Land–atmosphere interactions in midlatitude continental regions are particularly active during the warm season. It is still unclear whether and under what circumstances these interactions may involve positive or negative feedbacks between soil moisture conditions and rainfall occurrence. Assessing such feedbacks is crucially important to a better understanding of the role of land surface conditions on the regional dynamics of the water cycle. This work investigates the relationship between soil moisture and subsequent precipitation at the daily time scale in a midlatitude continental region. Sounding data from 16 locations across the midwestern United States are used to calculate two indices of atmospheric instability—namely, the convective available potential energy (CAPE) and the convective inhibition (CIN). These indices are used to classify rainfall as convective or stratiform. Correlation analyses and uniformity tests are then carried out separately for these two rainfall categories, to assess the dependence of rainfall occurrence on antecedent soil moisture conditions, using simulated soil moisture values. The analysis suggests that most of the positive correlation observed between soil moisture and subsequent precipitation is due to the autocorrelation of long stratiform events. The authors found both areas with positive and areas with negative feedback on convective precipitation. This behavior is likely due to the contrasting effects of soil moisture conditions on convective phenomena through changes in surface temperature and the supply of water vapor to the overlying air column. No significant correlation is found between daily rainfall intensity and antecedent simulated soil moisture conditions either for convective or stratiform rainfall.

. With remote sensing we can readily observe the Earth's surface, but direct observation of the sub-surface remains a challenge. In hydrology, but also in related disciplines such as agricultural and atmospheric sciences, knowledge of the dynamics of soil moisture in the root zone of vegetation is essential, as this part of the vadose zone is the core component controlling the partitioning of water into evaporative fluxes, drainage, recharge, and runoff. In this paper, we compared the catchment-scale soil moisture content in the root zone of vegetation, computed by a lumped conceptual model, with the remotely sensed Normalized Difference Infrared Index (NDII) in the Upper Ping River basin (UPRB) in northern Thailand. The NDII is widely used to monitor the equivalent water thickness (EWT) of leaves and canopy. Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to determine the NDII over an 8-day period, covering the study area from 2001 to 2013. The results show that NDII values decrease sharply at the end of the wet season in October and reach lowest values near the end of the dry season in March. The values then increase abruptly after rains have started, but vary in an insignificant manner from the middle to the late rainy season. This paper investigates if the NDII can be used as a proxy for moisture deficit and hence for the amount of moisture stored in the root zone of vegetation, which is a crucial component of hydrological models. During periods of moisture stress, the 8-day average NDII values were found to correlate well with the 8-day average soil moisture content (Su) simulated by the lumped conceptual hydrological rainfall–runoff model FLEX for eight sub-catchments in the Upper Ping basin. Even the deseasonalized Su and NDII (after subtracting the dominant seasonal signal) showed good correlation during periods of moisture stress. The results illustrate the potential of the NDII as a proxy for catchment-scale root zone moisture deficit and as a potentially valuable constraint for the internal dynamics of hydrological models. In dry periods, when plants are exposed to water stress, the EWT (reflecting leaf water deficit) decreases steadily, as moisture stress in the leaves is connected to moisture deficits in the root zone. Subsequently, when the soil moisture is replenished as a result of rainfall, the EWT increases without delay. Once leaf water is close to saturation – mostly during the heart of the wet season – leaf characteristics and NDII values are not well correlated. However, for both hydrological modelling and water management, the stress periods are most important, which is why this product has the potential of becoming a highly efficient model constraint, particularly in ungauged basins.\n

. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

. Satellite-derived soil moisture provides more spatially and temporally extensive data than in situ observations. However, satellites can only measure water in the top few centimeters of the soil. Root zone soil moisture is more important, particularly in vegetated regions. Therefore estimates of root zone soil moisture must be inferred from near-surface soil moisture retrievals. The accuracy of this inference is contingent on the relationship between soil moisture in the near-surface and the soil moisture at greater depths. This study uses cross correlation analysis to quantify the association between near-surface and root zone soil moisture using in situ data from the United States Great Plains. Our analysis demonstrates that there is generally a strong relationship between near-surface (5–10 cm) and root zone (25–60 cm) soil moisture. An exponential decay filter is used to estimate root zone soil moisture using near-surface soil moisture derived from the Soil Moisture and Ocean Salinity (SMOS) satellite. Root zone soil moisture derived from SMOS surface retrievals is compared to in situ soil moisture observations in the United States Great Plains. The SMOS-based root zone soil moisture had a mean R2 of 0.57 and a mean Nash–Sutcliffe score of 0.61 based on 33 stations in Oklahoma. In Nebraska, the SMOS-based root zone soil moisture had a mean R2 of 0.24 and a mean Nash–Sutcliffe score of 0.22 based on 22 stations. Although the performance of the exponential filter method varies over space and time, we conclude that it is a useful approach for estimating root zone soil moisture from SMOS surface retrievals.\n

. In the present work, we developed a new formulation for the estimation of the soil moisture in the root zone based on the measured value of soil moisture at the surface. It was derived from a simplified soil water balance equation for semiarid environments that provides a closed form of the relationship between the root zone and the surface soil moisture with a limited number of physically consistent parameters. The method sheds lights on the mentioned relationship with possible applications in the use of satellite remote sensing retrievals of soil moisture. The proposed approach was used on soil moisture measurements taken from the African Monsoon Multidisciplinary Analysis (AMMA) and the Soil Climate Analysis Network (SCAN) databases. The AMMA network was designed with the aim to monitor three so-called mesoscale sites (super sites) located in Benin, Mali, and Niger using point measurements at different locations. Thereafter the new formulation was tested on three additional stations of SCAN in the state of New Mexico (US). Both databases are ideal for the application of such method, because they provide a good description of the soil moisture dynamics at the surface and the root zone using probes installed at different depths. The model was first applied with parameters assigned based on the physical characteristics of several sites. These results highlighted the potential of the methodology, providing a good description of the root-zone soil moisture. In the second part of the paper, the model performances were compared with those of the well-known exponential filter. Results show that this new approach provides good performances after calibration with a set of parameters consistent with the physical characteristics of the investigated areas. The limited number of parameters and their physical interpretation makes the procedure appealing for further applications to other regions.\n

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基于GRACE卫星数据反演陆地水储量变化的能力在众多大尺度流域（>15 000 km2）中得到验证，但该数据空间分辨率较低，难以应用于中尺度流域。本研究基于分辨率更高的遥感土壤湿度，通过引入转换系数Kpm，对2010年到2015年汛期、非汛期前后的中尺度流域水储量变化（dSm/dt）进行估算，并通过水量平衡方程对该方法进行验证分析。结果表明基于土壤湿度得到的dSm/dt与P-Q-E在各个中尺度流域上的相关系数均大于0.7，呈显著相关，土壤湿度能捕捉流域季节性水储量变化；相关系数和均方根偏差在南部区域偏大，说明该方法虽然在湿润区捕捉变化的能力更强，但对量级的把握能力较弱，在北部偏干区域则表现呈相反趋势。该方法为陆地水储量计算提供新途径，计算结果可为区域水资源调度管理起指导作用。

Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a high-performance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties (pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately 5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate (Model Efficiency Coefficients from 0.71 to 0.36) at 0-5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.Copyright © 2021 Science China Press. Published by Elsevier B.V. All rights reserved.

以新疆全区草地为研究对象,利用2010-2014年天然草地地上生物量实测数据(above-ground biomass,AGB)和EOS Terra MODIS每日地表反射率产品MOD 09GA,采用空间分析方法分别计算了生长季(4-10月)草地NDVI_max,MSAVI_max,PVI_max,NDVI_mean,MSAVI_mean和PVI_mean 6种植被指数,探讨了NDVI_max,MSAVI_max,PVI_max与草地地上生物量之间的遥感反演模型,分析了新疆草地生长季多年NDVI_mean,MSAVI_mean和PVI_mean空间分布特征和空间变化特征。根据所选的最优模型反演了新疆2005-2014年的草地地上生物量,统计分析了地上生物量的空间变化特征。结果表明,新疆草地2010-2014年NDVI_mean,MSAVI_mean和PVI_mean总体上均具有由北向南、由西向东逐渐递减的空间分布特点,不同草地类型的生物量差异显著。2005-2014年低地草甸类的生物量最高,高寒荒漠类最低。统计分析2010-2014年的NDVI_mean,MSAVI_mean和PVI_mean变化趋势发现,北疆有28%以上草地处于改善状态,南疆草地则以稳定为主;全疆14个地、州、市草地以稳定为主,处于稳定比重的草地大于40%,博尔塔拉蒙古自治州、哈密地区、塔城地区、巴音郭楞蒙古自治州草地处于改善状态的草地超过15%,阿勒泰地区、博尔塔拉蒙古自治州、伊犁哈萨克自治州直属、克拉玛依市、昌吉回族自治州、克孜勒苏柯尔克孜自治州、阿克苏地区和乌鲁木齐市轻度改善的草地比重大于10%;除吐鲁番市和乌鲁木齐市外,其余地、州、市恶化草地比重低于10%,全疆草地整体改善以稳定为主,总体趋于良好。

利用2000-2012年的MODIS NDVI数据,结合中国北方187个气象基准站年均温度和年降水量资料,对2000-2012年中国北方草地NDVI的时空变化特征和同期年均温度、降水量动态变化进行了分析。结果表明:(1)草地NDVI无明显变化的区域占北方草地总面积的64.35%,以荒漠草地为主;草地退化区域的面积(占北方草地总面积的23.97%)大于改善区域的面积(占北方草地11.68%)。(2)NDVI变异系数分析结果表明,2000—2012年以来中国北方草地68.37%区域呈稳定状态。其中,荒漠草地植被变异性较小,处于相对稳定状态的草地占其总面积的79.73%;而灌丛草地和典型草地的变异性较大,变化显著的草地分别占其草地面积的41.55%和45.92%。(3)北方草地区中,54.04%的区域年均温度呈升高趋势,大于温度呈降低趋势的区域,温度升高幅度最大为0.159 ℃·a^-1;年降水量呈增加趋势的面积达71.01%,远大于呈减少趋势的面积,降水量增加的最大幅度为23.29 mm·a^-1。

. A process-based methodology is applied to estimate land-surface evaporation from multi-satellite information. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely-sensed observations to derive daily actual evaporation and its different components. Soil water stress conditions are defined from a root-zone profile of soil moisture and used to estimate transpiration based on a Priestley and Taylor equation. The methodology also derives evaporationfrom bare soil and snow sublimation. Tall vegetation rainfall interception is independently estimated by means of the Gash analytical model. Here, GLEAM is applied daily, at global scale and a quarter degree resolution. Triple collocation is used to calculate the error structure of the evaporation estimates and test the relative merits of two different precipitation inputs. The spatial distribution of evaporation – and its different components – is analysed to understand the relative importance of each component over different ecosystems. Annual land evaporation is estimated as 67.9 × 103 km3, 80% corresponding to transpiration, 11% to interception loss, 7% to bare soil evaporation and 2% snow sublimation. Results show that rainfall interception plays an important role in the partition of precipitation into evaporation and water available for runoff at a continental scale. This study gives insights into the relative importance of precipitation and net radiation in driving evaporation, and how the seasonal influence of these controls varies over different regions. Precipitation is recognised as an important factor driving evaporation, not only in areas that have limited soil water availability, but also in areas of high rainfall interception and low available energy.\n

This paper traces two decades of neural network rainfall-runoff and streamflow modelling, collectively termed ‘river forecasting’. The field is now firmly established and the research community involved has much to offer hydrological science. First, however, it will be necessary to converge on more objective and consistent protocols for: selecting and treating inputs prior to model development; extracting physically meaningful insights from each proposed solution; and improving transparency in the benchmarking and reporting of experimental case studies. It is also clear that neural network river forecasting solutions will have limited appeal for operational purposes until confidence intervals can be attached to forecasts. Modular design, ensemble experiments, and hybridization with conventional hydrological models are yielding new tools for decision-making. The full potential for modelling complex hydrological systems, and for characterizing uncertainty, has yet to be realized. Further gains could also emerge from the provision of an agreed set of benchmark data sets and associated development of superior diagnostics for more rigorous intermodel evaluation. To achieve these goals will require a paradigm shift, such that the mass of individual isolated activities, focused on incremental technical refinement, is replaced by a more coordinated, problem-solving international research body.

Soil moisture has a crucial role in both the global energy and hydrological cycles; it affects different ecosystem processes. Spatial and temporal variability of soil moisture add to its complex behaviour, which undermines the reliability of most current measurement methods. In this paper, two promising evolutionary data-driven techniques, namely (i) Evolutionary Polynomial Regression and (ii) Genetic Programming, are challenged with modelling the soil moisture response to the near surface atmospheric conditions. The utility of the proposed models is demonstrated through the prediction of the soil moisture response of three experimental soil covers, used for the restoration of watersheds that were disturbed by the mining industry. The results showed that the storage effect of the soil moisture response is the major challenging factor; it can be quantified using cumulative inputs better than time-lag inputs, which can be attributed to the effect of the soil layer moisture-holding capacity. This effect increases with the increase in the soil layer thickness. Three different modelling tools are tested to investigate the tool effect in data-driven modelling. Despite the promising results with regard to the prediction accuracy, the study demonstrates the need for adopting multiple data-driven modelling techniques and tools (modelling environments) to obtain reliable predictions.