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The integrated distributed hydrological model ParFlow-CLM was used to predict water and energy transport between subsurface, land surface, and atmosphere for the Stettbach headwater catchment, Germany. Based on this model, a global sensitivity analysis was performed using the Latin-Hypercube (LH) sampling strategy followed by the One-factor-At-a-Time (OAT) method to identify the most influential and interactive parameters affecting the main hydrologic processes. In total 12 parameters were evaluated including soil hydraulic properties, storage, Manning coefficient, leaf area index, stem area index, and aerodynamic resistance that characterize water and energy fluxes in soil and vegetation. In addition, the sensitivity analysis was also carried out for different slopes and meteorological conditions to test the transferability of the results to regions with other topographies and climates. Our results show that the simulated energy fluxes, i.e. latent heat flux and sensible heat flux are sensitive to the parameters such as wilting point, leaf area index, and stem area index, especially for steep slope and subarctic climate conditions. The simulated soil evaporation, plant transpiration, infiltration, and runoff, are most sensitive to soil porosity, the van Genuchten parameter n representing the soil pore size distribution, soil wilting point, and leaf area index. The subsurface soil water storage and groundwater storage are most sensitive to soil porosity, while the surface water storage was most sensitive to the soil roughness parameter. For the different slope and climate conditions, the rank order of input parameter sensitivity is consistent, but the magnitude of parameter sensitivity is very different. The strongest deviation in parameter sensitivity occurred for sensible heat flux under the different slope conditions as well as for transpiration under different climate conditions. Overall, this study provides an insight into the most important input parameters that control hydrological fluxes and how the simulated variables vary with the change in parameter values, which can improve our understanding of the key processes in the model and help us to reduce the computational demands of completing multiple simulations of expensive domains.

The concrete abrasion monitoring of water launch constructions is the key and hard factor in hydraulic engineering. With the development of technology, the present concrete abrasion monitoring techniques have made excellent progress; however, there are still some problems, such as the small monitoring range, and it is hard to totally mirror the diploma of abrasion damage. The smart sensing tools based totally on concrete abrasion is greater and extra used in engineering dimension due to the fact of its excessive precision and robust anti-interference ability. Based on the alive analysis accessory for accurate abrasion, this cardboard establishes the aegis ecology association anatomy of baptize barrage structures, selects the abstinent chafe advice of the project, incorporates out coaching modelling thru the education set, and forecasts the take a look at set. Through theoretical evaluation and accuracy verification test, it can be concluded that the smart sensing tools based totally on concrete abrasion can be used to measure the floor modifications of concrete materials, the size accuracy can attain 0.1 mm level, and the monitoring accuracy price can attain 95%, which can meet the necessities of concrete abrasion depth monitoring in hydraulic engineering.

Impervious surfaces (IS) constitute a quantifiable metric for urbanization and ecological environmental assessment. This study estimated the IS in the subtropical coastal area of Xiamen, southeastern China, from TM/OLI images obtained in 1994, 2000, 2004, 2010, and 2015. Two-dimension box-counting and two-dimension multifractal box-counting method were employed to quantitatively characterize the fractal and multifractal features of the spatial patterns of the IS. Results suggest that the spatial patterns observed during the study period are typical fractal structures with scale invariance, and the fractal dimension reveals the spatio-temporal complexity. Increasing the pixel binarization threshold decreases the spatial complexity of the IS pattern. The increasing dimension values over time show that the IS patterns become more complex and the spatial distribution becomes more clustered from 1994 to 2015. The two-dimension multifractal approach can transform an irregular IS pattern into a compact form and amplify small differences between different data series. The results revealed multifractality in the five study years, which varied throughout the study period, but the probability distribution shows a slightly decreasing trend. The probability of a given pixel having a high IS-fraction is consistently high in the study area, as indicated by the positive ratio between the regions where the probability measure appears most concentrated and most sparse.

Coastlines change with urbanization, and methods to extract coastlines have been previously reported. However, comparisons of these methods are rare. Based on remote sensing image, methods of coastline extraction, namely, the visual interpretation method, the threshold segmentation method, improved normalized water indexes and edge detection algorithms and were studied in Xiamen City, China. The best method to extract coastlines was then determined. The results show that the visual interpretation method for coastline extraction was inefficient. The threshold segmentation method was suitable for small-scale, but not large-scale, coastline extraction, based on coastline area. Improved normalized water indexes were insensitive to sediment shadows. The Sobel method (edge detection algorithms) was suitable for large-scale coastline extraction but could yield false edges. Finally, the block classification method, which combines the advantages of different extraction methods, specifically the threshold segmentation method and improved normalized water indexes, was studied. The results of this study show that coastline extraction by the block classification method is easier and produces better results than coastline extraction by other methods. Therefore, block classification is recommended for the study of coastlines and coastal ecology in large areas.

In order to investigate the spatial–temporal variability of land surface temperature (LST) spatial distribution in the context of rapid urbanization, we introduced the multifractal detrended fluctuation analysis (MFDFA) to the LST patterns in Xiamen city and Xiamen Island, China, during 1994–2015. Results reveal the almost same long-range dependence of the LST spatial pattern both in Xiamen city and Xiamen Island. LST has a long memory for a certain spatial range of LST values, such that a large increment in LST value is likely to be followed by a large increment in LST values of a certain spatial range. On the other hand, the LST spatial pattern possesses a multifractal nature, which shows an increasing trend over time as urbanization increased both in the whole study area and in the Xiamen Island. The irregularity of fractal structure exhibits a similar change from 1994 to 2015 in Xiamen city, as revealed by the multifractal spectrum with left-hook shapes. However, the multifractal spectrums exhibit different shapes for different study years in Xiamen Island, capturing the evolution from right-hook shape to left-hook shape and finally to a symmetrical shape. The difference in land surface change in small spatial scale leads to the variation in multifractal parameters. Meantime, differentiated from the previous study, we found that this long-range dependence is probably influenced by the natural factors, such as the local climate, atmospheric circulation, and any other factors.

Numerous studies have previously demonstrated the positive correlation between urban impervious surfaces (UIS) and land surface temperature (LST) from single or multitemporal images, but little attention has been paid to the basic mechanisms governing the process of UIS–LST relationship change. This study investigates how the UIS–LST relationship evolves with urbanization and how the spatiotemporal change in UIS quantitatively affects LST in Shanghai, China. UIS change trajectory is introduced to determine the spatiotemporal UIS change, and indices of LST deviation degree and contribution ratio are applied to determine the quantitative effect of spatiotemporal UIS change on LST. The results show a positive UIS–LST correlation during 1997 to 2010. The changes in the standard deviation of mean LST for corresponding UIS types suggest that the mean LST of all UIS types experienced broad variability in 1997, with these variations decreasing during 1997 to 2007 and then increasing in 2010. The trajector...

The plant metabolome is characterized by extensive diversity and is often regarded as a bridge between genome and phenome. Here we report metabolic and phenotypic genome-wide studies (mGWAS and pGWAS) in rice grain that, in addition to previous metabolic GWAS in rice leaf and maize kernel, show both distinct and overlapping aspects of genetic control of metabolism within and between species. We identify new candidate genes potentially influencing important metabolic and/or morphological traits. We show that the differential genetic architecture of rice metabolism between different tissues is in part determined by tissue specific expression. Using parallel mGWAS and pGWAS we identify new candidate genes potentially responsible for variation in traits such as grain colour and size, and provide evidence of metabotype-phenotype linkage. Our study demonstrates a powerful strategy for interactive functional genomics and metabolomics in plants, especially the cloning of minor QTLs for complex phenotypic traits., The plant metabolome is often considered as a bridge between genomic and phenotypic variation. Here, the authors perform metabolic and phenotypic genome-wide association studies in rice grain, and compare to prior studies on rice leaf and maize kernel, to demonstrate a new strategy to investigate complex traits.

Peatlands play a significant role in global carbon (C) cycles. Through incubation experiments of peat samples selected in the discontinuous permafrost zone of the Great Hing’an Mountains, we studied the effects of temperature and moisture on potential CO2 emission. Rates of C mineralization decreased with soil depth, increased with temperature, and reached the highest rates at 60% WHC (water holding capacity) at the same temperature. Total C mineralization ranged from 10.3 to 95.0 mg under the treatments for 40 d incubation and was significantly affected by soil temperature and moisture (P < 0.001). Calculated Q 10 values ranged from 2.1 to 2.7, and the average values could be used for multiple regression functions. We successfully predicted C mineralization as a function of incubation time, temperature, and moisture (P < 0.001) and estimated the peat C mineralization under future climate scenarios. The results indicated that C mineralization of peatlands would be accelerated under warming temperature. The peatlands in our study area would be a potential CO2 source to the atmosphere with future climatic changes.

Boreal peatlands represent a large global carbon pool. The relationships between carbon mineralization, soil temperature and moisture in the permafrost peatlands of the Great Hing'an Mountains, China, were examined. The CO2 emissions were measured during laboratory incubations of samples from four sites under different temperatures (5, 10, 15, and 20 degrees C) and moisture contents (0%, 30%, 60%, 100% water holding capacity (WHC) and completely water saturated). Total carbon mineralization ranged from 15.51 to 112.92 mg C under the treatments for all sites. Carbon mineralization rates decreased with soil depth, increased with temperature, and reached the highest at 60% WHC at the same temperature. The calculated temperature coefficient (Q10) values ranged from 1.84 to 2.51 with the soil depths and moisture. However, the values were not significantly affected by soil moisture and depth for all sites due to the different peat properties (P > 0.05). We found that the carbon mineralization could be successfully predicted as a two-compartment function with temperature and moisture (R2 > 0.96) and total carbon mineralization was significantly affected by temperature and moisture (P < 0.05). Thus, temperature and moisture would play important roles in carbon mineralization of permafrost peatlands in the Great Hing'an Mountains, indicating that the permafrost peatlands would be sensitive to the environment change, and the permafrost peatlands would be potentially mineralized under future climate change.

Studying the changes of species diversity in plant communities along latitude gradients is important to discover the correlation between biodiversity and environmental factors. Along the main ridges of the Great Xing’an Mountains, 12 natural permafrost wetlands in the valleys were investigated from north to south. Latitudinal changes in species diversity were analyzed with regressive analysis. About 150 plant species were recorded and were found to be in the 12 permafrost wetland plant communities. Most plants belong to the Compositae or Gramineae. The number of family, genus and species increased significantly in the herb layer with decreasing latitude ( P P

the objective of this paper is to study the spatial distribution of the Aridity Index in China during the period 1991-2000. Besides, the arid and humid is classified based on the Thornthwaite method, with the help of the climatic data including the monthly precipitation and temperature from 193 meteorological stations. The results showed that the temperature and precipitation was decreasing from the southeast to northwest. The potential evapotranspiration was higher in west than in the south. The aridity index was higher in southeast than in the northwest. The Penman method boundary was from the Liaoning Province to the southern Qinghai-Tibet Platean. The Thornthwaite boundary is low latitudes, which is from Shandong Province to Sichuan Province.

by the Excel, using the date of runoff in the year 2005 and 2006, the annual hydrological changes was analyzed in the Emuer River basin. The result showed that the maximum flow appeared rather at the beginning of the June than at the time when the maximum rainfall or the snow melting happened. The runoff was retardance when the snow began to melt. During the maximum rainfall periods, the maximum runoff did not appear because the evaporation was comparatively large and the plants needed more water. So the water of snow melting was important to the runoff in this basin. By Correlation Analysis and Curve Estimation of SPSS13.0, the result showed that there were significant relationship (p 0.05) between the runoff and rainfall, but the rainfall had impact on the runoff. Also we found that using the monthly mean runoff and the monthly mean rainfall to analysis the relationship of the runoff and rainfall was more reasonable than the daily data. The Curve Estimation showed that the runoff and rainfall had S relationship.

The response of stores of carbon in peatland to global warming is a major uncertainty in predicting the future carbon budget. We used a short-term laboratory incubation to simulate effects on the potential CO 2 emission of peatland soil in Mohe, China under soil temperature (5, 10 15 and 20degC) and soil moisture (0, 30, 60, 100%WHC and completely water saturated). The rates of peat carbon mineralized decreased greatly in the earlier phase, but became stabile after 20 days and total carbon mineralization ranged 20.04 to 112.92 mg across sites and experiment treatments. Carbon mineralization rates decreased with soil depth, increased with temperature and reached highest rates at 60%WHC at the same temperature for all treatments. The calculated Q 10 values ranged from 1.878 to 2.181, varying with the soil depths and soil moisture. The sensitivity of C flux to moisture depend on temperature. However the Q 10 was not significantly affected by soil moisture and depth. We developed a two compartment model to predict the measured CO 2 emission as a multiplicative function of temperature and moisture and the model predicted C flux very well (R 2 >0.888, P