Initial Soil Moisture Research Articles

Soil Moisture Dominates the Land Surface Feedback in the Development of Compound Drought–Heat Extremes in Tropical South America

Abstract Observational evidence has demonstrated that heat extremes consistently coincide with droughts in tropical South America. However, the underlying causes and associated physical mechanisms remain relatively unexplored. In this study, we employ numerical experiments using Community Earth System Model, version 2 (CESM2), with prescribed oceanic forcing to assess how extreme soil temperature and moisture affect subsequent hydrometeorological conditions during the premonsoon season in tropical South America, a season when land can play an important role in regional climate. The results reveal a different diurnal pattern in the strength of the surface air temperature response, with a stronger response at night to soil temperature anomalies and a stronger response at noon to soil moisture anomalies. Elevated initial soil temperature or reduced initial soil moisture tends to yield warmer and drier hydrometeorological conditions and increase the occurrence of both drought and heat extremes, with stronger and longer-lasting responses to soil moisture anomalies (than soil temperature anomalies). The simulated responses to initial soil moisture anomalies alone closely resemble those in the experiment with combined soil temperature and moisture anomalies. These findings underscore the dominant role of soil moisture feedback in shaping compound drought–heat extremes, in regions where evaporation plays a significant role, highlighting its importance in the modeling and prediction of hydrometeorological extremes.

Effects of freeze–thaw on the detachment capacity of soils with different textures on the Loess Plateau, China

Soil detachment capacity (Dc) is a crucial indicator for quantifying erosion intensity. However, the combined actions of freeze–thaw and water flow complicate the erosion process, leaving the variation mechanism of Dc under this condition systematically unexplored. This study examined five loess soils from a seasonal freeze–thaw area. The mechanism driving changes in Dc was quantified through freeze–thaw simulation combined with flow scouring tests, and a Dc prediction model was established. The results revealed that the shear strength (τm), cohesion (Coh), and internal friction angle (φ) in silt loam were higher than in sandy loam. After freeze–thaw cycles (FTC), τm, Coh, and φ of the five loess soils decreased by 1.02–1.37, 1.07–9.15, and 0.92–1.05 times, respectively. As FTC increased, τm and Coh gradually stabilized, while φ showed minimal fluctuation, indicating that FTC had a cumulative effect on the deterioration of soil mechanical properties. During FTC, Dc in Wuzhong sandy loam was the largest, being 1.14–3.24 times greater than in other soils, suggesting a significant main effect of soil type on Dc variation, with a contribution rate of 19.27 %. Dc eventually stabilized with increasing, indicating a critical FTC of around 10 for its impact on Dc. Compared to unfrozen soils, Dc increased by 33.69 %–102.40 % under the combined effects of freeze–thaw and water flow, clarifying that FTC aggravated soil instability. Effective stream power was the optimum hydraulic parameter, contributing the most to Dc (45.94 %). FTC (6.41 %) and initial soil moisture content (8.59 %) were less influential, as FTC initially degraded soil properties, and then the combined action with water flow intensified soil damage, causing the role of freeze–thaw factors to be obscured by other variables. A Dc prediction model using a general flow intensity index estimated well Dc, with both R2 and NSE at 0.94. Model performance comparison emphasized the need for validation when extending the application range beyond development conditions. These findings provide new insights into the detachment mechanisms of different textured soils under compound freeze–thaw and hydrodynamic influence in freeze–thaw region.

Devising a method for determining the moisture conductivity coefficient of subgrade soils taking into account European approaches and standards

The object of this study is the theoretical and methodological approaches to determining the coefficient of moisture conductivity of subgrade soils. The work focuses on considering the European approaches and standards when devising a method for determining the coefficient of moisture conductivity of soils. In the course of the research, a method was developed for determining the coefficient of moisture conductivity of soils K1, which characterizes the diffusion movement of water, through the filtration coefficient K0, calculated in accordance with European requirements based on laboratory test data. The proposed method is based on a mathematical model built on the basis of the differential equation of changes in soil moisture. The model is special in that, unlike existing ones, the movement of water was modeled from the bottom up, which reflects the process of moisture accumulation in the lower layers of the subgrade from groundwater or topwater. A good agreement of the mathematical model with the data by other authors was obtained (the relative error did not exceed 12.98 %). A direct relationship between the moisture conductivity coefficient of soils K1 and their initial moisture content W0 and an inverse relationship between K1 and the total moisture capacity of soils WFH were established in the paper. Dependences were derived in the range of changes in initial soil moisture W0 from 0.08 to 0.15 and WFH from 0.15 to 0.5. It was found that the values of the moisture conductivity coefficient of soils K1 increase from 4.64·10-6 to 3.81·10-5 m2/h with an increase in their initial moisture content and with a decrease in total moisture capacity. From the point of view of engineering practice of road construction, the proposed method makes it possible to predict seasonal changes in soil moisture in the subgrade, to determine the strength of the road structure. This makes it possible to make sound design decisions on the installation of drainage systems on roads in order to extend their service life

Theoretical Analysis of Landfill Gas Migration in Capillary Barrier Covers Considering Effects of Waste Temperature

The high-temperature and high-humidity conditions arising from the biochemical degradation of landfill waste result in significant temperature gradients within the landfill cover. The effects of waste temperature on landfill gas transport and microbial aerobic methane oxidation are not fully understood. In this study, a fully coupled theoretical model was developed to simulate the interactions of moisture, heat, and gas transport within a capillary barrier cover. A series of parametric studies were carried out to investigate the influence of the combined effects of temperature gradient, initial soil moisture content, and landfill gas generation rate on methane transport, oxidation, and emissions. The simulated results indicated that increasing waste temperature intensified the temperature gradient, leading to higher surface evaporation rates and variations in methane oxidation efficiencies. Additionally, variations in initial soil moisture content and landfill gas generation rates were found to significantly impact gas migration and methane oxidation in the cover. This study demonstrates the critical role of waste temperature in landfill gas migration within landfill cover systems, providing technical methodologies for the optimized design of soil cover systems.

Uptake, translocation and health risk assessment of nonylphenol in vegetables under reclaimed water irrigation

AbstractNonylphenol (NP) is one of the typical endocrine‐disrupting chemicals (EDCs), which can be harmful at very low concentrations. Municipal sewage and reclaimed water are its two main sources. EDCs can enter into the soil with reclaimed water irrigation and accumulate in plants, causing environmental and human health risks. The occurrence and migration of NP in soil‐crop systems were studied by pot experiments of lettuce and eggplants simulating long‐term irrigation with reclaimed water. The health risks were also evaluated. The experiments set treatments with different initial soil NP concentrations (0.28–6.42 mg/kg) and soil moisture (60%–90% field water capacity [FC]). After harvest, the NP in edible parts of lettuce and eggplants were 35.80–54.30 and 15.45–23.38 μg/kg, respectively. The residual amounts of NP in the soil‐lettuce system and soil‐eggplant system were limited with the residual rates of 0.9%–24.4% and 0.3%–14.5%, respectively. The results showed that the lettuce and eggplant tissues had the highest bioconcentration factors (BCFs) in 75% FC and the translocation factors (TFs) tended to decrease with the initial soil NP contents increased. The noncancer hazard quotients (HQ) for adults and children exposed to NP had the order of 10−5, which showed little health risk for reclaimed water irrigation.

Role of Initial Conditions and Meteorological Drought in Soil Moisture Drought Propagation: An Event‐Based Causal Analysis Over South Asia

AbstractThe role of meteorological droughts and initial conditions (land and atmosphere) in soil moisture drought (SMD) propagation are not yet fully understood. This work uses a drought event‐based causal framework to investigate the relative importance of meteorological drought (MD) duration and intensity and initial conditions that result in surface and rootzone SMD, considering their event‐level propagation time (PT) over South Asia. Initially, spatial variability of drought propagation is assessed by the Propagation Ratio (PR) computed based on MD counts that trigger SMD at various lags. PR depicts 2–3 months slower rootzone propagation than at surface. The gradual decrease in PR with increasing regional aridity indicates faster propagation over humid regions. The causal impact of initial conditions and MD parameters on propagating SMD are evaluated using normalized mutual information and a newly proposed normalized conditional mutual information. We found greater importance of triggering MD parameters followed by initial soil moisture condition on propagating SMD. This behavior is more evident for the surface layer propagation at shorter PT. There is a confounding effect of initial atmospheric conditions on drought propagation through initial soil moisture, depicting the significance of land‐atmosphere interactions prior to propagation. In the rootzone propagation, initial soil moisture has a greater influence on propagation, especially at longer PT, indicating the significance of soil moisture persistence. Stronger causal links obtained through the joint influence of MD parameters on SMD suggest the importance of accounting for MD duration and intensity simultaneously, which are not considered in drought index‐based propagation studies.

Effects of initial soil moisture on rill erodibility and critical shear stress factors in the WEPP model across diverse soil types

Rill erosion, a significant issue in agricultural regions, is intricately linked to initial soil moisture conditions, affecting the development of concentrated flow erosion processes. However, understanding its dynamics amidst varying soil moisture conditions remain challenging. This study aimed to assess the impact of different soil moisture levels on rill erodibility parameters in the Water Erosion Prediction Project (WEPP) model and to evaluate soil cohesion across a spectrum of soils. Through laboratory experiments employing a small V-shaped rill channel, we investigated rill erodibility (Kr) and critical hydraulic shear stress (τcr), under three soil moisture scenarios: initially dry, saturated, and drainage, with incremental surface inflow rates. Additionally, we examined the efficiency of soil cohesion obtained from an Automated Soil Cohesion Measurement Apparatus in predicting Kr and τcr across various soil textures. Our analysis encompassed twenty soils representing nine texture classes, revealing significant correlations between basic soil properties, cohesion parameters, and WEPP model rill erodibility. Notably, initial soil moisture conditions exerted substantial influence on erodibility potentials. Soils with higher silt contents demonstrated better fits in terms of Nash-Sutcliffe model efficiency, particularly under initially dry and saturated conditions. However, predictions for initially drained soils yielded poor fits, emphasizing the intricate interplay between soil properties and hydrological conditions. In conclusion, our findings emphasize the critical role of topsoil water dynamics in rill erodibility. We propose that soil cohesion serves as a valuable predictor, complementing friction forces within the soil and enhancing simulations of rill erodibility under shallow flow conditions in rills, particularly in next-generation process-based modeling approaches.

Initial soil moisture and soil texture control the impact of storm surges in coastal forests

Flooding and salinization triggered by storm surges threaten the survival of coastal forests. The forest root zone (top 40 cm of soil) is the most affected by surge flooding. Determining the effect of a storm surge on edaphic conditions is essential to estimate vegetation response. Pre-storm soil hydrology could mitigate or enhance the salinization effect, ultimately determining the resilience of the forest. Here we assess the influence of pre-storm soil water content and salinity on storm surge effects in coastal vegetated areas. A 1D model (HYDRUS-1D) is used to simulate saltwater infiltration from above through the unsaturated zone. Different water content and concentration scenarios, along with scenarios with variable storm surge salt concentration, storm surge height and storm surge flooding duration are considered. In soils characterized by silt and clay, the maximum salinized soil depth increases as water content increases, affecting the top 40 cm of soil, except for clay loam soil, for which only the top 20 cm are salinized. In sandy soils, the salinization process involves the entire soil column. The contribution of water content to salinization varies from 12 % to 30 % along the top 40 cm in fine soils. In fine soils, the storm-surge height also becomes relevant. A study case is presented to support the numerical results. Field data confirm that soil water content controls the salinization of the root zone in clay and silt soils. Overall, we conclude that the root zones of coastal forests with clay and silt soils and low water content are the most at risk during storm surges. These events have the potential to radically change edaphic conditions and affect ecosystems.

Soil moisture prediction based on integrating the CEEMDAN decomposition technique with LSTM in the proximity of Prosopis Juliflora

Human-made adaptations in plant diversity, particularly the introduction of alien invasive plant species, can result in problems such as depletion of groundwater, extraction of natural water resources, and modifications in the local biodiversity. In addition to regulating ecosystems and accurate farming, precise forecasting of soil moisture in the vicinity of these exotic plant species can greatly assist in various other agricultural applications. In this study, we present an improved approach for predicting soil moisture levels at different depths in the vicinity of Prosopis Juliflora (IAPS) by integrating long short-term memory (LSTM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). CEEMDAN is employed as a preprocessing and denoising technique to decompose the initial soil moisture data into several intrinsic mode functions (IMF). After that, LSTM is used to predict all the IMF (Intrinsic Mode Function) parts from the CEEMDAN process. The individual prediction outcomes of each IMF are combined to obtain the final prediction results. Soil moisture measurements were collected continuously in the Coimbatore area of Tamil Nadu at multi-layer soil depths of 10 cm, 30 cm, 60 cm, and 100 cm. The provided data was utilized for training models and achieving optimal accuracy in forecasting soil moisture levels one month prior. The proposed CEEMDAN-LSTM model achieved the highest R2, ranging from 0.985 to 0.995 at various depths when compared to RNN, LSTM, EMD-LSTM, EEMD-LSTM models, and existing literature models. The results show that the proposed method works well at predicting soil moisture at different depths, which can help improve the planting pattern and schedule of native species to increase the area’s green cover.

The Relation between Soil Moisture Phase Transitions and Soil Pore Structure under Freeze–Thaw Cycling

The process of soil moisture phase transitions (SMPT) under freeze–thaw cycling is considered a key factor driving changes in soil pore structure. However, there is still no consensus on which indicators related to SMPT affect the soil pore structure. The objectives of this study were to compare SMPT and soil pore characteristics under freeze–thaw cycling, and to analyze the inherent relationship between them as affected by different bulk densities. Hence, we employed thermal pulse time-domain reflection technology (T-TDR) and X-ray CT scanning technology (X-CT) to quantitatively study the process of SMPT and pore characteristics of soil core samples (60 mm diameter, 100 mm height) repacked with three different bulk density levels: 1.10 g·cm−3 (NC), 1.30 g·cm−3 (LC) and their combination (1.10 g·cm−3 for upper half, 1.30 g·cm−3 for lower half, SC) under freeze–thaw cycling. Our results showed that compared with NC, the porosity of LC’s 0–5 cm soil column decreased by 0.070 cm3·cm−3, the imaged porosity (ϕ>60μm) decreased by 0.034 cm3·cm−3, and the maximum soil ice content (MIC) decreased by 0.030 cm3·cm−3. The pores within the range of 200−300 mm (ϕ2) and 300–400 mm (ϕ3) contribute the most significantly to ϕ>60μm (50–60%). Soil initial moisture content (IMC) and MIC explained 50.1% of the change in ϕ2, and the bulk density explained 49.3% of the change in ϕ3. During the melting process, higher moisture content promotes the thaw collapse of soil particles, resulting in a decrease in ϕ>60μm. The mean pore radius of the limiting layer (MRLL) and the hydraulic radius (HR) show that changes in bulk density from 1.10 g·cm−3 to 1.30 g·cm−3 do not have significant differences. Our results show the relationship between SMPT and pore structure change during freeze–thaw cycles as affected by initial soil bulk density and moisture condition.

Study on the temperature field and infrared characteristics of unsaturated soils with shallow buried objects at different depths

Beneath the soil, there exist numerous concealed objects, some of which, like landmines, pose a considerable threat to human life. Infrared detection serves as a swift and secure method for detecting such hazards. However, in many current studies, the soil is often oversimplified as a single entity, overlooking the crucial internal moisture transport and phase transitions. In this paper, we propose a soil heat and moisture migration model, along with a ground infrared radiation model. Our comparisons with previous research reveal that the internal water movement, evaporation, and condensation processes in the soil have a profound influence on the soil's temperature field and infrared signature. To validate our model's reliability, we conducted three experiments with metal blocks buried 1 cm deep, having initial moisture contents of 0 %, 10 % and 20 %. A comparison of the simulation and experimental results confirms the credibility of our proposed model. Through simulations, we analyzed the daily variations in ground surface IR radiation, the effect of varying burial depths, and the influence of differing initial moisture contents. Our findings reveal that, when starting with zero initial soil moisture content, an increase in burial depth from 1 cm to 5 cm results in a substantial decrease in the peak difference between the surface center temperature and ambient temperature, dropping from 20.91 K to 5.35 K. Consequently, the optimal detection time shifts from 12:00 to 14:00 On the other hand, keeping the burial depth constant at 1 cm and increasing the initial soil moisture content from 5 % to 20 % leads to a reduction in the maximum temperature difference from 17.54 K to 12.48 K, while the optimal detection time remains unaffected. This study can provide data support for underground buried object detection and improve the accuracy of detection.

Characterizing Satellite Soil Moisture Drydown: A Bivariate Filtering Approach

AbstractDrying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss.

Variable soil moisture responses to rainfall events in fields under different management practices

AbstractSoil moisture response to rainfall is a key factor that dictates how well a landscape can support crop growth as well as its susceptibility to water runoff and leaching, however, few studies have investigated how agricultural management impacts this important soil function. This study compares two common agricultural soil treatments (cover crops and soil compaction) and their soil moisture response to rainfall in comparison to a control. In a humid temperate climate during March to November, individual rainfall events were delineated over two growing seasons and corresponding soil moisture responses were identified using in situ soil moisture sensors at four soil depths (20, 30, 40, and 60 cm). Results suggest that hydrological responses differed with both event type and management treatment. Not all rainfall events triggered a response: those that triggered responses at shallower soil depths were typically characterized by higher total event rainfall, and higher maximum and average rainfall intensity. In contrast, rainfall events triggering responses at deeper soil depths were characterized by longer event duration as well as higher 10‐day antecedent rainfall (AR). Soil moisture responses for the cover crop treatment were characterized by relatively lower initial and peak soil moisture at shallower depths but higher values at 60 cm depth, whereas soil moisture responses for the control and compacted soil treatments demonstrated the opposite. Matrix flow was most often generated for rainfall events with high magnitude and was not preferentially associated with any particular soil treatment. However, specific conditions were needed to generate vertical preferential flow, namely high total event rainfall for both horizons, or high AR for preferential flow in the Ap horizon, or high rainfall intensity for preferential flow in the Bt horizon. Our findings demonstrate the potential for detailed event‐based soil water process analysis using high‐frequency, multi‐depth soil moisture data.

Hydrometeorological controls of and social response to the 22 October 2019 catastrophic flash flood in Catalonia, north-eastern Spain

Abstract. On 22 October 2019, the Francolí River basin in Catalonia, north-eastern Spain, experienced a heavy precipitation event that resulted in a catastrophic flash flood, causing six fatalities. Few studies comprehensively address both the physical and human dimensions and their interrelations during extreme flash flooding. This research takes a step forward towards filling this gap in knowledge by examining the alignment among all these factors. The hydrometeorological factors are investigated using the new Triangle-based Regional Atmospheric Model, radar-derived precipitation estimates, post-flood field and gauge observations, and the Kinematic Local Excess Model. The social dimension is assessed by examining the relationship between catchment dynamics and warning response times and by quantifying human behaviour during the course of the flash flood through a post-event citizen science campaign. Results reveal that a persistent south-easterly airflow brought low-level moisture and established convective instability in the region, while local orography was instrumental to triggering deep moist convection. A convective train promoted intense, copious, and prolonged precipitation over the north-western catchment headwaters. Basin response was significantly modulated by the very dry initial soil moisture conditions. After the long-lasting rainfall, an acute burst of precipitation resulted in extreme flash flooding. Fast and abrupt increases in streamflow affect small spatial scales and leave limited time for the effective implementation of protective measures. The institutional organization–protection–prevention cycle unfolded at the spatial and temporal scales typically dominated by the meteorological rather than hydrological scales. Although the citizen science campaign reveals the effectiveness of the warnings in reaching the population living in the most affected areas, a significant proportion of the respondents expressed a lack of adequate information or were unfamiliar with the intended meaning. In addition, a majority of the interviewees did not perceive any significant threat to life or property. In view of these results, this study identifies potential areas for improving social preparedness for similar natural hazards in the future.

Study on the effect of ground stratification on heat transfer of vertical ground heat exchangers in lateritic clay areas

The potential effect of ground stratification on the efficiency of ground heat exchangers (GHEs) in lateritic clay regions remains unclear. Model experiments were conducted to investigate the effects of soil dry density, water content, and ground stratification on the heat exchange between GHEs and the surrounding lateritic clay. A 3D numerical model accounting for ground stratification was proposed and validated using experimental data. The model further examined the influence of soil moisture on the dynamic heat exchange process. The findings of the model experiments indicate that the heat from buried pipes increase soil temperature by 12.8 % when the initial water content of lateritic clay (with dry density of 1.1 g/cm3) rises from 5 % to 28 %. Raising the dry density from 1.1 to 1.3 g/cm3 at 28 % water content results in a 7.5 % temperature increase. The initial soil moisture and density have negligible effects on the temperature gradient within 18 cm of the geothermal pipes. The temperature increased faster in the sand layer than in the lateritic clay layer owing to the difference in thermal properties. The impact of heat produced by GHEs on the temperature field in homogeneous soil layers differs considerably from that in stratified soil layers. The 3D numerical model provided a good fit for the measured soil temperature in the stratified soil layer, including the soil temperature variation at the layered interface. The simulation results showed that for dry and saturated strata, the temperature of the lateritic clay around the GHEs increased with increasing initial stratum moisture. For unsaturated lateritic clay strata, because of the possible impacts of moisture and heat coupling migration, the temperature of the stratum around the GHEs first increased and subsequently decreased as the initial stratum moisture was enhanced.

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

In the design of offshore engineering foundations, a critical consideration involves determining the peak shear strength of marine soft clay sediment. To enhance the accuracy of estimating this value, a database containing 729 direct shear tests on marine soft clay sediment was established. Employing a machine learning approach, the Particle Swarm Optimization algorithm (PSO) was integrated with the Adaptive Boosting Algorithm (ADA) and Back Propagation Artificial Neural Network (BPANN). This novel methodology represents the initial effort to employ such a model for predicting the peak shear strength of the soil. To validate the proposed approach, four conventional machine learning algorithms were also developed as references, including PSO-optimized BPANN, Support Vector Machine (SVM), BPANN, and ADA-BPANN. The study results show that the PSO-BPANN model, which has undergone optimization via Particle Swarm Optimization (PSO), has prediction accuracy and efficiency in determining the peak shear performance of marine soft clay sediments that surpass that offered by traditional machine learning models. Additionally, a sensitivity analysis conducted with this innovative model highlights the notable impact of factors such as normal stress, initial soil density, the number of drying–wetting cycles, and average soil particle size on the peak shear strength of this type of sediment, while the impact of initial soil moisture content and temperature is comparatively minor. Finally, an analytical formula derived from the novel algorithm allows for precise estimation of the peak shear strength of marine soft clay sediment, catering to individuals lacking a background in machine learning.

Assessing crop yield and water balance in crop rotation irrigation systems: Exploring sensitivity to soil hydraulic characteristics and initial moisture conditions in the North China Plain

Multiple cropping is an effective measure to enhance the intensity of land use. The North China Plain is one of China’s most important grain production areas, with 70 % of the arable land under double rotation of winter wheat and summer maize. The allocation of irrigation water between two crop seasons depends on soil water flow and crop water consumption. This is because the amount of moisture left in the soil at the end of one season affects how much moisture is in the soil at the beginning of the next season. As a result, the amount of crops produced and the amount of water needed for irrigation are influenced by the initial soil moisture conditions and the soil’s hydraulic properties. This study aims to analyze how various factors affect crop yield when faced with water scarcity. The factors considered include initial soil moisture condition, soil texture, and irrigation scheduling. The simulation results indicate that, in most cases, initial soil moisture conditions have a more significant impact on crop yield than soil hydraulic characteristics. The impact of irrigation can differ based on the irrigation method and availability of water. Hence, when distributing irrigation water over a year, it is crucial to consider the soil’s water transport between two crop cycles to achieve the ideal full and deficit irrigation in a crop rotation system. Additionally, a joint optimal irrigation plan can significantly reduce the adverse effects of unfavorable soil hydraulic characteristics. Moreover, the optimal irrigation strategy can enhance crop water productivity and food security simultaneously.

SLEM (Shallow Landslide Express Model): A Simplified Geo-Hydrological Model for Powerlines Geo-Hazard Assessment

Powerlines are strategic infrastructures for the Italian electro-energetic network, and natural threats represent a potential risk that may influence their operativity and functionality. Geo-hydrological hazards triggered by heavy rainfall, such as shallow landslides, have historically affected electrical infrastructure networks, causing pylon failures and extensive blackouts. In this work, an application of the reworked version of the model proposed by Borga et al. and Tarolli et al. for rainfall-induced shallow landslide hazard assessment is presented. The revised model is called SLEM (Shallow Landslide Express Model) and is designed to merge in a closed-from equation the infinite slope stability with a simplified hydrogeological model. SLEM was written in Python language to automatise the parameter calculations, and a new strategy for evaluating the Dynamic Contributing Area (DCA) and its dependence on the initial soil moisture condition was included. The model was tested for the case study basin of Trebbia River, in the Emilia-Romagna region (Italy) which in the recent past experienced severe episodes of geo-hydrological hazards. The critical rainfall ratio (rcrit) able to trigger slope instability prediction was validated against the available local rainfall threshold curves, showing good performance skills. The rainfall return time (TR) was calculated from rcrit identifying the most hazardous area across the Trebbia basin with respect to the position of powerlines. TR was interpreted as an index of the magnitude of the geo-hydrological events considering the hypothesis of iso-frequency with precipitation. Thanks to its fast computing, the critical rainfall conditions, the temporal recurrence and the location of the most vulnerable powerlines are identified by the model. SLEM is designed to carry out risk analysis useful for defining infrastructure resilience plans and for implementing mitigation strategies against geo-hazards.

Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis

An unprecedented soil moisture drought broke out over the Yangtze River basin (YRB) in the summer of 2022 and lasted until the spring of 2023, caused great economic losses and serious environmental issues. With the rapid onset and long-lasting duration, the mega soil drought challenges the current seasonal prediction capacity. Whether the state-of-the-art climate models provide skillful predictions of the onset, persistence and recovery of the 2022–23 mega soil drought needs to be assessed. Identified by the drought area percentage, here we show that the mega soil drought over the YRB started from July, 2022, reached the peak in August, and diminished in April, 2023. Combined with real-time predictions of monthly precipitation released by three climate models participating in the North American multi-model ensemble (NMME) project, we predict the monthly evolution of the 2022–23 soil drought through a joint distribution between precipitation and soil moisture established by the copula method. The results indicate that the NMME/copula prediction well reproduced the spatiotemporal evolution of the mega soil drought at 1 month lead. Using the climatological prediction that relies on the information of initial soil moisture conditions as the reference forecast, the Brier skill score (BSS) values for NMME multi-model ensemble are 0.26, 0.23 and 0.2 for the forecast lead times increased from 1 to 3 months during the entire soil drought period. Specifically, the BSS is 0.14 at 2 months lead during drought onset stage, and 0.26 at 3 months lead during persistence stage, while it is close to zero at all leads during the recovery stage. Our study implies that climate models have great potential in probabilistic seasonal prediction of the onset and persistency of mega soil drought through combining with the copula method.

Runoff generation in a semiarid environment: The role of rainstorm intra-event temporal variability and antecedent soil moisture

Rainfall intensity and antecedent soil moisture are key variables affecting the initiation and accumulation of runoff, and therefore, impact on the occurence flood events. We used time series of rainfall intensities characterizing rainstorms with different mean, standard deviation, and skewness to simulate runoff response of a sealed loamy soil profile under various initial soil moisture conditions. We found that shifting from wet to dry initial soil moisture leads to a 300 % increase in ponding time and a 350 % decrease in total runoff. Intense rainfall events catalyze runoff development and runoff occurs earlier with increasing mean, maximum intensity, and variance of rainfall. Higher variance in intra-event storm-intensity temporal distribution yields more runoff. Earlier runoff formation aligned with negatively skewed storm structures, but largest cumulative runoff resulted from storms displaying near-zero skewness values. The combination between rainfall intensification and extended dry periods between storms might reduce potential runoff. These insights improve our understanding of anticipated impacts of climate variations on floods and may also contribute to ecosystem health evaluations, and bolster predictive capabilities of surface processes.