针对金沙江上游气象观测站点稀疏以及高寒山区复杂水文过程导致的径流模拟难题，提出一种基于融合ERA5-Land再分析数据与实测降水数据的VIC模型驱动方案。基于模型模拟结果，对比分析遥感监测以及模型输出的积雪覆盖率，并讨论了流域融雪径流变化特征以及气温对融雪径流的影响。结果表明：仅采用站点插值数据构建的VIC模型很难反映流域真实的径流状况，而采用融合降水资料将小时径流模拟的确定性系数在率定期和验证期分别提高至0.80和0.74，相对误差显著降低至20%以内。模型输出积雪覆盖与遥感数据监测的积雪覆盖呈现良好的一致性，流域融雪径流年际变化稳定，年内差异较大，呈现双峰特征，春季融雪径流与气温有较大的相关性。该结果可为高寒区径流模拟及洪旱灾害监测与预报提供参考。

针对金沙江上游气象观测站点稀疏以及高寒山区复杂水文过程导致的径流模拟难题，提出一种基于融合ERA5-Land再分析数据与实测降水数据的VIC模型驱动方案。基于模型模拟结果，对比分析遥感监测以及模型输出的积雪覆盖率，并讨论了流域融雪径流变化特征以及气温对融雪径流的影响。结果表明：仅采用站点插值数据构建的VIC模型很难反映流域真实的径流状况，而采用融合降水资料将小时径流模拟的确定性系数在率定期和验证期分别提高至0.80和0.74，相对误差显著降低至20%以内。模型输出积雪覆盖与遥感数据监测的积雪覆盖呈现良好的一致性，流域融雪径流年际变化稳定，年内差异较大，呈现双峰特征，春季融雪径流与气温有较大的相关性。该结果可为高寒区径流模拟及洪旱灾害监测与预报提供参考。

针对金沙江上游气象观测站点稀疏以及高寒山区复杂水文过程导致的径流模拟难题，提出一种基于融合ERA5-Land再分析数据与实测降水数据的VIC模型驱动方案。基于模型模拟结果，对比分析遥感监测以及模型输出的积雪覆盖率，并讨论了流域融雪径流变化特征以及气温对融雪径流的影响。结果表明：仅采用站点插值数据构建的VIC模型很难反映流域真实的径流状况，而采用融合降水资料将小时径流模拟的确定性系数在率定期和验证期分别提高至0.80和0.74，相对误差显著降低至20%以内。模型输出积雪覆盖与遥感数据监测的积雪覆盖呈现良好的一致性，流域融雪径流年际变化稳定，年内差异较大，呈现双峰特征，春季融雪径流与气温有较大的相关性。该结果可为高寒区径流模拟及洪旱灾害监测与预报提供参考。

近地表土壤冻融过程影响土壤和大气之间的水分与能量交换，研究其动态变化对揭示多年冻土退化过程具有重要意义。受限于高海拔地区地表观测数据的获取，过去多关注多年冻土区土壤冻融水热运移特征，缺乏对土壤冻融长期变化趋势及其时空变化特征的研究。青海三江源地区是青藏高原多年冻土分布区，是全球气候变化的敏感区和关键带。本文基于野外观测数据评估ERA5-Land地温数据的精度，分析1980—2021年三江源地区近地表土壤冻融时空变化特征。结果表明：（1） ERA5-Land表层土壤温度数据与国家气象局23个站点观测数据的相关系数均值为0.94，均方根误差均值为4.49℃；与6个活动层站点观测数据的相关系数均值为0.96，均方根误差均值为2.57℃，能够满足三江源地区土壤冻融时空变化研究要求。（2）三江源地区地表土壤温度年均值、春季、夏季、秋季和冬季空间分布均表现为东部高、西部低；近地表土壤温度夏季增幅最大，秋季增幅最小，春季、夏季增幅整体高于秋季、冬季。（3） 1980—2021年三江源地区冻结天数和冻结持续时间呈现显著减少趋势，减小幅度分别为4.99 d/10a和6.77 d/10a；冻结起始时间推迟...

近地表土壤冻融过程影响土壤和大气之间的水分与能量交换，研究其动态变化对揭示多年冻土退化过程具有重要意义。受限于高海拔地区地表观测数据的获取，过去多关注多年冻土区土壤冻融水热运移特征，缺乏对土壤冻融长期变化趋势及其时空变化特征的研究。青海三江源地区是青藏高原多年冻土分布区，是全球气候变化的敏感区和关键带。本文基于野外观测数据评估ERA5-Land地温数据的精度，分析1980—2021年三江源地区近地表土壤冻融时空变化特征。结果表明：（1） ERA5-Land表层土壤温度数据与国家气象局23个站点观测数据的相关系数均值为0.94，均方根误差均值为4.49℃；与6个活动层站点观测数据的相关系数均值为0.96，均方根误差均值为2.57℃，能够满足三江源地区土壤冻融时空变化研究要求。（2）三江源地区地表土壤温度年均值、春季、夏季、秋季和冬季空间分布均表现为东部高、西部低；近地表土壤温度夏季增幅最大，秋季增幅最小，春季、夏季增幅整体高于秋季、冬季。（3） 1980—2021年三江源地区冻结天数和冻结持续时间呈现显著减少趋势，减小幅度分别为4.99 d/10a和6.77 d/10a；冻结起始时间推迟...

The Mediterranean region experiences the annual destruction of thousands of hectares due to climatic conditions. This study examines forest fires in Turkiye's Antalya region, a Mediterranean high-risk area, from 2000 to 2023, analyzing 26 fires that each damaged over 50 hectares. Fire danger maps created from fire weather indexes (FWI) indicated that 85.7% of the analyzed fire areas were categorized within the high to very extreme danger categories. The study evaluated fire danger maps from EFFIS FWI and ERA5 FWI, both derived from meteorological satellite data, for 14 forest fires between 2019 and 2023. With its better spatial resolution, it was found that EFFIS FWI had a higher correlation (0.98) with in situ FWIs. Since FWIs are calculated from temperature and fire moisture subcomponents, the correlations of satellite-based temperature (MODIS Land Surface Temperature-LST) and soil moisture (SMAP) data with FWIs were investigated. The in situ FWI demonstrated a positive correlation of 0.96 with MODIS LST, 0.92 with EFFIS FWI, and 0.93 with ERA5 FWI. The negative correlation between all FWIs and SMAP soil moisture highlighted a strong relationship, with the highest observed in in situ FWI (-0.93) and -0.90 and -0.87 for EFFIS FWI and ERA5 FWI, respectively.

A number of global surface soil moisture (SM) datasets have been retrieved from the L-band frequency Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) missions to study the terrestrial water, energy, and carbon cycles. This paper presents the performance of the recently developed 9 km global SMAP product (hereafter SMAP-INRAE-BORDEAUX, SMAP-IB9). The product retrieves SM from the 9 km SMAP radiometric products using the forward model (L-MEB, L-band Microwave Emission of the Biosphere) of SMOS INRA-CESBIO (SMOS-IC) and SMOS L2 algorithms. We inter-compared SMAP-IB9 with two other products with a similar grid resolution (similar to 10 km): the SMAP Enhanced Level-3 SM dataset (SMAP-E) and the enhanced global dataset for the land component of the fifth generation of European reanalysis (ERA5-Land) with the main objective of assessing the discrepancy in accuracy between remotely sensed and model SM datasets. We found that ERA5-Land and SMAP-IB9 SM had the overall highest correlations (R = 0.62(+/- 0.15) for ERA5-Land vs. 0.60 (+/- 0.17) for SMAP-IB9 and 0.50(+/- 0.15) for SMAP-E) by comparing with the International Soil Moisture Network (ISMN) in-situ measurements from 22 networks. ERA5-Land showed better performances in the forest areas where SMAP-IB9 and SMAP-E still showed high potential in detecting the time variations of the observed SM, particularly in terms of median correlation values (0.62(+/- 0.18) for SMAP-IB9 vs. 0.66(+/- 0.16) for ERA5-and). The discrepancy in R between satellite and model SM products that were reported in some past studies has decreased to statistically insignificant levels over time. For instance, in the non-forest areas, we found that the latest versions of the SMAP SM products (SMAP-E and SMAP-IB9) had relatively comparable performances with ERA5-Land with regard to median ubRMSE (0.07(+/- 0.02) m(3)/m(3) for both SMAP-E and ERA5-Land) and R (0.59 (+/- 0.16) for SMAP-IB9 vs. 0.61(+/- 0.15) for ERA5-Land), respectively.

大渡河流域内站点分布较少，历史观测数据不足，给该地区的融雪径流预报带来困难。基于欧洲中期天气预报中心提供的最新一代高分辨率陆面再分析数据集ERA5-Land，将积雪覆盖率和积雪平均深度引入度日因子雪量计算公式中，对HBV模型的积融雪模块进行改进，以提升融雪径流计算的可靠性。以大渡河上游为研究对象，选取1961—2018年的水文气象资料对模型进行率定和验证，并以2019年为例进行试预报研究。结果表明，通过引入ERA5-Land再分析数据，以及对积融雪模块进行改进，发挥了其在模拟积融雪上的优势，有效提升了融雪径流预报精度，对大渡河流域具有适用性。研究成果可为稀缺资料地区融雪径流模拟预报提供经验。

大渡河流域内站点分布较少，历史观测数据不足，给该地区的融雪径流预报带来困难。基于欧洲中期天气预报中心提供的最新一代高分辨率陆面再分析数据集ERA5-Land，将积雪覆盖率和积雪平均深度引入度日因子雪量计算公式中，对HBV模型的积融雪模块进行改进，以提升融雪径流计算的可靠性。以大渡河上游为研究对象，选取1961—2018年的水文气象资料对模型进行率定和验证，并以2019年为例进行试预报研究。结果表明，通过引入ERA5-Land再分析数据，以及对积融雪模块进行改进，发挥了其在模拟积融雪上的优势，有效提升了融雪径流预报精度，对大渡河流域具有适用性。研究成果可为稀缺资料地区融雪径流模拟预报提供经验。

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products' (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T's causality is evenly distributed amongst all biomes with fractional covers between similar to 10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet's ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.