Integration of Soil Processes in Global Land Surface/Earth System Models

The importance of linking the soil and climate modeling is gaining importance as evidenced by the recent IPCC special report on Climate Change and Land (SRCCL - 2019).

The climate and soil/critical zone communities have been engaged in various activities to improve integration of soil and subsurface processes in current land surface models (for example via the GEWEX-SoilWat initiative, ISMC activities and other avenues). We want to build on these efforts; hence, this session seeks contributions in the general area of soil process representation (water- and heat dynamics, as well as biogeochemical processes) in land-surface models, including studies focusing on bridging scales between traditional soil profiles and processes at scales relevant to climate modeling. Contributions on new ways to improve soil information inputs (e.g. more realistic soil maps, novel pedotransfer functions, machine learning) to Earth system models including systematic evaluation of model performance with revised parameters are of particular interest. Links to ground water processes, vegetation processes (e.g. plant water stress and root water uptake) and dynamics, and observational or remote sensing studies of soil processes and energy- and water balance fluxes at large scales are also welcomed.

Convener: Anne Verhoef | Co-Conveners: Yuting Yang, Fubao Sun, Dani Or
| Wed, 19 May, 15:00–16:30 (CEST)
| Wed, 19 May, 16:30–18:00 (CEST)

Oral: Wed, 19 May

Chairpersons: Anne Verhoef, Yuting Yang, Attila Nemes
Félicien Meunier, Marc Peaucelle, Wim Verbruggen, Inês dos Santos Vieira, and Hans Verbeeck

Drought stress is an important threat for plants in tropical forests, especially in the context of human-induced increase of drought frequency and severity observed in regions like the Amazon basin. Terrestrial Biosphere Models are key tools to predict the future of tropical ecosystems as they allow estimating the resilience of ecosystems by simulating various future scenarios. Previous vegetation model runs have suggested a complete dieback of the Amazon or a rapid transition towards Savannah-like ecosystems because of increasing drought frequency while others have forecasted an overall greenup due to fertilization. A part of the discrepancy between those predictions stems from the representation of below-ground processes that substantially differ between models. How sensitive those models are to soil parameters and below-ground processes has been only sporadically addressed to this date, while answering this question is critical to make reliable predictions.

In this study, we compared the sensitivities of three state-of-the-art Terrestrial Biosphere Models, namely ORCHIDEE (big-leaf), ED2 (cohort-based), and LPJ-GUESS (individually-based) to the variability in soil properties over the Amazon. These three models are representative of the existing range of vegetation models but differ in their representation of the rooting system, the soil hydraulic properties and the plant below-ground functioning. We ran multiple model simulations with different soil maps and compared how the growth primary productivity, evapotranspiration and drought stress simulated by each model varied with soil properties. Those soil maps were derived from Soilgrids by retaining the most frequent soil class for each simulated pixel, or the one with the lowest or the highest clay content. The analysis revealed that all three models were weakly sensitive to soil texture, making clear that Terrestrial Biosphere Models require a better representation of below-ground processes in order to accurately simulate drought stress and water competition.

How to cite: Meunier, F., Peaucelle, M., Verbruggen, W., dos Santos Vieira, I., and Verbeeck, H.: Belowground processes in Terrestrial Biosphere Models, the forgotten half, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-2,, 2021.

Bertrand Guenet, Jérémie Orliac, Lauric Cécillon, Olivier Torres, and Laurent Bopp

Earth system models (ESMs) are numerical representations of the Earth system aiming at representing the climate dynamic including feedbacks between climate and carbon cycle. CO2 flux due to soil respiration including heterotrophic respiration coming from the soil organic matter (SOM) microbial decomposition and autotrophic respiration coming from the roots respiration is one of the most important flux between the surface and the atmosphere. Thus, even small changes in this flux may impact drastically the climate dynamic. It is therefore essential that ESMs reliably reproduce soil respiration. Until recently, such an evaluation at global scale of the ESMs was not straightforward because of the absence of observation-derived product to evaluate heterotrophic respiration fluxes from ESMs at global scale. Recently, several gridded products were published opening a new research avenue on climate-carbon feedbacks. In this study, we used simulations from 13 ESMs performed within the sixth coupled model intercomparison project (CMIP6) and we evaluate their capacities to reproduce the heterotrophic respiration flux using three gridded observation-based products. We first evaluate the total heterotrophic respiration flux for each model as well as the spatial patterns. We observed that most of the models are able to reproduce the total heterotrophic respiration flux but the spatial analysis underlined that this was partially due to some bias compensation between regions overestimating the flux and regions underestimating the flux. To better identify the causes of the identified bias in predicting the total heterotrophic respiration flux, we analysed the residues of ESMs using linear mixed effect models and we observed that lithology and climate were the most important drivers of the ESMs residues. Our results suggest that the response of SOM microbial decomposition to soil moisture and temperature must be improved in the next ESMs generation and that the effect of lithology should be better taken into account.

How to cite: Guenet, B., Orliac, J., Cécillon, L., Torres, O., and Bopp, L.: Are Earth system models able to reproduce the soil heterotrophic respiration fluxes?, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-23,, 2021.

Elizabeth Cooper, Eleanor Blyth, Hollie Cooper, Richard Ellis, Ewan Pinnington, and Simon Dadson

Accurate soil moisture predictions from land surface models are important in hydrological, ecological and agricultural applications. Despite increasing availability of wide area soil moisture measurements, few studies have combined soil moisture predictions from models with in-situ observations beyond the point scale. This work uses the LAVENDAR data assimilation framework to markedly improve soil moisture estimates from the JULES land surface model using field scale Cosmic Ray Neutron sensor observations from the UKCEH COSMOS-UK network. Rather than directly updating modelled soil moisture estimates towards measured values, we optimize constants in the underlying pedotransfer functions (PTF) which relate soil texture to soil hydraulics parameters. In this way we generate a single set of newly calibrated PTFs based on field scale observations from a number of UK sites with different soil types. We demonstrate that calibrating PTFs in this way can improve the performance of JULES. Further, we suggest that calibrating PTFs for the soils on which they are to be used and at the scales at which land surface models are applied (rather than on small-scale soil samples) will ultimately improve the performance of land surface models, potentially leading to improvements in flood, drought and climate projections.

How to cite: Cooper, E., Blyth, E., Cooper, H., Ellis, R., Pinnington, E., and Dadson, S.: Improving soil moisture predictions from a land surface model by optimisation of pedotransfer functions., 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-81,, 2021.

Alla Yurova, Daniil Kozlov, Maria Smirnova, and Pavel Fil

Historical soil maps with a reference to profile redoximorphic features have obvious utility for ecohydrological modelling. That is particularly pertinent in areas with shallow water tables where catchments have both dry and moist parts, latter due to moisture source from either upper or low boundary. However, there is no convenient method for converting maps to hydrological model state variables. Here we propose that the steady state continuity equation in kinematic wave form can be parameterized using expert knowledge of the typical water table depth (WTD) for soils with different hydromorphy degrees based on redoximorphic features. To test the approach, six hillslope-based computational units (catenas) were obtained, for use in simulations, by automated of the Samovetc and Izberdey catchments in the Tambov region (Russia) using lumpR software. Five of the six catenas began at poorly drained flat upslope positions with soils with various degrees of saturation by shallow groundwater and one began at a well-drained upslope position. We guided parameterization of the kinematic wave model by critical range of the WTD known to correspond to each soil group on historical soil map of hydromorphy degree. Application of expert knowledge in this manner alone yielded a broad range of possible WTD values (e.g. 1.5-5 m for a semi-hydromorphic soil) for each soil entity, but linking a catena by the fundamental physical constraint of flow continuity enabled narrowing of the range to 0.2-1 m thereby reducing it by ca. 80%. We further tested the shallow water table approximation in the WASA-SED ecohydrological model based on catenary approach to simulate soil moisture profiles referring explicitly to soil groups of different hydromorphy degree and distinguish stagnic and gleyic regimes of waterlogging. The results show that the approach could substantially improve crop and water management precision.

How to cite: Yurova, A., Kozlov, D., Smirnova, M., and Fil, P.: Historical soil maps with a reference to redoximorphic features as basis for ecohydrological modelling., 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-40,, 2021.

Hong Zhao, Yijian Zeng, Xujun Han, and Bob Su

Basic soil physical properties (i.e., soil texture and organic matter) and associated soil hydraulic properties (i.e., soil water retention curve and hydraulic conductivity) play an essential role in land surface models (LSMs) for estimating soil moisture. With the physical link between soil properties, LSMs and Radiative Transfer Models (RTMs), the soil physical properties can be retrieved, using a LSM coupled with a microwave L-band emission observation model in a data assimilation framework. To this purpose, this paper couples an enhanced physically-based discrete scattering-emission model with the Community Land Model 4.5 (CLM), to retreive soil physical properties using the Local Ensemble Transform Kalman Filter (LETKF) algorithm, assimilating Soil Moisture Active and Passive (SMAP) Level-1C (L1C) brightness temperature at H and V polarization ( and ) separately, assisted with in situ measurements at the Maqu site on the eastern Tibetan Plateau. Results show the improved estimate of soil properties at the topmost layer via assimilating SMAP ( H, V), as well as at profile using the retrieved top-layer soil properties and a prior depth ratio. The use of  and  shows varied sensitivities to retrievals of different soil compositions (i.e., sand, clay, silt) and soil moisture estimates. However, analyses show that the retrieved soil properties with fine accuracy are not sensitive factors affecting soil moisture estimates. Instead, uncertainties of CLM model structures shall be considered, such as the fixed PTFs (pedotransfer functions), the hydraulic function describing soil water retention curve and the water stress function determining root water update.

How to cite: Zhao, H., Zeng, Y., Han, X., and Su, B.: Retrieving Soil Physical Properties via Assimilating SMAP Brightness Temperature Observations in the Community Land Model, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-4,, 2021.

Eli Dennis and Hugo Berbery

Soil hydro-physical properties are necessary components in regional climate simulation; yet, the parameter inaccuracies introduce uncertainty in the representation of surface water and energy fluxes leading to differences in land-atmosphere interactions, and precipitation. This study examines the uncertainties in the North American atmospheric water cycle that result from the use of different soil datasets. Two soil datasets are considered: STATSGO from the United States Department of Agriculture and GSDE from Beijing Normal University.  Each dataset's dominant soil category allocations differ significantly at the model's resolution. Large regional discrepancies are found in the assignments of soil category, such that, for instance, in the Midwestern US (hereafter, Midwest), there is a systematic reduction in soil grain size allowing the impacts of the differing assignments to project onto regional scales.

The two simulations are conducted from June 1–August 31, 2016–2018 using the Weather Research and Forecasting Model coupled with the Community Land Model version 4. In the Midwest, where soil grain size decreases from STATSGO to GSDE, the GSDE simulation experiences reduced mean latent heat flux (–15 W m-2), and increased sensible heat flux (+15 W m-2).  The boundary layer thermodynamic structure responds to these changes resulting in differences in mean CAPE and CIN. In the GSDE simulation, there is more energy available for convection (CAPE: +200 J kg-1) in the Midwest, but it is more difficult to access that energy (CIN: +75 J kg-1). Differences arise in dynamic quantities, as well: the vertically-integrated moisture fluxes suggest a reduction in continental cyclonic rotation co-located with the decrease in latent heat flux and, the vertically-integrated moisture flux convergence is also affected. This combination of thermodynamic and dynamic responses culminate in a reduction of precipitation in the Midwest, which can be related to changes in the placement of soil hydro-physical properties.

How to cite: Dennis, E. and Berbery, H.: The role of soil hydro-physical properties in the atmospheric water cycle, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-27,, 2021.

Interactive: Wed, 19 May, 16:30–18:00<

Chairpersons: Dani Or, Martine van der Ploeg, Fubao Sun
Lianyu Yu, Yijian Zeng, Simone Fatichi, and Zhongbo Su

The vadose zone is a zone sensitive to environmental changes and exerts a crucial control in ecosystem functioning and even more so in cold regions considering the rapid change in the seasonally frozen ground under climate warming. While the way in representing the underlying physical process of the vadose zone differs among models, the effect of such differences on soil hydrothermal regimes, and then ecosystem functioning and its ecohydrological response to freeze–thaw cycles are seldom reported. Here, the detailed vadose zone process modeling framework STEMMUS (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil) was coupled with the ecohydrological model Tethys–Chloris (T&C) to investigate the role of influential physical processes during freeze-thaw cycles. The physical representation is increased from using T&C coupling without STEMMUS enabling the simultaneous mass and energy transfer in the soil system (liquid, vapor, ice) – and with explicit consideration of the impact of soil ice content on energy and water transfer properties – to using T&C coupling with it. We tested model performance with the aid of a comprehensive observation dataset collected at a typical meadow ecosystem on the Tibetan Plateau. Results indicated that explicitly considering the frozen soil process and vapor flow significantly improved the soil moisture/temperature profile simulations and facilitated our understanding of the water transfer processes within the soil-plant-atmosphere continuum. We further demonstrated the linkage between the vadose zone physics-induced difference in soil hydrothermal regimes and the ecosystem water/carbon cycles. This research highlights the important role of vadose zone physics for ecosystem functioning in cold regions and can support the development and application of future Earth system models.

How to cite: Yu, L., Zeng, Y., Fatichi, S., and Su, Z.: The Role of Vadose Zone Physics in the Soil Hydrothermal and Ecohydrological Response of a Tibetan Meadow Ecosystem to Freeze-Thaw Cycles, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-6,, 2021.

Soil thermal conductivity dataset
Hailong He
Peter Lehmann, Ben Leshchinsky, Surya Gupta, Ben Mirus, Samuel Bickel, Ning Lu, and Dani Or

Clay minerals dominate the soil colloidal fraction and often carry the largest specific surface area – a property that controls various soil hydraulic and mechanical properties (SHMPs; e.g. water retention, permeability, and internal friction). Differences in microscale structure among clay mineral types in tropical and temperate regions affect the specific surface area and result in higher permeability and internal friction angle values for tropical soils with inactive kaolinite clay minerals. Presently, the soil clay size fraction used to parameterize SHMPs with pedotransfer functions (PTFs) ignores clay mineral type, leading to inconsistent parameter representation. In this study, we present new PTFs informed by clay minerals, enabling enhanced estimation of friction angle and saturated hydraulic conductivity. To capture higher conductivity and lower air entry values in tropical soils, we developed a hierarchical packing model and validated this new PTF approach using literature data from various tropical regions. We leveraged recent global maps of clay minerals to demonstrate that a strong climatic and spatial segregation of active and inactive clays enable spatially resolved consideration of clay mineral type in SHMP estimation. We applied these clay-informed PTFs to improve SHMP representation regionally with implications for a wide range of hydrological and geomechanical Earth surface processes.

How to cite: Lehmann, P., Leshchinsky, B., Gupta, S., Mirus, B., Bickel, S., Lu, N., and Or, D.: Clays are not created equal – effects of clay mineral type on soil hydraulic and mechanical properties, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-99,, 2021.

Amy Thomas, Fiona Seaton, Jack Cosby, Bridget Emmett, Sabine Reinsch, Chris Feeney, Inma Lebron, Simon Smart, Claire Wood, Lindsey Maskell, and David Robinson

Soil porosity controls the flow of mass and energy through soil, and thus plays a fundamental role in regulating hydrological and biochemical cycling at the land surface. Global land surface and earth system models commonly derive porosity from soil texture using pedotransfer functions. This does not allow for response to change in environment or management, or potentially important climate feedbacks. Furthermore, the approach does not fully represent the baseline spatial variation in this important soil property. Here we show that porosity, and bulk density (BD), depend on SOM in temperate soils, using two comprehensive national data sets, covering the full range of soil organic matter (SOM) (n=1385 & n=2570). Our novel use of analytical models with machine learning (ML) algorithms opens up new physical insight into controls on porosity and BD, while generalized additive mixed models (GAMMs) provide further insights and opportunities for prediction. Our models allow us to consider influence of management on soil compaction and recent observations that soil porosity responds to climate change. The dependence of soil porosity on SOM, more so than texture, indicates the need for a paradigm shift in the conceptualization and modelling of these soil physical properties. Broad habitat was also an important control, and explained some of the variance in the relationship between SOM and porosity. This highlights that changes in soil porosity may occur due to land use or climate change, and will create feedbacks to hydrological and biogeochemical cycling which should be represented in Global land surface models. This will also be important for other pedotransfer functions, e.g. the use of BD to determine carbon stock from concentration.  In addition, we found opportunities for improved representation of the spatial pattern of porosity, even in the absence of measured data on SOM, based on climate and earth observation data.

How to cite: Thomas, A., Seaton, F., Cosby, J., Emmett, B., Reinsch, S., Feeney, C., Lebron, I., Smart, S., Wood, C., Maskell, L., and Robinson, D.: Soil organic matter dominates the magnitude of porosity and bulk density in temperate soils, with important implications for land surface models, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-51,, 2021.

Rose Shillito, Markus Berli, Ian Floyd, Li Chen, and Teamrat Ghezzehei

Several factors are believed to contribute to post-wildfire flooding and debris flows. One contributing factor—the occurrence of post-wildfire soil water repellency—lacks a quantitative mechanism to incorporate the effects in physically-based runoff models. For this study, a physically-based model was developed linking the contact angle (degree of water repellency) to sorptivity. The model was verified in laboratory experiments using a silica sand proxy. The effects of water repellency on infiltration were illustrated. Further, the effect of water repellency on runoff was simulated using the AGWA-KINEROS2 watershed model with data from rainfall following the 2009 Station fire in the San Gabriel Mountains of southern California, USA. Results show water repellency has a quantifiable effect on runoff production, an effect enhanced by the dry soil moisture conditions common after wildfires.

How to cite: Shillito, R., Berli, M., Floyd, I., Chen, L., and Ghezzehei, T.: Quantifying the effect of post-wildfire soil water repellency on runoff, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-102,, 2021.

John Edwards

The parametrization of land-atmosphere interactions in numerical weather prediction and climate models is a topic of active and growing interest, especially in connection with extreme events such as heat waves and droughts. Semiarid regions are sensitive to drought and are currently expanding, but they are often poorly represented in numerical models. On forecasting timescales, comparisons of simulated land surface temperature against retrievals from satellites often show significant cold biases around noon, whilst, on climate timescales, land surface models often fail to represent droughts realistically. Inadequate treatment of the land surface, and particularly of soil properties and soil moisture, is likely to contribute to such errors.

Efforts to develop improved parametrizations of soil processes in the JULES land surface model for application in weather prediction and climate simulations are underway. Whilst processes at the soil surface are a central part of this, to obtain acceptable performance it is also important to consider the surface flux budget as a whole, including the treatment of the plant canopy. Here, we shall describe the current status of developments aimed at improving the representation of evapotranspiration and ground heat fluxes in the model, noting the major issues encountered. The importance of accurately representing the impact of soil moisture on thermal properties will be stressed. Results from initial studies will be presented and we shall offer a perspective on future developments.

How to cite: Edwards, J.: Parametrizating Soil Surface Fluxes in the JULES Land Surface Model, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-49,, 2021.

Patrick C. McGuire, Pier Luigi Vidale, Martin J. Best, David H. Case, Imtiaz Dharssi, Maria Carolina Duran Rojas, Rosalyn S. Hatcher, Grenville M.S. Lister, Alberto Martinez de la Torre, Carsten Montzka, Omar V. Müller, Valeriu Predoi, Eddy Robertson, Markus Todt, Anne Verhoef, and Simon S. Wilson

    We have updated the soil properties used in JULES (Joint UK Land Environment Simulator), which is the land-surface component of the UM (Unified Model, the UK Met Office’s climate model). JULES models the interaction of the land surface with the atmosphere, and simulates the energy, water, and carbon fluxes. JULES allows either: (i) the Brooks & Corey (BC) model for estimating soil hydraulic properties, or (ii) the van Genuchten (VG) model but using hydraulic parameters translated from the BC model. One advantage of the VG model over the BC model is the smoother dependence of water retention upon matric potential for nearly saturated soils. Herein, we report on our work towards fully implementing the VG model in JULES and in the UM, through the implementation and evaluation of several VG pedotransfer functions (PTFs) for estimating the soil hydraulic parameters used in the hydraulic functions.

    We have tested three VG PTFs in global offline JULES runs (driven with WFDEI data over 1979-2012): the combination of Tóth et al. PTFs 17 & 20, the Weynants et al. PTF, and the Zhang & Schaap ROSETTA3 H1 PTF (modified for sandy soils). We also modernized the soil basic properties that are conventionally used for JULES and the UM, from the UM version of the Harmonized World Soil Database (HWSD) to the SoilGrids database.

    Evaluation of JULES simulations shows (i) that the modified version of the Zhang & Schaap ROSETTA3 H1 PTF is the best VG option, and (ii) that it compares favorably with the BC control model (which uses the Cosby et al. PTF and the UM/HWSD soils), in terms of the surface energy balance and the mitigation of near-surface temperature biases over mid-latitude continental regions. This modified version of the Zhang & Schaap ROSETTA3 H1 PTF with SoilGrids soils is also currently being used in coupled land-atmosphere UM runs.

How to cite: McGuire, P. C., Vidale, P. L., Best, M. J., Case, D. H., Dharssi, I., Duran Rojas, M. C., Hatcher, R. S., Lister, G. M. S., Martinez de la Torre, A., Montzka, C., Müller, O. V., Predoi, V., Robertson, E., Todt, M., Verhoef, A., and Wilson, S. S.: Improving the global modeling of soils in JULES and the Unified Model: Updating from UM/HWSD to SoilGrids soil properties and from the Brooks & Corey to the van Genuchten soil-hydraulics model, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-63,, 2021.

Vedran Krevh, Jasmina Defterdarović, Lana Filipović, Zoran Kovač, Steffen Beck-Broichsitter, Jannis Groh, Jaromir Dusek, Horst H. Gerke, Radka Kodešová, and Vilim Filipović

SUPREHILL is a new (2020) and first Croatian critical zone observatory (CZO), focused on local scale agricultural e.g., vineyard hillslope processes. The experimental setup includes an extensive sensor-based network accompanied by weighing lysimeters and instruments for surface and subsurface hydrology measurement. The field measurements are supported by novel laboratory and numerical quantification methods for the determination of water flow and solute transport. This combined approach will allow the research team to accurately determine soil water balance components (soil water flow, preferential flow/transport pathways, surface runoff, evapotranspiration), the temporal origin of water in hillslope hydrology (isotopes), transport of agrochemicals, and to calibrate and validate numerical modeling procedures for describing and predicting soil water flow and solute transport. First results from sensors indicate increased soil moisture on the hilltop, which is supported by precipitation data from rain gauges and weighing lysimeters. The presence of a compacted soil horizon and compacted inter-row parts (due to trafficking) of the vineyard seems to be highly relevant in regulating water dynamics. Wick lysimeters confirm the sensor soil moisture data, while showing a significant difference in its repetitions which suggests a possibility of a preferential flow imposed by local scale soil heterogeneity. Measured values of surface and subsurface runoff suggest a crucial role of these processes in the hillslope hydrology, while slope and structure dynamics additionally influence soil hydraulic properties. We are confident that the CZO will give us new insights in the landscape heterogeneity and substantially increase our understanding regarding preferential flow and nonlinear solute transport, with results directly applicable in agricultural (sloped agricultural soil management) and environmental (soil and water) systems. Challenges remain in characterizing local scale soil heterogeneity, dynamic properties quantification and scaling issues for which we will rely on combining CZO focused measurements and numerical modeling after substantial data is collected.

How to cite: Krevh, V., Defterdarović, J., Filipović, L., Kovač, Z., Beck-Broichsitter, S., Groh, J., Dusek, J., Gerke, H. H., Kodešová, R., and Filipović, V.: Agricultural hillslope soil heterogeneity challenges: first experimental results from SUPREHILL critical zone observatory, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-44,, 2021.

Andrés F. Almeida-Ñauñay, Ernesto Sanz, Miguel Quemada, Juan C. Losada, Rosa M. Benito, and Ana M. Tarquis

Grassland dynamics are constantly changing at a variety of spatial and temporal scales. Remote-sensing techniques are used to detect, identify, and monitor ecosystem changes at multi-temporal scales. Particularly, Normalized Difference Vegetation Index (NDVI)-based time series are important to obtain numerical observations related to vegetation dynamics.

It is within this context that Recurrence Plots (RPs), Cross Recurrence Plots (CRPs) and Recurrence Quantification Analysis (RQA) offer new insight into the analysis of non-linear processes. Altogether, recurrence techniques could describe the whole dynamics of the system, explore its temporal behaviour, and quantify its structure through complexity measures. The goal of this study is to compute recurrence techniques to visualize and quantify the temporal dynamics of the semiarid grassland-climate system.

In this work, we chose a semiarid grassland area in the centre of Spain, characterized by a Mediterranean climate. Multispectral images were composed for 8-days and they were acquired from MODIS TERRA (MOD09Q1.006) product from 2002 to 2018. Then, NDVI time-series was generated from four pixels with a spatial resolution of 250 x 250 m2. Temperature and precipitation time-series were obtained from a nearby meteorological station and transformed into an 8-day time step.

Our results demonstrated that RPs showed the seasonality of the NDVI time-series. Furthermore, abrupt changes in NDVI time series were detected at specific times, implying that an atypical event occurred during that time. Temperature-NDVI CRPs showed a periodical pattern between them, on the other hand, precipitation-NDVI CRPs showed more erratic behaviour. We also found that a maximum lag between the two time-series could be detected through recurrence techniques. Overall, our findings suggest that temperature and precipitation present a dynamic complexity that is revealed in NDVI response. Therefore, RPs and CRPs are an alternative and complementary method to analyse and quantify non-stationary process, such as vegetation dynamics.


Almeida-Ñauñay, A. F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. Complexity of the Vegetation-Climate System Through Data Analysis. In International Conference on Complex Networks and Their Applications. Springer, Cham., 609-619, 2020


The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330 and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020.


How to cite: Almeida-Ñauñay, A. F., Sanz, E., Quemada, M., Losada, J. C., Benito, R. M., and Tarquis, A. M.: Visualization and quantification of NDVI dynamics in semiarid grasslands through recurrence tools., 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-47,, 2021.

Ernesto Sanz, Andrés Almeida-Ñauñay, Carlos G. Diaz Ambrona, Antonio Saa-Requejo, Margarita Ruiz-Ramos, Alfredo Rodríguez, and Ana M. Tarquis

Rangelands ecosystem comprises more than a third of the global land surface, sustaining key ecosystem services and livelihoods. Unfortunately, they suffer from severe degradation on a global scale. Tailored-monitoring of rangeland will allow us to improve their management and maintain their social-ecological systems.

MODIS data are commonly used to calculate Normalized Differenced Vegetation Index (NDVI) and NDVI anomaly (NDVIa) to monitor rangelands. In this study, we compare summary statistics and multifractal analysis to see if using complexity based tools improves our ability to differentiate land uses and types using remote sensing.

We collected time series using satellite data of MODIS (MOD09Q1.006) from 2000 to 2019. An area from southeastern Spain (Murcia province) of 6.25 Km2 was selected. This area comprised 132 pixels with a spatial resolution of 250 x 250 m2 and a temporal resolution of 8 days. This area includes irrigated and rainfed crops, shrubs and forested patches.

Multifractal detrended fluctuation analysis (MF-DFA) focuses on measuring variations of the moments of the absolute difference of their values at different scales. This allows us to use different multifractal exponent such as generalized Hurst exponent (H(q)), and its range (ΔH) to characterize the area. Here, we have selected H(1), H(2) and ΔH, to reflect variance, persistence and multifractality, respectively. Then, we compare them to the average, standard deviation and kurtosis of our NDVI and NDVIa series.

Our results indicate that MF-DFA, allow us to see more clearly the differences among the pixels than the summary statistics. Particularly H(1) and H(2) of NDVI reflects more precisely the vegetation profile and land uses of the selected area. On the other hand, NDVIa allows us to highlight those pixels where several uses occur, or some feature such as roads interact with NDVI. MF-DFA appears as a promising tool to classify and monitor rangelands.

Acknowledgements: The authors acknowledge the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain, “Garantía Juvenil” scholarship from Comunidad de Madrid, and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020.

How to cite: Sanz, E., Almeida-Ñauñay, A., Diaz Ambrona, C. G., Saa-Requejo, A., Ruiz-Ramos, M., Rodríguez, A., and Tarquis, A. M.: Spatial rangeland variability: using summary statistics and multifractal analysis to classify and monitor rangelands., 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-48,, 2021.