Search

6th WGNE workshop on systematic errors in weather and climate models What: Scientists, ranging from early career to highly experienced, involved in the development of weather and climate models and in the diagnosis of model errors, held an international workshop to discuss the nature, causes and remedies of systematic errors across timescales and across Earth system modeling components. When: 31 Oct - 04 Nov 2022 Where: Reading, UK and online

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

Land surface and Earth System models require reliable soil maps to represent the influence of spatial variability of soil properties on ecosystem fluxes and storages. However, mapping soils using conventional in situ survey protocols is time-consuming and costly. We addressed the outdated spatial information on soil physico-chemical properties for a tropical region with a ~700-km longitudinal gradient of contrasting topography, climate, and vegetation (~98,000 km2; NE Brazil), by developing a novel hybrid machine learning framework and applying it to this region. This framework reduces prediction redundancies due to high multicollinearity by implementing a recursive feature selector algorithm for input selection; its core is composed of the Soil-Landscape Estimation and Evaluation Program (SLEEP) and a calibrated Gradient Boosting Model (GBM) capable of modeling the spatial distribution of soil properties at multiple and dynamic soil depths. The use of SLEEP and GBM allowed us to explain the spatial distribution of various soil properties and their environmental modulators. The model training and testing approach used six topographical, ten meteorological and two vegetation properties, and data from 223 soil profiles across the study area. Our models demonstrated a consistent performance with spatial extrapolations exhibiting r2 values of 0.79–0.98, and -1.39–1.14% percent bias. The properties related to topography and climate were dominating when estimating the number of soil layers, soil texture, and the sum of bases. Our framework features high flexibility and it is transferable to other tropical regions, while reducing capital investments and increasing accuracy when compared to traditional mapping protocols.

The Global Energy and Water Cycle Exchanges (GEWEX) project was created more than 30 years ago within the framework of the World Climate Research Programme (WCRP). The aim of this initiative was to address major gaps in our understanding of Earth’s energy and water cycles given a lack of information about the basic fluxes and associated reservoirs of these cycles. GEWEX sought to acquire and set standards for climatological data on variables essential for quantifying water and energy fluxes and for closing budgets at the regional and global scales. In so doing, GEWEX activities led to a greatly improved understanding of processes and our ability to predict them. Such understanding was viewed then, as it remains today, essential for advancing weather and climate prediction from global to regional scales. GEWEX has also demonstrated over time the importance of a wider engagement of different communities and the necessity of international collaboration for making progress on understanding and on the monitoring of the changes in the energy and water cycles under ever increasing human pressures. This paper reflects on the first 30 years of evolution and progress that has occurred within GEWEX. This evolution is presented in terms of three main phases of activity. Progress toward the main goals of GEWEX is highlighted by calling out a few achievements from each phase. A vision of the path forward for the coming decade, including the goals of GEWEX for the future, are also described.

Over the last decades, there has been much interest in natural flood management (NFM). However, there is still a lack of evidence on the effectiveness of NFM in general and particularly land and soil management based NFM in medium to large scale catchments. This study investigates the effects of soil and land management based NFM in the lowland Pang and Blackwater catchments in the UK. It uses the Soil and Water Assessment Tool (SWAT) to model the effects of broadscale land use and crop rotation scenarios on peak flows in selected catchments, while considering uncertainties. The broadscale land use scenarios consisted of the conversion of the catchments’ land to broadleaf woodland and cropland, respectively, except for water and urban areas. The results indicate that the NFM effects vary across the catchments and depend on landscapes characteristics. In addition, the blanket conversion to broadleaf woodland or cropland has a larger effect on small peak flows than on large floods such as those of January 9 and February 6, 2014. Afforestation leads to a reduction of 10 to 16% of the modelled 2014 winter flood events. In contrast, implementing crop rotation scenarios increases the peak flows, with the increase depending on the crops used and tillage practice. These findings suggest that via bespoke woodland planting and farming practices, combined with other measures that can reduce the amount of flow reaching the river channel or delay the timing of the peak flow (eg. leaky barriers), flood risks can be minimized. The results of this study provide information that can benefit future decision making on flood risk reduction in suitable catchments.

Soil moisture is a critical component in many hydrological, agricultural, and meteorological applications and processes, and understanding the spatiotemporal dynamics and changes is critical to further their understanding. They are also an important parameter for use within soil- and land-based Natural Flood Management (NFM) schemes, to determine a relationship between surface wetness and soil water storage. Satellite-based remote sensing offers the ability to capture this spatiotemporal information on soil moisture on the synoptic scale; compared to more site-based in-situ field measurements, made up of numerous national and international soil moisture networks. In this study, we use Sentinel-1 SAR imagery over the course of six water years (from 2016 to 2021), utilizing the TU-Wein change-detection algorithm to calculate the relative Surface Soil Moisture (rSSM) across the River Thames Catchment in Southern England, equating to approximately 11,000 km2. As part of this, two pairs of backscatter normalisation factors were considered, in order to negate the impact from varying local incidence angles: a simple direct-slope and a complex multiple regression slope, both calculated annually and monthly. Whilst the monthly normalisation factor does exhibit a seasonal cycle (attributed to the growth and harvest of arable crops within the study area) in both the simple and multiple regression methodology, the impact upon the rSSM, when compared to the traditional annual method is small. In order to assess the spatiotemporal patterns of soil moisture across the River Thames Catchment, the rSSM timeseries was calculated using multiple spatial scales (1km, 500m, 250m, and 100m), to effectively estimate the rSSM across the catchment, sub-catchment, inter-field, and intra-field spatial scales. Comparisons with the Cosmic-ray Soil Moisture Observing System, United Kingdom (COSMOS-UK), show that, although there is an overestimation in rSSM over the summer months during the growing season of Arable farmland, we were able to effectively capture the general temporal dynamics of the relative Surface Soil Moisture across the region, with an average uncertainty of 30%, across both pairs of backscatter normalisation factors, and across all four spatial scales. Having catchment-wide datasets of rSSM such as this would be advantageous for evaluating land- and soil-based NFM measures across catchment and sub-catchment scales and have the potential for further application to improve hydrological model outputs.

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

In the UK, land use and land management change are being considered as part of a strategy to tackle flooding; the term natural flood management (NFM) is used in the UK to refer to this approach. Although evidence is limited, it is thought that practices considered as NFM, such as woodland planting or cover cropping, increase soil water storage and infiltration through improved soil structure, potentially reducing flooding within catchments where applied. Moreover, we know that trees and woodland have higher interception than shorter vegetation, increasing soil moisture deficit. We simulate what effect these land use and management changes have in the River Coln catchment (Upper Thames, UK), which is characterised by permeable geology and arable farming. The catchment has experienced both surface water and groundwater-driven flooding in recent history and is also important in maintaining summer low flows in the River Thames. The land surface water balance was modelled with the detailed, field-scale hydrological model SWAP. Recharge from the SWAP model was passed to a semi-distributed groundwater model, which produces groundwater baseflow, and direct runoff from SWAP was passed to a linear reservoir model to produce surface runoff. Soil information (such as soil hydraulic parameters) was based on the NATMAP database; vegetation parameters (e.g. typical crop rotations or forest types) were derived from stakeholder workshops, surveys and interviews, and expert elicitation. Soil and farm management decisions were implemented by perturbing the SWAP soil and vegetation parameters. The parameters of the surface water routing and groundwater components were calibrated within a Monte Carlo framework.Overall, we found that land use and land management measures have very limited potential for reducing flooding in permeable catchments, where the primary driver of high flows is high winter groundwater levels. We found that only large-scale coniferous planting had the potential to reduce winter peak flows and flooding in the Coln (e.g. reduction of 21−27% with two-thirds of catchment as coniferous woodland), but that this would decrease summer mean flows (12% reduction in July mean flow, 8% in August). The levels of coniferous woodland required to achieve these reductions in winter high flows are unrealistic, given the large area of productive arable land that would need to be converted to woodland and the limited ecological benefits of coniferous woodland.

Results: Soil temperature is a crucial variable in Land Surface Models (LSMs) because it affects the fractions of frozen and unfrozen water content in the soil. For example, getting the coupling between below-ground heat- and water transfer correct in LSMs is very important in permafrost regions because these are particularly sensitive to climate change. Poor predictions of the energy- and water balance in these regions will lead to large uncertainties in predicted carbon fluxes, and related land-atmosphere feedbacks. Also, simulated near-surface soil temperatures can be used to diagnose and explain model differences in skin temperatures and soil heat fluxes, both of which are pivotal in the prediction of the surface energy balance.Soil temperature is generally under-researched as part of LSM intercomparisons. Here we present an analysis of the spatial distribution (including the vertical distribution along the soil profile) and seasonal evolution of soil temperature simulated by eight LSMs as part of the Soil Parameter Model Intercomparison Project (SP-MIP). We found large inter-model differences in key metrics of the annual soil temperature wave, including the amplitude, phase shift and damping depth, which were partly attributed to diversity in hydraulic as well as thermal soil properties. Soil layer discretisation also played a role.Methods: Via manipulation of model soil hydraulic properties, and the soil texture inputs required to calculate these properties, controlled multi-model experiments have been conducted as part of SP-MIP, this MIP was originally proposed at the GEWEX-SoilWat workshop held in Leipzig (June 2016).The model experiments closely followed the LS3MIP protocol (van den Hurk et al. 2016). Eight land models (CLM5, ISBA, JSBACH, JULES, MATSIRO, MATSIRO-GW, NOAH-MP and ORCHIDEE) were run globally on 0.5° with GSWP3 forcing, from 1980-2010, for vertically homogeneous soil columns. There were 4 model experiments, leading to 7 model runs: Experiment 1. Global soil hydraulic parameter maps provided by SP-MIP; Experiment 2. Soil-hydraulic parameters derived from common soil textural properties, provided by SP-MIP, using model-specific pedotransfer functions (PTFs); Experiment 3. Reference run with all models applying their default soil hydraulic settings (including their own soil maps to derive the parameters); Experiment 4: four runs using spatially uniform soil hydraulic parameters for the whole globe (loamy sand, loam, clay and silt) provided by SP-MIP.Differences between the model experiments will allow the assessment of the inter-model variability that is introduced by the different stages of preparing model parameters. Soil parameters for Experiments 1 and soil textures for Experiment 2 at 0.5° resolution were prepared from dominant soil classes of the 0-5 cm layer of SoilGrids (Hengl et al. 2014) at 5 km resolution. Brooks and Corey hydraulic parameters come from Table 2 of Clapp and Hornberger (1978), Mualem-Van Genuchten hydraulic parameters are ROSETTA class average hydraulic parameters (Schaap et al. 2001), and soil textures are from Table 2 of Cosby et al. (1984). Experiments 4 a-d use the USDA soil classes, using the same PTFs for Brooks and Corey and Mualem-van Genuchten parameters as in Experiment 1.

Soil hydrological processes (SHP) support ecosystems, modulate the impact of climate change on terrestrial systems and control feedback mechanisms between water, energy and biogeochemical cycles. However, land-use changes and extreme events are increasingly impacting these processes. In this Review, we describe SHP across scales and examine their links with soil properties, ecosystem processes and climate. Soil structure influences SHP such as infiltration, soil water redistribution and root water uptake on small scales. On local scales, SHP are driven by root water uptake, vegetation and groundwater dynamics. Regionally, SHP are impacted by extreme events such as droughts, floods, heatwaves and land-use change; however, antecedent and current SHP partially determine the broader effects of extreme events. Emerging technologies such as wireless and automated sensing, soil moisture observation through novel synthetic aperture radars satellites, big data analysis and machine learning approaches offer unique opportunities to advance soil hydrology. These advances, in tandem with the inclusion of more key soil types and properties in models, will be pivotal in predicting the role of SHP during global change.

Spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. There is growing interest in monitoring soil moisture in relation to soil- and land-based natural flood management (NFM), to understand the soil’s ability, via land-use and management changes, and to delay the arrival of flood peaks in nearby watercourses. This article monitors relative surface soil moisture (rSSM) across the Thames Valley, U.K., using Sentinel-1 data, and the Vienna University of Technology (TU-Wien) Change Detection Algorithm, with a novel exploration of monthly and annual normalization factors and spatial averaging. Two pairs of normalization factors are introduced to remove impacts from varying local incidence angles through direct and multiple regression slopes. The spatiotemporal distribution of rSSM values at various spatial resolutions (1000, 500, 250, and 100 m) is assessed. Comparisons with in situ soil moisture data from the COSMOS-UK network show that, while general temporal trends agree, the difference in effective depth of measurements, coupled with vegetation impacts during the growing season, makes comparison with soil moisture observations difficult. Temporal rSSM trends can be retrieved at spatial resolutions down to 100 m, and the rSSM RMSE was found to decrease as the spatial resolution increases. The vegetation effects upon the rSSM are further explored by comparing the two dominant land cover types: Arable and Horticulture, and Improved Grassland. It was found that, while the rSSM retrieval for these land covers was possible, and the general soil moisture trend is clear, overlying vegetation during the summer artificially increased the rSSM values.

Groundwater is by far the largest unfrozen freshwater resource on the planet. It plays a critical role as the bottom of the hydrologic cycle, redistributing water in the subsurface and supporting plants and surface water bodies. However, groundwater has historically been excluded or greatly simplified in global models. In recent years, there has been an international push to develop global scale groundwater modeling and analysis. This progress has provided some critical first steps. Still, much additional work will be needed to achieve a consistent global groundwater framework that interacts seamlessly with observational datasets and other earth system and global circulation models. Here we outline a vision for a global groundwater platform for groundwater monitoring and prediction and identify the key technological and data challenges that are currently limiting progress. Any global platform of this type must be interdisciplinary and cannot be achieved by the groundwater modeling community in isolation. Therefore, we also provide a high-level overview of the groundwater system, approaches to groundwater modeling and the current state of global groundwater representations, such that readers of all backgrounds can engage in this challenge.

Many remote sensing-based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman–Monteith Mu model (PM-MOD), and Penman–Monteith Nagler model (PME-VI). ET was predicted satisfactorily by all four models, with correlations consistently higher (R2 > 0.6) for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of percent bias (- 10

The accurate and timely assessment of pasture quantity and quality (i.e., nutritive characteristics) is vital for effective pasture management. Remotely sensed data can be used to predict pasture quantity and quality. This study investigated the ability of Sentinel-2 multispectral bands, convolved from proximal hyperspectral data, in predicting various pasture quality and quantity parameters. Field data (quantitative and spectral) were gathered for experimental plots representing four pasture types—perennial ryegrass monoculture and three mixtures of swards representing increasing species diversity. Spectral reflectance data at the canopy level were used to generate Sentinel-2 bands and calculate normalised difference indices with each possible band pair. The suitability of these indices for prediction of pasture parameters was assessed. Pasture quantity parameters (biomass and Leaf Area Index) had a stronger influence on overall reflectance than the quality parameters. Indices involving the 1610 nm band were optimal for acid detergent fibre, crude protein, organic matter and water-soluble carbohydrate concentration, while being less affected by biomass or LAI. The study emphasises the importance of accounting for the quantity parameters in the spectral data-based models for pasture quality predictions. These explorative findings inform the development of future pasture quantity and quality models, particularly focusing on diverse swards.

Inundation of river water during flooding deposits contaminated sediments onto floodplain topsoil. Historically, floodplains were considered an important sink for potentially toxic elements (PTEs). With increasing flood frequency and duration, due to climate change and land use change, it is important to understand the impact that further flooding may have on this legacy contamination. In this study a field-based approach was taken, extracting soil pore waters by centrifugation of soils sampled on multiple occasions from multiple locations across a floodplain site, which lies adjacent to the River Loddon in southeast England. Flooding generally decreased pore water PTE concentrations and significantly lower pore water concentrations of Cd, Cu, and Cr were found post-flood compared to pre-flood. The dominant process responsible for this observation was precipitation with sulphides resulting in PTE removal from the pore water post-flood. The changes in pH were found to be associated with the decreased pore water concentration of Cu, which suggests the pH rise may have aided adsorption mechanisms or precipitation with phosphates. The impact of flooding on the release and retention of PTEs in floodplain soils is the net effect of several key processes occurring concurrently. It is important to understand the dominant processes that drive mobility of individual PTEs on specific floodplains so that site-specific predictions can determine the impact of future floods on the environmental fate of legacy contaminants.

Both climate change and anthropogenic activities contribute to the deterioration of terrestrial water resources and ecosystems worldwide, with Central Asia and its endorheic lakes being among the most severely affected. We used a digital elevation model, bathymetry maps and Landsat images to estimate the areal water cover extent and volumetric storage changes for eleven small terminal lakes in Burabay National Nature Park (BNNP) in Northern Kazakhstan from 1986 to 2016. Based on the analysis of hydrometeorological observations, lake water balance, lake evaporation and Budyko equations, driven by gridded climate and global atmospheric reanalysis datasets, we evaluate the impact of historical climatic conditions on the water balance of the BNNP lake catchments. The total surface water area of the BNNP lakes decreased by around 7% for that period, mainly due to a reduction in the extent of three main lakes. In contrast, for some smaller lakes, the surface area increased. Overall, we attribute the decline of the BNNP lakes’ areal extent and volume to the prolonged periods of water balance deficit when lake evaporation exceeded precipitation. However, during the most recent years (2013–2016) precipitation increased and the BNNP lake levels stabilized.

A large proportion of the global land surface is covered by pasture. The advent of the Sentinel satellites program provides free datasets with good spatiotemporal resolution that can be a valuable source of information for monitoring pasture resources. We combined optical remote sensing data (proximal hyperspectral and Sentinel 2A) with a radiative transfer model (PROSAIL) to estimate leaf Area Index (LAI), and biomass, in a dairy farming context. Three sites in Southern England were used: two pasture farms that differed in pasture type and management, and a set of small agronomy trial plots with different mixtures of grasses, legumes and herbs, as well as pure perennial ryegrass. The proximal and satellite spectral data were used to retrieve LAI via PROSAIL model inversion, which were compared against field observations of LAI. The potential of bands of Sentinel 2A that corresponded with a 10 m resolution was studied by convolving narrow spectral bands (from a handheld hyperspectral sensor) into Sentinel 2A bands (10 m). Retrieved LAI, using these spectrally resampled S2A data, compared well with measured LAI, for all sites, even for those with mixed species cover (although retrieved LAI was somewhat overestimated for pasture mixtures with high LAI). This proved the suitability of 10 m Sentinel 2A spectral bands for capturing LAI dynamics for different types of pastures. We also found that inclusion of 20 m bands in the inversion scheme did not lead to any further improvement in retrieved LAI. Sentinel 2A image based retrieval yielded good agreement with LAI measurements obtained for a typical perennial ryegrass based pasture farm. LAI retrieved in this way was used to create biomass maps (that correspond to indirect biomass measurements by Rising Plate Meter (RPM)), for mixed-species paddocks for a farm for which limited field data were available. These maps compared moderately well with farmer-collected RPM measurements for this farm. We propose that estimates of paddock-averaged and within-paddock variability of biomass are more reliably obtained from a combined Sentinel 2A-PROSAIL approach, rather than by manual RPM measurements. The physically based radiative transfer model inversion approach outperformed the Normalized Difference Vegetation Index based retrieval method, and does not require site specific calibrations of the inversion scheme.

Changes to climate are projected over the next 50 years for many peatland areas. As decomposition of peat-forming vegetation is likely to be intrinsically linked to these changes in climate, a clear understanding of climate-peat dynamics is required. There is concern that increased temperature and decreased precipitation could increase the rate of decomposition and put the carbon sink status of many peatlands at risk, yet few studies have examined the impact of both climatic factors together. To better understand the sensitivity of peatland decomposition to changes in both temperature and precipitation and their interaction, we conducted a short-term laboratory experiment in which plant litters and peat soil were incubated, in isolation, in a factorial design. Treatments simulated baseline and projected climate averages derived from the latest UK climate change projections (UKCP09) for Exmoor, a climatically marginal peatland in SW England. Regular carbon dioxide flux measurements were made throughout the simulation, as well as total mass loss and total dissolved organic carbon (DOC) leached. The largest effect on carbon loss in this multifactor experiment was from substrate, with Sphagnum/peat releasing significantly less C in total during the experiment than dwarf shrubs/graminoids. Climate effects were substrate specific, with the drier rainfall treatment increasing the DOC leaching from Calluna, but decreasing it from Sphagnum. Partitioning between CO2 and DOC was also affected by climate, but only for the peat and Sphagnum samples, where the future climate scenarios (warmer and drier) resulted in a greater proportion of C lost in gaseous form. These results suggest that indirect effects of climate through changes in species composition in peatlands could ultimately turn out to be more important for litter decomposition than direct effects of climate change from increased temperatures and decreased rainfall.

Environmental models often require soil maps to represent the spatial variability of soil attributes. However, mapping soils using conventional in-situ survey protocols is time-consuming and costly. As an alternative, digital soil mapping offers a fast-mapping approach that might be used to monitor soil attributes and their interrelationships over large areas. In Brazil, conventional survey methods are still widely used, and thus maps still in development are considered as the state-of-the-art products for decades. In this study, we address this lack of updated spatial information on many soil attributes by producing regional statistical soil models using an innovative framework. This new framework attempts to reduce prediction redundancies due to high multicollinearity, by implementing a Feature Selector algorithm. This is expected to improve a model’s strength by decreasing its unexplained variance. The framework’s core is composed of the Soil-Landscape Estimation and Evaluation Program (SLEEP) and a calibrated Gradient Boosting Model capable of modelling the spatial distribution of soil attributes at multiple soil depths. These models allowed us to explain the spatial distribution of some basic soil attributes (physical and chemical), and its environmental drivers. The model training and testing approach used 30 environmental attributes, and data from 223 soil profiles for the state of Pernambuco, Brazil. Our models demonstrated a consistent potential to perform spatial extrapolations with r2 ranging from 0.8 to 0.97, and PBIAS from -0.51 to 2.03. The properties related to topographic and climatic conditions were dominating when estimating the number of horizons, percentage of silt and the sum of bases (a measure of soil fertility). We believe that our framework features high flexibility, while reducing capital investments when compared to in situ surveys and traditional mapping protocols. These findings also have implications for the improvement and testing of pedotransfer functions. We thank FACEPE for funding this through APQ 0646-9.25/16.

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.

International audience; Up to now, relatively little effort has been dedicated to the quantitative assessment of the differences in spatial patterns of model outputs. In this paper, we employed a variogram-based methodology to quantify the differences in the spatial patterns of root-zone soil moisture, net radiation, and latent and sensible heat fluxes simulated by three land surface models (SURFEX/ISBA, JULES and CHTESSEL) over three European geographic domains – namely, UK, France and Spain. The model output spatial patterns were quantified through two metrics derived from the variogram: i) the variogram sill, which quantifies the degree of spatial variability of the data; and ii) the variogram integral range, which represents the spatial length scale of the data. The higher seasonal variation of the spatial variability of sensible and latent heat fluxes over France and Spain, compared to the UK, is related to a more frequent occurrence of a soil-moisture-limited evapotranspiration regime during summer dry spells in the south of France and Spain. The small differences in spatial variability of net radiation between models indicate that the spatial patterns of net radiation are mostly driven by the climate forcing data set. However, the models exhibit larger differences in latent and sensible heat flux spatial variabilities, which are related to their differences in i) soil and vegetation ancillary datasets and ii) physical process representation. The highest discrepancies in spatial patterns between models are observed for soil moisture, which is mainly related to the type of soil hydraulic function implemented in the models. This work demonstrates the capability of the variogram to enhance our understanding of the spatiotemporal structure of the uncertainties in land surface model outputs. Therefore, we strongly encourage the implementation of the variogram metrics in model intercomparison exercises.

Climate projections suggest that rainfall events will become more frequent and intense, which may lead to more widespread flooding. Floodplains can be used to help reduce the magnitude of floods downstream by storing excess flood water, thus making them useful for flood risk management. This means that floodplains are subjected to repeated drying and rewetting, which has implications for biogeochemical cycling of chemical elements in floodplain soils.Floodplains have been considered a sink for contaminants in urban catchments, where high river flows transport contaminated sediments downstream and deposit them onto the floodplain topsoil. With increasing flooding frequency and duration, floodplains may become sources of legacy pollution through desorption of contaminants into soil porewater or resuspension of particulate matter into the overlying floodwater. Therefore, flooding could re-mobilise potentially toxic elements (PTEs) such as Cadmium (Cd), Copper (Cu), Chromium (Cr), Nickel (Ni), and Lead (Pb) that are present in the floodplain soil as a result of historic deposition. Mobilising PTEs in floodplain soils may cause adverse ecological impacts for soil microorganisms, plants, and both terrestrial and aquatic fauna.The mobility of PTEs from the floodplain soil can increase or decrease due to the net effect of five key processes that influence dispersion and accumulation; 1) soil redox potential for which decreases can directly alter the speciation, and hence mobility, of redox sensitive PTEs (e.g. As and Cr), 2) soil pH for which an increase usually reduces the mobility of metal cations (e.g. Cd2+, Cu2+, Ni2+, Pb2+), 3) dissolved organic matter which can mobilise PTEs were strongly bound to soil particles, 4) iron (Fe) and manganese (Mn) hydroxides undergo reductive dissolution, releasing adsorbed and co-precipitated PTEs, and 5) reduction of sulphate which immobilises PTEs due to precipitation of metal sulphides.We took a field-based approach; extracting soil pore waters from a floodplain downstream of a typical urban catchment in southeast England before, during and after a flooding event. During the flood, there was increased mobility of Cd and Pb, and decreased mobility for Cu and Cr, compared to the mobility before flooding. After the flood, Ni mobility increased, whereas the other PTEs had lower mobility than they had prior to the flood. We also measured explanatory variables (e.g. pH, redox, Fe and Mn) that might explain the changes in mobility of PTEs that we found. Reductive dissolution of Mn is a possible mechanism for the increased mobility of Cd and Pb and redox likely played a role in the reduced Cr mobility.Flooding did not influence the mobility of all PTEs in the same way. The duration of flooding is thought to influence the mobilisation due to the length of time for key processes to take place. It is therefore difficult to predict what PTEs might be mobilised into the environment with any given flooding event, further work is required to identify which soil properties should be measured in order to improve our capability to predict how a flooding event will influence the mobility of individual PTEs in geochemically contrasting floodplain soils.

Burabay National Nature Park (BNNP), which is famous for its beautiful lakes and pine forests, is an important tourist destination and biodiversity hot spot in cold, semi-arid Northern Kazakhstan, Central Asia. BNNP lake system is being influenced by increasing anthropogenic pressures and climate change impacts. Lake level declines observed from 2008 to 2013 followed by rebound from 2013 onwards raised concerns about the future of these unique lakes. Previous studies on BNNP lakes showed that its steady long-term water storage decline was mainly due to a natural water balance deficit, with evaporation (from the lakes and catchments) exceeding precipitation. Next, to obtain a deeper understanding of this complex lake system, we studied the BNNP’s catchments by applying a hydrological model. This work is the first attempt to simulate the hydrological processes in two key BNNP lakes (Ulken Shabakty and Burabay) using a semi-distributed hydrological model, Soil and Water Assessment Tool (SWAT). The available daily lake level measurements were transformed into lake volumes using the data from a recent bathymetric survey and Surface Volume tool of ArcGIS. The level of Burabay Lake is determined by its main outlet, Gromotukha river, that discharges the excess water from Burabay Lake to Ulken Shabakty. Therefore, it acts as a natural reservoir and allows to use the Reservoir function of SWAT. Calibration of the model by lake volumes was done for years 2010-2013 and the model performed well for both lakes (NSE 0.71 and 0.57; KGE 0.77 and 0.73; PBIAS -0.9 and -0.4 for Ulken Shabakty and Burabay, respectively). However, during validation for years 2014-2016 the model performance decreased considerably (NSE -23.94 and -0.35, KGE 0.12 and -0.35, PBIAS 7.6 and -0.3 for Ulken Shabakty and Burabay, respectively). SWAT substantially overestimated the lake volumes for Ulken Shabakty by 0.01 km3 on average for the validation period. This extreme overestimation highlights the specific features of both basins, which has to do with the local subsurface flows. Due to the relatively simplistic representation of groundwater in SWAT and the absence of comprehensive groundwater data, the calibrated model might not have been able to fully capture the complexity of the actual hydrogeologic system. As a result, smaller in size surface catchment boundary (in the case of Ulken Shabakty Lake) is considered in comparison to potentially larger groundwater catchment boundary. In addition, two years (2010 and 2012) used for calibration were drought years, during which the model might have compensated for the lower groundwater flows by simulating enhanced surface runoff and lateral flow. As a result, during the following years with normal and higher precipitation amounts (2013-2016) significantly higher surface runoff was generated. Further studies using coupled groundwater and surface water models are necessary to understand the interactions between groundwater and surface water.

The frequency and duration of flooding events are increasing due to land-use changes increasing run-off of precipitation, and climate change causing more intense rainfall events. Floodplain soils situated downstream of urban or industrial catchments, which were traditionally considered a sink of potentially toxic elements (PTEs) arriving from the river reach, may now become a source of legacy pollution to the surrounding environment if PTEs are mobilised by unprecedented flooding events.\ud \ud When a soil floods, the mobility of PTEs can increase or decrease due to the net effect of five key processes; (i) the soil redox potential decreases which can directly alter the speciation, and hence mobility, of redox sensitive PTEs (e.g. Cr, As), (ii) pH increases which usually decreases the mobility of metal cations (e.g. Cd2+, Cu2+, Ni2+, Pb2+, Zn2+), (iii) dissolved organic matter (DOM) increases, which chelates and mobilises PTEs, (iv) Fe and Mn hydroxides undergo reductive dissolution, releasing adsorbed and co-precipitated PTEs, and (v) sulphate is reduced and PTEs are immobilised due to precipitation of metal sulphides. These factors may be independent mechanisms, but they interact with one another to affect the mobility of PTEs, meaning the effect of flooding on PTE mobility is not easy to predict. Many of the processes involved in mobilising PTEs are microbially mediated, temperature dependent and the kinetics are poorly understood.\ud \ud Soil mineralogy and texture are properties that change spatially and will affect how the mobility of PTEs in a specific soil may be impacted by flooding. As a result, knowledge based on one river catchment may not be particularly useful for predicting the impacts of flooding at another site. This review provides a critical discussion of the mechanisms controlling the mobility of PTEs in floodplain soils. It summarises current understanding, identifies limitations to existing knowledge, and highlights requirements for further research.

10 pàginas.- 5 figuras.- 2 tablas.- referencias, Understanding the relationship between tree production and water use, as well as the main environmental and plant-related drivers of water use, is crucial for the development of production prediction models and reliable water management strategies under different climatic conditions. However, the relation between tree water use and biomass production has never been assessed throughout the entire rotation for poplar trees. Meanwhile, it remains poorly understood how the transpiration driving factors of poplars will change with stand age. Therefore, we investigated the relationship between transpiration (E) and aboveground biomass (ABM), as well as the main drivers of E for 2- to 5-year-old (2016–2019) Populus tomentosa trees under three water treatments. The annual increase in ABM depended on annually accumulated E and their relationship could be fitted with a logistic curve in each growing season (R2 > 0.89). Throughout the whole rotation period, compared with the non-irrigated trees, full irrigation trees produced 59% more biomass with only 12% more E, while deficit irrigation trees attained 46% more biomass with 32% more E. The daily E had a strong exponential relationship with vapor pressure deficit (D) during years 3–5 of the rotation, and the asymptote of this relationship increased with tree age (1.6 kPa (2017), 2 kPa (2018), 2.5 kPa (2019)). The E was also strongly linearly correlated to solar radiation (Rs) for each year although with slightly weaker relationships than for D. Similar to other poplar species, P. tomentosa showed effective stomatal control on E. However, soil water content had almost no effect on E for all treatments, no matter which soil layer was considered. D and Rs were the major drivers of P. tomentosa transpiration during the growing season, even during drought periods, in the study region. Our findings will not only help to deepen the understanding of poplar water use characteristics and the underlying mechanisms, but also help to develop models for predicting the biomass production., This research was jointly supported by “National Natural Science Foundation of China” (32001304, 31872702, 31971640) and the National Key Research and Development Program of China (2016YFD0600403).

The potential effect of climate change on regional suitability for cocoa cultivation is a serious economic concern for West Africa—especially for Ghana and Côte d’Ivoire, whose cocoa cultivation accounts for respectively ∼19% and ∼45% of world production. Here, we present a modelling and observational study of cocoa net primary productivity (NPP) in present day and future West African climates. Our analysis uses a data assimilation technique to parameterise a process-based land-surface model. The parameterisation is based on laboratory observations of cocoa, grown under both ambient and elevated CO2. Present day and end of 21st century cocoa cultivation scenarios are produced by driving the parameterised land-surface model with output from a high-resolution climate model. This represents a significant advance on previous work, because unlike the CMIP5 models, the high-resolution model used in this study accurately captures the observed precipitation seasonality in the cocoa-growing regions of West Africa—a key sensitivity for perennials like cocoa. We find that temperature is projected to increase significantly and precipitation is projected to increase slightly, although not in all parts of the region of interest. We find, furthermore, that the physiological effect of higher atmospheric CO2 concentration ameliorates the impacts of high temperature and variation in precipitation thereby reducing some of the negative impacts of climate change and maintaining NPP in West Africa, for the whole 21st Century, even under a high emissions scenario. Although NPP is an indicator of general vegetation condition, it is not equivalent to yield or bean quality. The study presented here is, nevertheless, a strong basis for further field and modelling studies of cultivation under elevated CO2 conditions.

Modeling of the land surface water‐, energy‐, and carbon balance provides insight into the behavior of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large‐scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyze the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS‐1D were simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in‐depth analysis of the soil SHPs and derived soil characteristics was performed to analyze why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time‐integrated behavior of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter‐comparison studies to avoid artifacts originating from the choice of PTF rather from different model structures., Journal of Advances in Modeling Earth Systems, 13 (3), ISSN:1942-2466

Understanding temporal variability in groundwater levels is essential for water resources management. In sub-Saharan Africa, groundwater level dynamics are poorly constrained due to limited long term observations. Here we present the first published analysis of temporal variability in groundwater levels at the national scale in sub-Saharan Africa, using 12 multi-decadal (c. 1980s – present) groundwater level hydrographs in Burkina Faso. For each hydrograph, we developed lumped parameter models which achieved acceptable calibrations (NSE = 0.5–0.99). For eight sites not showing significant (p

According to the Spectral Variation Hypothesis (SVH), spectral variance has the potential to predict taxonomic composition in grasslands over time. However, in previous studies the relationship has been found to be unstable. We hypothesise that the diversity of phenological stages is also a driver of spectral variance and could act to confound the species signal. To test this concept, intra-annual repeat spectral and botanical sampling was performed at the quadrat scale at two grassland sites, one displaying high species diversity and the other low species diversity. Six botanical metrics were used, three taxonomy based and three phenology based. Using uni-temporal linear permutation models, we found that the SVH only held at the high diversity site and only for certain metrics and at particular time points. We also tested the seasonal influence of phenological stage dominance, alongside the taxonomic and phenological diversity metrics on spectral variance using linear mixed models. A term of percentage mature leaves, alongside an interaction term of percentage mature leaves and species diversity, explained 15-25% of the model variances, depending on the spectral region used. These results indicate that the dominant canopy phenology stage is a confounding variable when examining the spectral variance-species diversity relationship. We emphasise the challenges that exist in tracking species or phenology-based metrics in grasslands using spectral variance but encourage further research that contextualises spectral variance data within seasonal plant development alongside other canopy structural and leaf traits.

Studies simultaneously quantifying litter weight losses and rates of CO2-C evolved are few, though essential for accurate estimates of forest carbon budgets. A 120-day dry matter loss and a 130-day carbon emission experiments were concurrently conducted at the soil laboratory of the University of Reading, UK. Leaf litters of tree species comprising cocoa (Theobroma cacao), Newbouldia laevis (dominant shade tree in Eastern region (ER)) and Persea americana (dominant shade tree in Western region (WR)) of Ghana were incubated using a single tree leaf litter and/or a 1:1 mixed species leaf litters to determine and predict the litter decomposition and C dynamics in cocoa systems with or without the shade trees. Decomposition and C release trends in the ER systems followed: shade > mixed cocoa-shade = predicted mixed litter > cocoa; and in the WR, the order was: cocoa = mixed cocoa-shade > predicted mixed > shade. Differences between released C estimated from litter weight loss and CO2-C evolution measurement methods were not consistent. Regression analysis revealed a strong (R2 = 0.71) relationship between loss of litter C and the CO2-C evolution during litter decomposition. The large C pool for shaded cocoa systems indicates the potential to store more C and thus, its promotion could play a significant role in atmospheric CO2 mitigations.

The Caatinga is a seasonally dry tropical forest, which is the dominant vegetation type in the northeastern region of Brazil. Its many plant species have adapted to the semiarid climate through different biophysical and physiological traits and drought survival strategies. In recent years, this region has endured a number of prolonged droughts that have adversely affected this already severely water-limited region. Despite the relatively small amounts of rainfall (with annual rainfall ranging approximately between 100–800 mm/year), there is an almost perpetual occurrence of clouds due to the regional atmospheric circulation; broadly speaking cumulus or cumulonimbus in the rainy season, and mostly stratocumulus during the transition from wet to dry, and dry seasons. We studied the effect of cloud cover on the radiation balance, as well on the surface energy- and carbon balance of a pristine Caatinga area from 2011 to 2018.This study used radiation and weather data obtained from a SONDA BSRN radiation station, as well from a flux tower installed in the study area; both were near the urban areaofPetrolina, Brazil. Furthermore, radio-sounding data collected nearby were employed to obtain column integrated atmospheric water vapour, to estimate atmospheric emissivity.We derived cloudiness from a number of indirect methods (using shortwave- and longwave incoming radiation) at diurnal, seasonal and multi-year timescales. We also employed observed cloud cover data, including those from sky-cameras, for verification.Estimates of clear-sky atmospheric emissivity were required to determine cloud cover. These were obtained from well-known equations (e.g., Brunt, Brutsaert and Prata) using tower air temperature and/or vapour pressure; calibration of the constants in these equations was required and their performance varied considerably. Occasionally, there were large differences between column integrated atmospheric water vapour and near-surface humidity; this had implications for estimates of atmospheric emissivity and hence of cloud cover.Seasonal variations in turbidity varied by a factor of 2. Clear-sky conditions occurred for between 8-18% of the time, with the lowest percentage occurring for the wettest year (2011).Despite its considerable effect on the radiation balance, the variation in cloud cover had a relatively modest effect only on the energy- and carbon balance fluxes. This has implications for our understanding of the Caatinga vegetation functioning, as well as for the development and testing of land surface models for this ecosystem.This work has been supported by The Natural Environment Research Council (NE/N012488/1) and Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (Caatinga-FLUX Phase 2 APQ 0062-1.07/15).

The need for a detailed investigation of the Vea catchment water balance components cannot be overemphasized due to its accelerated land-cover dynamics and the associated impacts on the hydrological processes. This study assessed the possible consequences of land-use change scenarios (i.e. business as usual, BAU, and afforestation for the year 2025) compared to the 2016 baseline on the Vea catchment’s water balance components using the Soil and Water Assessment Tool (SWAT) model. The data used include daily climate and discharge, soil and land use/land cover maps. The results indicate that the mean annual water yield may increase by 9.1% under the BAU scenario but decrease by 2.7% under the afforestation scenario; actual evapotranspiration would decrease under BAU but increase under afforestation; and groundwater recharge may increase under both scenarios but would be more pronounced under the afforestation scenario. These outcomes highlight the significance of land-cover dynamics in water resource management and planning at the catchment.

There is a paucity of information on nutrient stocks and distribution in the cocoa ecosystem for the management of production sites to improve its productivity. Apart, sites with long histories of cocoa production could differ in nutrient stocks and distribution relative to recent production regions. Therefore, some existing cocoa farms in Ghana were sampled on the basis of shade management (shaded and unshaded) and production site longevity (Eastern region > Western North region) to determine the nutrient stock and distributions in them. Over 93% of the total ecosystems’ elementary nutrients were stored in the soil. Higher nutrient stocks occurred under shaded cocoa ecosystem. Nutrient element concentrations in cocoa tree biomasses followed the order: N > Ca > K > Mg > P > S > Al = Fe > Zn = Mn, and mostly concentrated in leaf > root = husk > branch > stem. On average, region as a main factor affected nutrient distributions. There was a sharp distinction between macronutrient and micronutrient accumulations in favour of Eastern region and Western North region, respectively. Therefore, the regional distinction with respect to macro- and micronutrients could be used as a guide to fertilizer recommendation for cocoa systems in the two regions.