Soil Moisture Fluxes Research Articles

Two Perspectives on Amplified Warming over Tropical Land Examined in CMIP6 Models

Abstract Climate change projections show amplified warming associated with dry conditions over tropical land. We compare two perspectives explaining this amplified warming: one based on tropical atmospheric dynamics and the other focusing on soil moisture and surface fluxes. We first compare the full spatiotemporal distribution of changes in key variables in the two perspectives under a quadrupling of CO2 using daily output from the CMIP6 simulations. Both perspectives center around the partitioning of the total energy/energy flux into the temperature and humidity components. We examine the contribution of this temperature/humidity partitioning in the base climate and its change under warming to rising temperatures by deriving a diagnostic linearized perturbation model that relates the magnitude of warming to 1) changes in the total energy/energy flux, 2) the base-climate temperature/humidity partitioning, and 3) changes in the partitioning under warming. We show that the spatiotemporal structure of warming in CMIP6 models is well predicted by the inverse of the base-climate partition factor, which we term the base-climate sensitivity: conditions that are drier in the base climate have a higher base-climate sensitivity and experience more warming. On top of this relationship, changes in the partition factor under intermediate (between wet and dry) surface conditions further enhance or dampen the warming. We discuss the mechanistic link between the two perspectives by illustrating the strong relationships between lower-tropospheric temperature lapse rates, a key variable for the atmospheric perspective, and surface fluxes, a key component of the land surface perspective. Significance Statement Understanding what conditions give rise to the largest magnitude of warming in response to rising CO2 concentrations is not only scientifically important but also critical from a climate impact standpoint. Two main perspectives, one focusing on atmospheric dynamics and the other focusing on land surface processes, have been proposed to explain the stronger warming associated with drier conditions in the tropics. Here, we compare and contrast these two perspectives. We demonstrate that amplified warming in CMIP6 models can largely be predicted from base-climate dryness alone in both perspectives but is further modified based on changes in the partitioning of energy between temperature and moisture. We highlight the spatiotemporal conditions where assumptions in the two perspectives hold and where deviations occur within CMIP6 climate models.

Modeling the spatial distribution of soil physical properties in a semiarid tropical region

In semiarid regions, sustainably managing the limited available water resources is critical for food, water, and economic security. An important aspect of water management is monitoring, analyzing, and predicting soil moisture and hydrologic fluxes, especially under the threats of climate change. Unfortunately, since monitoring of soil moisture and water fluxes is often sparse in these regions, one needs to rely on hydrological modeling. The latter requires accurate knowledge of soil properties (e.g., texture, porosity, hydraulic conductivity), which in semiarid regions is also complicated by high level of spatial heterogeneity that is not captured by the low density of soil sampling efforts as well as by globally gridded soil databases. Using 300 monitoring sites across the Semiarid region of NE Brazil and a soil moisture model, this study aims to estimate soil properties at the sites through inverse-modeling and then spatially interpolation these properties via geostatistics. Inverse modeling with the Levenberg–Marquardt algorithm accurately captured soil properties across the sites, while clear spatial patterns in these properties allowed us to confidently interpolation them across the region. Comparing our results with a global soil dataset shows that the latter does not capture spatial variability in the region, underlining the need to leverage small scale information. The derived soil property datasets may then particularly be valuable for regional water and ecosystem management applications.

An experiential account with recommendations for the design, installation, operation and maintenance of a farm-scale soil moisture sensing and mapping system

Context The research explores the benefits of real time tracking of soil moisture for various land management contexts and the importance of spatio-temporal modelling and mapping to gain clear and visual understanding of soil moisture fluxes across a farm. Aims This research aims to outline the key processes required for building an operational on-farm soil moisture monitoring system where the product is highly granular daily soil moisture maps depicting variations temporally, spatially and vertically. Methods We describe processes of capacitance soil moisture probe installation, data collection infrastructure, sensor calibration, spatio-temporal modelling, and mapping. Key results An out-of-bag soil moisture evaluation modelling system was tested for nearly 2 years. We found a model accuracy (RMSE) estimate of 0.002 cm−3 cm−3 and concordance of 0.96 were found. This result is averaged over this period but fluctuated daily, and related to rainfall patterns across the target farm, which were not directly incorporated into the modelling framework. As expected, incorporating prior estimates of soil moisture into the modelling framework contributed to very accurate estimates of real time available soil moisture. Conclusions This research promotes the importance of iterative improvements to the soil moisture monitoring system, particularly in areas of sensor recalibration and spatio-temporal modelling. We stress the need for a longer-term view and plan for ongoing maintenance and improvement of such systems in the emerging digital farming ecosystem. Implications The results of this research will be useful for researchers and practitioners involved in the design and implementation of on-farm soil monitoring systems.

Assessing the informativeness of a coupled surface–subsurface watershed model for understanding debris flow: a hydrological perspective

ABSTRACT Characterization of debris flow is critical to both risk assessment and hazard mitigation. Recent technologies enable onsite environmental monitoring sensors for geological disaster monitoring. However, the spatiotemporal understanding of debris flow in remote mountainous areas is still limited due to difficulties in observation networks and its complex driving conditions. Here we apply a coupled surface–subsurface hydrological model to examine the characteristics of the water movements near debris flow sites in southwest China. Our approach captured the temporal dynamics of infiltration, redistribution of soil moisture, groundwater storage, and lateral groundwater fluxes. The lateral groundwater flux and groundwater storage were informative indicators in identifying debris flow location. Such informativeness was only effective when hourly dynamics were analysed. Our findings provide new insight into quantifying debris flow susceptibility. This study suggests that the coupled surface–subsurface watershed modelling approach can be informative for preliminary monitoring, planning and management of debris flow.

Global 1 km land surface parameters for kilometer-scale Earth system modeling

Abstract. Earth system models (ESMs) are progressively advancing towards the kilometer scale (“k-scale”). However, the surface parameters for land surface models (LSMs) within ESMs running at the k-scale are typically derived from coarse-resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1 km land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31 % to 54 % of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSM and ESM modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The 1 km land surface parameters are publicly available at https://doi.org/10.5281/zenodo.10815170 (Li et al., 2024).

Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output

Abstract. Land–atmosphere (L–A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L–A coupling (LoCo) metrics capture relevant L–A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L–A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land–atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics. Here, we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyze, and better understand L–A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behavior. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant timescales, (ii) enable demonstration of inter-model spread in L–A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.

Evaluation of Land–Atmosphere Coupling Processes and Climatological Bias in the UFS Global Coupled Model

Abstract This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS’s ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model’s ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales.

LandBench 1.0: A benchmark dataset and evaluation metrics for data-driven land surface variables prediction

The advancements in deep learning methods have presented new opportunities and challenges for predicting land surface variables (LSVs) due to their similarity with computer sciences tasks. However, few researchers focus on the benchmark datasets for LSVs predictions that hampers fair comparisons of different data-driven deep learning models. Hence, we propose a LSVs benchmark dataset and prediction toolbox to boost research in data-driven LSVs modeling and improve the consistency of data-driven deep learning models for LSVs. LSVs benchmark dataset contains a large number of hydrology-related variables, such as global soil moisture, runoff, etc., which can verify the simulation of hydrological processes. Various global data from European Centre for Medium-Range Weather Forecasts reanalysis 5 (ERA5), ERA5-land, global gridded soil information (SoilGrid), soil moisture storage capacity (SMSC), and moderate-resolution imaging spectroradiometer (MODIS) datasets have been pre-processed into daily data at 0.5-, 1-, 2-, and 4-degree resolutions to facilitate their use in data-driven models. Simple statistical metrics, i.e., the root mean squared error and correlation coefficient, are chosen to evaluate the performance of different deep learning (DL) models, including convolutional neural network, long short-term memory and convolution long short-term memory models, with lead times of 1 and 5 days. A processed-based model serves as a physic baseline, soil moisture and surface sensible heat fluxes are taken as the target variables. The developed benchmark dataset and evaluation metrics for predicting LSVs using data-driven approaches, named as the LandBench toolbox, were implemented using Pytorch. This toolbox facilitates the reimplementation of existing methods, the development of novel predictive models, and the utilization of unified evaluation metrics. Additionally, the toolbox incorporates address mapping technology to enable high-resolution global predictions with constrained computing resources. We hope LandBench will not only serves as a standardized framework, fostering equitable model comparisons, but also provides indispensable data and a robust scientific foundation essential for advancing climate change research, disaster management, and sustainable development initiatives.

Thermal performance of wedge pile slab foundation without frost protection insulation and highly insulated slab on ground floor – first year measurements

In the Nordic countries frost insulation is necessary if the foundation is above the frost-free depth. The thermal performance of the pile foundations is between a shallow ground foundation and a frost wall foundation. The pile ends extend below the frost line to help insulate the ground beneath a building’s foundation. The ends of the piles extend below the frost line to prevent the foundation of the building from frost heaving. However, there may be a risk that the ground below the slab on ground may freeze and frost heaving may occur there. The risk of frost below the slab on ground floor and/or basement increases because increasing thickness of floor insulation remarkable influences frost insulation dimensioning. In this study a 6.5 × 17-meter size uninsulated wedge pile slab foundation with highly insulated (U=0.07 W/(m2·K)) slab on ground floor was constructed for a heated building and equipped with temperature, soil moisture, relative humidity, and heat flux sensors. Seven temperature measurement piles below the building and one for reference 8 meters far from the building reached a depth of up to 3.2 meters, which is deeper than the usual 1.4 m depth to avoid freezing in foundation design. In three places, the temperature below the insulation of slab on the ground is measured at 0.5 meters step up to 3 meters from the outer wall of the building. The first-year measurement results showed that temperature did not drop below the zero degree below the foundation and floor. Even though during winter the daily average absolute minimum temperature was -24 °C the sum of degree hours below the zero degree was -7300 °Ch. The historic 50-year absolute sum of minimum degree hours below the zero degree was -28600 °Ch. In addition to real measurement results this three-dimensional temperature sensor field helps to calibrate and validate dynamic model for temperature simulation of thermal performance of wedge pile slab foundation without frost protection insulation and highly insulated slab on ground floor.

Vegetation communities and summer net ecosystem CO2 exchange on western Axel Heiberg Island, Canadian High Arctic

Climate change is expected to result in the Arctic transitioning from a carbon sink to a carbon source environment, with models predicting half of the carbon stock of the upper 3 m soil layer to be released by the year 2300 (van Huissteden and Dolman 2012). However, uncertainty in latitudinal warming and changes in Arctic ecosystem functions, such as gross carbon ecosystem exchange (GEE), are poorly understood, in part a reflection of a high variability in vascular plant community diversity that is dependent upon and sensitive to physiographic controls, such as soil moisture, topography, and seasonal active layer depth (Walker et al. 2005). This heterogeneity complicates assessments of carbon fluxes on a landscape scale and how they will change in the future (Shaver et al. 2007), especially given their sensitivity to local changes in climate, such as warming and higher rates of rainfall (Bintanja 2018, Bintanja and Andry 2017). As part of the creation of a long-term ecological and environmental monitoring program at the McGill Arctic Research Station at Expedition Fiord, western Axel Heiberg Island, field-based studies in 2021-2022 of plant surveys and summer net ecosystem CO2 exchange monitoring were undertaken to: define the major vegetation communities; quantify and investigate CO2 fluxes with chambers and their analogous biophysical variables; and upscale plot level CO2 measurements to the landscape scale using high spatial resolution remote sensing data. define the major vegetation communities; quantify and investigate CO2 fluxes with chambers and their analogous biophysical variables; and upscale plot level CO2 measurements to the landscape scale using high spatial resolution remote sensing data. The Expedition Fiord area is recognized as a polar oasis, with high plant species richness existing within an environment of heterogeneous physiography. At the moment, five vegetation communities have been identified (xeric dwarf shrub barren, xeric-mesic dwarf shub barren, mesic dwarf shrub tundra, cassiope heath, and sedge meadow) that varied as a function of species diversity, percent cover, soil moisture, and net ecosystem carbon exchange. Barren vegetation communities having stronger respiration fluxes (i.e., carbon source environments) while more vegetated communities have stronger photosynthesis fluxes (i.e., carbon sink environments). Landcover classification revealed with high accuracy (79.3%) that barren ground and barren vegetation communities cover a much larger area compared to wetter habitats. Upscaling summer season measured carbon fluxes based on the landcover map revealed that Expedition Fiord is a carbon source environment, with an average efflux of +94.6 g CO2/day. Ongoing work focuses on the expansion of carbon flux and subsurface monitoring locations, as well as studies of soil carbon and microbial diversity across the different land cover classifications, which will help to better resolve how soil microorganisms, plant detritus, labile organic carbon, soil moisture, slope, aspect, and bedrock geology influence CO2 fluxes throughout the summer season in this high Arctic setting.

The influence of bias correction of global climate models prior to dynamical downscaling on projections of changes in climate: a case study over the CORDEX-Australasia domain

We investigate the influence of bias correction of Global Climate Models (GCMs) prior to dynamical downscaling using regional climate models (RCMs), on the change in climate projected. We use 4 GCMs which are bias corrected against ERA-Interim re-analysis as a surrogate truth, and carry out bias corrected and non-bias corrected simulations over the CORDEX Australasia domain using the Weather Research and Forecasting model. Our results show that when considering the effect of bias correction on current and future climate separately, bias correction has a large influence on precipitation and temperature, especially for models which are known to have large biases. However, when considering the change in climate, i.e the Δ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta$$\\end{document}change (future minus current), we found that while differences between bias-corrected and non-corrected RCM simulations can be substantial (e.g. more than 1∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\\,^\\circ$$\\end{document}C for temperatures) these differences are generally smaller than the models’ inter-annual variability. Overall, averaged across all variables, bias corrected boundary conditions produce an overall reduction in the range, standard deviation and mean absolute deviation of the change in climate projected by the 4 models tested, over 61.5%, 62% and 58% of land area, with a larger reduction for precipitation as compared to temperature indices. In addition, we show that changes in the Δ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta$$\\end{document}change for DJF tasmax are broadly linked to precipitation changes and consequently soil moisture and surface sensible heat flux and changes in the Δ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta$$\\end{document}changefor JJA tasmin are linked to downward longwave heat flux. This study shows that bias correction of GCMs against re-analysis prior to dynamical downscaling can increase our confidence in projected future changes produced by downscaled ensembles.

Impacts of seasonality, drought, nitrogen fertilization, and litter on soil fluxes of biogenic volatile organic compounds in a Mediterranean forest

Biogenic volatile organic compounds (BVOCs) play critical roles in ecosystems at various scales, influencing above- and below-ground interactions and contributing to the atmospheric environment. Nonetheless, there is a lack of research on soil BVOC fluxes and their response to environmental changes. This study aimed to investigate the impact of drought, nitrogen (N) fertilization, and litter manipulation on soil BVOC fluxes in a Mediterranean forest. We assessed the effects of drought and N fertilization on soil BVOC exchanges and soil CO2 fluxes over two consecutive years using a dynamic chamber method, and solid-phase microextraction was utilized to quantify soil BVOCs in one year. Our findings revealed that the soil acted as an annual net sink for isoprenoids (1.30–10.33 μg m−2 h−1), with the highest uptake rates observed during summers (25.90 ± 9.36 μg m−2 h−1). The increased summer uptake can be attributed to the significant concentration gradient of BVOCs between atmosphere and soil. However, strong seasonal dynamics were observed, as the soil acted as a source of BVOCs in spring and autumn. The uptake rate of isoprenoids exhibited a significant positive correlation with soil temperature and atmospheric isoprenoid concentrations, while displaying a negative correlation with soil moisture and soil CO2 flux. The effects of drought and N fertilization on soil BVOCs were influenced by the type of VOCs, litter layer, and season. Specifically, drought significantly affected the exchange rate and quantities of sesquiterpenes. N fertilization led to increased emissions of specific BVOCs (α-pinene and camphene) due to the stimulation of litter emissions. These findings underscore the importance of the soil as a sink for atmospheric BVOCs in this dry Mediterranean ecosystem. Future drought conditions may significantly impact soil water content, resulting in drier soils throughout the year, which will profoundly affect the exchange of soil BVOCs between the soil and atmosphere.

Response of Liquid Water and Vapor Flow to Rainfall Events in Sandy Soil of Arid and Semi-Arid Regions

In arid and semi-arid regions, rainfall takes on a critical significance to both agricultural and engineering construction activities, and the transport process and driving mechanism of soil water under rainfall conditions are in need of further investigation. To clarify the variations in soil moisture, temperature, and liquid and vapor flux under various rainfall scenarios, the Mu Us Sandy Land was selected as the study region, and a water–vapor–heat transport model was established using the Hydrus-1D software with in situ observed soil and meteorological data. The simulated results were in good agreement with the measured data during both the calibration and validation periods, suggesting that the model was accurate and applicable to the study region. The variations in the selected dry and rainy periods proved the significant effect of rainfall events on soil matric potential, temperature, and driving forces. When rainfall occurred, the hydraulic conductivity for liquid water rose by three to five orders of magnitude, driving the liquid water flow downward. In contrast, the vapor flux played a vital role in soil water movement, accounting for about 15% of the total water flux in the shallow layer when the soil was dry, while it became non-significant during rainy periods due to the reduction in hydraulic conductivity for vapor and the temperature gradient. These results clarified the mechanisms of soil liquid water and vapor movement in arid areas, which could provide scientific support for future studies on vegetation restoration and ecosystem sustainability in ecologically fragile areas.

Abrupt permafrost thaw drives spatially heterogeneous soil moisture and carbon dioxide fluxes in upland tundra.

Permafrost thaw causes the seasonally thawed active layer to deepen, causing the Arctic to shift toward carbon release as soil organic matter becomes susceptible to decomposition. Ground subsidence initiated by ice loss can cause these soils to collapse abruptly, rapidly shifting soil moisture as microtopography changes and also accelerating carbon and nutrient mobilization. The uncertainty of soil moisture trajectories during thaw makes it difficult to predict the role of abrupt thaw in suppressing or exacerbating carbon losses. In this study, we investigated the role of shifting soil moisture conditions on carbon dioxide fluxes during a 13-year permafrost warming experiment that exhibited abrupt thaw. Warming deepened the active layer differentially across treatments, leading to variable rates of subsidence and formation of thermokarst depressions. In turn, differential subsidence caused a gradient of moisture conditions, with some plots becoming consistently inundated with water within thermokarst depressions and others exhibiting generally dry, but more variable soil moisture conditions outside of thermokarst depressions. Experimentally induced permafrost thaw initially drove increasing rates of growing season gross primary productivity (GPP), ecosystem respiration (Reco ), and net ecosystem exchange (NEE) (higher carbon uptake), but the formation of thermokarst depressions began to reverse this trend with a high level of spatial heterogeneity. Plots that subsided at the slowest rate stayed relatively dry and supported higher CO2 fluxes throughout the 13-year experiment, while plots that subsided very rapidly into the center of a thermokarst feature became consistently wet and experienced a rapid decline in growing season GPP, Reco , and NEE (lower carbon uptake or carbon release). These findings indicate that Earth system models, which do not simulate subsidence and often predict drier active layer conditions, likely overestimate net growing season carbon uptake in abruptly thawing landscapes.

Modeling of soil moisture and water fluxes in a maize field for the optimization of irrigation

Precision irrigation is becoming more and more important in agricultural crop production due to the limited water resources resulting from the negative effects of climate change. Modeling of irrigation can support decision makers in the maintenance of the quantitative and qualitative parameters of the cultivated crops, while improving the efficiency of water consumption. In the present paper, a 3D hydrodynamic model was created in the HYDRUS environment to support the modeling of the temporal changes and spatial variations in the water balance (WB) of a maize cultivated Hungarian study site, thereby providing inputs for irrigation scheduling and variable rate irrigation for stakeholders. Beside soil physical parameters, crop evapotranspiration (ETc) values were also considered as model inputs for the phenological crop development stages of the maize from the sowing to the harvesting. This period was between the 3rd of May and the 10th of September in 2020 and the 20th of May and 14th of September for the year 2021. The performance of the model for soil moisture conditions were validated by on-field soil moisture measurements. The overall performance (full vegetation period) of the model was good (r2 = 0.88 for 2020 and r2 = 0.91 for 2021), but slightly varied among different phenological stages. Based on the model, the water balance of the investigated area was determined without any irrigation, alongside the cumulative water fluxes (CWF) through the boundaries including the root water uptake (RWU) of the maize as well. The results revealed that the incoming precipitation was not sufficient to supplement the water content in the soil to the optimal soil moisture range, except when the precipitation level is high enough to balance it. It was concluded that the water balance was negative for the investigated time periods without any irrigation. The specific water deficit (WD) values were calculated considering the area of the study site, which is 1439 m3/ha for 2020 and 2068 m3/ha for 2021. From the obtained results, optimal irrigation schedules were presented to keep the soil moisture content in the optimal range (20.92–14.41 V/V%) in the modeled area for two different vegetation periods. It was found that due to the irrigation the crop water productivity (CWP) increased by 6% and 4% in 2020 and 2021. Overall, the model is able to cope with changing circumstances that could help to mitigate the negative effects of climate change with the reasonable use of limited water resources in the future.

Impact of alternative soil data sources on the uncertainties in simulated land-atmosphere interactions

Numerical weather- and climate prediction models rely on soil data to accurately model land surface processes. However, as soil data are produced using soil profiles and maps with multiple sources of uncertainty, wide discrepancies prevail in global soil datasets. Comparison of four commonly used soil datasets in Earth system climate models, i.e., Food and Agriculture Organization soil data, Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids, yields widespread differences in southern Africa. This study investigates the simulated land-atmosphere interactions in southern Africa in the context of the uncertainties from applying different global soil datasets. We conducted ensemble simulations using the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) incorporated with each of the global soil datasets mentioned above. Model simulations were performed at 4-km convection-permitting scale from January 2015 to June 2016. By quantifying model's internal variability and comparing the modeling results, results show that the simulated temperature, soil moisture, and surface energy fluxes are largely impacted by soil texture differences. For instance, changes in soil texture and associated hydrophysical parameters result in large differences in air temperature up to 1.7°C and surface heat flux up to 25 W/m2, and disparities in averaged surface soil moisture differ up to 0.1 m3/m3 in austral summer months. Differences in soil texture characteristics also regulate local climatic conditions differently in the wet and dry seasons as well as in different climatic regions. Furthermore, the thermodynamic differences in surface energy fluxes caused by soil texture demonstrate physical feedback perspective on atmospheric processes, resulting in distinct changes in planetary boundary layer height. This study demonstrates the non-negligible impact of soil data on land surface-atmosphere coupled modeling and highlights the need for consistent consideration of modeling uncertainties from soil data in modeling applications.

Delineating soil moisture dynamics within an evapotranspiration surface barrier using spatial‐temporal analysis

AbstractEvapotranspiration (ET) surface barriers store infiltrated precipitation during the recharge period and release the stored water to the atmosphere via ET. The primary purpose of a surface barrier is to reduce or eliminate drainage to the underlying waste zone. The objective of this study is to analyze the spatial and temporal dynamics of soil moisture within an ET surface barrier based on observed and simulated data. This study characterizes the water movement processes using contour plots of soil moisture content and flux rate in the depth‐time domain. Zero‐flux planes (ZFPs) divide the depth‐time domain into stored water, ET, and drainage zones. Some flow dynamics (e.g., flow rate and direction) that were not observed in the field were elaborated with simulation results to identify the depth of the recharge front of infiltrated water, the release front of stored water, and the bottom of the ET zone. The ET‐drainage divide marks the bottom of the ET zone and the top of the drainage zone. The results showed that the temporal analysis of soil moisture storage could indicate the degree of usage of the storage capacity of a surface barrier. The spatial‐temporal analyses of soil moisture content and flux rate can characterize the durations of the recharge/release processes and the depth of the stored water. Quantification of these processes and related zones provides beneficial understanding of the state and dynamics of soil moisture for a range of weather and vegetation conditions and is useful in optimizing the design of an ET surface barrier.

Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model.

This paper coupled a unified passive and active microwave observation operator-namely, an enhanced, physically-based, discrete emission-scattering model-with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature TBp (p = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of TBH lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of TBV reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.

Evaluation of CLM5.0 for simulating surface energy budget and soil hydrothermal regime in permafrost regions of the Qinghai-Tibet Plateau

Surface energy budget and soil hydrothermal regime are crucial for understanding the interactions between the atmosphere and land surface. However, large uncertainties in current land surface process models exist, especially for the permafrost regions in the Qinghai-Tibet Plateau. In this study, observed soil temperature, moisture, and surface energy fluxes at four sites in permafrost regions are chosen to evaluate the performance of CLM5.0. Furthermore, the soil property data, different thermal roughness length schemes, and dry surface layer (DSL) scheme are investigated. The results show that the soil property data is important for CLM5.0. The default scheme in CLM5.0 yields large errors for surface energy fluxes. The combination of the thermal roughness length and DSL scheme significantly improved the simulation of surface energy fluxes, especially for latent heat flux. The optimization of DSL scheme significantly improved soil temperature simulation and decreased the RMSE from 1.95 °C, 2.07 °C, 2.02 °C, and 2.95 °C to 1.34 °C, 1.35 °C, 1.35 °C and 2.29 °C in TGL site, respectively. The combination of the thermal roughness length and DSL scheme performed the best in shallow soil moisture, decreasing the RMSE from 0.136 m3 m−3 to 0.049 m3 m−3 in the XDT site but slightly enhancing the errors in middle soil. The interactions between surface energy and soil hydrothermal regime also discussed. However, the thermal roughness length and the DSL schemes are highly dependent on the condition of the underlying surface. Different schemes should be selected for different regions.

Deep soil CO2 flux with strong temperature dependence contributes considerably to soil-atmosphere carbon flux

The diffusion of CO2 produced by carbon mineralization in vertical profiles is an important CO2 emission process. However, because of the relatively slow rate of change in the subsoil environment compared with the surface soil, carbon mineralization and diffusion are often ignored, and the mechanisms that transfer deep carbon to the soil-atmosphere interface are still unclear. We studied vertical differences in CO2 flux and its driving mechanism in layers situated from 0 to 200 cm in Robinia pseudoacacia plantations with different stand ages on the Loess Plateau under a typical temperate continental semi-arid monsoon climate. The results showed that (1) in the 0–200 cm layer, CO2 flux showed a bimodal seasonal trend, and total CO2 flux of Robinia pseudoacacia forests with a 10 years stand age fluctuated more actively among the months (Standard Deviation: 3.28%–9.57%). (2) Dynamic evaluation of contribution rate showed a stable (21.81–24.42%) contribution of deep CO2 flux to soil atmosphere interface. (3) Temperature sensitivity of CO2 flux (expressed as Q10) significantly increased with soil depth, moisture and soil CO2 flux presented stronger quadratic function relation in deep layers than shallow layers. There was a significant positive correlation between deep CO2 flux and soil organic carbon (SOC), but there was a weak negative correlation in the shallow layers. The CO2 flux in the shallow layers was mainly regulated by soil organic carbon, whereas that in the deep layers was more regulated by soil temperature.(4) Compared with the traditional hydrothermal two-factor model, the new T&M&C (temperature & moisture & organic carbon) model can improve the model precision by 20–25%, especially in the prediction of CO2 flux in deep layers. Overall, this study is significant for understanding the stability and dynamic changes in the soil carbon pool, and the mechanism of deep organic carbon diffusion to the soil-atmosphere interface in the Loess Plateau.