Evaluation of the SMOS and SMAP soil moisture products under different vegetation types against two sparse in situ networks over arid mountainous watersheds,Northwest China

Assessment of the suitability of satellite soil moisture products at large scales is urgently needed for numerous climatic and hydrological researches, particularly in arid mountainous watersheds where soil moisture plays a key role in landatmosphere exchanges. This study presents evaluation of the SMOS(L2) and SMAP(L2_P_E and L2_P) products against ground-based observations from the Upstream of the Heihe River Watershed in situ Soil Moisture Network(UHRWSMN) and the Ecological and Hydrological Wireless Sensor Network(EHWSN) over arid high mountainous watersheds, Northwest China.Results show that all the three products are reliable in catching the temporal trend of the in situ observations at both point and watershed scales in the study area. Due to the uncertainty in brightness temperature and the underestimation of effective temperature, the SMOS L2 product and both the SMAP L2 products show "dry bias" in the high, cold mountainous area. Because of the more accurate brightness temperature observations viewing at a constant angle and more suitable estimations of single scattering albedo and optical depth, both the SMAP L2 products performed significantly better than the SMOS product.Moreover, comparing with station density of in situ network, station representation is much more important in the evaluation of the satellite soil moisture products. Based on our analysis, we propose the following suggestions for improvement of the SMOS and SMAP product suitability in the mountainous areas: further optimization of effective temperature; revision of the retrieval algorithm of the SMOS mission to reduce the topographic impacts; and, careful selection of in situ observation stations for better representation of in situ network in future evaluations. All these improvements would lead to better applicability of the SMOS and SMAP products for soil moisture estimation to the high elevation and topographically complex mountainous areas in arid regions.     

基于简化参数方法的蒙古干旱区土壤湿度被动微波遥感

卫星被动微波遥感土壤湿度,是准确分析大空间尺度上陆表水分变化信息的有效手段.美国航天局(NASA)发布的基于AMSR-E观测亮温资料的全球土壤湿度反演产品,在蒙古干旱区的实际精度并不令人满意.本文基于对地表微波辐射传输中地表粗糙度和植被层影响的简化处理方法,采用AMSR-E的6.9 GHz,10.7 GHz和18.7 GHz之V极化亮温资料,应用多频率反演算法,并以国际能量和水循环协同观测计划(The Coordinated Energy and Water Cycle Observations Project)即CEOP实验在蒙古国东部荒漠地区的地面实验资料作为先验知识,获取被动微波遥感模型的优化参数,以期获得蒙古干旱区精度更高的土壤湿度遥感估算结果.分析表明,本文方法反演的白天和夜间土壤湿度结果与地面验证值之间的均方根误差(RMSE)接近0.030 cm³/cm³, 证明所用方法在不需要其他辅助资料或参数帮助下,可较精确地反演干旱区表层土壤湿度信息,能够全天候、动态监测大空间尺度的土壤湿度变化,可为干旱区气候变化研究及陆面过程模拟和数据同化研究提供高精度的表层土壤湿度初始场资料.     

Could operational hydrological models be made compatible with satellite soil moisture observations?

Soil moisture is a significant state variable in flood forecasting. Nowadays more and more satellite soil moisture products are available, yet their usage in the operational hydrology is still limited. This is because the soil moisture state variables in most operational hydrological models (mostly conceptual models) are over‐simplified—resulting in poor compatibility with the satellite soil moisture observations. A case study is provided to discuss this in more detail, with the adoption of the XAJ model and the Soil Moisture and Ocean Salinity (SMOS) level‐3 soil moisture observation to illustrate the relevant issues. It is found that there are three distinct deficiencies existed in the XAJ model that could cause the mismatch issues with the SMOS soil moisture observation: (i) it is based on runoff generation via the field capacity excess mechanism (interestingly, such a runoff mechanism is called the saturation excess in XAJ while in fact it is clearly a misnomer); (ii) evaporation occurs at the potential rate in its upper soil layer until the water storage in the upper layer is exhausted, and then the evapotranspiration process from the lower layers will commence – leading to an abrupt soil water depletion in the upper soil layer; (iii) it uses the multi‐bucket concept at each soil layer – hence the model has varied soil layers. Therefore, it is a huge challenge to make an operational hydrological model compatible with the satellite soil moisture data. The paper argues that this is possible and some new ideas have been explored and discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Root-zone soil moisture estimation from assimilation of downscaled Soil Moisture and Ocean Salinity data

The crucial role of root-zone soil moisture is widely recognized in land–atmosphere interaction, with direct practical use in hydrology, agriculture and meteorology. But it is difficult to estimate the root-zone soil moisture accurately because of its space-time variability and its nonlinear relationship with surface soil moisture. Typically, direct satellite observations at the surface are extended to estimate the root-zone soil moisture through data assimilation. But the results suffer from low spatial resolution of the satellite observation. While advances have been made recently to downscale the satellite soil moisture from Soil Moisture and Ocean Salinity (SMOS) mission using methods such as the Disaggregation based on Physical And Theoretical scale Change (DisPATCh), the assimilation of such data into high spatial resolution land surface models has not been examined to estimate the root-zone soil moisture. Consequently, this study assimilates the 1-km DisPATCh surface soil moisture into the Joint UK Land Environment Simulator (JULES) to better estimate the root-zone soil moisture. The assimilation is demonstrated using the advanced Evolutionary Data Assimilation (EDA) procedure for the Yanco area in south eastern Australia. When evaluated using in-situ OzNet soil moisture, the open loop was found to be 95% as accurate as the updated output, with the updated estimate improving the DisPATCh data by 14%, all based on the root mean square error (RMSE). Evaluation of the root-zone soil moisture with in-situ OzNet data found the updated output to improve the open loop estimate by 34% for the 0–30 cm soil depth, 59% for the 30–60 cm soil depth, and 63% for the 60–90 cm soil depth, based on RMSE. The increased performance of the updated output over the open loop estimate is associated with (i) consistent estimation accuracy across the three soil depths for the updated output, and (ii) the deterioration of the open loop output for deeper soil depths. Thus, the findings point to a combined positive impact from the DisPATCh data and the EDA procedure, which together provide an improved soil moisture with consistent accuracy both at the surface and at the root-zone.     

Long-term SMOS soil moisture products: A comprehensive evaluation across scales and methods in the Duero Basin (Spain)

The European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) Level 2 soil moisture and the new L3 product from the Barcelona Expert Center (BEC) were validated from January 2010 to June 2014 using two in situ networks in Spain. The first network is the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), which has been extensively used for validating remotely sensed observations of soil moisture. REMEDHUS can be considered a small-scale network that covers a 1300 km² region. The second network is a large-scale network that covers the main part of the Duero Basin (65,000 km²). At an existing meteorological network in the Castilla y Leon region (Inforiego), soil moisture probes were installed in 2012 to provide data until 2014. Comparisons of the temporal series using different strategies (total average, land use, and soil type) as well as using the collocated data at each location were performed. Additionally, spatial correlations on each date were computed for specific days. Finally, an improved version of the Triple Collocation (TC) method, i.e., the Extended Triple Collocation (ETC), was used to compare satellite and in situ soil moisture estimates with outputs of the Soil Water Balance Model Green-Ampt (SWBM-GA). The results of this work showed that SMOS estimates were consistent with in situ measurements in the time series comparisons, with Pearson correlation coefficients (R) and an Agreement Index (AI) higher than 0.8 for the total average and the land-use averages and higher than 0.85 for the soil-texture averages. The results obtained at the Inforiego network showed slightly better results than REMEDHUS, which may be related to the larger scale of the former network. Moreover, the best results were obtained when all networks were jointly considered. In contrast, the spatial matching produced worse results for all the cases studied.These results showed that the recent reprocessing of the L2 products (v5.51) improved the accuracy of soil moisture retrievals such that they are now suitable for developing new L3 products, such as the presented in this work. Additionally, the validation based on comparisons between dense/sparse networks and satellite retrievals at a coarse resolution showed that temporal patterns in the soil moisture are better reproduced than spatial patterns.     

Intercomparison of the JULES and CABLE land surface models through assimilation of remotely sensed soil moisture in southeast Australia

Numerous land surface models exist for predicting water and energy fluxes in the terrestrial environment. These land surface models have different conceptualizations (i.e., process or physics based), together with structural differences in representing spatial variability, alternate empirical methods, mathematical formulations and computational approach. These inherent differences in modeling approach, and associated variations in outputs make it difficult to compare and contrast land surface models in a straight-forward manner. While model intercomparison studies have been undertaken in the past, leading to significant progress on the improvement of land surface models, additional framework towards identification of model weakness is needed. Given that land surface models are increasingly being integrated with satellite based estimates to improve their prediction skill, it is practical to undertake model intercomparison on the basis of soil moisture data assimilation. Consequently, this study compares two land surface models: the Joint UK Land Environment Simulator (JULES) and the Community Atmosphere Biosphere Land Exchange (CABLE) for soil moisture estimation and associated assessment of model uncertainty. A retrieved soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission was assimilated into both models, with their updated estimates validated against in-situ soil moisture in the Yanco area, Australia. The findings show that the updated estimates from both models generally provided a more accurate estimate of soil moisture than the open loop estimate based on calibration alone. Moreover, the JULES output was found to provide a slightly better estimate of soil moisture than the CABLE output at both near-surface and deeper soil layers. An assessment of the updated membership in decision space also showed that the JULES model had a relatively stable, less sensitive, and more highly convergent internal dynamics than the CABLE model.     

Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau

Long-term highly accurate surface soil moisture data of TP(Tibetan Plateau)are important to the research of Asian monsoon and global atmospheric circulation.However,due to the sparse in-situ networks,the lack of soil moisture observations has seriously hindered the progress of climate change researches of TP.Based on the Dual-Channel soil moisture retrieval algorithm and the satellite observation data of AMSR-E(Advanced Microwave Scanning Radiometer for EOS),we have produced the surface soil moisture data of TP from 2003 to 2010 and analyzed the seasonal characteristic of the soil moisture spatial distribution and its multi-year changing trend in area of TP.Compared to the in-situ observations,the accuracy of the soil moisture retrieved by the proposed algorithm is evaluated.The evaluation result shows that the new soil moisture product has a better accuracy in the TP region than the official product of AMSR-E.The spatial distribution of the annual mean values of soil moisture and the seasonal variations of the monthly-averaged soil moisture are analyzed.The results show that the soil moisture variations in space and time are consistent with the precipitation distribution and the water vapor transmission path in TP.Based on the new soil moisture product,we also analyzed the spatial distribution of the changing trend of multi-year soil moisture in TP.From the comparisons with the precipitation changing trend obtained from the meteorological observation sites in TP,we found that the spatial pattern of the changing trend of soil moisture coincides with the precipitation as a whole.     

Factors and interactions controlling infiltration,runoff, and soil loss at the microscale in a patchy Mediterranean semiarid landscape

In semiarid ecosystems, the transfer of water, sediments, and nutrients from bare to vegetated areas is known to be crucial to ecosystem functioning. Rainfall simulation experiments were performed on bare‐soil and vegetated surfaces, on both wet and dry soils, in semiarid shrub‐steppe landscapes of SE Spain to investigate the spatial and temporal factors and interactions that control the fine‐scale variation in water infiltration, runoff and soil loss, and hence the water and sediment flows in these areas. Three types of shrub‐steppe landscapes varying in plant community and physiography, and four types of plant patches (oak shrub, subshrub, tussock grass, and short grass mixed with chamaephytes) were studied. Higher infiltration and lower runoff and soil loss were measured on vegetation patches than on bare soils, for both dry and wet conditions. The oak‐shrub patches produced no runoff, while the subshrub patches showed the highest runoff and soil loss. Despite these differences among patch types, the influence of vegetation patch type on the variables analysed was not significant. The response of bare soil surfaces clearly varied between landscape types, yet the differences were only relevant under dry soil conditions. Stone cover, particularly the cover of embedded stones, and crust cover, were the key explanatory variables for the hydrological behaviour of bare soils. The study documents quantitatively how bare soils and vegetation patches function as runoff sources and runoff sinks, respectively, for a wide range of soil moisture conditions, and illustrates that landscape‐type effects on bare‐soil runoff sources may also exert an important control on the site hydrology, while the role of the vegetation patch type is less important. The effects of the control factors are modulated by antecedent soil moisture, with dry soils showing the most contrasting soil water infiltration between landscapes and surface types. Copyright © 2009 John Wiley & Sons, Ltd.     

Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture

This study aims at evaluating the uncertainty in the prediction of soil moisture (1D, vertical column) from an offline land surface model (LSM) forced by hydro-meteorological and radiation data. We focus on two types of uncertainty: an input error due to satellite rainfall retrieval uncertainty, and, LSM soil-parametric error. The study is facilitated by in situ and remotely sensed data-driven (precipitation, radiation, soil moisture) simulation experiments comprising a LSM and stochastic models for error characterization. The parametric uncertainty is represented by the generalized likelihood uncertainty estimation (GLUE) technique, which models the parameter non-uniqueness against direct observations. Half-hourly infra-red (IR) sensor retrievals were used as satellite rainfall estimates. The IR rain retrieval uncertainty is characterized on the basis of a satellite rainfall error model (SREM). The combined uncertainty (i.e., SREM + GLUE) is compared with the partial assessment of uncertainty. It is found that precipitation (IR) error alone may explain moderate to low proportion of the soil moisture simulation uncertainty, depending on the level of model accuracy—50–60% for high model accuracy, and 20–30% for low model accuracy. Comparisons on the basis of two different sites also yielded an increase (50–100%) in soil moisture prediction uncertainty for the more vegetated site. This study exemplified the need for detailed investigations of the rainfall retrieval-modeling parameter error interaction within a comprehensive space-time stochastic framework for achieving optimal integration of satellite rain retrievals in land data assimilation systems.     

Evaluation of SMOS soil moisture retrievals over the central United States for hydro-meteorological application

Soil moisture has been widely recognized as a key variable in hydro-meteorological processes and plays an important role in hydrological modelling. Remote sensing techniques have improved the availability of soil moisture data, however, most previous studies have only focused on the evaluation of retrieved data against point-based observations using only one overpass (i.e., the ascending orbit). Recently, the global Level-3 soil moisture dataset generated from Soil Moisture and Ocean Salinity (SMOS) observations was released by the Barcelona Expert Center. To address the aforementioned issues, this study is particularly focused on a basin scale evaluation in which the soil moisture deficit is derived from a three-layer Xinanjiang model used as a hydrological benchmark for all comparisons. In addition, both ascending and descending overpasses were analyzed for a more comprehensive comparison. It was interesting to find that the SMOS soil moisture accuracy did not improve with time as we would have expected. Furthermore, none of the overpasses provided reliable soil moisture estimates during the frozen season, especially for the ascending orbit. When frozen periods were removed, both overpasses showed significant improvements (i.e., the correlations increased from r = −0.53 to r = −0.65 and from r = −0.62 to r = −0.70 for the ascending and descending overpasses, respectively). In addition, it was noted that the SMOS retrievals from the descending overpass consistently were approximately 11.7% wetter than the ascending retrievals by volume. The overall assessment demonstrated that the descending orbit outperformed the ascending orbit, which was unexpected and enriched our knowledge in this area. Finally, the potential reasons were discussed.     

青藏高原春季土壤湿度与我国长江流域夏季降水的联系及其可能机理

土壤湿度作为陆面过程的重要因子,对局地及邻近地区的大气环流和天气气候有重要影响.青藏高原的土壤湿度观测站点稀少,时间较短,鉴于此,本文使用经过部分观测站点检验的卫星反演数据,研究了春季高原土壤湿度的年际变化与后期夏季我国东部降水的联系和可能机理.结果表明:在全球变暖的背景下,高原土壤湿度总体呈现出显著增加的趋势,去除该线性趋势后,我们定义了一个高原土壤湿度指数TPSMI来定量表征高原土壤湿度的年际变化特征,发现表层、中层、深层的土壤湿度年际变率趋于一致,且春季土壤湿度与夏季土壤湿度显著相关(相关系数可达0.56).当TPSMI偏大时,即高原东部土壤湿度偏大,而西部偏小时,夏季在高原东部(西部)存在一个潜热(感热)热源,二者共同作用下,在对流层中高层从高原西部经我国大陆直至东北地区激发出一个气旋—反气旋—气旋波列,该波列呈相当正压结构,有利于东北冷涡的加强及冷空气向南爆发;与此同时,南亚高压加强东伸,西太副高西伸加强,低空南方暖湿气流与北方干冷气流在长江流域汇合,伴随着上升运动加强,从而有利于夏季长江流域降水增多;反之,当TPSMI偏小时,夏季长江流域降水减少.     

The effect of roughness in simultaneously retrieval of land surface parameters

Using remotely-sensed data, various soil moisture estimation models have been developed for bare soil areas. Previous studies have shown that the brightness temperature (BT) measured by passive microwave sensors were affected by characteristics of the land surface parameters including soil moisture, vegetation cover and soil roughness. Therefore knowledge of vegetation cover and soil roughness is important for obtaining frequent and global estimations of land surface parameters especially soil moisture.In this study, a model called Simultaneous Land Parameters Retrieval Model (SLPRM) that is an iterative least-squares minimization method is proposed. The algorithm estimates surface soil moisture, land surface temperature and canopy temperature simultaneously in vegetated areas using AMSR-E (Advance Microwave Scanning Radiometer-EOS) brightness temperature data. The simultaneous estimations of the three parameters are based on a multi-parameter inversion algorithm which includes model construction, calibration and validation using observations carried out for the SMEX03 (Soil Moisture Experiment, 2003) region in the South and North of Oklahoma.Roughness parameter has also been included in the algorithm to increase the soil parameters retrieval accuracy. Unlike other methods, the SLPRM method works efficiently in all land covers types.The study focuses on soil parameters estimation by comparing three different scenarios with the inclusion of roughness data and selects the most appropriate one. The difference between the resulted accuracies of scenarios is due to the roughness calculation approach.The analysis on the retrieval model shows a meaningful and acceptable accuracy on soil moisture estimation according to the three scenarios.The SLPRM method has shown better performance when the SAR (Synthetic Aperture RADAR) data are used for roughness calculation.     

Time stability and variability of Electronically Scanned Thinned Array Radiometer soil moisture during Southern Great Plains hydrology experiments

Variability and time‐stability analysis for field‐scale (800 m) Electronically Scanned Thinned Array Radiometer soil moisture within a satellite scale footprint (∼ 50 km) were quantified using observations from the Southern Great Plains Hydrology Experiment 1997 and 1999 (SGP97 and SGP99). The pixels' time‐stability properties were examined with respect to soil, vegetation and topographic parameters in order to determine which physical parameters can be used to identify good candidate observation locations for validating soil moisture from satellite observations and global‐scale model output. The results show that the time‐stability concept remains valid at the satellite scale. The root mean square error values were 1·47, 1·51, 1·93 and 2·32% for the 1st, 2nd, 50th and 100th most stable fields, respectively. The most stable locations had sand and clay percentages consistent with sandy loam soils and moderate to high normalized difference vegetation index values. Neither land cover nor topography properties could be used to identify potentially stable fields in the study region. Copyright © 2010 John Wiley & Sons, Ltd.     

Comparison of SMOS,modelled and in situ long-term soil moisture series in the northwest of Spain

ABSTRACT

This work aimed to evaluate the capability of modelled vs in situ soil moisture observations in the northwest of Spain for a period of four years (2010–2013) in order to validate the SMOS L2 product. Comparisons were performed for a set of representative stations of the Soil Moisture Measurement Stations network of the University of Salamanca (REMEDHUS) at both point and area scales. The SMOS series showed good correlation with the modelled series, better than that obtained with the in situ observations (0.77 vs 0.68 average correlation coefficients). However, some underestimation or overestimation of the SMOS series, related to the soil characteristics, was observed with respect to both the in situ and the modelled series. The SMOS data normalization produced a notable improvement in the results, highlighting the capability of the modelled data to validate the SMOS soil moisture series. This research provides a solid foundation for the future validation of SMOS at large scales, overcoming the spatial representativeness issues arising from the use of in situ point measurements.

Editor M.C. Acreman; Associate editor N. Verhoest     

The role of vegetation patterns in structuring runoff and sediment fluxes in drylands

The dynamics of vegetation‐driven spatial heterogeneity (VDSH) and its function in structuring runoff and sediment fluxes have received increased attention from both geomorphological and ecological perspectives, particularly in arid regions with sparse vegetation cover. This paper reviews the recent findings in this area obtained from field evidence and numerical simulation experiments, and outlines their implications for soil erosion assessment. VDSH is often observed at two scales, individual plant clumps and stands of clumps. At the patch scale, the local outcomes of vegetated patches on soil erodibility and hydraulic soil properties are well established. They involve greater water storage capacity as well as increased organic carbon and nutrient inputs. These effects operate together with an enhanced capacity for the interception of water and windborne resources, and an increased biological activity that accelerates breakdown of plant litter and nutrient turnover rates. This suite of relationships, which often involve positive feedback mechanisms, creates vegetated patches that are increasingly different from nearby bare ground areas. By this way a mosaic builds up with bare ground and vegetated patches coupled together, respectively, as sources and sinks of water, sediments and nutrients. At the stand scale within‐storm temporal variability of rainfall intensity controls reinfiltration of overland flow and its decay with slope length. At moderate rainfall intensity, this factor interacts with the spatial structure of VDSH and the mechanism of overland flow generation. Reinfiltration is greater in small‐grained VDSH and topsoil saturation excess overland flow. Available information shows that VDSH structures of sources and sinks of water and sediments evolve dynamically with hillslope fluxes and tune their spatial configurations to them. Rainfall simulation experiments in large plots show that coarsening VDSH leads to significantly greater erosion rates even under heavy rainfall intensity because of the flow concentration and its velocity increase. Copyright © 2005 John Wiley & Sons, Ltd.     

Vegetation response to rainfall intermittency in drylands: Results from a simple ecohydrological box model

Vegetation in arid and semi-arid regions is affected by intermittent water availability. We discuss a simple stochastic model describing the coupled dynamics of soil moisture and vegetation, and study the effects of rainfall intermittency. Soil moisture dynamics is described by a ecohydrological box model, while vegetation is represented by site occupancy dynamics in a spatially-implicit model. We show that temporal rainfall intermittency allows for vegetation persistence at low values of annual rainfall volume, whereas it would go extinct if rainfall were constant. Rainfall intermittency also generates long-term fluctuations in vegetation cover, even in the absence of significant inter-annual variations in the statistical properties of precipitation.     

Factors controlling the spatial variability of surface soil moisture within revegetated‐stabilized desert ecosystems of the Tengger Desert,Northern China

The spatial structure of surface soil moisture was investigated at a grid scale with 10 × 10 m intervals on a plot of 4500 m² in a re‐vegetated desert area in Shapotou, the Tengger Desert. The site topography varies from dune crest to dune hollow, and again to dune crest. Volumetric soil moisture contents were measured 21 times over 6 months in 2006 by using Delta‐T Theta‐Probes in the 0–6 cm surface soil layer before and after rainfall. At the same time, soil texture, relative elevation, and plant coverage were measured, to examine (i) the spatial variability of surface soil moisture; (ii) the main factors controlling the spatial variability patterns; and (iii) how the importance of these factors varies with the seasonal variations in soil moisture content. The results indicated that the normal distribution of surface soil moisture was more obvious in wet conditions than in dry conditions; the spatial variability of surface soil moisture was inherent and decreased with increased soil moisture content; and precipitation increased the spatial dependence of surface soil moisture. The relative elevation of the landscape, the shrub coverage of the community, and the soil texture were the main factors influencing surface soil moisture variability, while the effect of soil texture strengthened gradually following the heavy precipitation events. The correlation between the spatial variability of surface soil moisture and the environmental factors, such as, the dry and wet conditions, the landscape coverage and the relative elevation suggests that increasing stability of the soil moisture resulted in a significant increase of soil moisture. Copyright © 2009 John Wiley & Sons, Ltd.     

Root‐zone moisture replenishment in a native vegetated catchment under Mediterranean climate

The root‐zone moisture replenishment mechanisms are key unknowns required to understand soil hydrological processes and water sources used by plants. Temporal patterns of root‐zone moisture replenishment reflect wetting events that contribute to plant growth and survival and to catchment water yield. In this study, stable oxygen and hydrogen isotopes of twigs and throughfall were continuously monitored to characterize the seasonal variations of the root‐zone moisture replenishment in a native vegetated catchment under Mediterranean climate in South Australia. The two studied hillslopes (the north‐facing slope [NFS] and the south‐facing slope [SFS]) had different environmental conditions with opposite aspects. The twig and throughfall samples were collected every ~20 days over 1 year on both hillslopes. The root‐zone moisture replenishment, defined as percentage of newly replenished root‐zone moisture as a complement to antecedent moisture for plant use, calculated by an isotope balance model, was about zero (±25% for the NFS and ± 15% for the SFS) at the end of the wet season (October), increased to almost 100% (±26% for the NFS and ± 29% for the SFS) after the dry season (April and May), then decreased close to zero (±24% for the NFS and ± 28% for the SFS) in the middle of the following wet season (August). This seasonal pattern of root‐zone moisture replenishment suggests that the very first rainfall events of the wet season were significant for soil moisture replenishment and supported the plants over wet and subsequent dry seasons, and that NFS completed replenishment over a longer time than SFS in the wet season and depleted the root zone moisture quicker in the dry season. The stable oxygen isotope composition of the intraevent samples and twigs further confirms that rain water in the late wet season contributed little to root‐zone moisture. This study highlights the significant role of the very first rain events in the early wet season for ecosystem and provides insights to understanding ecohydrological separation, catchment water yield, and vegetation response to climate changes.     

A temperature-dependent multi-relaxation spectroscopic dielectric model for thawed and frozen organic soil at 0.05–15 GHz

A dielectric model for thawed and frozen Arctic organic-rich soil (50% organic matter) has been developed. The model is based on soil dielectric measurements that were collected over ranges of gravimetric moisture from 0.03 to 0.55 g/g, dry soil density from 0.72 to 0.87 g/cm³, and temperature from 25 to −30 °C (cooling run) in the frequency range of 0.05–15 GHz. The refractive mixing dielectric model was applied with the Debye multi-relaxation equations to fit the measurements of the soil’s complex dielectric constant as a function of soil moisture and wave frequency. The spectroscopic parameters of the dielectric relaxations for the bound, transient bound, and unbound soil water components were derived and were complimented by the thermodynamic parameters to obtain a complete set of parameters for the proposed temperature-dependent multi-relaxation spectroscopic dielectric model for moist soils. To calculate the complex dielectric constant of the soil, the following input variables must be assigned: (1) density of dry soil, (2) gravimetric moisture, (3) wave frequency, and (4) temperature. The error of the dielectric model was evaluated and yielded RMSE_ε′ values of 0.348 and 0.188 for the soil dielectric constant and the loss factor, respectively. These values are on the order of the dielectric measurement error itself. The proposed dielectric model can be applied in active and passive microwave remote sensing techniques to develop algorithms for retrieving the soil moisture and the freeze/thaw state of organic-rich topsoil in the Arctic regions.