Long-term, high-resolution soil moisture (SM) is a vital variable for understanding the water-energy cycle and the impacts of climate change on the Qinghai-Tibet Plateau (QTP). However, most existing satellite SM data are only available at coarse scale (+/- 25 km) and suffer a lot from data gaps due to satellite orbit coverage and snow cover, especially on the QTP. Although substantial efforts have been devoted to downscale SM utilizing multiple soil moisture indices (SMIs) or diverse machine learning (ML) methods, the potentials of different SMIs and ML approaches in SM downscaling on the complex plateau remain unclear, and there is still a necessity to obtain an accurate, long-term, high-resolution and seamless SM data over the QTP. To address this issue, this study generated the long-term, high-accuracy and seamless soil moisture dataset (LHS-SM) over the QTP during 2001-2020 using a two-step downscaling method (first downscaling then merging). Firstly, the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1 km utilizing five ML approaches. Then, a dynamic data merging method that considers spatiotemporal nonstationary error was applied to derive the final LHS-SM data. The performance of fifteen SMIs was also assessed and the optimal indexes for downscaling were identified. Results indicated that the shortwave infrared band-based indices had better performance than the near infrared band-based and energy-based indices. The generated LHS-SM data exhibited satisfying accuracy (mean R = 0.52, ubRMSE = 0.047 m(3)/m(3)) and certain improvement to the ESA CCI SM data both at station and network scales. Compared with existing 1 km SM datasets, the LHS-SM data also showed the best performance (mean R = 0.62, ubRMSE = 0.047 m(3)/m(3)), while existing datasets either failed to fully characterize the spatial details or had some data gaps and unreasonable distributions. Strong spatial heterogeneity was observed in the SM dynamics during 2001-2020 with the southwest and northeast showing a dry gets wetter scheme and the southeast presenting a wet gets drier trend. Overall, the LHS-SM dataset gained its added values by compensating the drawbacks of existing 1 km SM products over the QTP and was much valuable for many regional applications.

Soil Moisture (SM) is a key parameter in northern Arctic and sub-Arctic (A-SA) environments that are highly vulnerable to climate change. We evaluated six SM satellite passive microwave datasets using thirteen ground-based SM stations across Northwestern America. The best agreement was obtained with SMAP (Soil Moisture Active Passive) products with the lowest RMSD (Root Mean Square Difference) (0.07 m$3$3 m${-3}$-3) and the highest R (0.55). ESA CCI (European Space Agency Climate Change Initiative) also performed well in terms of correlation with a similar R (0.55) but showed a strong variation among sites. Weak results were obtained over sites with high water body fractions. This study also details and evaluates a dedicated retrieval of SM from SMOS (Soil Moisture and Ocean Salinity) brightness temperatures based on the $\tau -\omega$tau-omega model. Two soil dielectric models (Mironov and Bircher) and a dedicated soil roughness and single scattering albedo parameterization were tested. Water body correction in the retrieval shows limited improvement. The metrics of our retrievals (RMSD = 0.08 m$3$3 m${-3}$-3 and R = 0.41) are better than SMOS but outperformed by SMAP. Passive microwave satellite remote sensing is suitable for SM retrieval in the A-SA region, but a dedicated approach should be considered.

Due to climate change the drop in spring-water discharge poses a serious issue in the Himalayan region, especially in the higher of Himachal Pradesh. This study used different climatic factors along with long-term rainfall data to understand the decreasing trend in spring-water discharge. It was determined which climate parameter was most closely correlated with spring discharge volumes using a general as well as partial correlation plot. Based on 40 years (1981-2021) of daily average rainfall data, a rainfall-runoff model was utilised to predict and assess trends in spring-water discharge using the MIKE 11 NAM hydrological model. The model's effectiveness was effectively proved by the validation results (NSE = 0.79, R2 = 0.944, RMSE = 0.23, PBIAS = 32%). Model calibration and simulation revealed that both observed and simulated spring-water runoff decreased by almost 29%, within the past 40 years. Consequently, reduced spring-water discharge is made sensitive to the hydrological (groundwater stress, base flow, and stream water flow) and environmental entities (drinking water, evaporation, soil moisture, and evapotranspiration). This study will help researchers and policymakers to think and work on the spring disappearance and water security issues in the Himalayan region.

Prolonged and excessive use of chemical fertilizers has resulted in serious harm to soil health and ecosystems. This study aimed to reduce the cultivation costs for apricot trees, nearly 1/3(rd) of which are spent on fertilizers. The research was conducted on fully grown apricot trees of the cultivar New Castle, in the Solan district of Himachal Pradesh, India. The experiment consisted of fourteen treatment combinations evaluated in triplicate and statistically analyzed using a randomized block design (RBD). Results revealed that treatment T-12 [50% Nitrogen (Calcium Nitrate) + 50% Nitrogen (Urea) + Azotobacter + Phosphate Solubilizing Bacteria + Vermicompost] resulted in the highest percent increase in tree trunk girth (6.82%), highest leaf chlorophyll content (3.00 mg g(-1) fresh weight), leaf area (58.29 cm), fruit set (61.00%) and total yield (61.9 kg tree(-1)). In terms of nutrient status, T-12 had the highest leaf N (2.95%), leaf K (2.60%), soil N (386.33 kg ha(-1)), soil P (51.00 kg ha(-1)) and soil organic carbon (1.81%). The highest net return and profit over recommended dose of fertilizers (RDF) was also recorded in treatment T-12. The results of this study show that judicious fertilizer use along with integrated organic manure and bio-fertilizers can reduce cultivation costs, improve soil health, and increase fruit production with minimum ecosystem damage.

Giant reed (Arundo donax L.) is a plant species with a high growth rate and low requirements, which makes it particularly interesting for the production of different bioproducts, including natural fibers. This work assesses the use of fibers obtained from reed culms as reinforcement for a high-density polyethylene (HDPE) matrix. Two different lignocellulosic materials were used: i) shredded culms and ii) fibers obtained by culms processing, which have not been reported yet in literature as fillers for thermoplastic materials. A good stress transfer for the fibrous composites was observed, with significant increases in mechanical properties; composites with 20% fiber provided a tensile elastic modulus of almost 1900 MPa (78% increase versus neat HDPE) and a flexural one of 1500 MPa (100% increase), with an improvement of 15% in impact strength. On the other hand, composites with 20% shredded biomass increased by 50% the tensile elastic modulus (reaching 1560 MPa) and the flexural one (up to 1500 MPa), without significant changes in impact strength. The type of filler is more than its ratio; composites containing fibers resulted in a higher performance than the ones with shredded materials due to the higher aspect ratio of fibers.

Sinkholes pose a significant hazard in Mexico City (CDMX), causing substantial economic damage. While the link between sinkhole formation and groundwater extraction has been studied, specific mechanisms vary by site. Our overall aim is to characterize the phenomenon of sinkholes in CDMX. To achieve this, we create a database with 13 influencing factors, including population density, well density, distance to faults, fractures, roads, streams, elevation, slope, clay thickness, lithology, subsidence rate, geotechnical zones, and soil texture. Sinkhole locations were obtained from CDMX's Risk Atlas (2017-2019). We shaped a susceptibility map based on statistical regression methods derived from applying linear regression models. For the susceptibility map, results showed that 40% of variables are significantly correlated with sinkhole density. Despite the regression model explained 24% of sinkhole density variability, it helped choosing variables for the susceptibility map that correlate better (89.7%). Hence, we identified that the northeast CDMX was the most susceptible zone. Therefore, the compound assessment of environmental factors is useful for the evaluation of susceptibility maps to identify prone factors for the generation of sinkholes. This framework provides relevant information for better use of the territory throughout the development of public policies.

Land degradation threatens environmental and agricultural development in the 21st century. To alleviate this problem, bench terracing has been implemented in eastern and southern Ethiopia. This paper investigates how farmers perceive the attributes and effectiveness of bench terracing in Ethiopia. A Multi-stage sampling techniques were applied to select 384 sample households. For this study, data were collected through primary and secondary sources, and the collected data were analyzed using descriptive statistics and content analysis methods. Primary data were collected using semi-structured questionnaires, focus groups, and key informant interviews; secondary data came from local authority reports. We found that bench terraces restored damaged land and improved crop yield where they were aptly implemented and maintained. The findings also disclose that 57.3% of farmers perceived that bench terracing was more cost-effective; 60.7% responded that it is compatible with the socio-cultural context; and 59.8% perceived Its outcomes are observable to others. However, when a farmer lacks sufficient social, human, or financial capital holdings and capabilities, it often fails. We conclude that the technology was adopted through a multifaceted process, promoted or hindered by both its attributes and effectiveness. Policy-makers and Planners should center those restraints on designing, implementing, and maintaining bench terracing. [GRAPHICS]

近年来，季节冻土区滑坡灾害的发生频率逐渐增加且危害加重，引起人们的广泛关注。相对于非季节冻土区，季节冻土区的积雪消融和土体冻融的物理过程是否对滑坡产生影响，有待进一步研究。2002年5月9日发生在中国天山伊犁地区的一个巨型黄土滑坡群（加郎普特滑坡群）为本研究提供了一个理想案例。本研究基于实地勘察、遥感影像判识、气象数据分析和黄土特征试验等方法，探究加朗普特滑坡群的形成过程，揭示其破坏模式和失稳机理。研究表明，加朗普特黄土滑坡群由3个滑坡构成，总堆积方量约1 735.5×104 m3，滑动过程断断续续持续了2天，其形成与发展是多级、多次的推移式滑动破坏过程。加朗普特滑坡群的发生是早期融雪和后期暴雨耦合触发的结果。春季气温异常升高驱动的积雪融水影响斜坡前期变形演化，极端暴雨是滑坡发生的激发因素。另外，特殊坡体结构和地层组合为黄土滑坡发生提供了物质结构基础。结合斜坡变形过程我们建立了考虑降水入渗和冻融循环作用的黄土斜坡变形破坏模式，并提出了黄土斜坡滑面静态液化和坡脚滑动液化的联合是诱发黄土滑坡发生的重要机理。随着气候变化驱动的异常升温事件增多，未来天山季节冻土区发生大型黄土滑坡的风险极高。本...

In order to solve the problem of low freeze-thaw deformation strength of railway road genes in cold regions, railway subgrade soil was improved with polypropylene fibres. The failure mechanism of fibres improved foundation soil is revealed by experiments. The test results showed the following: (1) The strength decreased with the increase in the water content in the melting state and reached its maximum when the water content was 12% in the freezing state. The strength reached the maximum when fibre incorporation was 0.3% and fibre length was 15 mm. (2) The shear strength of the improved subgrade soil gradually decreased and tended to be stable with the increase in the number of freeze-thaw cycles in the frozen state. There was no significant change with the increasing number of freeze-thaw cycles in the thawing state. (3) Before and after the cyclic loading of the fibre-modified subgrade soil, the strength after cyclic loading was greater than that before. (4) Through scanning electron microscopy, the optimal fibre content was determined to be 0.3%. The research results can provide a strong reference for the improvement of railway subgrades, and they have broad application prospects.

A bacterial wilt disease (R. solanacearum) severely damages potato crops. The pathogen infects several crops in various agroclimatic areas, and it has a broad pathogenic diversity. Six phylotypes, twenty-three sequevars, five races, and six biovars have been identified to indicate the pathogenic diversity of the pathogen. Twenty-eight isolates of Phylotype II were separated into seven classes and identified 97.06% diversity. It survives in the soil for a long time. Temperature and soil moisture, affected the infection, growth, and epidemics of the pathogen. In the last three decades, scholars have reported Mondial, CIP385312-2, Cruza 148, and CIP388285-14 resistant clones and cultivars. Five quantitative trait loci responsible for resistance were identified on different potato chromosomes. LYZ-C resistance gene and the receptor kinase gene CLAVATA 1 were used to develop potato resistance. For potato resistance, a clustered regularly interspaced short palindromic repeat has been used since bacteria do not have Ribonucleic acid interference. Biochar, compost, and bio-organic fertilizer cultural practices are important to control the disease. It has been stated that bacteria exceed fungus as a biological control. Moreover, new or unusual biological controls such as Enterobacter sp., Pseudomonas sp., and Paenibacillus sp have been suggested. Several studies showed the effects of cultural and physical practices on other soil-borne diseases, however not on the potato bacterial wilt disease. Resistant potato clones against bacterial wilt disease are not available in developing countries. Then, the current review was proposed to assess various findings available on potato bacterial wilt pathogenic variability and management practices.