The aerosol size distribution, particularly the number and mass distributions, plays a crucial role in understanding changes in optical properties due to hygroscopic growth, which affects visibility and radiative forcing on a regional scale. The Indo-Gangetic Plain (IGP), including National Capital Region (NCR) of Delhi, experiences severe fog and haze with reduced visibility during the post-monsoon to winter months (October-February) every year. This study reports aerosol mass size distribution over Delhi during a winter fog campaign (December 15, 2015-February 15, 2016) using a ground-based optical particle counter. The fine and coarse mode aerosols were contributed to similar to 85% and 15% to the total aerosol mass concentration during the campaign period. The characteristic changes in aerosol size distribution, effective radius, and the influence of meteorological factors, particularly relative humidity (RH) and temperature, under three visibility conditions: Vis-1 (1200 m) were investigated. Fine-mode aerosols accounted for similar to 85 % of the total aerosol mass, with their concentration increasing by a factor of 3.7 during Vis-1 and 2.3 during Vis-2 compared to Vis-3, when the effective radius of aerosol was lowest (R-eff: 0.44 mu m). Fine particle concentrations showed a positive correlation with RH (R = 0.35) and a negative correlation with visibility (R = -0.65), suggesting that the high RH and fine-mode aerosols contribute to fog formation and reduced visibility in Delhi-NCR.

Revetment breakwaters on reclaimed coral sand have demonstrated vulnerability to seismic damage during strong earthquakes, wherein soil liquefaction has been identified as a substantial contributor. Based on the results of three centrifuge shaking table tests, this study investigates the characteristic seismic response of revetment breakwater on reclaimed coral sand and the influence of soil liquefaction. The basic mechanical properties of reclaimed coral sand were measured using undrained triaxial and hollow cylinder torsional shear tests. The centrifuge test results indicate that liquefaction of coral sand can result in significant displacement and even failure of revetment breakwaters, encompassing: (a) tilting, horizontal displacement, and settlement of the crest wall; (b) seismic subsidence in the foundation and backfill. The liquefaction consequence of the reclaimed coral sand increased with a decrease in soil density and rise in sea water level (SWL). Post-earthquake rapid reinforcement measure via sandbags is found to be effective in limiting excess pore pressure buildup in foundation soil and structure deformation under a second shaking event. Based on the test results, the effectiveness of current simplified design procedures in evaluating the stability and deformation of breakwaters in coral sand is assessed. When substantial excess pore pressure generation and liquefaction occur within the backfill and foundation coral sand, the pseudo-static and simplified dynamic methods are inadequate in assessing the stability and deformation of the breakwater.

Rigid pile composite foundation (RPCF) has been widely used in Yellow River Alluvial Plain (YRAP) due to remarkable reinforcement and economical effects. However, current design of RPCF in this area are typically based on saturated soil mechanic principles assuming drained condition, despite the fact that the soil is typically in unsaturated condition. Due to long time water scouring, the silt in YRAP generally exhibits high particle sphericity and poor particle gradation. Even after standard compaction, it is still in a relatively loose state with developed capillary pores. Water content increment induced by infiltration can lead to considerable soil mechanical properties degradations due to matric suction reduction associated with soil micro-structure rearrangement. Consequently, the RPCF will suffer serious bearing characteristic deteriorations, exhibiting additional settlement. In this study, extending unsaturated soil mechanics, initially the influences of matric suction on mechanical properties of YRAP silt were demonstrated. Then total RPCF settlement was calculated as the sum of the compression deformation of the soil between piles in the reinforcement zone and the underlying soil stratum. The former one was estimated through the modified load transfer curve method considering the pile-soil interface behaviors deteriorations with matric suction reduction, while the later one was estimated through the traditional stress diffusion method. The feasibility of the proposed method was validated through a model RPCF test subjected to ground water level fluctuations. Good comparisons on RPCF mechanical behaviors indicate the proposed method can be a valuable tool in the design of RPCF in YRAP under extreme weather conditions.

Glaciers playa vital role in providing water resources for drinking, agriculture, and hydro-electricity in many mountainous regions. As global warming progresses, accurately reconstructing long-term glacier mass changes and comprehending their intricate dynamic relationships with environmental variables are imperative for sustaining livelihoods in these regions. This paper presents the use of eXplainable Machine Learning (XML) models with GRACE and GRACE-FO data to reconstruct long-term monthly glacier mass changes in the Upper Yukon Watershed (UYW), Canada. We utilized the H2O-AutoML regression tools to identify the best performing Machine Learning (ML) model for filling missing data and predicting glacier mass changes from hydroclimatic data. The most accurate predictive model in this study, the Gradient Boosting Machine, coupled with explanatory methods based on SHapley Additive eXplanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) analyses, led to automated XML models. The XML unveiled and ranked key predictors of glacier mass changes in the UYW, indicating a decrease since 2014. Analysis showed decreases in snow water equivalent, soil moisture storage, and albedo, along with increases in rainfall flux and air temperature were the main drivers of glacier mass loss. A probabilistic analysis hinging on these drivers suggested that the influence of the key hydrological features is more critical than the key meteorological features. Examination of climatic oscillations showed that high positive anomalies in sea surface temperature are correlated with rapid depletion in glacier mass and soil moisture, as identified by XML. Integrating H2OAutoML with SHAP and LIME not only achieved high prediction accuracy but also enhanced the explainability of the underlying hydroclimatic processes of glacier mass change reconstruction from GRACE and GRACE-FO data in the UYW. This automated XML framework is applicable globally, contingent upon sufficient high-quality data for model training and validation.

Vegetation indices (VIs) are widely applied to estimate leaf area index (LAI) for monitoring vegetation vigor and growth dynamics. However, the saturation issues in VIs caused by crown closure during the growing season pose significant challenges to the application of VIs in LAI estimation, particularly at the individual tree level. To address this, the feasibility of common VIs for LAI estimation at the individual tree level throughout the growing season was analyzed using data from digital hemispherical photography (DHP) and Unmanned Aerial Vehicle (UAV) acquisition. Additionally, the physical mechanisms underlying a generic VI-based estimation model were explored using the PROSAIL model and Global Sensitivity Analysis (GSA). Furthermore, the relationships between observed LAI derived from DHP and UAV-based VIs across different phenological development phases throughout the growing season were analyzed. The results suggested that the normalized difference vegetation index (NDVI) and its faster substitute infrared percentage vegetation index (IPVI) exhibited the best capabilities for LAI estimation (R2 = 0.55 and RMSE = 0.77 for both) across the entire growing season. The LAI-VI relationship varied seasonally due to the saturation issues on VIs, with R2 values increasing from the leaf budburst to the growing stage, decreasing during maturation, and rising again in the senescence stage. This indicated that seasonal effects induced by phenological changes should be considered when estimating LAI using VIs. Additionally, the saturation of VIs was influenced by soil background, leaf properties (especially leaf chlorophyll content [Cab] and dry matter content [Cm]), and canopy structures (especially average leaf inclination angle, ALA). Compared to satellites, UAV-based sensors were more effective at mitigating spectral saturation at finescale due to their finer spatial resolution and narrower bandwidth. The drone-based VIs used in this study provided reliable estimates and effectively described temporal variability in LAI, contributing to a better understanding of VI saturation effects.

This study reveals the mechanical behavior of silt in the Yellow River floodplain under 3D stress. A true triaxial apparatus was used to conduct consolidated drained shear tests under different intermediate principal stress coefficients (b) and consolidation confining pressures to investigate the influence of the intermediate principal stress on the deformation and shear strength of silt. The stress-strain curves exhibited strong strain-hardening characteristics during shearing. Due to enhanced particle interlocking and microstructural reorganization, the silt demonstrated complex b-dependent deformation and strength characteristics. The cohesion rose with increasing b, whereas the internal friction angle followed a non-monotonic pattern, increasing and decreasing slightly as b approached 1. The strength envelope of the silt fell between that predicted by the Lade-Duncan and the extended von Mises strength criteria., which is best predicted by the generalized nonlinear strength criterion when the soil parameter alpha was 0.533. The findings reveal the stress-path-dependent mechanisms of Yellow River floodplain silt and provide essential parameters for optimizing the design of underground engineering projects in this region.

Powdery Mildew Blumeria graminis (PMBG) is one of the most dangerous diseases for winter wheat plants, causing damage to all above-ground plant organs. The main aim of this study is to develop and validate machine learning (ML) models with explainable AI (XAI) capabilities for accurate risk prediction of PMBG in winter wheat crops at the pre-symptomatic stage. Multiple heterogeneous ML classifiers with XAI for PMBG risk prediction have been developed in this study. The weather data used in this study were collected from two regions in Ukraine and included hourly air temperature, solar radiation, leaf wetness duration and other measurements of soil and climatic parameters. Several different feature selection approaches were leveraged to retrieve the most salient features. The multistack of ML models has been used to find the best-performing pipeline, which achieved an accuracy of 82 %. Further, diverse XAI methods such as Shapley Additive Values (SHAP), ELI5, Anchor and Local Interpretable Model-agnostic Explanations (LIME) have been applied to understand the model predictions. The precision, recall, f1-score and AUC obtained were 85%, 82%, 82% and 72 % respectively. As a result a decision support system has been developed to predict the risk of wheat powdery mildew using soil and climatic parameters monitoring, ML, and XAI. This study provides the holistic risk prediction of PMBG for the enhancement of wheat stress resistance during the full cycle of its cultivation in open-field conditions.

Agricultural drought significantly affects crop growth and food production, making accurate drought thresholds essential for effective monitoring and discrimination. This study aims to monitor the threshold ranges for different drought levels of winter wheat during three growth periods using a multispectral Unmanned Aerial Vehicle (UAV). Firstly, based on controlled field experiments, six vegetation indices were used to develop UAV optimal inversion models for the Leaf Area Index (LAI) and Soil-Plant Analysis Development (SPAD) during the jointing-heading period, heading-filling period, and filling-maturity period of winter wheat. The results show that during the three growth periods, the DVI-LAI, NDVI-LAI, and RVI-LAI models, along with the DVI-SPAD, RVI-SPAD, and TCARI-SPAD models, achieved the highest inversion accuracy. Based on the UAV-inversed LAI and SPAD indices, threshold ranges for different drought levels were determined for each period. The accuracy of LAI threshold monitoring during three periods was 92.8%, 93.6%, and 90.5%, respectively, with an overall accuracy of 92.4%. For the SPAD index, the threshold monitoring accuracy during three periods was 93.1%, 93.0%, and 92%, respectively, with an overall accuracy of 92.7%. Finally, combined with yield data, this study explores UAV-based drought disaster monitoring for winter wheat. This research enriches and expands the crop drought monitoring system using a multispectral UAV. The proposed drought threshold ranges can enhance the scientific and precise monitoring of crop drought, which is highly significant for agricultural management.

Beyond flood protection to prevent severe damage, the restored floodplain grassland in Austria provides ecosystem services in terms of carbon balance. Net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco) were quantified by the eddy covariance (EC) method before, during and after a severe flooding event. Our results show that the carbon balance is heavily influenced by water level in the study site. The diurnal variations influenced by various degree from the flood are analysed, showing the average daily GPP of the floodplain grassland in Marchegg dropping from 1.048 g C m-2 day-1 before the flood, down to 0.470 g C m-2 day-1 during the flood. The study demonstrates that the restored floodplain grassland in Marchegg functions as a robust CO2 sink with a cumulative NEE of 38.8 g carbon per m2 over the three-month study period, despite temporary disruptions caused by flooding events. The findings emphasise the considerable potential of floodplain grassland restoration for carbon storage and climate change mitigation, with the new data from the EC station offering valuable insights for future restoration projects. Finally, this supports the adoption of the new EU Nature Restoration Law and the need for restoring wetlands, floodplains and rivers to secure water availability and biodiversity in these unique ecosystems. NBS and more specifically as Soil and Water Bioengineering (SWBE) are methods with ecological advantages and a huge potential for sustainable recreation of nearnatural ecosystems. It is of crucial importance to prove these beneficial effects, and to quantify them transparently in terms of quality assurance and use of resources in a sustainable and eco-friendly way.

During the construction of deep and large foundation pits in floodplain areas, it is inevitable to cause stratum disturbance and endanger the safety of the surrounding environment. This paper focuses on the influence of deep foundation pit excavation on surrounding environment based on a soft soil deep foundation pit project in Nanjing floodplain area. A series of laboratory tests were conducted to obtain the parameters of the small strain hardening (HSS) model for the typical soil layers. Then PLAXIS 3D software is used to simulate the excavation process of the foundation pit. On the basis of field measurement and numerical model, the deformation characteristics of deep foundation pit and surrounding environment are analyzed. The HSS model and the appropriate model parameters can effectively simulate the deformation behavior during the excavation of the foundation pit. Aiming at the problem of excessive deformation of foundation pit and surrounding pipelines, the reinforcement effect of reinforced soil in active and passive areas under different reinforcement parameters is analyzed. The optimal reinforcement width and depth should be determined after reasonable analysis to obtain the best economic benefits.