Soil Vegetation Atmosphere Transfer Model Research Articles

Extended HYDRUS-1D freezing module emphasizes thermal conductivity schemes for simulation of soil hydrothermal dynamics

Soil thermal conductivity (λ) is required to investigate coupled heat and water transport in disciplines such as agriculture, hydrology and engineering. Parameterization schemes or models of λ are also the critical input parameter for various numerical simulation programs like the widely used HYDRUS, one of the most commonly used models for mimicking water, heat, and solute transport. However, λ has not received enough attention in HYDRUS, and it remains unclear how different λ schemes affect the simulated soil water and thermal regimes. Thus, we programmed 24 λ schemes (including two built-in schemes) used in mainstream land surface, hydrological, and soil–vegetation–atmosphere transfer models into HYDRUS-1D (freezing module) to assess the effects of different λ schemes on soil temperature and water content simulations under freezing–thawing. The results showed that the 24 λ schemes performed differently in the simulation of soil temperature within 1 m below ground, with eight λ schemes, i.e., de Vries 1963 scheme/DV1963 (R=0.99), Camillo and Schmugge 1981/CS1981 scheme (R=0.98), Desborough and Pitman 1998/DP1998 scheme (R=0.97), Cass et al. 1984/CS1984 scheme (R=0.96), Shmakin 1998/SA1998 scheme (R=0.95), Dharssi et al. 2009/DI2009 scheme (R=0.95), Becker et al. 1992/BB1992 scheme (R=0.95), Hubrechts 1998/HL1998 scheme (R=0.94), performing superiorly to the built-in λ schemes. This study highlights the importance of choosing appropriate λ schemes in soil water and heat simulations.

Impact of vegetation composition and seasonality on sensitivity of modelled CO2 exchange in temperate raised bogs

Encroachment of vascular plants (VP) in temperate raised bogs, as a consequence of altered hydrological conditions and nutrient input, is widely observed. Effects of such vegetation shift on water and carbon cycles are, however, largely unknown and identification of responsible plant physiological traits is challenging. Process-based modelling offers the opportunity of gaining insights into ecosystem functioning beyond observations, and to infer decisive trait shifts of plant functional groups. We adapted the Soil–Vegetation–Atmosphere Transfer model pyAPES to a temperate raised bog site by calibration against measured peat temperature, water table and surface CO2 fluxes. We identified the most important traits determining CO2 fluxes by conducting Morris sensitivity analysis (MSA) under changing conditions throughout the year and simulated VP encroachment. We further investigated transferability of results to other sites by extending MSA to parameter ranges derived from literature review. We found highly variable intra-annual plant traits importance determining ecosystem CO2 fluxes, but only a partial shift of importance of photosynthetic processes from moss to VP during encroachment. Ecosystem respiration was dominated by peat respiration. Overall, carboxylation rate, base respiration rate and temperature sensitivity (Q10) were most important for determining bog CO2 balance and parameter ranking was robust even under the extended MSA.

Recent Developments to the SimSphere Land Surface Modelling Tool for the Study of Land–Atmosphere Interactions

Soil–Vegetation–Atmosphere Transfer (SVAT) models are a promising avenue towards gaining a better insight into land surface interactions and Earth’s system dynamics. One such model developed for the academic and research community is the SimSphere SVAT model, a popular software toolkit employed for simulating interactions among the layers of vegetation, soil, and atmosphere on the land surface. The aim of the present review is two-fold: (1) to deliver a critical assessment of the model’s usage by the scientific and wider community over the last 15 years, and (2) to provide information on current software developments implemented in the model. From the review conducted herein, it is clearly evident that from the models’ inception to current day, SimSphere has received notable interest worldwide, and the dissemination of the model has continuously grown over the years. SimSphere has been used so far in several applications to study land surface interactions. The validation of the model performed worldwide has shown that it is able to produce realistic estimates of land surface parameters that have been validated, whereas detailed sensitivity analysis experiments conducted with the model have further confirmed its structure and architectural coherence. Furthermore, the recent inclusion of novel functionalities in the model, as outlined in the present review, has clearly resulted in improving its capabilities and in opening up new opportunities for its use by the wider community. SimSphere developments are also ongoing in different aspects, and its use as a toolkit towards advancing our understanding of land surface interactions from both educational and research points of view is anticipated to grow in the coming years.

Implications of seasonal changes in photosynthetic traits and leaf area index for canopy CO2 and H2O fluxes in a Japanese cedar (Cryptomeria japonica D. Don) plantation

Recently, requests for forest management to conserve water resources and sequester carbon have increased in Japan, where approximately 27% and 12% of the total land area are covered by forest plantations and Japanese cedar (Cryptomeria japonica D. Don). To formulate better forest management practices that maximize forest functions by maintaining water and carbon cycles, an understanding of how leaf-level ecophysiological traits influence canopy-level CO2 and H2O exchanges in Japanese cedar plantations is required. In this study, observations of eddy covariance flux and leaf ecophysiology in a Japanese cedar plantation in southern Japan indicate that significantly greater productivity and seasonal variations tend to occur in canopy CO2 and H2O fluxes, with seasonal patterns of photosynthetic traits (maximum carboxylation rate normalized at 25 °C, Vcmax,25) and leaf area index (LAI). We also examined how biotic (e.g., Vcmax,25 and LAI) and abiotic (e.g., meteorological variables) factors govern canopy fluxes using a multi-layer soil–vegetation–atmosphere transfer (SVAT) model. The model validation suggests that seasonal variations in Vcmax,25 and LAI must be included to reproduce the fluxes observed in the plantation. Quantitative experiments using the validated SVAT model indicate that considering cold acclimation during the winter improved the reproducibility of the model with regard to the measured fluxes, constant high intra-annual LAI had the least impact on forest productivity, and a consistently high Vcmax,25 value increased forest productivity but considerably decreased the nitrogen use efficiency and required a larger nitrogen supply from the ecosystem during the winter. These findings highlight the necessity of decreasing photosynthetic ability during the winter, which safeguards the ecosystem's nitrogen resources to sustain the productivity of the Japanese cedar plantation throughout the year.

Temporal interpolation of land surface fluxes derived from remote sensing – results with an unmanned aerial system

Abstract. Remote sensing imagery can provide snapshots of rapidly changing land surface variables, e.g. evapotranspiration (ET), land surface temperature (Ts), net radiation (Rn), soil moisture (θ), and gross primary productivity (GPP), for the time of sensor overpass. However, discontinuous data acquisitions limit the applicability of remote sensing for water resources and ecosystem management. Methods to interpolate between remote sensing snapshot data and to upscale them from an instantaneous to a daily timescale are needed. We developed a dynamic soil–vegetation–atmosphere transfer model to interpolate land surface state variables that change rapidly between remote sensing observations. The “Soil–Vegetation, Energy, water, and CO2 traNsfer” (SVEN) model, which combines the snapshot version of the remote sensing Priestley–Taylor Jet Propulsion Laboratory ET model and light use efficiency GPP models, now incorporates a dynamic component for the ground heat flux based on the “force-restore” method and a water balance “bucket” model to estimate θ and canopy wetness at a half-hourly time step. A case study was conducted to demonstrate the method using optical and thermal data from an unmanned aerial system at a willow plantation flux site (Risoe, Denmark). Based on model parameter calibration with the snapshots of land surface variables at the time of flight, SVEN interpolated UAS-based snapshots to continuous records of Ts, Rn, θ, ET, and GPP for the 2016 growing season with forcing from continuous climatic data and the normalized difference vegetation index (NDVI). Validation with eddy covariance and other in situ observations indicates that SVEN can estimate daily land surface fluxes between remote sensing acquisitions with normalized root mean square deviations of the simulated daily Ts, Rn, θ, LE, and GPP of 11.77 %, 6.65 %, 19.53 %, 14.77 %, and 12.97 % respectively. In this deciduous tree plantation, this study demonstrates that temporally sparse optical and thermal remote sensing observations can be used to calibrate soil and vegetation parameters of a simple land surface modelling scheme to estimate “low-persistence” or rapidly changing land surface variables with the use of few forcing variables. This approach can also be applied with remotely-sensed data from other platforms to fill temporal gaps, e.g. cloud-induced data gaps in satellite observations.

Characterizing uncertainty in the hydraulic parameters of oil sands mine reclamation covers and its influence on water balance predictions

Abstract. One technique to evaluate the performance of oil sands reclamation covers is through the simulation of long-term water balance using calibrated soil–vegetation–atmosphere transfer models. Conventional practice has been to derive a single set of optimized hydraulic parameters through inverse modelling (IM) based on short-term (<5–10 years) monitoring datasets. This approach is unable to characterize the impact of variability in the cover properties. This study utilizes IM to optimize the hydraulic properties for 12 soil cover designs, replicated in triplicate, at Syncrude's Aurora North mine site. The hydraulic parameters for three soil types (peat cover soil, coarse-textured subsoil, and lean oil sand substrate) were optimized at each monitoring site from 2013 to 2016. The resulting 155 optimized parameter values were used to define distributions for each parameter/soil type, while the progressive Latin hypercube sampling (PLHS) method was used to sample parameter values randomly from the optimized parameter distributions. Water balance models with the sampled parameter sets were used to evaluate variations in the maximum sustainable leaf area index (LAI) for five illustrative covers and quantify uncertainty associated with long-term water balance components and LAI values. Overall, the PLHS method was able to better capture broader variability in the water balance components than a discrete interval sampling method. The results also highlight that climate variability dominates the simulated variability in actual evapotranspiration and that climate and parameter uncertainty have a similar influence on the variability in net percolation.

Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard

Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field-scale using a data assimilation strategy.

Ability of a soil–vegetation–atmosphere transfer model and a two-source energy balance model to predict evapotranspiration for several crops and climate conditions

Abstract. The heterogeneity of Agroecosystems, in terms of hydric conditions, crop types and states, and meteorological forcing, is difficult to characterize precisely at the field scale over an agricultural landscape. This study aims to perform a sensitivity study with respect to the uncertain model inputs of two classical approaches used to map the evapotranspiration of agroecosystems: (1) a surface energy balance (SEB) model, the Two-Source Energy Balance (TSEB) model, forced with thermal infrared (TIR) data as a proxy for the crop hydric conditions, and (2) a soil–vegetation–atmosphere transfer (SVAT) model, the SEtHyS model, where hydric conditions are computed from a soil water budget. To this end, the models' skill was compared using a large and unique in situ database covering different crops and climate conditions, which was acquired over three experimental sites in southern France and Morocco. On average, the models provide 30 min estimations of latent heat flux (LE) with a RMSE of around 55 W m−2 for TSEB and 47 W m−2 for SEtHyS, and estimations of sensible heat flux (H) with a RMSE of around 29 W m−2 for TSEB and 38 W m−2 for SEtHyS. A sensitivity analysis based on realistic errors aimed to estimate the potential decrease in performance induced by the spatialization process. For the SVAT model, the multi-objective calibration iterative procedure (MCIP) is used to determine and test different sets of parameters. TSEB is run with only one set of parameters and provides acceptable performance for all crop stages apart from the early growing season (LAI < 0.2 m2 m−2) and when hydric stress occurs. An in-depth study on the Priestley–Taylor key parameter highlights its marked diurnal cycle and the need to adjust its value to improve flux partitioning between the sensible and latent heat fluxes (1.5 and 1.25 for France and Morocco, respectively). Optimal values of 1.8–2 were highlighted under cloudy conditions, which is of particular interest due to the emergence of low-altitude drone acquisition. Under developed vegetation (LAI > 0.8 m2 m−2) and unstressed conditions, using sets of parameters that only differentiate crop types is a valuable trade-off for SEtHyS. This study provides some scientific elements regarding the joint use of both approaches and TIR imagery, via the development of new data assimilation and calibration strategies.

An improved perspective in the spatial representation of soil moisture: potential added value of SMOS disaggregated 1 km resolution “all weather” product

Abstract. This study uses the synergy of multi-resolution soil moisture (SM) satellite estimates from the Soil Moisture Ocean Salinity (SMOS) mission, a dense network of ground-based SM measurements, and a soil–vegetation–atmosphere transfer (SVAT) model, SURFEX (externalized surface), module ISBA (interactions between soil, biosphere and atmosphere), to examine the benefits of the SMOS level 4 (SMOS-L4) version 3.0, or “all weather” high-resolution soil moisture disaggregated product (SMOS-L43.0; ∼1 km). The added value compared to SMOS level 3 (SMOS-L3; ∼25 km) and SMOS level 2 (SMOS-L2; ∼15 km) is investigated. In situ SM observations over the Valencia anchor station (VAS; SMOS calibration and validation – Cal/Val – site in Europe) are used for comparison. The SURFEX (ISBA) model is used to simulate point-scale surface SM (SSM) and, in combination with high-quality atmospheric information data, namely from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Système d'analyse fournissant des renseignements atmosphériques à la neige (SAFRAN) meteorological analysis system, to obtain a representative SSM mapping over the VAS. The sensitivity to realistic initialization with SMOS-L43.0 is assessed to simulate the spatial and temporal distribution of SSM. Results demonstrate the following: (a) All SMOS products correctly capture the temporal patterns, but the spatial patterns are not accurately reproduced by the coarser resolutions, probably in relation to the contrast with point-scale in situ measurements. (b) The potential of the SMOS-L43.0 product is pointed out to adequately characterize SM spatio-temporal variability, reflecting patterns consistent with intensive point-scale SSM samples on a daily timescale. The restricted temporal availability of this product dictated by the revisit period of the SMOS satellite compromises the averaged SSM representation for longer periods than a day. (c) A seasonal analysis points out improved consistency during December–January–February and September–October–November, in contrast to significantly worse correlations in March–April–May (in relation to the growing vegetation) and June–July–August (in relation to low SSM values < 0.1 m3 m−3 and low spatial variability). (d) The combined use of the SURFEX (ISBA) SVAT model with the SAFRAN system, initialized with SMOS-L43.0 1 km disaggregated data, is proven to be a suitable tool for producing regional SM maps with high accuracy, which could be used as initial conditions for model simulations, flood forecasting, crop monitoring and crop development strategies, among others.

A Critical Evaluation on the Role of Aerodynamic and Canopy–Surface Conductance Parameterization in SEB and SVAT Models for Simulating Evapotranspiration: A Case Study in the Upper Biebrza National Park Wetland in Poland

Evapotranspiration (ET) estimation through the surface energy balance (SEB) and soil-vegetation-atmosphere-transfer (SVAT) models are uncertain due to the empirical parameterizations of the aerodynamic and canopy-substrate conductances (gA and gS) for heat and water vapor transfers. This study critically assessed the impact of conductance parameterizations on ET simulation using three structurally different SEB and SVAT models for an ecologically important North-Eastern European wetland, Upper Biebrza National Park (UBNP) in two consecutive years 2015 and 2016. A pronounced ET underestimation (mean bias −0.48 to −0.68 mm day−1) in SEBS (Surface Energy Balance System) was associated with an overestimation of gA due to uncertain parameterization of momentum roughness length and bare soil’s excess resistance to heat transfer (kB−1) under low vegetation cover. The systematic ET overestimation (0.65–0.80 mm day−1) in SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) was attributed to the overestimation of both the conductances. Conductance parameterizations in SEBS and SCOPE appeared to be very sensitive to the general ecohydrological conditions, with a tendency of overestimating gA (gS) under humid (arid) conditions. Low ET bias in the analytical STIC (Surface Temperature Initiated Closure) model as compared to SEBS/SCOPE indicated the critical need for calibration-free conductance parameterizations for improved ET estimation.

Tree-, stand- and site-specific controls on landscape-scale patterns of transpiration

Abstract. Transpiration is a key process in the hydrological cycle, and a sound understanding and quantification of transpiration and its spatial variability is essential for management decisions as well as for improving the parameterisation and evaluation of hydrological and soil–vegetation–atmosphere transfer models. For individual trees, transpiration is commonly estimated by measuring sap flow. Besides evaporative demand and water availability, tree-specific characteristics such as species, size or social status control sap flow amounts of individual trees. Within forest stands, properties such as species composition, basal area or stand density additionally affect sap flow, for example via competition mechanisms. Finally, sap flow patterns might also be influenced by landscape-scale characteristics such as geology and soils, slope position or aspect because they affect water and energy availability; however, little is known about the dynamic interplay of these controls. We studied the relative importance of various tree-, stand- and site-specific characteristics with multiple linear regression models to explain the variability of sap velocity measurements in 61 beech and oak trees, located at 24 sites across a 290 km2 catchment in Luxembourg. For each of 132 consecutive days of the growing season of 2014 we modelled the daily sap velocity and derived sap flow patterns of these 61 trees, and we determined the importance of the different controls. Results indicate that a combination of mainly tree- and site-specific factors controls sap velocity patterns in the landscape, namely tree species, tree diameter, geology and aspect. For sap flow we included only the stand- and site-specific predictors in the models to ensure variable independence. Of those, geology and aspect were most important. Compared to these predictors, spatial variability of atmospheric demand and soil moisture explains only a small fraction of the variability in the daily datasets. However, the temporal dynamics of the explanatory power of the tree-specific characteristics, especially species, are correlated to the temporal dynamics of potential evaporation. We conclude that transpiration estimates on the landscape scale would benefit from not only consideration of hydro-meteorological drivers, but also tree, stand and site characteristics in order to improve the spatial and temporal representation of transpiration for hydrological and soil–vegetation–atmosphere transfer models.

A modernized version of a 1D soil vegetation atmosphere transfer model for improving its future use in land surface interactions studies

SimSphere is a land biosphere model that provides a mathematical representation of vertical ‘views’ of the physical mechanisms controlling Earth's energy and mass transfers in the soil/vegetation/atmosphere continuum. Herein, we present recent advancements introduced to SimSphere code, aiming at making its use more integrated to the automation of processes within High Performance Computing (HPC) that allows using the model at large scale. In particular, a new interface to the model is presented, so-called “SimSphere-SOA” which forms a command line land biosphere tool, a Web Service interface and a parameters verification facade that offers a standardised environment for specification execution and result retrieval of a typical model simulation based on Service Oriented Architecture (SOA). SimSphere-SOA library can now execute various simulations in parallel. This allows exploitation of the tool in a simple and efficient way in comparison to the currently distributed approach. In SimSphere-SOA, an Application Programming Interface (API) is also provided to execute simulations that can be publicly consumed. Finally this API is exported as a Web Service for remotely executing simulations through web based tools. This way a simulation by the model can be executed efficiently and subsequently the model simulation outputs may be used in any kind of relevant analysis required.The use of these new functionalities offered by SimSphere-SOA is also demonstrated using a “real world” simulation configuration file. The inclusion of those new functions in SimSphere are of considerable importance in the light of the model's expanding use worldwide as an educational and research tool.

Conversion of Blue Water into Green Water for Improving Utilization Ratio of Water Resources in Degraded Karst Areas

Vegetation deterioration and soil loss are the main causes of more precipitation leakages and surface water shortages in degraded karst areas. In order to improve the utilization of water resources in such regions, water storage engineering has been considered; however, site selection and cost associated with the special karstic geological structure have made this difficult. According to the principle of the Soil Plant Atmosphere Continuum, increasing both vegetation cover and soil thickness would change water cycle process, resulting in a transformation from leaked blue water (liquid form) into green water (gas or saturated water form) for terrestrial plant ecosystems, thereby improving the utilization of water resources. Using the Soil Vegetation Atmosphere Transfer model and the geographical distributed approach, this study simulated the conversion from leaked blue water (leakage) into green water in the environs of Guiyang, a typical degraded karst area. The primary results were as follows: (1) Green water in the area accounted for <50% of precipitation, well below the world average of 65%; (2) Vegetation growth played an important role in converting leakage into green water; however, once it increased to 56%, its contribution to reducing leakage decreased sharply; (3) Increasing soil thickness by 20 cm converted the leakage considerably. The order of leakage reduction under different precipitation scenarios was dry year > normal year > rainy year. Thus, increased soil thickness was shown effective in improving the utilization ratio of water resources and in raising the amount of plant ecological water use; (4) The transformation of blue water into green water, which avoids constructions of hydraulic engineering, could provide an alternative solution for the improvement of the utilization of water resources in degraded karst area. Although there are inevitable uncertainties in simulation process, it has important significance for overcoming similar problems.

Do the energy fluxes and surface conductance of boreal coniferous forests in Europe scale with leaf area?

Earth observing systems are now routinely used to infer leaf area index (LAI) given its significance in spatial aggregation of land surface fluxes. Whether LAI is an appropriate scaling parameter for daytime growing season energy budget, surface conductance (Gs ), water- and light-use efficiency and surface-atmosphere coupling of European boreal coniferous forests was explored using eddy-covariance (EC) energy and CO2 fluxes. The observed scaling relations were then explained using a biophysical multilayer soil-vegetation-atmosphere transfer model as well as by a bulk Gs representation. The LAI variations significantly alter radiation regime, within-canopy microclimate, sink/source distributions of CO2 , H2 O and heat, and forest floor fluxes. The contribution of forest floor to ecosystem-scale energy exchange is shown to decrease asymptotically with increased LAI, as expected. Compared with other energy budget components, dry-canopy evapotranspiration (ET) was reasonably 'conservative' over the studied LAI range 0.5-7.0m2 m-2 . Both ET and Gs experienced a minimum in the LAI range 1-2m2 m-2 caused by opposing nonproportional response of stomatally controlled transpiration and 'free' forest floor evaporation to changes in canopy density. The young forests had strongest coupling with the atmosphere while stomatal control of energy partitioning was strongest in relatively sparse (LAI ~2m2 m-2 ) pine stands growing on mineral soils. The data analysis and model results suggest that LAI may be an effective scaling parameter for net radiation and its partitioning but only in sparse stands (LAI <3m2 m-2 ). This finding emphasizes the significance of stand-replacing disturbances on the controls of surface energy exchange. In denser forests, any LAI dependency varies with physiological traits such as light-saturated water-use efficiency. The results suggest that incorporating species traits and site conditions are necessary when LAI is used in upscaling energy exchanges of boreal coniferous forests.

Construction of Minirhizotron Facilities for Investigating Root Zone Processes

Core Ideas Horizontal straight holes for rhizotube installation were bored by a homemade system. Dynamics of root growth and soil moisture could be described by rhizotron facilities. Soil moisture could be monitored by TDR and GPR approaches in rhizotron facilities. Minimally invasive monitoring of root development and soil states (soil moisture, temperature) in undisturbed soils during a crop growing cycle is a challenging task. Minirhizotron (MR) tubes offer the possibility to view root development in situ with time. Two MR facilities were constructed in two different soils, stony vs. silty, to monitor root growth, root zone processes, and their dependence on soil water availability. To obtain a representative image of the root distribution, 7‐m‐long tubes were installed horizontally at 10‐, 20‐, 40‐, 60‐, 80‐, and 120‐cm depths. A homemade system was developed to install MR tubes in the silty soil in horizontally drilled straight holes. For the stony soil, the soil rhizotubes were installed in an excavated and subsequently backfilled pit. In both facilities, three subplots were established with different water treatments: rain sheltered, rainfed, and irrigated. To monitor soil moisture, water potential, and soil temperature, time domain reflectometer probes, tensiometers, and matrix water potential sensors were installed. Soil water content profiles in space and time were obtained between two MR tubes using cross‐hole ground‐penetrating radar along the tubes at different depths. Results from the first growing season of winter wheat (Triticum aestivum L.) after installation demonstrate that differences in root development, soil water, and temperature dynamics can be observed among the different soil types and water treatments. When combined with additional measurements of crop development and transpiration, these data provide key information that is essential to validate and parameterize root development and water uptake models in soil–vegetation–atmosphere transfer models.

Optimising crop production and nitrate leaching in China: Measured and simulated effects of straw incorporation and nitrogen fertilisation

Abstract The sustainability of growing a maize—winter wheat double crop rotation in the North China Plain (NCP) has been questioned due to its high nitrogen (N) fertiliser use and low N use efficiency. This paper presents field data and evaluation and application of the soil–vegetation–atmosphere transfer model Daisy for estimating crop production and nitrate leaching from silty loam fields in the NCP. The main objectives were to: i) calibrate and validate Daisy for the NCP pedo-climate and field management conditions, and ii) use the calibrated model and the field data in a multi-response analyses to optimise the N fertiliser rate for maize and winter wheat under different field managements including straw incorporation. The model sensitivity analysis indicated that a few measurable crop parameters impact the simulated yield, while most of the studied topsoil parameters affect the simulated nitrate leaching. The model evaluation was overall satisfactory, with root mean squared residuals ( RMSR ) for simulated aboveground biomass and nitrogen content at harvest, monthly evapotranspiration, annual drainage and nitrate leaching out of the root zone of, respectively, 0.9 Mg ha −1 , 20 kg N ha −1 , 30 mm, 10 mm and 10 kg N ha −1 for the calibration, and 1.2 Mg ha −1 , 26 kg N ha −1 , 38 mm, 14 mm and 17 kg N ha −1 for the validation. The values of mean absolute deviation, model efficiency and determination coefficient were also overall satisfactory, except for soil water dynamics, where the model was often found erratic. Re-validation run showed that the calibrated Daisy model was able to simulate long-term dynamics of crop grain yield and topsoil carbon content in a silty loam field in the NCP well, with respective RMSR of 1.7 and 1.6 Mg ha −1 . The analyses of the model and the field results showed that quadratic, Mitscherlich and linear-plateau statistical models may estimate different economic optimal N rates, underlining the importance of model choice for response analyses to avoid excess use of N fertiliser. The analyses further showed that an annual fertiliser rate of about 300 kg N ha −1 (100 for maize and 200 for wheat) for the double crop rotation with straw incorporation is the most optimal in balancing crop production and nitrate leaching under the studied conditions, given the soil replenishment with N from straw mineralisation, atmospheric deposition and residual fertiliser. This work provides a sound reference for determining N fertiliser rates that are agro-environmentally optimal for similar and other cropping systems and regions in China and extends the application of the Daisy model to the analyses of complex agro-ecosystems and management practices under semi-arid climate.

Impact of Bias Correction Methods on Estimation of Soil Moisture When Assimilating Active and Passive Microwave Observations

In this paper, bias correction approaches are investigated to understand their impact on assimilating active and/or passive microwave observations on near-surface soil moisture (SM) estimates. Synthetic and field observations were assimilated in a soil–vegetation–atmosphere transfer model linked with an integrated active–passive model at L-band for bare soil. The two bias correction methods included in this study are the online bias correction with feedback (BCWF) with extended implementation with nonlinear observation operators and the simultaneous state parameter (SSP) update. New equations for BCWF were derived for the case of nonlinear observation operators because current versions of this approach were not applicable for improving SM by assimilating microwave observations. In SSP, the bias is compensated by tunning the values of the parameters. The two approaches resulted in similar accuracy for improving SM estimates compared with the uncorrected estimates. SSP showed the highest certainty for both synthetic and field observations. Using the bias correction methods, the mean estimates of SM improved by up to 88%, 87%, and 94%, when passive, active, and active–passive synthetic observations were assimilated, respectively, compared with the open-loop estimates. In contrast, when assimilating field observations from the Eleventh Microwave Water Energy Balance Experiment, the mean estimates of SM improved by up to 44%, 18%, and 48%, when passive, active, and active–passive observations were assimilated, respectively, compared with open-loop estimates. The decrement in improving the SM estimates suggests sources of uncertainty other than those from model parameters and forcings.

A deuterium-based labeling technique for the investigation of rooting depths, water uptake dynamics and unsaturated zone water transport in semiarid environments

Summary Non- or minimum-invasive methods for the quantification of rooting depths of plants are rare, in particular in (semi-)arid regions; yet, this information is crucial for the parameterization of SVAT (Soil–Vegetation–Atmosphere Transfer) models and understanding of processes within the hydrological cycle. We present a technique utilizing the stable isotope deuterium (2H) applied as artificial tracer to investigate the vertical extent of the root zone, characterize water uptake dynamics of trees and shrubs at different depths and monitor transport of water through the unsaturated zone of dry environments. One liter of 35% deuterated water (2H2O) was punctually applied at several depths (0.5 m, 1 m, 2 m, 2.5 m and 4 m) at six different plots at a natural forested site in the Cuvelai–Etosha Basin (CEB), Namibia/Angola. Subsequently, uptake of the tracer was monitored by collecting plant samples (xylem and transpired water) up to seven days after tracer injection. Soil profiles at the plots were taken after the campaign and again after six months in order to evaluate the transport and distribution of 2H within the unsaturated zone. Of 162 plant samples taken, 31 samples showed clear signals of artificially introduced 2H, of which all originate from the plots labeled up to 2 m depth. No artificially injected 2H was found in plants when tracer application occurred deeper than 2 m. Results further indicate a sharing of water resources between the investigated shrubs and trees in the upper 1 m whilst tree roots seem to have better access to deeper layers of the unsaturated zone. The soil profiles taken after six months reveal elevated 2H-concentrations from depths as great as 4 m up to 1 m below surface indicating upward transport of water vapor. Purely diffuse transport towards the soil surface yielded an estimated 0.4 mm over the dry season. Results are of particular significance for a more precise parameterization of SVAT models and the formulation of water balances in semiarid areas. The developed methodology is beneficial for site-specific investigations in complex and data scarce environments, where the root zone plays a major role for the overall water balance. For arid and semiarid environments experiencing low recharge rates, water transported in its vapor phase is found to play an important role for the overall soil water balance. The use of 2H2O is cost-effective and provides the opportunity to investigate multiple effects along the soil–vegetation interface that have been difficult to deal with previously.

Quantifying the prediction accuracy of a 1-D SVAT model at a range of ecosystems in the USA and Australia: evidence towards its use as a tool to study Earth's system interactions

Abstract. This paper describes the validation of the SimSphere SVAT (Soil–Vegetation–Atmosphere Transfer) model conducted at a range of US and Australian ecosystem types. Specific focus was given to examining the models' ability in predicting shortwave incoming solar radiation (Rg), net radiation (Rnet), latent heat (LE), sensible heat (H), air temperature at 1.3 m (Tair 1.3 m) and air temperature at 50 m (Tair 50 m). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from eight sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation. Overall, results showed a good agreement between the model predictions and the in situ measurements, particularly so for the Rg, Rnet, Tair 1.3 m and Tair 50 m parameters. The simulated Rg parameter exhibited a root mean square deviation (RMSD) within 25 % of the observed fluxes for 58 of the 72 selected days, whereas an RMSD within ~ 24 % of the observed fluxes was reported for the Rnet parameter for all days of study (RMSD = 58.69 W m−2). A systematic underestimation of Rg and Rnet (mean bias error (MBE) = −19.48 and −16.46 W m−2) was also found. Simulations for the Tair 1.3 m and Tair 50 m showed good agreement with the in situ observations, exhibiting RMSDs of 3.23 and 3.77 °C (within ~ 15 and ~ 18 % of the observed) for all days of analysis, respectively. Comparable, yet slightly less satisfactory simulation accuracies were exhibited for the H and LE parameters (RMSDs = 38.47 and 55.06 W m−2, ~ 34 and ~ 28 % of the observed). Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. The Nash–Sutcliffe efficiency index for all parameters ranges from 0.720 to 0.998, suggesting a very good model representation of the observations. To our knowledge, this study presents the most detailed evaluation of SimSphere done so far, and the first validation of it conducted in Australian ecosystem types. Findings are important and timely, given the expanding use of the model both as an educational and research tool today. This includes ongoing research by different space agencies examining its synergistic use with Earth observation data towards the development of global operational products.

An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth’s surface and their interactions with vegetation and atmosphere. When it comes to studying vegetation properties, RTMs allows us to study light interception by plant canopies and are used in the retrieval of biophysical variables through model inversion. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. Emulators are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We hereby present an “Emulator toolbox” that enables analysing multi-output machine learning regression algorithms (MO-MLRAs) on their ability to approximate an RTM. The toolbox is included in the free-access ARTMO’s MATLAB suite for parameter retrieval and model inversion and currently contains both linear and non-linear MO-MLRAs, namely partial least squares regression (PLSR), kernel ridge regression (KRR) and neural networks (NN). These MO-MLRAs have been evaluated on their precision and speed to approximate the soil vegetation atmosphere transfer model SCOPE (Soil Canopy Observation, Photochemistry and Energy balance). SCOPE generates, amongst others, sun-induced chlorophyll fluorescence as the output signal. KRR and NN were evaluated as capable of reconstructing fluorescence spectra with great precision. Relative errors fell below 0.5% when trained with 500 or more samples using cross-validation and principal component analysis to alleviate the underdetermination problem. Moreover, NN reconstructed fluorescence spectra about 50-times faster and KRR about 800-times faster than SCOPE. The Emulator toolbox is foreseen to open new opportunities in the use of advanced RTMs, in which both consistent physical assumptions and data-driven machine learning algorithms live together.