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.

In many high altitude river basins, the hydro-climatic regimes and the spatial and temporal distribution of precipitation are little known, complicating efforts to quantify current and future water availability. Scarce, or non-existent, gauged observations at high altitudes coupled with complex weather systems and orographic effects further prevent a realistic and comprehensive assessment of precipitation. Quantifying the contribution from seasonal snow and glacier melt to the river runoff for a high altitude, melt dependent region is especially difficult. Global scale precipitation products, in combination with precipitation-runoff modelling may provide insights to the hydro-climatic regimes for such data scarce regions. In this study two global precipitation products; the high resolution (0.1 degrees x 0.1 degrees), newly developed ERA5-Land, and a coarser resolution (0.55 degrees x 0.55 degrees) JRA-55, are used to simulate snow/glacier melts and runoff for the Gilgit Basin, a sub-basin of the Indus. A hydrological precipitation-runoff model, the Distance Distribution Dynamics (DDD), requires minimum input data and was developed for snow dominated catchments. The mean of total annual precipitation from 1995 to 2010 data was estimated at 888 mm and 951 mm by ERA5-Land and JRA-55, respectively. The daily runoff simulation obtained a Kling Gupta efficiency (KGE) of 0.78 and 0.72 with ERA5-Land and JRA-55 based simulations, respectively. The simulated snow cover area (SCA) was validated using MODIS SCA and the results are quite promising on daily, monthly and annual scales. Our result showed an overall contribution to the river flow as about 26% from rainfall, 37-38% from snow melt, 31% from glacier melt and 5% from soil moisture. These melt simulations are in good agreement with the overall hydro-climatic regimes and seasonality of the area. The proxy energy balance approach in the DDD model, used to estimate snow melt and evapotranspiration, showed robust behaviour and potential for being employed in data poor basins. (c) 2021 Published by Elsevier B.V.

土壤冻融交替是陆地表层极其重要的物理过程,土壤冻融状态的频繁变化对地气能量交换、地表径流、植被生长、生态系统及土壤碳氮循环等均具有重要的影响。本文基于1981—2019年ERA5-LAND逐小时土壤温度数据,借助GIS空间分析功能,利用Python编程处理分析了中国东北地区近地表土壤冻融状态的时空变化特征。结果表明:从不同冻融状态起始日期的空间分布来看,近地表不同阶段的起始日期主要受纬度和地形的影响,具有明显的纬度地带性和垂直地带性。春季冻融过渡期和完全融化期的起始日期由东南向西北均呈逐渐推迟趋势,而秋季冻融过渡期与完全冻结期起始日期则由东南向西北随纬度升高越来越早。就不同冻融状态发生天数的空间分布而言,研究区南部春季冻融过渡期发生天数多于北部,西部多于东部,年均发生天数均在30 d以内;秋季发生冻融的天数空间差异不大,研究区一半以上的地区年均发生天数在10 d以内。完全融化期发生天数最多,从东南向西北呈逐渐减少趋势,年均发生天数主要介于150～240 d之间;完全冻结期发生天数则由南向北日益增多,其空间分布表现为一向南开口的簸箕形,各地年均发生天数集中于90～180 d之间。从时间变...