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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.

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.

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

【Objective】Qitai County, a major grain-producing area in Xinjiang, is adopting modern agricultural technologies such as aerial pest control, Beidou navigation, precision sowing, and integrated water-fertilizer management. Taking Yaozhanzi village as an example, this paper analyzes the spatial distribution of soil quality in the region to provide a baseline for intelligent and precise agricultural management. 【Method】After harvesting wheat and corn, 69 soil sampling points were selected at 700 m intervals across the region. Samples were taken from the 0-40 cm soil layer for determining soil particle size composition and nutrient contents. Classical statistical methods were used to analyze the correlations between indicators, and geostatistical methods were used to assess the spatial variation of each indicator. Soil quality was evaluated using the soil quality index (SQI).【Result】Soil texture in the study area is coarse and alkaline. Soil pH, soil organic matter (SOM), and soil bulk density showed low spatial variability, whereas other soil indicators show moderate variability. Soil available iron, manganese, zinc, SOM, and soil bulk density showed moderate spatial autocorrelation, while other indicators showed strong spatial autocorrelation. Overall, soil quality was above average.【Conclusion】The coarse nature of soil texture in the region resulted in poor water and nutrient retention. Therefore, a high-frequency, low-volume drip irrigation strategy is recommended to reduce nutrient and water leaching. Additionally, applying an appropriate amount of acidic organic fertilizer can increase soil organic matter, improve soil structure, and enhance nitrogen and zinc fertilizer use efficiency.

The Republic of Azerbaijan suffers from low agricultural productivity caused by soil salinization and erosion, and limited and insufficient soil data are available for economic and political reasons. In this study, soil salinity and heavy metal levels were assessed. Environmental risk assessment was conducted to evaluate the potential risk posed by soils to human health. Soil guideline values were proposed to monitor soil pollution in the Republic of Azerbaijan. Water extraction and spatial variability analysis were conducted to understand soil salinization and heavy metal pollution. Among the 20 studied elements, the elements Ca, Cl, and S and the heavy metals Cr, Ni, and Pb were classified as problematic on the basis of the geoaccumulation index, and As was also identified as posing a possible risk on the basis of the potential ecological risk index. Based on the developed soil guideline values for agricultural soil, the As, Cr, and Ni in the soil samples exceeded their respective guidelines by 31.3, 41.8, and 61.6%, respectively. Water extraction results confirmed that 99% of the leached ions were cationic salts, and the most problematic ion was Na, followed by Ca, Cl, and S. The extractability values of Cr and Ni were significantly lower than those of other heavy metals, which implies that their actual leaching potential may be overestimated. The linear regression and spatial variability analysis confirmed that leachable salts have accumulated in lowland areas due to the capillary rise of water and evaporation, but the distribution of heavy metals confirmed that As, Cr, and Ni were abundant in agricultural soils. Our results clearly showed that heavy metal soil contamination and high salinity levels are major problems that should be considered when assessing food safety and health hazards in the Mugan Plain of Azerbaijan. Therefore, future studies should be performed for additional environmental risk assessment, detailed hazard identification, and health risk assessment.

Tree stems from wetland, floodplain and upland forests can emit CH4. This emerging field of research has revealed a high spatial and temporal variability on CH4 stem emissions between trees and species, and within and across ecosystems, which is not completely understood. Additionally, there is no consensus on the biophysical mechanisms that could support stem CH4 emissions, including the origin of these emissions. This hinders our understanding of spatial and temporal patterns and hamper the identification of biophysical drivers. Here, we summarize up to 30 opportunities and challenges on stem CH4 emissions research in order to improve estimates of magnitudes, patterns, drivers and trace the potential origin of CH4 emissions. We propose two main challenges: the need for long-term high frequency measurements of stem CH4 emissions, and the need for a mechanistic model including passive and active transport of CH4 from the soil-tree-atmosphere continuum. The first challenge would allow to constrain magnitudes and patterns of CH4 emissions at different temporal scales, and the second would require discovery and integration of pathways and mechanisms of CH4 production and emissions to be integrated into process-base models. Addressing these challenges might improve upscaling of CH4 emissions from trees to the ecosystem scale and the quantification of the role of stem CH4 emissions for the local-to-global CH4 budget.

The effects of ground motion spatial variability (GMSV) or fluid-structure interaction (FSI) on the seismic responses of deep-water bridges have been extensively examined. However, there are few studies on the seismic performance of bridges considering GMSV and FSI effects simultaneously. In this study, the original multiple-support response spectrum (MSRS) method is extended to consider FSI effect for seismic analysis of deep-water bridges. The solution of hydrodynamic pressure on a pier is obtained using the radiation wave theory, and the FSI-MSRS formulation is derived according to the random vibration theory. The influence of FSI effect on the related coefficients is analyzed. A five-span steel-concrete continuous beam bridge is adopted to conduct the numerical simulations. Different load conditions are designed to investigate the variation of the bridge responses when considering the GMSV and FSI effects. The results indicate that the incoherence effect and wave passage effect decrease the bridge responses with a maximum percentage of 86%, while the FSI effect increases the responses with a maximum percentage of 26%. The GMSV and FSI effects should be included in the seismic design of deep-water bridges.

Catchment Morphing (CM) is a newly proposed approach to apply fully distributed models for ungauged catchments and has been trialled in several catchments in the UK. As one of the most important input datasets for hydrological models, rainfall spatial variability is influential on the stream variabilities and simulation performance. A homogenous rainfall was utilized in the previous experiments with Catchment Morphing. This study applied a spatially distributed rainfall from CEH-GEAR rainfall dataset in the morphed catchment for ungauged catchments as the follow-on study. Three catchments in the UK were used for rainfall spatial analysis and CEH-GEAR rainfall data were adopted for additional spatial analysis. The results demonstrate the influence of rainfall spatial information to the model performance with CM and illustrate the ability of morphed catchment to deal with spatially varied information. More spatially distributed information is expected to be introduced for a wider application of CM.

The characterization of intense rainfall in engineering projects is fundamental, mainly regarding the estimate of design flows necessary for designing hydraulic works. Intense rainfall events are commonly measured by Equations and curves that relate their intensity, duration, and frequency. Such relations, known as IDF, enable the hydrological characterization of a given region. This article presents a methodological design and results from both determination and spatialization of IDF curve parameters for the state of Maranhão. Historical series of maximum daily rainfalls obtained from National Water and Sanitation Agency (ANA) were used in 126 rainfall gauge stations and the Gumbel probability distribution estimated the maximum daily rainfall for 5, 10, 15, 25, 50, and 100 return periods. The Isozonal Method obtained the IDF correlations of intense rainfall events for 0,1. 1, and 24 h durations, and their performance were conducted by Nash-Sutcliffe R2 coefficient and Root Mean Square Relative Error (RMSE). “K, a, b, and c” parameters of intense rainfall equations were determined by optimization and convergence processes and their spatialization was carried out by interpolation by Inverse Distance Weighted (IDW), which enabled to determine the values of each parameter in regions without physical measurements of rainfall. Similarly, rainfall intensity was spatialized for the entire state. According to the results, the rainfall distribution in the state of Maranhão shows a variation in the indexes of precipitation, with the highest values found in areas located in central-southern, southwestern, and southeastern regions.

Seasonal variations of soil water content over 16 months at three typical sites of the Qilian Mountain were investigated to understand the controlling factors in regulating soil moisture content and dynamics. In this study, we quantified soil moisture response to cumulative rainfall events and freezing and thawing processes. The significant response depth of soil moisture after rainfall was 0–40 cm, and the forms of effective rainfall in this semiarid region: consecutive, with small dry intervals, and a few small rainfall events, with the cumulative amount of no less than 18.5 mm. Not only vegetation but also topography and soil texture were the primary controlling variables for soil moisture dynamics in this mountain ecosystem. The freezing temperature in 0–100-cm soil depth was in the range of −1.66 to 1.73 °C, and the thawing temperature was in the range of −0.54 to 0.84 °C; soil aspect and water content were the main controlling factors in the freezing and thawing points in this mountain region. The results of this study contributed to identifying the major controls on soil moisture content in mountain ecosystems and will provide basis for ecological restoration in arid regions.