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]

已建寒区隧道大多冻害频发，衬砌冻胀开裂严重，隧道冻胀力计算是寒区隧道防抗冻设计亟待解决的难题之一。冻融圈整体冻胀模型是应用最广的冻胀力计算模型，对寒区隧道设计至关重要。本文首先浅析冻结围岩的力学状态和强度理论，从弹性与弹塑性角度探讨冻融圈整体冻胀物理模型，并总结均匀冻胀和不均匀冻胀假定下的冻胀力弹性计算模型与弹塑性计算模型；阐明不同冻胀变形假定下冻胀力弹性计算模型的计算思路及求解方法，分析弹性与弹塑性冻胀力计算模型的联系，并归纳不均匀冻胀假定下各冻胀因素的表达方式。在总结现有研究的基础上，探讨寒区隧道冻胀力计算模型进一步的研究方向，以期为寒区隧道工程施工设计提供参考。

表碛厚度是冰川消融模拟及冰川径流精确量化的关键因素，可为表碛覆盖型冰川动力学、物质平衡、水文模型及下游地区的防灾减灾和水资源管理研究提供数据支持。基于Landsat 8遥感影像，利用能量平衡方程法反演喜马拉雅山南坡朗塘流域冰川表碛厚度，分析了典型冰川表碛厚度空间分布特征，并探讨了表碛厚度空间分布异质性成因。研究结果表明：（1）朗塘流域冰川表碛平均厚度为（0.25±0.02） m，其中Lirung为（0.55±0.02） m、Shalbachum为（0.48±0.02） m、Langshisha为（0.31±0.02） m、Langtang为（0.25±0.02） m;(2)沿纵剖面，各冰川表碛厚度从消融区上部到下部呈现增厚趋势，其中，Lirung、Shalbachum和Langtang冰川表碛堆积速率沿纵剖面从上到下先减小、后增大，而Langshisha冰川则逐渐减小；沿横剖面，Lirung、Shalbachum、Langtang冰川运动方向右侧表碛厚度大于左侧，而Langshisha两侧表碛厚、中间薄；（3）冰川纵剖面表碛堆积速率的差异主要由消融区下部冰川表面流速差异所引起；（4）冰...

积雪作为数值模式重要的下垫面之一，尤其是在季节性积雪覆盖地区，其准确性对于积雪表面的地表通量计算以及随后的大气变量模拟至关重要。为检验数值模式中气温模拟对积雪初始场的敏感性，选取辽宁地区2020年1月作为冬季代表月，利用ERA5-Land再分析数据、中国1980—2020年雪水当量25 km逐日产品以及辽宁地区国家级气象观测站的积雪深度和雪水当量数据，分别更新中尺度数值天气预报模式（WRF）的积雪深度和雪水当量初始场，开展积雪初始场对冬季气温影响的模拟试验。结果显示：（1）三套积雪资料均改进了数值模式积雪初始场的正偏差，进而使得日最高气温的冷偏差减小了0.6℃以上，平均气温的冷偏差改进了0.74～0.85℃，最低气温的冷偏差变为暖偏差，对应的平均气温和极端气温的RMSE减小0.38～0.62℃，相关系数增加；（2）受陆面模式中积雪参数化方案的影响，雪水当量初始场的改进要比积雪深度初始场的改进更重要；（3）积雪初始场的改变主要是通过积雪反照率效应影响气温的模拟，而积雪水文效应则较小。