Surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and energy fluxes at the land surface/atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture is critical for numerous environmental studies. Recent technological advances in satellite remote sensing have shown that soil moisture can be measured by a variety of remote sensing techniques, each with its own strengths and weaknesses. This paper presents a comprehensive review of the progress in remote sensing of soil moisture, with focus on technique approaches for soil moisture estimation from optical, thermal, passive microwave, and active microwave measurements. The physical principles and the status of current retrieval methods are summarized. Limitations existing in current soil moisture estimation algorithms and key issues that have to be addressed in the near future are also discussed.
Headwaters, defined here as first- and second- order streams, make up 70%-80% of the total channel length of river networks. These small streams exert a critical influence on downstream portions of the river network by： retaining or transmitting sediment and nutrients; providing habitat and refuge for diverse aquatic and riparian organisms; creating migration corridors; and governing connectivity at the watershed-scale. The upstream-most extent of the channel network and the longitudinal continuity and lateral extent of headwaters can be difficult to delineate, however, and people are less likely to recognize the importance of headwaters relative to other portions of a river network. Consequently, headwaters commonly lack the legal protections accorded to other portions of a river network and are more likely to be significantly altered or completely obliterated by land use.
Earth＇s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities （e.g., cropland, pastureland, urban land, wood harvest）. Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments. However, due to unaccounted changes in land cover （resulting from both indirect anthropogenic and natural causes）, heterogeneity in land-use/cover （LUC） conversions among grid cells, even for the same land use activity, and uncertainty associated with potential vegetation mapping and historical estimates of human land use result in land cover estimates that are substantially different compared to results acquired from remote sensing observations. Here, we present a method to implicitly account for the differences arising from these uncertainties in order to provide historical estimates of land cover that are consistent with satellite estimates for recent years. Due to uncertainty in historical agricultural land use, we use three widely accepted global estimates of cropland and pastureland in combination with common wood harvest and urban land data sets to generate three distinct estimates of historical land-cover change and underlying LUC conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and the extent to which different ecosystems have undergone changes. The annual land cover maps and LUC conversion maps are reported at 0.5°×0.5° resolution and describe the area of 28 land- cover types and respective underlying land-use transitions. The reconstructed data sets are relevant for studies addressing the impact of land-cover change on biogeo- physics, biogeochemistry, water cycle, and global climate.
Multi-model ensembles are one of the most common ways to deal with epistemic uncertainty in hydrology. This is a problem because there is no known way to sample models such that the resulting ensemble admits a measure that has any systematic (i.e., asymptotic, bounded, or consistent) relationship with uncertainty. Multi-model ensembles are effectively sensitivity analyses and cannot – even partially – quantify uncertainty. One consequence of this is that multi-model approaches cannot support a consistent scientific method – in particular, multi-model approaches yield unbounded errors in inference. In contrast, information theory supports a coherent hypothesis test that is robust to (i.e., bounded under) arbitrary epistemic uncertainty. This paper may be understood as advocating a procedure for hypothesis testing that does not require quantifying uncertainty, but is coherent and reliable (i.e., bounded) in the presence of arbitrary (unknown and unknowable) uncertainty. We conclude by offering some suggestions about how this proposed philosophy of science suggests new ways to conceptualize and construct simulation models of complex, dynamical systems.
Regional carbon emissions research is necessary and helpful for China in realizing reduction targets. The LMDI I （Logarithmic Mean Divisia Index I） technique based on an extended Kaya identity was conducted to uncover the main five driving forces for energy-related carbon emissions in Xinjiang, an important energy base in China. Decomposition results show that the affluence effect and the population effect are the two most important contributors to increased carbon emissions. The energy intensity effect had a positive influence on carbon emissions during the pre-reform period, and then became the dominant factor in curbing carbon emissions after 1978. The renewable energy penetration effect and the emission coefficient effect showed important negative but relatively minor effects on carbon emissions. Based on the local realities, a comprehensive suite of mitigation policies are raised by considering all of these influencing factors. Mitigation policies will need to significantly reduce energy intensity and pay more attention to the regional economic development path. Fossil fuel substitution should be considered seriously. Renewable energy should be increased in the energy mix. All of these policy recommendations, if implemented by the central and local government, should make great contributions to energy saving and emission reduction in Xinjiang.
Only one-dimensional (1D) deformation along the radar line of sight (LOS) can be obtained using differential interferometry synthetic aperture radar (D-InSAR), and D-InSAR observation is insensitive to deformation in the north direction. This study inferred three-dimensional (3D) deformation of a mining subsidence basin by combining the north-south deformation predicted by a probability integral method with the LOS deformation obtained by D-InSAR. The 15235 working face in Fengfeng mining area (Hebei Province, China) was used as the object of study. The north-south horizontal movement was predicted by the probability integral method according to the site's geological and mining conditions. Then, the vertical and east-west deformation fields were solved by merging ascend-orbit RadarSAT-2, descend-orbit TerraSAR, and predicted north-south deformation based on a least squares method. Comparing with the leveling data, the results show that the vertical deformation accuracy of the experimental method is better than the inversed vertical deformation neglecting the horizontal deformation. Finally, the impact of the relationship between the azimuth of the working face and the SAR imaging geometry on the monitoring of the mining subsidence basin was analyzed. The results can be utilized in monitoring mining subsidence basins by single SAR image sources.
This study examined the influence of spatial resolution on model parameterization, output, and the parameter transferability between different resolutions using the Storm Water Management Model. High-resolution models, in which most subcatchments were homogeneous, and high-resolution-based low-resolution models (in 3 scenarios) were constructed for a highly urbanized catchment in Beijing. The results indicated that the parameterization and simulation results were affected by both spatial resolution and rainfall characteristics. The simulated peak inflow and total runoff volume were sensitive to the spatial resolution, but did not show a consistent tendency. High-resolution models performed very well for both calibration and validation events in terms of three indexes: 1) the Nash-Sutcliffe efficiency, 2) the peak flow error, and 3) the volume error; indication of the advantage of using these models. The parameters obtained from high-resolution models could be directly used in the low-resolution models and performed well in the simulation of heavy rain and torrential rain and in the study area where sub-area routing is insignificant. Alternatively, sub-area routing should be considered and estimated approximately. The successful scale conversion from high spatial resolution to low spatial resolution is of great significance for the hydrological simulation of ungauged large areas.
Intensive anthropogenic activities can lead to soil heavy metal contamination resulting in potential risks to the environment and to human health. To reveal the concentrations, speciation, sources, pollution level, and ecological risk of heavy metals in vegetable garden soil, a total of 136 soil samples were collected from three vegetable production fields in the suburbs of Xianyang City, Northwest China. These samples were analyzed by inductively coupled plasma- atomic emission spectrometry and atomic fluorescence spectrometry. The results showed that the mean concentrations of Cd, Co, Cu, Mn, Pb, Zn, and Hg in vegetable garden soil were higher than the corresponding soil element background values of Shaanxi Province. The heavy metals studied in vegetable garden soil were primarily found in the residual fraction, averaging from 31.26% (Pb) to 90.23% (Cr). Considering the non-residual fractions, the mobility or potential risk was in the order of Pb (68.74%)>Co (60.54%)>Mn (59.28%) >Cd (53.54%) ≫Ni (23.36%) >Zn (22.73%)>Cu (14.93%)>V (11.81%)>Cr (9.78%). Cr, Mn, Ni, V, and As in the studied soil were related to soilforming parent materials, while Cu, Hg, Zn, Cd, Co, and Pb were associated with the application of plastic films, fertilizers, and pesticides, as well as traffic emissions and industrial fumes. Cr, Ni, V, and As presented low contamination levels, whereas Co, Cu, Mn, Pb, and Zn levels were moderate, and Cd and Hg were high. Ecological risk was low for Co, Cr, Cu, Mn, Pb, Zn, and As, with high risk observed for Cd and Hg. The overall pollution level and ecological risk of these heavy metals were high.
The purpose of this work is to study the weathering processes of the granulite rocks of the Serre Massif (southern Calabria, Italy) using a multidisciplinary approach based on field studies, geochemical modeling, and minero-petrographical analyses. The granulite rocks are plagioclase-rich with minor amphibole, clinopyroxene, orthopyroxene, biotite, and garnet and their texture are coarse-grained. The reaction path modeling was performed to simulate the evolution of groundwaters upon interaction with local granulite by means of the software package EQ3/6, version 8.0a. Simulations were performed in kinetic (time) mode under a closed system at a constant temperature of 11.5°C, (which reproduces the average temperature of local area) and fixing the fugacity of CO2 at 10–2.34 bar (mean value). During the most advanced stage of weathering the main mineralogical changes are: partial destruction and transformation of biotite and plagioclase associated with neoformation of ferruginous products and secondary clay minerals producing a change in the origin rock fabric. The secondary solid phases observed during the geochemical modeling (kaolinite, vermiculite and ferrihydrite) are similar to those found in this natural system. Thus, the soil-like material mainly characterized by mostly sand to gravel grain-size fractions is the final result of the weathering processes.