Identification and characteristics analysis of Meiyu in Anhui Province based on the National Standard of Meiyu monitoring indices

Abstract

Meiyu is the term used to depict the consecutive rainy weather advancing in the months before the flooding season in East Asia. However, the temporal-spatial climatic characteristics of Meiyu can be differently specified by different evaluation criteria. In this study, we employ both the atmospheric circulation conditions and meteorological factors to identify the spatial characteristics of precipitation of Meiyu in Anhui Province using the collected data of 1957–2020. We further conduct a comparison analysis of the precipitation characteristics in the northern Huaihe River of Aihui province (NHA) with Meiyu rainfall features in two other regions: south of the Yangtze River in Anhui Province (SYA) and the region between the Yangtze River and Huaihe River in Anhui Province (YHA). Finally, the relation between the intensity index between Meiyu and flood or drought is investigated. The results showed that the climatic feature in NHA is a transitional region between Meiyu and non-Meiyu. Also, we proposed a Meiyu intensity index determined by the precipitation amount, intensity, and days of heavy rain. This index performs better than the Meiyu intensity index of National Standard in terms of flood and drought identification.

System Structure–Based Drought Disaster Risk Assessment Using Remote Sensing and Field Experiment Data

“…Based on the reference values of maize crop parameters in the AquaCrop mode ual [47], this study further adopts two-season field experiment data to calibrate and partial crop parameters, which change with the actual planting conditions (Ta Firstly, the simulated canopy cover degree (Figure 11), aboveground biomass (Figu and biomass yield (Table 5) of summer maize in Bengbu using the calibrated param indicate that the simulated results are all highly consistent with the field measured v the simulation accuracy meets the requirements. Furthermore, the simulated yield of maize caused by severe droughts in Bengbu from 1982 to 2017 (Table 6) are all cordance with the historical drought situations [41,55] and relevant studies [40,52, addition, the calibrated maize crop parameters in this study (Table 2) are basicall sistent with the studies of Han et al [62], Wolka et al [63], and Wu et al [64], who ob the parameters by field experiments in the Heihe River Basin of China, the BokoleK watershed of southwest Ethiopia, and Wuwei City of northwest China, respec Therefore, it can be considered that the obtained crop parameters of summer mai the AquaCrop model in Table 2 are reasonable. Moreover, these parameters can be f verified and modified by continuous field experiments in future work.…”

“…According to the historical drought data in Bengbu, the period of 1990-1992 was three continuous drought years, 1994-1995 were the most severe drought years, and 2000-2001 was another period of serious drought, following 1978 and 1994 [15]. For instance, in 2001, the precipitation in Anhui Province was low; the flood season encountered an empty plum rain period [55]. On 27 July, the upstream water level of Bengbu Sluice declined to the lowest value for the same period in history [40].…”

Decline in net primary productivity caused by severe droughts: evidence from the Pearl River basin in China

“…Additionally, rainfall erosivity in some regions also shows an upward trend that the probability of water and soil erosion is increasing correspondingly (Peng & Wang 2012;Wang et al 2013), which further exacerbates drought conditions in the upstream. On the other hand, because the basin is located in the monsoon region, the climate is strongly influenced by global circulation patterns, such as the El Nino-Southern Oscillation (ENSO) (Deng et al 2018;Zhou et al 2021). Large-scale atmospheric circulation can induce drought changes, as it alters the amount of water vapor available for precipitation and could affect the key components of the hydrologic cycle, such as soil moisture and ETa (Liu et al 2013).…”

Identification of Meiyu process and spatiotemporal characteristics of different precipitation levels during the Meiyu period over the Yangtze-Huai River Basin

“…识别梅雨过程 [19][20] ， 但各地在确定区域入梅日和出 梅日时所采用的指标和阈值不尽相同 [8,21] 。伴随着 非典型梅雨的出现， 梅雨的降水规律和监测范围发 生变化， 梅雨判别标准也随之变化 [21][22] 。中国气象 局 2014 年印发的 《梅雨监测业务规定(试行)》 规范 了国家级和省级梅雨监测业务， 赵俊虎等 [20] 、 陈旭 等 [23] 和罗小杰等 [24] 基于此规定识别和分析江淮梅 雨。国家气候中心 2017 年发布的 《梅雨监测指标》 (GB/T 33671-2017)(以下简称 "国标" )统一了国家 和地方的梅雨监测业务， 国标采用逐日降水和气温 数据， 结合副高脊线位置识别梅雨过程， 适用于梅 雨监测、 预报预测、 评估及服务 [1,6,25] 。梅雨监测指标 相关的研究一类为直接使用国家气候中心提供的 梅雨监测数据分析梅雨异常特征及其成因 [4][5][6]26] ， 另 一类依据国标识别某局部区域的梅雨过程， 进而分 析梅雨监测指标的特征 [8,25,[27][28][29] 。 在气候变化和城市化背景下， 中国东部的降水 特征发生了变化， 主要表现为极端降水的频率和强 度明显增加， 连续降水及弱降水减少， 强降水增加， 空间异质性增强 [2,[30][31][32][33][34][35][36][37] ， 目前多是在全年或雨季( 4 域 [1,4] 用气候平均值补充 [1,38] 。最终有分布均匀的 239 个 代表站满足条件， Ⅰ区、 Ⅱ区和Ⅲ区分别有 53、 138 和 48 个站点(图 1)。 选用 NCEP/NCAR 逐日再分析资料集中的 500 hPa 位势高度场和纬向风场确定副高脊线位置 [1,8,25,39] ， 其水平分辨率为 2.5°×2.5°。下载网址为： ftp://ftp. cdc.noaa.gov/pub/Datasets。选用 1980、 1990、 2000、 2010 和 2020 年江淮流域土地利用/土地覆被变化 (LUCC)资料动态判别城市站点与农村站点 [40][41] ， 数 据空间分辨率为 1°×1°， 来源于中国科学院资源环 境科学与数据中心(https://www.resdc.cn/)。 1.2 梅雨过程识别及梅雨强度计算 1.2.1 梅雨过程识别 梅雨过程识别步骤简述如下(详细过程请参见 国标 [1] )： (1)…”

“…站点为城市站点， 否则为农村站点 [41] 。由一元线性 回归方程可得城市站点和农村站点强降水指标的变 化速率 k u 和 k r ， 城市化贡献率 C 计算公式为 [33,[40][41] ： 国标在统一国家和地方梅雨监测业务的同时 给出了 277 个梅雨监测代表站 [1] 。有学者用比国标 规定的子区域更小范围内的站点成功识别出区域 梅雨过程 [8,25,[27][28][29] 。本文使用江淮流域 239 个站点数 据识别的梅雨期长度和梅雨雨强与国家气候中心 的基本一致 [4] ， 说明使用国标识别梅雨过程并没有 长江三角洲地区极端降水的频率和强度 [13,[34][35] 。本 文发现城市化增加了梅雨期的强降水量(图 7~8)， 城市化主要通过城市热岛效应和城市下垫面变化 影响降水的强度和时间 [2,31] 。城市热岛效应是指城 市气温明显高于外围郊区及乡村的现象， 城市热岛 扰动大气边界层， 破坏大气层结构稳定性， 加之在 水汽充足且凝结核丰富或者其他有利的天气形势 下， 易形成对流云和对流性降水， 或对暴雨产生诱 导、 强化作用， 导致城市下风处的降水受此影响较 大 [18,[31][32][33] 。城市下垫面变化一方面通过改变地表能 量平衡， 使局地蒸发减少、 感热通量增加和边界层 水汽混合更均匀， 从而对局地降水产生影响 [18,31,35] ； 另一方面由于城市冠层结构的存在， 增大空气动力 学粗糙度， 扰动边界层水汽和能量过程， 阻滞降水 系统停留时间， 降低近地面风速， 迫使降水时间延 长， 导致降水量增加 [2,[30][31]35] [31,34,40,46] [2,[30][31]47]…”