Site-specific estimates of precipitation can be used to assess crop productivity and identify areas vulnerable to crop damages caused by extreme weather events such as droughts and floods. Spatial interpolation of precipitation such as Parameter-elevation Regressions on Independent Slopes Model (PRISM) has been used to estimate precipitation in an area of interest. However, the reliability of spatial interpolation is often affected by the availability of precipitation measurements from weather stations in a given region especially under complex terrain conditions. Here we propose an alternative approach for site-specific estimation of precipitation using both radar reflectivity data and topographic features. At first, radar reflectivity data are used as inputs to an artificial neural network (ANN) for estimation of precipitation. These radar precipitations at each grid cell are used to represent the observations at virtual weather stations for spatial interpolation using PRISM. Furthermore, the radar precipitations are compared with the observations at actual weather stations for their bias correction. This approach is referred to as PRISM and Radar Estimation for Precipitation (PREP). A case study was conducted in Jeollabuk-do, South Korea to compare the degree of agreement between PREP and PRISM. It was found that PREP had higher degree of agreement for the daily estimates of precipitation than PRISM in the given region with a complex terrain including coast and mountains. For example, the root mean square error (RMSE) of precipitation estimates for PREP was 22.1% less than that for PRISM in 2020. PREP also had greater value of the critical success index (CSI) than PRISM especially under heavy precipitation events, e.g.,>180 mm, and no rainfall conditions. These findings indicate that the PREP would improve the reliability of site-specific estimates of precipitation, which would facilitate decision-making in agriculture and early warning of extreme weather events.

The strict Clean Air Action Plan has been in place by central and local government in China since 2013 to alleviate haze pollution. In response to implementation of the Plan, daytime PM2.5 (particulate matter with aerodynamic diameter less than 2.5 um) showed significant downward trends from 2015 to 2019, with the largest reduction during spring and winter in the North China Plain. Unlike PM2.5, O-3 (ozone) showed a general increasing trend, reaching 29.7 mu g m(-3) on summer afternoons. Increased O-3 and reduced PM2.5 simultaneously occurred in more than half of Chinese cities, increasing to approximately three-fourths in summer. Declining trends in both PM2.5 and O-3 occurred in only a few cities, varying from 19.1% of cities in summer to 33.7% in fall. Meteorological variables helped to decrease PM2.5 and O-3 in some cities and increase PM2.5 and O-3 in others, which is closely related to terrain. High wind speed and 24 h changing pressure favored PM2.5 dispersion and dilution, especially in winter in southern China. However, O-3 was mainly affected by 24 h maximum temperature over most cities. Soil temperature was found to be a key factor modulating air pollution. Its impact on PM2.5 concentrations depended largely on soil depth and seasons; spring and fall soil temperature at 80 cm below the surface had largely negative impacts. Compared with PM2.5, O-3 was more significantly affected by soil temperature, with the largest impact at 20 cm below the surface and with less seasonal variation. (C) 2020 Elsevier Ltd. All rights reserved.

The Durance watershed (14 000 km(2)), located in the French Alps, generates 10% of French hydropower and provides drinking water to 3 million people. The Catchment land surface model (CLSM), a distributed land surface model (LSM) with a multilayer, physically based snow model, has been applied in the upstream part of this watershed, where snowfall accounts for 50% of the precipitation. The CLSM subdivides the upper Durance watershed, where elevations range from 800 to 4000 m within 3580 km(2), into elementary catchments with an average area of 500 km(2). The authors first show the difference between the dynamics of the accumulation and ablation of the snow cover using Moderate Resolution Imaging Spectroradiometer (MODIS) images and snow-depth measurements. The extent of snow cover increases faster during accumulation than during ablation because melting occurs at preferential locations. This difference corresponds to the presence of a hysteresis in the snow-cover depletion curve of these catchments, and the CLSM was adapted by implementing such a hysteresis in the snow-cover depletion curve of the model. Different simulations were performed to assess the influence of the parameterizations on the water budget and the evolution of the extent of the snow cover. Using six gauging stations, the authors demonstrate that introducing a hysteresis in the snow-cover depletion curve improves melting dynamics. They conclude that their adaptation of the CLSM contributes to a better representation of snowpack dynamics in an LSM that enables mountainous catchments to be modeled for impact studies such as those of climate change.