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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.