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This study investigated the effect of NaCl stress on Na+ and K+ absorption and transport by roots, nitrogen and phosphorus content in leaves, PSII photochemical activity and reactive oxygen species (ROS) in leaves of mulberry own-root seedlings and grafted seedlings. To determine the response, own-root seedlings of a high yielding mulberry cultivar, Tieba mulberry (Morus alba L.), and the grafted seedlings, obtained by using Qinglong mulberry with high salt tolerance as rootstock and Tieba mulberry as scion, were used. The Na+ content in roots and leaves of grafted seedlings was significantly lower than that in own-root seedlings under salt stress; while K+ content in roots and leaves of grafted seedlings was significantly higher than that in own-root seedlings. The root activity in grafted seedlings was significantly higher than that in own-root seedlings, as well as the content of nitrogen, phosphorous and water. PSII photochemical activity in leaves of grafted seedlings was less significantly affected by salt stress compared to own-root seedlings. The electron transport at the acceptor side of PSII from QA to QB was less affected by salt stress, which resulted in a significantly lower ROS content in leaves of grafted seedlings than that of own-root seedlings. Therefore, grafting high-yielding and good-quality Tieba mulberry with salt tolerant Qinglong mulberry as rootstock showed a relatively high salt tolerance. This may be because (1) the root system of rootstock presented high Na+ resistance and has selective absorption capacity for Na+ and K+ (2) the root system of rootstock prevented excess Na+ from being transported to aerial parts in order to reduce adverse effects of Na+ (3) the root system of rootstock had enhanced root activity under salt stress, which accelerated water and nutrient absorption (4) the leaves of grafted seedlings had higher PSII photochemical activity and electron transport rate compared with those of own-root seedlings under salt stress, which effectively reduced ROS burst mediated by photosynthesis and reduced oxidative damage.

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Tropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment factors [daytime surface temperature (LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude, and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of precipitation is achieved with the geographically weighted regression model (GWR) and the backward-propagation artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling. The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within Hubei Province from 0.25° (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through 2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The high-resolution precipitation datasets obtained with the proposed downscaling model with GWR relying on the NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.

Loess tilled surface is the geographical unit for the quantitative study of the soil erosion process. Contour tillage has been proved to be the most effictive measure for soil and water conservation in the Chinese Loess Plateau. Studies on the spatial heterogeneity of loess tilled surfaces will contribute to the understanding of the mechanism of erosion evolution. For this, a laboratory experiment was performed on contour tilled sloping surfaces where the hydrological-erosive processes were simulated. At different erosion stages, point cloud measurements were made using a terrestrial laser scanning system (TLS); then the heterogeneity depending on detrending and directionality were analysed by both the semivariogram method and the rescaled range analysis method. Results showed that: (1) the fractal dimensions D S and D R were equivalent when determined from either the semivariogram method or the rescaled range analysis method, although the semivariogram method appears to be more reliable in identifying the specific stage of the erosion evolution process; (2) the contour tilled microtopographies had an anisotropy behavior depending on direction; (3) the fractal dimension (either D S or D R ) in different erosion stages was less than 1.5, which indicates that the microtopography of the sloping surface exhibits characteristics of persistent fractional Brownian motion and positive spatial autocorrelation. Irrespective of tillage measure and slope percentage, the sloping surface can be regarded as having random roughness. The results reveals a quantitative relationship between microtopography and sloping erosion. Also, it may provide guidance for further studies regarding the spatial variability and heterogeneity of various tilled slopes on the microtopographic scale. ARTICLE HISTORY

Abstract:As the era of big data approaches, big data has attracted increasing amounts of attention from researchers. Various types of studies have been conducted and these studies have focused particularly on the management, organization, and correlation of data and calculations using data. Most studies involving big data address applications in scientific, commercial, and ecological fields. However, the application of big data to government management is also needed. This paper examines the application of multi-source government data to urban construction and supervision in Tianjin, China. The analytic hierarchy process and a new approach called the correlation degree algorithm are introduced to calculate the degree of correlation between different approval items in one construction project and between different construction projects. The results show that more than 75% of the construction projects and their approval items are highly correlated. The results of this study suggest that most of the examined construction projects are well supervised, have relatively high probabilities of satisfying the relevant legal requirements, and observe their initial planning schemes.

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

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