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This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool–heat and heat–cool island phenomena, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8 and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of the LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May, it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the combination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in an urban context compared with a rural context, while the land-cover LCZs within rural had a higher level of suitability.

The mechanical properties of soil, resulting from the weathering of rocks through physical and chemical processes, exhibit spatial variability. This variability introduces uncertainties in the design and characteristics of excavation projects. To address these uncertainties caused by soil spatial variability, safety factors are commonly used in excavation design. However, using the same safety factor for different indicators of soil spatial variability is illogical. Therefore, specialized research on the characteristics of deep excavations in the context of soil spatial variability is necessary, as it provides the theoretical basis for rational excavation design. In this study, we assumed that soil parameters follow a lognormal distribution, while spatial correlation adheres to a Gaussian function. We developed a random finite element algorithm for deep excavations, which incorporated Python programming and the ABAQUS computational platform. This algorithm was created within the framework of random field theory and Monte Carlo simulation. The results of our study indicate that, influenced by soil spatial variability, the lateral wall movements and ground surface settlements exhibit discrete distributions near the deterministic results. The maximum deformation of the excavation follows a normal distribution, while the pattern of ground surface settlements demonstrates diversity and chaotic characteristics. The extent to which soil spatial variability affects deep excavations is correlated with indicators of this variability. As the coefficient of soil spatial variability increases, the diversity and chaotic characteristics of ground surface settlements become more prominent. The locations of maximum ground surface settlement and maximum deformation becomes more scattered. Consequently, the probability of excavation failure increases, and the reliability index of the excavation decreases. In summary, soil spatial variability significantly impacts deformation prediction and safety control during the design and construction stages of deep excavations. Therefore, it is crucial to consider the influence of soil spatial variability when designing deep excavations, based on the variability indicators.

Some properties of landslide soils are generally recognized as inherently spatially variable because of the heterogeneity of natural geological deposits. Fluctuations in water levels of the Three Gorges Reservoir cause the depth of the groundwater table at landslide toes to change, resulting in fluctuations in the soil water content and significant soil degradation. The spatial variability and temporal weakening of soil properties should be incorporated into the landslide stability analysis. More importantly, rational deformation prediction and stability analysis of landslide numerical models require an advanced soil constitutive model. Herein, taking the Tangjiao landslide as a case study, the statistical characteristics of shear strength parameters were studied based on valuable soil test data. Then, the spatial variability of these parameters was modeled as a random field for the sliding mass. A hypoplastic constitutive model for clay was used to simulate the landslide deformation over 6 years caused by precipitation and changes in the reservoir water level. In addition, soil degradation induced by the fluctuating groundwater level was accounted for in key model parameters on the basis of experimental results. Eventually, the non-intrusive random finite element method was used to compute the landslide deformation and stability for the random field model. Landslide simulation of the deterministic model ignoring the spatial variation of soil parameters was also performed. Simulation results indicate that the difference in the landslide safety factor between the deterministic and random field models is up to 0.14 for the leading edge and up to 0.12 for the trailing edge. Random field models predict greater deformation and less stability than the deterministic model, suggesting that they are more conservative in this specific case. This research can serve as a useful reference for probabilistic stability analyses of riverside landslides considering soil spatial and temporal variability, which may be quantified more precisely in future research based on multi-source data inversion.

This paper presents the results of a two-year radon risk study conducted at the Historic Silver Mine in Tarnowskie Góry. During this period, continuous measurements of radon activity concentration were carried out in three-month cycles at 30 points distributed along the tourist route. The average radon activity concentration was 1160 Bq/m 3 for the first year of measurements and 1210 Bq/m 3 in the second year. Based on the collected data, seasonal correction factors considering seasonal variations in radon activity concentration (SCF) were determined. The obtained factors are in the range of 0.8–1.4. In addition, the spatial variation of radon activity concentration was studied at selected locations of the mine at different heights of the location of the detectors and their distribution on opposite sides of the excavation. Based on the collected data, effective doses were calculated. Assuming annual working time of 300 h, which was specified for workers, the average annual dose is 0.6 and 1.3 mSv for the conversion factor of 1.4 mSv/(mJ/m 3 ⋅h) indicated in Polish law and 3.1 mSv/(mJ/m 3 ⋅h) as recommended in the ICRP report no 137 for underground mines respectively. For the annual working time of 1800 h, the corresponding doses would be 3.4 mSv and 7.4 mSv.

The rate of soil degradation is increasing in the Himalayan ecosystem and inducing soil nutrient loss due to numerous environmental effects. The site-specific soil management zones (MZs) are necessary for such terrain with variability in spatial soil nutrient distribution. The present study investigates the spatial variability of soil nutrients and delineates the MZs in a mid-Himalayan watershed. Overall, 100 m grid 65 surface soil samples (0–30 cm) were collected from an area of 102 ha mini watershed located in the Tehri Garhwal district of Uttarakhand state in India. The samples were processed and analyzed for soil pH, electrical conductivity (EC), aggregate stability (AS), clay content (Cl), total carbon (TC), total nitrogen (TN), available phosphorous (AP) and available potassium (AK). The soil pH, EC, AS, Cl, TC, AP, AK and TN had mean values of 5.15, 104.57 dS cm−1, 0.80, 17.78%, 2.55%, 32.14 kg ha−1, 163.59 kg ha−1 and 0.24%, respectively. Geostatistical analysis showed a spatial distribution pattern of soil variables with moderate to weak spatial dependency. The principal component analysis (PCA) divulged five principal components (PCs) with eigenvalue > 1 with 69.48% total variance. The fuzzy c means algorithm for the scores of the selected PCs was carried out to establish the optimum number of clusters, i.e., management zones (MZs). The geostatistical analysis, PCA and fuzzy clustering resulted in four optimum soil management zones. The analysis of variance indicated that there is a significant difference between the soil and terrain parameters among the MZs. The soil pH and TN among MZ1 and MZ4, whereas the EC, AK, TC and elevation were significantly different among all the delineated MZs. In addition, the delineated MZs using cluster analysis resulted in within-zone variability and could be used as a guide for farmers to adopt site-specific soil management.

Mapping of soil nutrient parameters using experimental measurements and geostatistical approaches to assist site-specific fertiliser advisories is anticipated to play a significant role in Smart Agriculture. FarmerZone is a cloud service envisioned by the Department of Biotechnology, Government of India, to provide advisories to assist smallholder farmers in India in enhancing their overall farm production. As a part of the project, we evaluated the soil spatial variability of three potato agroecological zones in India and provided soil health cards along with field-specific fertiliser recommendations for potato cultivation to farmers. Specifically, 705 surface samples were collected from three representative potato-growing districts of Indian states (Meerut, UP; Jalandhar, Punjab and Lahaul and Spiti, HP) and analysed for soil parameters such as organic carbon, macronutrients (NPK), micronutrients (Zn, Fe, Mn, and Cu), pH, and EC. The soil parameters were integrated into a geodatabase and subjected to kriging interpolation to create spatial soil maps of the targeted potato agroecological zones through best-fit experimental semivariograms. The spatial distribution showed a deficiency of soil organic carbon in two studied zones and available nitrogen among all studied zones. The available phosphorus and potassium varied among the agroecological zones. The micronutrient levels were largely sufficient in all the zones except at a few specific sites where nutrient advisories are recommended to replenish. The general management strategies were recommended based on the nutrient status in the studied area. This study clearly supports the significance of site-specific soil analytics and interpolated spatial soil mapping over any targeted agroecological zones as a promising strategy to deliver reliable advisories of fertiliser recommendations for smart farming. Graphical abstract

Soil multifunctionality (SMF) and the soil quality index (SQI) are essential indicators of soil function, productivity, and health. Additionally, the spatial variability of soil multifunctionality (SVM) signifies soil heterogeneity. Biological soil crusts (Biocrusts) can affect these indicators. However, there is little information about the role of biocrusts in regulating the response of multiple ecosystem functions to climate change. We evaluated the relative importance of climate, soil environment, and biocrusts variables as drivers of SMF, SQI, and SVM at 74 sites in the Gurbantunggut Desert. Soil SMF, and SQI increase with the coverage of lichen and moss crust. Biocrusts index, SMF and SQI increase with an increase in the mean annual temperature. Biocrusts index, SMF and SQI increase first with an increase in mean annual precipitation (MAP)< 163 mm and then decrease. SVM display a significant decreasing trend with the increase of MAP. The structural equation model (SEM) demonstrate that the spatial distribution can significantly influence the biocrusts, soil SQI and SVM. Biocrusts has a significant positive influence on soil SMF (0.47)and SQI (0.31). Soil SMF has a significant negative effect on SVM (-0.50), and SQI (0.59) has a significant positive effect. We provide the first quantitative evidence that biocrust type and a 163 mm precipitation threshold govern SMF through opposing direct vs. indirect temperature pathways, offering a predictive rule-of-thumb for dryland management under climate change. The findings contribute decidedly to our understanding of the patterns and mechanisms driving SMF, SQI, and SVM in drylands, which is important for predicting changes in ecosystem function under climate change.

The interaction between rainfall spatial–temporal variability and watershed response has been extensively studied in recent decades. Due to the influence of spatiotemporal non‐uniformity and variability in urban rainfall processes, the urban drainage system can exhibit different capabilities of handling flood risk. In this study, we evaluate the urban drainage system performance responsive to rainfall spatial variability by constructing nonuniformly structured rainfall events with varying intensities, employing several systematic performance indices, and accounting for the cumulative transmission effects of different stormwater management strategies. Our results show that when storm centers are located in the central part of the basin, the spatial variation extent of rainfall—quantified by the standard deviation ( σ ) of a Gaussian distribution—has minimal influence on flood severity. Comparative analysis of flood spatial patterns under varying storm centers allows for the identification of critical rainfall zones, and heavy rainfall concentrated in these zones significantly amplifies system‐wide flood risk. While rainfall intensity affects system performance, uncertainty in storm positioning leads to substantial variability in hydrological response, leading to a wider range of flooding node ratios, especially under return periods of 20‐year or 30‐year. The performance of stormwater management infrastructure is also modulated by hydrological forcing variables, notably rainfall intensity and storm center location. The findings provide us with insight into the impact that rainfall structure can pose on the urban drainage system performance and stormwater management efficiency.

PurposeInherent spatial variability of properties of loess deposit along depth direction plays important roles in soil reclamation, agricultural irrigation, and risk assessment and management of geo-hazards on the Loess Plateau. This study aims to develop a database for loess deposit properties in Shaanxi Province first, followed by evaluating and reporting comprehensively the abovementioned spatial variability along depth.Materials and methodsA comprehensive literature review and numerous laboratory tests were conducted for 37, 81, and 177 loess profiles respectively for sandy, silty, and clayey loess to examine their properties in Shaanxi Province, China. For each property collected in the database, the means, coefficients of variation (COV), most suitable probability distribution functions, and spatial correlation lengths along the depth direction were evaluated and reported.Results and discussionThe physical and index properties and strength properties of the three loess soil generally exhibit relatively low variabilities with a mean COV of less than 20.0% in most cases. In contrast, the deformation properties exhibit medium to high variabilities with a mean COV varying from roughly 27.0 to 85.0%. For all of these properties mentioned above, the spatial correlation length along depth was estimated to be approximately 3 ~ 8 m.Conclusions(1) The COV of loess properties at one site can be significantly different from that at other sites. Thus, the site-specific variabilities of loess deposit properties shall be considered in geological design and analysis. (2) When site-specific data of properties for loess deposits are not available for on-going geological/geotechnical projects, the typical ranges of loess properties obtained in this study, including their statistics and correlation lengths, can be used as guidelines for approximation.

Many groundwater recharge estimation methods require extensive data, costly equipment, sensors, and human effort. These activities include monitoring groundwater level changes, water balance assessments, or isotopic tracers. However, in many regions, the high cost of data collection makes these methods infeasible. This study presents an observation-constrained LSM to estimate groundwater recharge for a large, data-limited water scarcity region in the Rift Valley basin in Ethiopia. This model leverages publicly available data from the National Aeronautics and Space Administration (NASA) GLDAS Noah model, spanning the years 2000 to 2022, to provide a cost-effective means for groundwater recharge estimation for larger regions. We conduct a comprehensive analysis of groundwater recharge, precipitation, land cover, and land use while integrating both temporal and spatial variability. Validating this estimation is challenging due to data limitations, so we compare our findings with literature on rainfall conversion to groundwater recharge in similar regions. Our method estimates that only 5% of rainfall is converted to groundwater recharge in the study area. This value is consistent with reported estimates in similar regions. This framework could make groundwater recharge estimation feasible in data and water-limited regions around the world.