It rains and then? Numerical challenges with the 1D Richards equation in kilometer-resolution land surface modelling

Over the last decade kilometer-scale weather predictions and climate projections have become established. Thereby both the representation of atmospheric processes, as well as land-surface processes need adaptions to the higher-resolution. Soil moisture is a critical variable for determining the exchange of water and energy between the atmosphere and the land surface on hourly to seasonal time scales, and a poor representation of soil processes will eventually feed back on the simulation quality of the atmosphere. Especially the partitioning between infiltration and surface runoff will feed back on the hydrological cycle. Several aspects of the coupled system are affected by a shift to kilometer-scale, convection-permitting models. First of all, the precipitation-intensity distribution changes to more intense events. Second, the time-step of the numerical integration becomes smaller. The aim of this study is to investigate the numerical convergence of the one-dimensional Richards Equation with respect to the soil hydraulic model, vertical layer thickness and time-step during the infiltration process. Both regular and non-regular (unequally spaced) grids typical in land surface modelling are considered, using a conventional semi-implicit vertical discretization. For regular grids, results from a highly idealized experiment on the infiltration process show poor numerical convergence for layer thicknesses larger than approximately 5 cm and for time steps greater than 40 s, irrespective of the soil hydraulic model. The velocity of the wetting front decreases systematically with increasing time step and decreasing vertical resolution. For non-regular grids, a new discretization based on a coordinate transform is introduced. In contrast to simpler vertical discretizations, it is able to represent the solution second-order accurate. The results for non-regular grids are qualitatively similar, as a fast increase in layer thickness with depth is equivalent to a lower vertical resolution. It is argued that the sharp gradients in soil moisture around the propagating wetting front must be resolved properly in order to achieve an acceptable numerical convergence of the Richards Equation. Furthermore, it is shown that the observed poor numerical convergence translates directly into a poor convergence of infiltration-runoff partitioning for precipitation time series characteristic of weather and climate models. As a consequence, soil simulations with low resolution in space and time may produce almost twice the amount of surface runoff within 24 hours than their high-resolution counterparts. Our analysis indicates that the problem is particularly pronounced for kilometer-resolution models.

A flux tower dataset tailored for land model evaluation

Eddy covariance flux towers measure the exchange of water, energy and carbon fluxes between the land and atmosphere. They have become invaluable for theory development and evaluating land models. However, flux tower data as measured (even after site post-processing) are not directly suitable for land surface modelling due to data gaps in model forcing variables, inappropriate gap-filling, formatting and varying data quality. Here we present a quality-control and data-formatting pipeline for tower data from FLUXNET2015, La Thuile and OzFlux syntheses and the resultant 170-site globally distributed flux tower dataset specifically designed for use in land modelling. The dataset underpins the second phase of the PLUMBER land surface model benchmarking evaluation project, an international model intercomparison project encompassing > 20 land surface and biosphere models. The dataset is provided in the Assistance for Land-surface Modelling Activities (ALMA) NetCDF format and is CF-NetCDF compliant. For forcing land surface models, the dataset provides fully gap-filled meteorological data that has had periods of low data quality removed. Additional constraints required for land models, such as reference measurement heights, vegetation types and satellite-based monthly leaf area index estimates, are also included. For model evaluation, the dataset provides estimates of key water, carbon and energy variables, with the latent and sensible heat fluxes additionally corrected for energy balance closure. The dataset provides a total of 1040 site years covering the period 1992–2018, with individual sites spanning from 1 to 21 years. The dataset is available at http://dx.doi.org/10.25914/5fdb0902607e1 (Ukkola et al., 2021).

Reviews and syntheses: Gaining insights into evapotranspiration partitioning with novel isotopic monitoring methods

Disentangling ecosystem evapotranspiration (ET) into evaporation (E) and transpiration (T) is of high relevance for a wide range of applications, from land surface modelling to policymaking. Identifying and analysing the determinants of the ratio of T to ET (T/ET) for various land covers and uses, especially in view of climate change with an increased frequency of extreme events (e.g. heatwaves and floods), is prerequisite for forecasting the hydroclimate of the future and tackling present issues, such as agricultural and irrigation practices. One partitioning method consists of determining the water stable isotopic compositions of ET, E, and T (δET, δE, and δE, respectively) from the water retrieved from the atmosphere, the soil, and the plant vascular tissues. The present work emphasizes the challenges this particular method faces (e.g. the spatial and temporal representativeness of the T/ET estimates, the limitations of the models used, and the sensitivities to their driving parameters) and the progress that needs to be made in light of the recent methodological developments. As our review is intended for a broader audience beyond the isotopic ecohydrological and micrometeorological communities, it also attempts to provide a thorough review of the ensemble of techniques used for determining δET, δE, and δE and solving the partitioning equation for T/ET. From the current state of research, we conclude that the most promising way forward to ET partitioning and capturing the subdaily dynamics of T/ET is by making use of non-destructive online monitoring techniques of the stable isotopic composition of soil and xylem water. Effort should continue towards the application of the eddy covariance technique for high-frequency determination of δET at the field scale as well as the concomitant determination of δET, δE, and δE at high vertical resolution with field-deployable lift systems.

Advances in Land Surface Modelling

Land surface models have an increasing scope. Initially designed to capture the feedbacks between the land and the atmosphere as part of weather and climate prediction, they are now used as a critical tool in the urgent need to inform policy about land-use and water-use management in a world that is changing physically and economically. This paper outlines the way that models have evolved through this change of purpose and what might the future hold. It highlights the importance of distinguishing between advances in the science within the modelling components, with the advances of how to represent their interaction. This latter aspect of modelling is often overlooked but will increasingly manifest as an issue as the complexity of the system, the time and space scales of the system being modelled increase. These increases are due to technology, data availability and the urgency and range of the problems being studied.

ECLand: an ECMWF land surface modelling platform

<p>The ECMWF operational land surface model, based on the Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) is the baseline for global weather, climate and environmental applications at ECMWF. In order to expedite its progress and benefit from international collaboration, an ECLand platform has been designed to host advanced and modular schemes. ECLand is paving the way toward a land model that could support a wider range of modelling applications, facilitating global kilometer scales testing as envisaged in the Copernicus and Destination Earth programmes. This presentation introduces the CHTESSEL and its recent new developments that aims at hosting new research applications.</p><p>These new improvements touch upon different components of the model: (i) vegetation, (ii) snow, (iii) soil hydrology, (iv) open water/lakes (v) rivers and (vi) urban areas. The developments are evaluated separately with either offline simulations or coupled experiments, depending on their level of operational readiness, illustrating the benchmarking criteria for assessing process fidelity with regards to land surface fluxes and reservoirs involved in water-energy-carbon exchange, and within the Earth system prediction framework, as foreseen to enter upcoming ECMWF operational cycles.</p><p>Reference: Souhail Boussetta, Gianpaolo Balsamo*, Anna Agustì-Panareda, Gabriele Arduini, Anton Beljaars, Emanuel Dutra, Glenn Carver, Margarita Choulga, Ioan Hadade, Cinzia Mazzetti, Joaquìn Munõz-Sabater, Joe McNorton, Christel Prudhomme, Patricia De Rosnay, Irina Sandu, Nils Wedi, Dai Yamazaki, Ervin Zsoter, 2021: ECLand: an ECMWF land surface modelling platform, MDPI Atmosphere, (in prep).</p>

Application of an optimality-based model to operate at half-hourly timestep to implement plant acclimation within a land-surface modelling framework

<p>Vegetation and atmosphere are linked through the perpetual exchange of water, carbon and energy. An accurate representation of the processes involved in these exchanges is crucial in forecasting Earth system states. Although vegetation has become an undisputed key component in land-surface modelling (LSMs), the current generation of models differ in terms of how key processes are formulated. Plant processes react to environmental changes on multiple time scales. Here we differentiate a fast (minutes) and a slower (acclimated – weeks to months) response. Some current LSMs include plant acclimation, even though they require additional parameters to represent this response, but the majority of them represent only the fast response and assume that this also applies at longer time scales. Ignoring acclimation in this way could be the cause of inconsistent future projections. Our proposition is to include plant acclimation in a LSM schema, without having to include new plant-functional-type-dependent parameters. This is possible by using an alternative model development strategy based on eco-evolutionary theory, which explicitly predicts the acclimation of photosynthetic capacities and stomatal behaviour to environmental variations. So far, this theory has been tested only at weekly to monthly timescales. Here we develop and test an approach to apply an existing optimality-based model of gross primary production (GPP), the P model, at the sub-daily timestep necessary for use in an LSM, making an explicit differentiation between the fast and slow responses of photosynthesis and stomatal conductance. We test model performance in reproducing the diurnal cycle of GPP as recorded by flux tower measurements across different biomes, including boreal and tropical forests. The extended model requires only a few meteorological inputs, and a satellite-derived product for leaf area index or green vegetation cover. It is able to manage both timescales of acclimation without PFT-dependent photosynthetic parameters and has shown to operate with very good performance at all sites so far investigated. The model structure avoids the need to store past climate and vegetation states. These findings therefore suggest a simple way to include both instantaneous and acclimated responses within a LSM framework, and to do so in a robust way that does not require the specification of multiple parameters for different plant functional types.</p>

Reviews and syntheses: Gaining insights into evapotranspiration partitioning with novel isotopic monitoring methods

Disentangling ecosystem evapotranspiration (ET) into evaporation (E) and transpiration (T) is of high relevance for a wide range of applications, from land surface modelling to policy making. Identifying and analysing the determinants of the ratio of T to ET (T / ET) for various land covers and uses, especially in view of climate change with increased frequency of extreme events (e.g., heatwaves and floods), is prerequisite for forecasting the hydroclimate of the future and tackling present issues, such as agricultural and irrigation practices. A powerful partitioning method consists in determining the water stable isotopic compositions of ET, E, and T (δET, δE, and δT, respectively) from the water retrieved from the atmosphere, the soil, and the plant vascular tissues. The present work emphasises the challenges this particular method faces (e.g., the spatial and temporal representativeness of the T / ET estimates, the limitations of the models used and the sensitivities to their driving parameters) and the progress that needs to be made in light of the recent methodological developments. As our review is intended for a broader audience beyond the isotopic ecohydrological and micrometeorological communities, it also attempts to provide a thorough review of the ensemble of techniques used for determining δET, δE, and δT, and solving the partitioning equation for T / ET. From the current state of research, we conclude that the most promising way forward to ET partitioning and capturing the sub-daily dynamics of T / ET is in making use of non-destructive online monitoring techniques of the stable isotopic composition of soil and xylem water. Effort should continue towards the application of the eddy covariance technique for high-frequency determination of δET at the field scale as well as the concomitant determination of δET, δE, and δT at high vertical resolution with field-deployable lift systems.

Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis

A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, make use of land surface modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and this may introduce predictive uncertainty when LSM outputs are used as inputs to inform a given application. For useful predictions for a specific application, one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multiway data analysis, which extends singular value decomposition (SVD) to multidimensional tables and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fennoscandian and north-west Russian regions, and the approach is explored by comparing net primary production (NPP) estimates over the period 1998–2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high-latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability, is essentially a spatial correction applied to ORCHIDEE-HLveg that significantly improves the fit to this LSM, with only small improvements for the other LSMs. Subsequent correction terms gradually improve the overall and individual LSM fits but capture at most 1.7 % of the overall variability. Analysis of differences between LSMs provides information on the times and places where the LSMs differ and by how much, but in this case no single spatio-temporal pattern strongly dominates the variability. Hence interpretation of the analysis requires the summation of several such patterns. Nonetheless, the three best principal tensors capture around 76 % of the variability in the LSM differences and to a first approximation successively indicate the times and places where ORCHIDEE-HLveg, CLM5 and ORCHIDEE-MICT differ from the other LSMs. Differences between the climate forcing GCMs had a marginal effect up to 6 % on NPP predictions out to 2100 without specific spatio-temporal GCM interaction.