductivity are lost, respectively), and one that quantifies the efficiency of water movement within the plant (e.g., maximum hydraulic conductance). Regrettably, we do not yet know how to obtain regional- or global-scale hydraulic parameters from local-scale measurements, nor how to connect them to other plant traits. In this study, we propose strategies to leverage cross-species hydraulic diversity when scaling traits from the species level into model parameters. We also emphasise the importance of accounting for (i) within-species trait variability across space (e.g., interactions between hydraulic traits and their environment) and (ii) cross-functional trait covariation (i.e., interactions – or lack thereof – among traits that characterise different functional axes). Beyond advancing regional and global plant hydraulic modelling, efforts to address the suggested strategies would ready models for simulations that capture the resilience of vegetation communities worldwide.

Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the diagnosis of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest feature importance to detect the input variables that most influence simulated hydrological processes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoffs. The machine learning model could reproduce GHM/LSMs outputs with a coefficient of determination over 0.85 in all cases and often considerably better. The GHM/LSMs agreed precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regards to the relative importance of soil texture and topographic slope.

The assessment of soil health has evolved from focusing primary on agricultural productivity to an integrated evaluation of soil biota and biotic processes that impact soil properties. Consequently, soil health assessment has shifted from a predominantly physico-chemical approach to incorporating ecological, biological and molecular microbiology methods. These methods enable a comprehensive exploration of soil microbial community properties and their responses to environmental changes arising from climate change and anthropogenic disturbances. Despite the increasing availability of soil health indicators (physical, chemical, and biological), a holistic mechanistic linkage between indicators and soil functions across multiple spatiotemporal scales has not yet been fully established. This article reviews the state-of-the-art of soil health monitoring, focusing on understanding how soil-microbiome-plant processes contribute to feedback mechanisms and causes of changes in soil properties, as well as the impact these changes have on soil functions. Furthermore, we survey the opportunities afforded by the soil-plant digital twin approach, an integrative framework that amalgamates process-based models, Earth Observation data, data assimilation, and physics-informed machine learning, to achieve a nuanced comprehension of soil health. This review delineates the prospective trajectory for monitoring soil health by embracing a digital twin approach to systematically observe and model the soil-plant system. We further identify gaps and opportunities, and provide perspectives for future research for an enhanced understanding of the intricate interplay between soil properties, soil hydrological processes, soil-plant hydraulics, soil microbiomes, and landscape genomics.

Groundwater-fed peatlands are a rare and vital ecosystem providing rich biodiversity, carbon storage and regulation of the hydrological cycle. Management of these species and carbon stores are essential for maintaining a healthy ecosystem. In parallel to this, groundwater aquifers are a common source of relatively clean drinking water, but they are under pressure from population growth and climate change. Groundwater abstraction can lead to a reduction in groundwater levels within groundwater-fed peatlands, affecting their condition, for example by facilitating tree encroachment. Therefore, sustainable water supply needs to balance water demand against other unintentional environmental impacts on these ecosystems. Greywell Fen is located in Southern England, situated above a chalk aquifer that is used to provide drinking water to the area. The fen has been designated a site of special scientific interest (SSSI) in recognition of its important flora. However, the significant fen vegetation species have been declining in recent decades in favour of extensive tree growth throughout the site. New management of the area has included the reintroduction of grazing and large areas of tree clearance. Our research concerns the impacts of groundwater abstraction and woodland management on the health of the fen. Extensive water level monitoring connected to areas with variable tree expansion and clearance is being used to determine if tree management is having an effect on water levels within the fen. In addition, peat cores have been sampled in the different areas to determine if tree management and/or water level changes are impacting peat properties, as an indication of drying and decline in fen health. Peat properties studied include pH, water content, C:N, and organic matter decomposition. The latter was performed using FTIR spectroscopy. The monitoring data have been fed into the Soil-Water-Atmosphere-Plant (SWAP) model to improve our understanding of the hydrology of the system and how the groundwater abstraction affects fen water levels. The model has also been used to determine how management can be optimised to enable sustainable abstraction. The results of the in-depth monitoring and modelling are presented here.

Hydro-pedotransfer functions (PTFs) relate easy-to-measure and readily available soil information to soil hydraulic properties (SHPs) for applications in a wide range of process-based and empirical models, thereby enabling the assessment of soil hydraulic effects on hydrological, biogeochemical, and ecological processes. At least more than 4 decades of research have been invested to derive such relationships. However, while models, methods, data storage capacity, and computational efficiency have advanced, there are fundamental concerns related to the scope and adequacy of current PTFs, particularly when applied to parameterise models used at the field scale and beyond. Most of the PTF development process has focused on refining and advancing the regression methods, while fundamental aspects have remained largely unconsidered. Most soil systems are not represented in PTFs, which have been built mostly for agricultural soils in temperate climates. Thus, existing PTFs largely ignore how parent material, vegetation, land use, and climate affect processes that shape SHPs. The PTFs used to parameterise the Richards–Richardson equation are mostly limited to predicting parameters of the van Genuchten–Mualem soil hydraulic functions, despite sufficient evidence demonstrating their shortcomings. Another fundamental issue relates to the diverging scales of derivation and application, whereby PTFs are derived based on laboratory measurements while often being applied at the field to regional scales. Scaling, modulation, and constraining strategies exist to alleviate some of these shortcomings in the mismatch between scales. These aspects are addressed here in a joint effort by the members of the International Soil Modelling Consortium (ISMC) Pedotransfer Functions Working Group with the aim of systematising PTF research and providing a roadmap guiding both PTF development and use. We close with a 10-point catalogue for funders and researchers to guide review processes and research.

We use two comprehensively instrumented field observatories to understand groundwater recharge processes in African drylands. The observatories are located on crystalline basement geology in semi-arid parts of Ghana and Burkina Faso, aridity indices 0.43 and 0.29, respectively, and we report 2017-2019 observations. Groundwater recharge was quantified by inverse water table fluctuation models using specific yield estimates derived from magnetic resonance soundings. Evidence for recharge drivers and mechanisms comes from high resolution meteorological observations, soil moisture (logged hourly and weekly along hillslope transects), overland flow plots, river stage, and stable isotopes of O and H in rainfall events and groundwater. Groundwater recharge varied between 87 and 175 mm/y, i.e. 7-15 % of annual rainfall. Rainfall was twice the volume of water lost via actual evapotranspiration across the four peak months (Jun-Sep) of the monsoon. This seasonal water surplus of similar to 350 mm/y is not characterised by the annual scale of the aridity index. Overland flow was rare and soil moisture deficits were overcome at all monitoring locations. Large rainfall events only produced appreciable recharge when the antecedent soil moisture was close to field capacity, yet always produced large responses in river stage. Stable isotopes of O and H in groundwater indicate no evidence of evapotranspiration prior to infiltration and their composition is akin to depleted isotopic rainfall at the monsoon peak. Stable isotopes indicate recharge season timing and not a relationship between intense rainfall and groundwater recharge. We contend that the mechanism for groundwater recharge is predominantly diffuse in these semi-arid African settings.

Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF.

Over the last 20 years, there has been a surge of interest in the use of reflectance data collected using satellites and aerial vehicles to monitor vegetation diversity. One methodological option to monitor these systems involves developing empirical relationships between spectral heterogeneity in space (spectral variation) and plant or habitat diversity. This approach is commonly termed the ‘Spectral Variation Hypothesis’. Although increasingly used, it is controversial and can be unreliable in some contexts. Here, we review the literature and apply three-level meta-analytical models to assess the test results of the hypothesis across studies using several moderating variables relating to the botanical and spectral sampling strategies and the types of sites evaluated. We focus on the literature relating to grasslands, which are less well studied compared to forests and are likely to require separate treatments due to their dynamic phenology and the taxonomic complexity of their canopies on a small scale. Across studies, the results suggest an overall positive relationship between spectral variation and species diversity (mean correlation coefficient = 0.36). However, high levels of both within-study and between-study heterogeneity were found. Whether data was collected at the leaf or canopy level had the most impact on the mean effect size, with leaf-level studies displaying a stronger relationship compared to canopy-level studies. We highlight the challenges facing the synthesis of these kinds of experiments, the lack of studies carried out in arid or tropical systems and the need for scalable, multitemporal assessments to resolve the controversy in this field.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Ariane Frassoni, ariane.frassoni@inpe.br

A solid understanding of the global water cycle and how land surface processes respond to both changes in climate and pressure due to water use is essential for society. Although Land Surface Models (LSM) and Global Hydrological Models (GHM) are able to simulate the spatiotemporal variability of the water balance relatively reliably, intercomparison studies have indicated considerable differences between the models. Each LSM and GHM present a unique set of equations, parameters and configurations that contribute to the spread of simulated hydrological responses to meteorological forcings. In order to improve our understanding of modeling uncertainties, we propose a variable importance assessment for 5 LSM/GHM (JULES, HTESSEL, PCR-GLOBWB, SURFEX and ORCHIDEE) from the EartH2Observe (E2O) project. The output of the models and the meteorological forcings were collected from the Water Resources Reanalysis Tier 2 of the E2O project, which consists of a global dataset with spatial resolution of 0.25ox0.25o. We used soil texture and land cover datasets that most resemble the inputs used by each LSM/GHM during the E2O project. The models’ outputs were used to estimate 6 hydrological indices for every land cell: Evaporation-Precipitation ratio; Runoff-Precipitation ratio; Surface Runoff-Total Runoff ratio; median Soil Moisture variation caused by a Rainfall event; median Surface Runoff caused by a Rainfall event; and Soil Moisture temporal autocorrelation. Then, we evaluate the input features (meteorological, land cover, and soil texture) importance to the hydrological indices of each model using machine learning. With the analysis we aim to examine a) How much the models differ and why? b) To what extent are the output differences related to the input features or/and to the models formulation? and c) How significant is each feature to the respective hydrological index?