Soil-plant-atmosphere interaction (SPAI) plays a significant role on the safety and serviceably of geotechnical infrastructure. The mechanical and hydraulic soil behaviour varies with the soil water content and pore water pressures (PWP), which are in turn affected by vegetation and weather conditions. Focusing on the hydraulic reinforcement that extraction of water through the plant roots offers, this study couples advances in ecohydrological modelling with advances in geotechnical modelling, overcoming previous crude assumptions around the application of climatic effects on the geotechnical analysis. A methodology for incorporating realistic ecohydrological effects in the geotechnical analysis is developed and validated, and applied in the case study of a cut slope in Newbury, UK, for which field monitoring data is available, to demonstrate its successful applicability in boundary value problems. The results demonstrate the positive effect of vegetation on the infrastructure by increasing the Factor of Safety. Finally, the effect of climate change and changes in slope vegetation cover are investigated. The analysis results demonstrate that slope behaviour depends on complex interactions between the climate and the soil hydraulic properties and cannot be solely anticipated based on climate data, but suctions and changes in suction need necessarily to be considered.

Predicting cumulative surface slope displacements induced by rainfall infiltration is crucial for accurately assessing the risks to potentially affected infrastructure. In this paper the numerical modelling of the case history of Miscano slope is presented. Plaxis 2D code has been used adopting two constitutive laws: the linear elastoplastic model (Mohr-Coulomb, MC) and the Hardening Soil with small strain stiffness (HSsmall). The aim is to test the suitability of these constitutive laws in predicting the hydro-mechanical behaviour of clayey soil slope. Based on long-term field measurements, the parameters of MC and HSsmall have been determined by back analysing the first-year field measurements in terms of cumulative surficial horizontal displacements and pore water pressure. Subsequently, the numerical models have been validated against the analogous field measurements collected from the second year. The numerical models predict with a good agreement the field measurements for both years. In terms of cumulative surficial horizontal displacements, the HSsmall underestimates the field measurements by 21.2% at the end of the first year, while that based on MC exhibits a 32.8% overestimation. Moreover, the initialization procedure clearly affects the cumulative surficial horizontal displacements results obtained with both the HSsmall and MC models for the second year. In fact, the best results have been achieved when the second-year net rainfall have been applied starting from the initial phase used to generate the lithostatic stress state.

In this study, a novel 2D method for measuring soil surface suction, leveraging infrared thermal imaging technology is presented. The main principle of this method is the establishment of a correlation between soil surface water content and a normalized interfacial temperature difference. Subsequently, we link unsaturated soil surface suction to the normalized interfacial temperature difference through the soil-water characteristic curve. To validate the proposed method, an in-situ calibration test was conducted to ascertain the requisite parameters. Then, the method was tested under varying meteorological conditions at two distinct in-situ sites using the same test protocol as the calibration phase. The results demonstrate a strong agreement compared to measured values, affirming the feasibility and robustness of the proposed approach. This method offers several noteworthy advantages, including rapidity, non-contact operation, non-destructiveness, and robustness to environmental fluctuations. It holds promise for advancing investigation of the spatial and temporal evolution of hydro-mechanical properties of in-situ soil under the influence of climate change.

In the summer of 2022, a record-breaking heatwave and drought event occurred in the Yangtze River (YR) Basin of China, causing great damage to the society and ecosystem. However, the role of land-atmosphere (LA) interactions in driving and reinforcing this event has not been fully studied. In this study, using air temperature, soil moisture (SM), surface sensible heat fluxes, surface latent heat fluxes and radiation fluxes data from ERA5, we analyze the process of this event and reveal the contribution of the LA feedbacks. The results indicate that during the 2022 YR Basin heatwave and drought event, the regional average maximum air temperature and SM reached unprecedented levels of 2.7 standard deviations (SDs) and -3.5 SDs, respectively, compared to the climatology from 1980 to 2021. In August 2022, SM rapidly declined, pushing the region into a rare dry state. The dry soil increased the sensitivity of daily maximum air temperature to SM, intensifying the occurrence of heatwaves in the area. Simultaneously, increased downward solar radiation reached surface and most of that converted to sensible heat fluxes due to low soil moisture limitations leading to elevated air temperatures. While similar events have been reported multiple times in regions like Europe and western North America, their occurrence in the moist region of the YR Basin of China is exceptionally rare, which suggests an increasing likelihood of such extreme events in this region. Land-atmosphere interactions play an increasingly crucial role in exacerbating extreme conditions, and therefore, more studies such as this are needed for improving predictability of extreme events on a sub-seasonal time scale.

Skill in predicting where damaging convective storms will occur is limited, particularly in the tropics. In principle, near-surface soil moisture (SM) patterns from previous storms provide an important source of skill at the mesoscale, yet these structures are often short-lived (hours to days), due to both soil drying processes and the impact of new storms. Here, we use satellite observations over the Sahel to examine how the strong, locally negative, SM-precipitation feedback there impacts rainfall patterns over subsequent days. The memory of an initial storm pattern decays rapidly over the first 3-4 days, but a weak signature is still detected in surface observations 10-20 days later. The wet soil suppresses rainfall over the storm track for the first 2-8 days, depending on aridity regime. Whilst the negative SM feedback initially enhances mesoscale rainfall predictability, the transient nature of SM likely limits forecast skill on sub-seasonal time scales. Early warning of severe weather is particularly important in Africa, where resilience to storm hazards such as flash flooding is weak. Given large-scale atmospheric conditions favorable for convective activity, understanding where storms will occur is challenging for conventional weather prediction models. In semi-arid regions such as the Sahel, the spatial distribution of SM provides additional predictability of convective rain, via its impact on heating and moistening of the atmosphere. Given that convection is favored over drier soils and that storms create new SM patterns every few days during the wet season, the extent to which knowledge of today's SM aids rainfall prediction in future days is unclear. Here we use 17 years of satellite observations to document how surface properties evolve over 20 days after a storm, and how the surface influences subsequent rainfall patterns. We find that even in regions of West Africa where storms are frequent, the suppression of rain over recently-wetted soils is evident out to 2 days. In climatologically drier regions, this predictability extends out to 8 days. Overall, the feedback between SM and rainfall enhances rainfall predictability in the short-term (days), but effectively degrades the skill of longer-term (weeks) forecasts. Satellite observations over the Sahel reveal how the land surface evolves in the 20 days after a Mesoscale Convective System (MCS) After an MCS, rainfall is suppressed over wet soils for 2 days in humid regions and up to 8 days in drier areas Initially soil moisture enhances rainfall predictability, but the strong land feedback degrades skill at longer lead times

In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe-Middle East, central Siberia-Mongolia-North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is signifi- cant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system.

Rainfall erosion can cause environmental and economic damage by decreasing the storage capacity of water reservoirs because of the detachment of soil particles. The purpose of this study was to develop a one-dimensional physicomathematical model that can help predict the effects of rainfall erosion on the banks of water reservoirs. The model was developed using the Mein-Larson model to describe water infiltration, the kinematic wave approximation to represent overland flow generation, and the steady state sediment continuity equation to estimate sediment transport. The model was validated using rainfall simulator tests and lateritic soil samples with a bimodal soil-water retention curve. The results showed conformity with the experimental data, identifying a threshold in the models for discharge per unit area and sediment yield rate, as well as a linear increase in the models for total runoff and sediment load per unit area. However, the model failed to capture the peak in sediment yield rate owing to raindrop impact during the initial minutes of rainfall. Parametric analysis highlighted the impact of increasing the calibration constant of splash erosion, erodibility coefficient, and critical shear stress on the slope of the sediment load per unit area model. Despite its limitations, the model demonstrates satisfactory predictive capability for sediment load per unit area under high-intensity rainfalls, achieving an R2 greater than 0.92 in five of the six cases examined.

Understanding the soil-root mechanical interaction is crucial to advancing the utilisation of vegetation as a nature-based approach to designing more stable slopes and resilient urban forestry against tree windthrown. Although numerical and analytical models for detailed analysis of soil-root interaction exist, these models are seldom validated due to the lack of field data and the significant challenges in quantifying such interactions due to the complex nature of root system. The centrifuge modelling technique is an effective alternative for unravelling the complexities of the hydromechanical behaviour of vegetated soils by recreating prototype stress levels in small-scale physical models and testing them under more controlled conditions. This work presents a critical review on existing centrifuge modelling methods for vegetated soils, paying particular attention on the (i) fundamentals of centrifuge modelling, where principles, scaling laws and applications relevant to modelling vegetated soils are detailed; (ii) methods for modelling soils, including choice of soil material and sample preparation; and (iii) methods for modelling roots by means of natural plants and root analogues, where the replication of root morphology, mechanical properties and capabilities of modelling transpiration effects are discussed. In every topic, the challenges that could further advance the centrifuge modelling of vegetated soils and the possible ways to address them are highlighted. Finally, the prospect for future studies is discussed, highlighting the potential to enhance the understanding of the underlying mechanisms amongst plant roots, soil, water and external loading.

Engineered soil barriers have been proposed to prevent rainwater infiltration into the underlying soil, thus improving stability of sloping ground. The use of engineered barriers on flat ground as means of preventing flooding has also been explored. This paper aims to provide proof-of-concept as to the potential efficiency of engineered barriers in minimising soil shrinkage and swelling arising from seasonal variations of water content and pore water pressures within the ground due to its interaction with the atmosphere. A series of 2-dimensional, hydro-mechanically coupled finite element analyses were conducted to this effect. Emphasis was placed on accurately modelling the stiffness of the underlying soil, accounting for its small-strain behaviour, as well as the hydraulic behaviour of all the layers involved. The results confirm that it is possible to engineer barriers to minimise shrinkage/swelling in greenfield, as well as urban, conditions and highlight the influence of barrier geometry and configuration, so that recommendations for the design of such barriers can be made.

The tau -omega model is expanded to properly simulate L -band microwave emission of the soil-snow-vegetation continuum through a closed -form solution of Maxwell's equations, considering the intervening dry snow layer as a loss -less medium. The error standard deviations of a least -squared inversion are 0.1 and 3.5 for VOD and ground permittivity, over moderately dense vegetation and a snow density ranging from 100 to 400 kg m -3 , considering noisy brightness temperatures with a standard deviation of 1 kelvin. Using the Soil Moisture Active Passive (SMAP) satellite observations, new global estimates of VOD and ground permittivity are presented over the Arctic boreal forests and permafrost areas. In the absence of dense in situ observations of ground permittivity and VOD, the retrievals are causally validated using ancillary variables including ground temperature, above -ground biomass, tree height, and net ecosystem exchange of carbon dioxide. Time -series analyses promise that the new data set can expand our understanding of the land-atmosphere interactions and exchange of carbon fluxes over Arctic landscapes.