Soil Moisture Model Research Articles

Hydrologic‐based modelling of burn depth potentials in degraded peat soils

AbstractPeatland drainage may degrade system resilience to high intensity, soil‐consuming fires. Peat soil fires are unique in that they can smoulder vertically through the soil column, with a multitude of consequences including large carbon emissions, altered hydrology, and dramatic shifts in vegetation communities. In this work we developed and verified a new method to model peat burn depths with readily available water level and peat hydraulic property data at the Great Dismal Swamp National Wildlife Refuge (VA and NC, USA). To model peat burn depths across 11 sites in the Great Dismal Swamp National Wildlife Refuge we combined water table time series data and soil moisture release curves, developed at multiple depths, with moisture‐to‐ignition thresholds. A subset of the results from this empirical modelling approach of peat burn depth severity were compared against those made using a mechanistic model of soil moisture, HYDRUS 1‐D. By comparing modelled burn depth potentials between these two approaches for a range of peats, we confirmed that our simpler, water table‐based approach had similar performance to HYDRUS 1‐D in drained and degraded peats, like those found in the Great Dismal Swamp National Wildlife Refuge. A comparative analysis of modelled burn depths across our study site found that water table position and peat water holding capacity were the key governing controls on burn depth potential. Our findings suggest that drainage weakens both short‐ and long‐term controls on peat burn depths by reducing soil moisture and by decreasing peat water holding capacity. This new approach offers land managers with an additional tool for assessing risk while offering insight into the drivers of peatland wildfire severity.

Monitoring the Catastrophic Flood With GRACE-FO and Near-Real-Time Precipitation Data in Northern Henan Province of China in July 2021

Zhengzhou and its surrounding areas, located in northern Henan Province, China, receive continuous extreme rainfall from July 17 to July 22, 2021. Northern Henan Province experiences extensive flash floods and urban floods, causing severe casualties and property damage. Understanding the variation of hydrologic features during this flood event could be valuable for future flood emergency response work and flood risk management. This study first demonstrates the rainstorm process based on near-real-time precipitation data from the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0). To meet the temporal resolution required for monitoring this short-term flood event, reconstructed daily terrestrial water storage anomalies (TWSAs) based on GRACE and GRACE-FO data and CLDAS-V2.0 datasets are first introduced. The spatial and temporal evolution of the reconstructed daily TWSA is analyzed in the study area during this heavy rainfall event. We further employ a wetness index based on the reconstructed daily TWSA for flood warnings. Furthermore, the modeled soil moisture data and daily runoff data are used for flood monitoring. Results show that the reconstructed daily TWSA increases by 437.7 mm in just six days (from July 17 to July 22, 2021), with a terrestrial water storage increment of 9.4 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Compared with ITSG-Grace2018, the reconstructed daily TWSA has better potential for near-real-time flood monitoring for short-term events in a small region. The wetness index derived from reconstructed daily TWSA is potential for flood early warning.

Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat

Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat

Calibration Strategy for Compact Polarimetric GNSS-R Instruments

Polarimetric Global Navigation Satellite System - Reflectometry (GNSS-R) is being proposed by different teams as a solution to directly estimate soil moisture. Up to date, two approaches using polarimetric GNSS-R have been proposed to estimate soil moisture, using the ratio between the right-hand and left-hand circularly polarized received signals, and using the ratio between the linear horizontal and vertical components at a set of incidence angles, known as Hybrid Compact Polarimetric (HCP) GNSS-R. In this manuscript, the necessary calibrations of a received HCP GNSS-R signal are presented. The Stokes parameters of the signal are required to compensate all non-idealities. A methodology to calibrate the receiver effects, scene-antenna polarimetric misalignment, Faraday rotation, and transmitter non-idealities is proposed. Finally, the calibration performance is evaluated using several statistical parameters and polarimetric ratio models based on two different soil moisture products. Results show a correlation coefficient increase of 4.7% with respect to the model derived from the Soil Moisture Active Passive (SMAP) soil moisture product, and a 6.5% improvement with respect to the model derived from the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA-5) monthly average soil moisture product. Furthermore, the proposed calibrations show an unbiased root-mean-square error reduction of 6.3% and 6.8% for the SMAP soil moisture model and the ERA-5 model, respectively. Due to the SMAP antenna and pointing design, SMAP-Reflectometry (SMAP-R) is implicitly insensitive to some of the corrections. More significant corrections should be expected for future polarimetric GNSS-R instruments as European Space Agency (ESA) HydroGNSS.

Spatiotemporal modeling to predict soil moisture for sustainable smart irrigation

In this paper, we have proposed spatiotemporal soil moisture modeling to improve understanding of soil moisture and variability for sustainable irrigation practices. Increasing population growth and climatic changes have intensified the need to implement effective measures to conserve water in irrigation practices. Effective irrigation in parkland influences the overall plant growth processes including the final appearance of the plants. Furthermore, over-irrigation and poor water management in parks and gardens lead to wastage of water which may result in seepage, runoff, and leaching of nutrients into nearby streams. Conversely, under-irrigation results in reduced plant growth and unappealing appearance. Thus, appropriate irrigation management is required to maintain a lush green landscape. Due to large variability in soil properties, environmental conditions and landscape features, the soil moisture level might not be uniformly distributed within a given landscape. Hence, it is essential to understand the soil moisture distribution in the field. There are few existing soil moisture modeling exist but these modelings have not considered multiple vertical depth and time domain. Using Internet of Things(IoT) enabled Cyber–physical system and machine learning techniques, this paper has presented a soil moisture modeling for multiple vertical soil depth for a robust understanding of soil’s and plants need for sustainable sprinkling of the water. The proposed model has used both statistical and deep learning techniques to understand moisture variability in both vertical depth and time. The proposed model has used Correlation Index (CI) to understand multi depth moisture variability on the influence of soil types and local weather parameters, such as precipitation and temperature. To predict moisture data in multi depth, the model has evaluated the use of both statistical machine learning models, such as Support vector regression(SVR) and linear regression (LR), and deep learning models, such as Long Short-Term Memory (LSTM) using seasonal and non-seasonal dataset. The experiment has revealed closely related variability pattern between wind speed and soil moisture in multi depth whereas soil type shows a loosely related variability pattern with moisture in higher depth. With these diverse datasets, the proposed machine learning model has achieved almost 90% success rate to predict moisture content in higher depth using data of lower depth to reduce cumbersome sensors array deployment in higher depth for live moisture visualization. This proposed multiple depth and time domain-based model can enable a smart water dispersing system to conserve water and make a positive contribution to a sustainable future.

Exploring Sentinel-1 backscatter time series over the Atacama Desert (Chile) for seasonal dynamics of surface soil moisture

Recent research indicates an inverse relation between Synthetic Aperture Radar (SAR) signal and near-surface soil moisture (SM) over very dry sediments, arid to hyper-arid soils resp., caused by subsurface scattering effects. This phenomenon can lead to large errors when it comes to modelling and remote sensing-based estimation of SM. While the effect of subsurface scattering and its influence on SM estimates is well described and modelled in literature, its actual presence in recorded SAR data is largely unknown. Here we investigate the relation between C-Band SAR backscatter and SM in the hyper-arid environment of the Atacama Desert (Chile). Time series (2018–2020) of Sentinel-1 VV/VH intensities are compared to in situ SM, measured at 17 stations located across the Atacama Desert. Linear and non-linear regression modelling is applied to uncover the relationship between the SAR intensities and in situ SM, while, in addition, SM variations triggered by seasonal varying humidity (i.e., not by precipitation) are investigated. Results indicate (i) a very weak linear relationship between SM variations and SAR intensities (VV/VH) for most meteorological stations in the Atacama (R2 < 0.5). In particular, noticeable and significant exceptions are found for stations located in the northern uplands of the Coastal Cordillera and on sediments, which are characterized by thick atmospheric dust deposits on top of subsurface cemented crusts; (ii) over these sites a strong inverse linear relationship is present (R2 up to 0.84) and the observable seasonal variations in SAR intensity are linked to the seasonal variations in SM; (iii) remarkably low changes in SM (ranging from approx. 1 to 3%) lead to comparable high changes in SAR intensity (up to 5.5 dB in VH and up to 4.0 dB in VV); (iv) employing a non-linear regression modelling to the SAR time series allowed identifying the occurrence and the strength of seasonal variations over the central desert. This study contributes to a better understanding of SAR scattering over arid to hyper-arid soils, indicates a moisture-regulated complex interplay of surface and subsurface scattering, and proves the presence of subsurface scattering effects in Sentinel-1C-Band data to occur over large parts of the Atacama Desert.

Surface soil moisture modeling using C-band SAR observations at different stages of agricultural crops

Surface soil moisture modeling using C-band SAR observations at different stages of agricultural crops

Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture

Abstract. Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We designed an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The integrated model, LPJ-PM (LPJ-PT-JPLSM Model) as the underlying model, was coupled from the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM version 3.01) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the Global LAnd and Surface Satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 ( coefficient of determination) of 0.91 and 0.81 and an ubRMSD (unbiased root mean square deviation) reduced by 40.3 % and 29.9 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The GPP and ET resulting from the assimilation demonstrated a better performance in the arid and semi-arid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS data had a slight difference, and the SMAP soil moisture data performed better than SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, and GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm per month; estimated standard deviation of μ: 1.91 mm per month). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions

Abstract. To meet the sustainable development goals and enable sustainable management and protection of peatlands, there is a strong need for improving the mapping of peatlands. Here we present a novel approach to identify peat soils based on a high-resolution digital soil moisture map that was produced by combining airborne laser scanning-derived terrain indices and machine learning to model soil moisture at 2 m spatial resolution across the Swedish landscape. As soil moisture is a key factor in peat formation, we fitted an empirical relationship between the thickness of the organic layer (measured at 5479 soil plots across the country) and the continuous SLU (Swedish University of Agricultural Science) soil moisture map (R2= 0.66, p &lt; 0.001). We generated categorical maps of peat occurrence using three different definitions of peat (30, 40, and 50 cm thickness of the organic layer) and a continuous map of organic layer thickness. The predicted peat maps had a higher overall quality (MCC = 0.69–0.73) compared to traditional Quaternary deposits maps (MCC = 0.65) and topographical maps (MCC = 0.61) and captured the peatlands with a recall of ca. 80 % compared to 50 %–70 % on the traditional maps. The predicted peat maps identified more peatland area than previous maps, and the areal coverage estimates fell within the same order as upscaling estimates from national field surveys. Our method was able to identify smaller peatlands resulting in more accurate maps of peat soils, which was not restricted to only large peatlands that can be visually detected from aerial imagery – the historical approach of mapping. We also provided a continuous map of the organic layer, which ranged 6–88 cm organic layer thickness, with an R2 of 0.67 and RMSE (root mean square error) of 19 cm. The continuous map exhibits a smooth transition of organic layers from mineral soil to peat soils and likely provides a more natural representation of the distribution of soils. The continuous map also provides an intuitive uncertainty estimate in the delineation of peat soils, critically useful for sustainable spatial planning, e.g., greenhouse gas or biodiversity inventories and landscape ecological research.

Do Model Results Vary under Different Routing Algorithms Based on a Distributed Ecohydrological Model?

Abstract Although several flow routing (FR) algorithms are developed for hydrological modeling, it is still uncertain how the selection of algorithms may affect model results. This study aims to explore the similarity and dissimilarity in model results among different FR algorithms characterized by single flow direction (SD) and multiple flow direction (MD). The Coupled Hydro-Ecological Simulation System (CHESS) was incorporated with six different FR algorithms (D8, D∞, MD∞, MD8, MFD-md, and RMD∞) and then applied for modeling ecohydrological processes for a semiarid mountainous watershed in the western United States during 1991–2012. Comparisons were made between the model results at the catchment and the grid scale. After slightly adjusting one of the most sensitive soil parameters, all algorithms behave similarly in simulating stream hydrographs. When averaged for the watershed, the modeled ecohydrological variables mostly do not differ significantly (&lt;5%) among the six FR algorithms. Nevertheless, the simulated ecohydrological variables are spatially more autocorrelated under the more dispersive MD algorithms. In addition, there exist significant (&gt;5%) cell-level differences in modeled soil moisture among different FR algorithms, with propagated influences on the simulated evapotranspiration and vegetation growth variables. In hillslopes, the cell-level differences in model results tend to increase significantly as the flows move to the streams. Overall, this study proves that the watershed-level differences in model results among FR algorithms are low after model calibration, while significant differences still occur at the cell level. Thus, observational data are essential for testing which routing algorithm captures better the reality of local ecohydrological processes. Significance Statement The consideration of flow routing is essential for accurately simulating land surface ecohydrological processes. However, less is known about how the selection of flow routing algorithms may affect the model results. Based on model experiments, we found that the model results under different algorithms do not significantly differ from each other when averaged for the watershed. However, significant differences in model results exist at the individual cell level. These findings are useful for guiding future modeling-related research and also suggest the importance of field studies for testing which routing algorithm can better represent local ecohydrological processes.

An Assessment of the National Water Model’s Ability to Reproduce Drought Series in the Northeastern United States

Abstract Portions of the northeastern United States (NE) have experienced drought every year since 2016. The U.S. Drought Monitor (USDM) has played an important role in drought characterization and management by providing weekly drought maps across the entire United States, including the NE. Unfortunately, the USDM lacks consistency between input variables leading to difficulties in defining boundaries between drought categories. This paper evaluates the National Water Model’s (NWM) ability to model streamflow and soil moisture, two important hydrological products that are frequently incorporated in drought indices. Using a 26-yr NWM retrospective simulation, comparisons were conducted between NWM output and observations of streamflow and soil moisture, as well as between drought categories derived from the NWM and observations and the USDM. Results indicate that NWM provides moderate predictions of streamflow at NE stations when comparing to historical observations, that NWM streamflow estimators are generally upwardly biased, and performance is worse at lower streamflow magnitudes. The NWM’s ability to predict soil moisture is worse than streamflow, with again a positive bias at most sites and strong variations in anomaly correlation across sites. When predicting drought categories, NWM streamflow is as strong a predictor of USDM drought categories as observed streamflow. Extending the NWM streamflow series using a maintenance of variance technique and only past records provides slight improvements over drought categories derived from the entire 26-yr retrospective simulation. Output from the NWM appears to have some skill in characterizing drought in the NE and provides a spatial resolution to improve the designation of drought boundaries.

Climatological Drought Monitoring in Switzerland Using EUMETSAT SAF Satellite Data

Climatological drought monitoring in Switzerland relies heavily on station-based precipitation and temperature data. Due to the high spatial variability and complexity of droughts, it is important to complement station-based drought indices with gridded information and to couple multiple drought indicators within the monitoring system. Here, long-term satellite-based drought parameters from the EUMETSAT SAF network are analyzed in terms of dry anomalies within their climatology’s, namely ASCAT soil water index (SWI), CM SAF land surface temperature (LST), complemented with NOAA vegetation data, and LSA SAF Meteosat evapotranspiration data. The upcoming EUMETSAT SAF climate data records on land surface temperature and evapotranspiration will cover for the first time the WMO climatological 30-year reference period. This study is the first study investigating the potential of those long-term data records for climate monitoring of droughts in Europe. The satellite datasets are compared with the standardized precipitation index (SPI), soil moisture observations from the SwissSMEX measurement network, with a modelled soil moisture index (SMI) based on observations, and with evapotranspiration measurements, focusing on the temporal dynamics of the anomalies. For vegetation and surface temperature, the dry years of 2003, 2015, and 2018 are clearly visible in the satellite data. CM SAF LSTs show strong anomalies at the beginning of the drought period. The comparison of in situ and modelled soil moisture and evapotranspiration measurements with the satellite parameters shows strong agreement in terms of anomalies. The SWI indicates high anomaly correlations of 0.56 to 0.83 with measurements and 0.63 to 0.76 with the SMI at grassland sites. The Meteosat evapotranspiration data strongly agree with the measurements, with anomaly correlations of 0.63 and 0.67 for potential and actual evapotranspiration, respectively. Due to the prevailing humid climate conditions at the considered sites, evapotranspiration anomalies during the investigated dry periods were mostly positive and thus not water limited, but were also a driver for soil moisture drought. The results indicate that EUMETSAT SAF satellite data can well complement the station-based drought monitoring in Switzerland with spatial information.

The Relative Importance of Water Vapor Flux from the Perspective of Heat and Mass Movement

Water movement is normally modelled in the soil to quantify spatial and temporal soil moisture distribution. This is important given that soil moisture indicates the amount of water available to plant consumption and also imply the necessity of water sourcing, storage and distribution system to maintain agricultural activities. Modelling soil moisture content in soil is often limited to water mass flux in the mass balance equation. A limited account is given to water vapor contribution to mass flux. Adding to the complexity, the liquid water, and water vapor mass fluxes are influenced by soil heat flux. In this study, five mechanisms driving overall mass fluxes, and seven mechanisms driving overall heat fluxes were quantified based on the published experimental data from Heitman and his co-workers. The study was carried out on silt loam and sandy soil in a drier soil condition at 0.1 and 0.08 m3∙m-3, respectively. The relative comparison between the mechanisms and the soil types clearly shows that water vapor mass flux dominates in the overall mass fluxes, while water vapor heat flux repeatedly ranked second the most important among the seven mechanisms quantified on overall heat fluxes in which there were only three water vapor-heat flux mechanisms exist, the rest four mechanisms are from liquid water-heat flux. Clearly, water vapor flux is a necessary inclusion in heat and mass movement estimation.

Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma—Part I: Individual Product Assessment

Improvements in soil moisture observations and modeling play a vital role in drought, water resources, flooding, and landslide management and forecasting. However, the lack of multisensor products that integrate different spatial scales (i.e., from 1 m2 to 102 km2) is a pressing need in the management and forecasting chain. Up to date, surface soil moisture estimates could be obtained through three primary approaches: (1) in situ measurements and their interpolations, (2) remote sensing observations, and (3) land surface model (LSM) outputs. Each source of soil moisture has its own spatiotemporal resolution, strengths, and weaknesses. Therefore, their correct interpretation and application require an in-depth understanding of their accuracy and appropriateness. In this study, we explore the utility of the triple collocation (TC) method for an independent assessment of three soil moisture products to characterize their uncertainty structures and make recommendations toward a potential product merge. The state of Oklahoma is an ideal domain to test the hypotheses of this work because of the presence of marked west-to-east gradients in climate, vegetation, and soils. The three target soil moisture products include (1) the remotely sensed microwave soil moisture active passive (SMAP) L3_SM_P_E (9 km, daily), (2) the physically based LSM estimates from NLDAS_NOAH0125_H (1/8°, hourly; Noah), and (3) the Oklahoma Mesonet ground sensor network (point, 30 min). The product assessment was conducted from April 2015 to July 2019. The results indicate that, in general, Mesonet and Noah are the most reliable products, although their performance varies geographically and by land cover type, reflecting the main spatiotemporal characteristics and scope of each product. Specifically, Mesonet provides the best estimates of volumetric soil moisture with a mean Pearson correlation coefficient of 0.805, followed by Noah with 0.747. However, Noah represents the true soil moisture variation better than the interpolated Mesonet product on the mesoscale, with an averaged RMSE of 0.026 m3⁄m3. Over different land cover types, Mesonet had the best performance in shrub/scrub, herbaceous, hay/pasture, and cultivated crops with an average correlation coefficient of 0.79, while Noah achieved the best performance in evergreen, mixed, and deciduous forests, with an average correlation coefficient of 0.74. The period-integrated TC intercomparison results over nine climate divisions indicated that Noah outperformed in the central, northeast, and east-central regions. TC provides not only a new perspective for comparatively assessing multisource soil moisture products but also a basis for objective data merging to capitalize on the strengths of multisensor, multiplatform soil moisture products.

Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States.

High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. In-situ measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale. This study aimed to provide calibrated soil moisture models and methodology for generating gridded estimates of soil moisture at multiple depths, according to user-defined temporal periods, spatial resolution and extent. We applied nearly one million national library soil moisture records from over 100 sites, spanning the U.S. Midwest and West, to build Quantile Random Forest (QRF) calibration models. The QRF models were built on covariates including soil moisture estimates from North American Land Data Assimilation System (NLDAS), soil properties, climate variables, digital elevation models, and remote sensing-derived indices. We also explored an alternative approach that adopted a regionalized calibration dataset for the Western U.S. The broad-scale QRF models were independently validated according to sampling depths, land cover type, and observation period. We then explored the model performance improved with local samples used for spiking. Finally, the QRF models were applied to estimate soil moisture at the field scale where evaluation was carried out to check estimated temporal and spatial patterns. The broad-scale QRF model showed moderate performance (R2 = 0.53, RMSE = 0.078 m3/m3) when data points from all depth layers (up to 100 cm) were considered for an independent validation. Elevation, NLDAS-derived moisture, soil properties, and sampling depth were ranked as the most important covariates. The best model performance was observed for forest and pasture sites (R2 > 0.5; RMSE < 0.09 m3/m3), followed by grassland and cropland (R2 > 0.4; RMSE < 0.11 m3/m3). Model performance decreased with sampling depths and was slightly lower during the winter months. Spiking the national QRF model with local samples improved model performance by reducing the RMSE to less than 0.05 m3/m3 for grassland sites. At the field scale, model estimates illustrated more accurate temporal trends for surface than subsurface soil layers. Model estimated spatial patterns need to be further improved and validated with management data. The model accuracy for top 0-20 cm soil depth (R2 > 0.5, RMSE < 0.08 m3/m3) showed promise for adopting the methodology for soil moisture monitoring. The success of spiking the national model with local samples showed the need to collect multi-year high frequency (e.g., hourly) sensor-based field measurements to improve estimates of soil moisture for a longer time period. Future work should improve model performance for deeper depths with additional hydraulic properties and use of locally-selected calibration datasets.

Water Retention Characteristics of Mineral Forest Soils in Finland: Impacts for Modeling Soil Moisture

Soil hydraulic properties are central for soil quality and affect forest productivity and the impacts of climate change on forests. The water retention characteristics (WRC) of mineral forest soils in Finland are not well known, and practical tools to predict them for hydrological, biogeochemical and forest models are lacking. We statistically analyzed mineral forest soils WRC from over 130 sites in Finland, focusing on the humus layer and main root zone (0–19 cm depth). We showed that mineral forest soils can be grouped into five WRC classes that are well predictable from soil bulk density, organic matter content and clay fraction. However, the majority of the forest soils are hydrologically rather similar. We found that neither topsoil maps nor any combination of open geospatial data were able to predict WRC. Thus, in the absence of site-specific soil data, parameterizing WRC as a function of forest site fertility type was proposed. We demonstrated the approach in soil moisture modeling at a small forest headwater catchment and showed that the soil moisture response to weather conditions is jointly affected by WRC, stand attributes and topography. We showed that drought risks are highest for dense mature forests at nutrient-poor, coarse-textured sites and lower for young stands on peatlands and lowland herb-rich sites with groundwater influence. The results improve hydrological predictions for Finnish forests, and the open dataset can contribute to the larger synthesis and development of boreal forest soil pedo-transfer functions.

SpatialAquaCrop, an R Package for Raster-Based Implementation of the AquaCrop Model.

Modeling crop water use and soil moisture availability is becoming increasingly critical, particularly in light of recent drought events. Our study focuses on the spatial application of the AquaCrop model, using a raster-based approach in an R-based environment. The formulated methodology was initially applied and tested on two point-based examples in the Central region of Hungary, followed by the spatial application of the model at the Rákos Stream catchment in the same region. For evaluation purposes, we also utilized satellite-based NDVI data. The results showed that there is a strong correlation between NDVI values and the model-based biomass estimation. We also found that the model simulated the soil moisture content fairly well, with a correlation coefficient of 0.82. While our results support the validity of the applied methodology, it is also clear that input data availability and quality are still critical issues in spatial application of the AquaCrop model.

Multiscale extrapolative learning algorithm for predictive soil moisture modeling & applications

We present Multiscale Extrapolative Learning Algorithm (MELA) as a novel artificial-intelligence (AI)-based data extrapolator. MELA is capable of extending temporally limited local hydroclimatic measurements at fine spatial resolution to longer periods, using remotely-sensed hydroclimatic data readily available for longer periods but at coarse spatial resolution. We demonstrate the implementation of MELA to extrapolate the monthly local soil moisture measurements at multiple depths from 2015–2021 to 1958–2021 in a semi-arid region. Such data extrapolators are imperative to generate longer historical data needed to adequately train and test AI models while enhancing the chance of capturing the effects of extreme climates on spatially variable soil moisture. The MELA-extrapolated local soil moisture subsequently allowed the construction of monthly time-series of field-scale soil moisture distributions with a normalized accuracy of 72% and prediction of countywide annual winter wheat yields – using MELA-extrapolated soil moisture data and eXplainable AI (XAI) – with a normalized accuracy of 81%. Furthermore, the XAI model ranked the predictors based on their importance in estimating winter wheat yields, in which the soil moisture near the surface and in the root zone and precipitation totals were found to be more influential than temperature on crop yields in the semi-arid region. The XAI model also unveiled the inflection points of the predictors beyond which crop yields would increase or decrease. Moreover, the AI-based analyses in conjunction with climate projections from global climate models suggest potential reductions in rainfed crop yields in the study area by 2050 and 2100 in the absence of climate-resilient mitigation and adaptation plans.

Modeling shallow soil moisture dynamics in mountainous landslide active regions

Under the worsening climate change, the mountainous landslide active regions are more likely to suffer severe disasters threatening residents. To predict the occurrence of landslides, shallow soil moisture lying in the interface of the hydrological processes has been found as one of the critical factors. However, shallow soil moisture data are often scarce in the landslide active regions. To overcome the severe measurement deficiencies and provide predictions of soil moisture dynamics, we construct a physically-based shallow soil moisture model based on the assumptions of ideal flow, homogeneous and isotropic soil textures, and 1-dimensional water movement dominant by gravity forces. In the model, the meteorological conditions and the physical soil properties are taken into consideration. With limited field measurements, the model can provide reasonably accurate soil moisture predictions. In recognition of the seasonal weather characteristics, we perform a series of sensitivity analyses to examine the response of shallow soil moisture and relate the hydrological processes to air temperature, precipitation intensity, duration, and combinations thereof. Complex interactions of hydrological processes are found with variations in precipitation and air temperature, depending on the interlinked boundary conditions of the soil and water. It demonstrates a strong need for a decent forecast of the complex shallow soil moisture dynamics and the associated hydrologic processes in mountain regions to cope with climate change for landslide preparation and agricultural adaptation in the future.

Water retention characteristics of mineral forest soils in Finland: impacts for modeling soil moisture

Water retention characteristics of mineral forest soils in Finland: impacts for modeling soil moisture