Entropy‐based spatiotemporal patterns of precipitation regimes in the Huai River basin, China

Precipitation plays a fundamental role in terrestrial modelling and is extremely vulnerable to climate change. It is particularly challenging to describe precipitation due to its intermittency and fluctuation at nearly all temporal and spatial scales. Precipitation variability represents the degree of unevenness in its distribution or its lack of uniformity over different scales. This study examined the seasonal variability of meteorological station precipitation observations during 1961–2018 over mainland China using entropy theory. The intensity disorder index (IDI) and apportionment disorder index (ADI) were used to characterize the distribution unevenness, including the number of precipitation days, and precipitation amounts. Six special regions were selected according to their geographical location and climate conditions to show the regional differences of the disorder indices. At the daily scale, the regional mean IDI was 0.4081 for the eastern Tibetan Plateau and the regional mean ADI was 0.9984 for north China, which were the largest value among the six regions investigated. The regional mean IDI was 0.1128 and the regional mean ADI was 0.4071 both for the Yangtze River Basin, which were the smallest value among the six regions investigated. Temporal variations of the DIs were also detected. The IDI have decreased not significantly in most regions, except increasing by 0.0223/decade and 0.0129/decade in southwest China during nighttime and daytime, respectively. The ADI decreased by −0.0191, −0.0275, −0.0229, and −0.0203/decade and passed the significant test at .05 level for northwest, northeast, north China, and eastern Tibetan Plateau at the daily scale, respectively. The results quantify the precipitation unevenness over mainland China, which can be treated as a reference for water resources management and land surface model evaluation.

Section: Methodsmentioning

confidence: 99%

“…Alongside China's rapid economic development and continued environmental degradation, water shortages have become more common in recent decades. Researchers have evaluated precipitation variability based on entropy theory, but this has been limited to different regional scales (Zhao et al ., 2015; Zhang et al ., 2016; Cheng et al ., 2017).…”

Section: Introductionmentioning

confidence: 99%

Seasonal precipitation variability in mainland China based on entropy theory

Qin

Huang

et al. 2021

“…(2020) used the standardized variability index (SVI) within the range of [0,1] to evaluate the precipitation variability. Since then, the DI and SVI are used widely for precipitation variability assessment in different regions (Rodrigues da et al ., 2016; Zhang et al ., 2016; Roushangar et al ., 2019; Gu et al ., 2020; Guntu et al ., 2020; Li et al ., 2022; Sreeparvathy and Srinivas, 2022).…”

Section: Introductionmentioning

confidence: 99%

Temporal variability of precipitation over the Qinghai‐Tibetan Plateau and its surrounding areas in the last 40 years

Gao

et al. 2022

The Qinghai‐Tibet Plateau (QTP) plays a crucial role in regulating the climate and hydrology over Asia, and studies indicate that the QTP tends to be warm and humid as a whole. Although precipitation variability on the QTP has a great impact on floods, soil erosion, and droughts, it is poorly understood owing to sparse meteorological observations. This study has investigated the temporal variability of precipitation on the QTP and its surrounding areas on different time scales over the past 40 years using the standardized variability index (SVI) based on entropy as well as its changing trends. The results show that the variability of precipitation has different spatial patterns on different time scales. In general, the variability of both the precipitation days and amounts are low in the southeast and northwest corners of the plateau and high in the southwest and north. It was found that the number of light precipitation days in the high vegetation coverage area in the southeastern plateau decreased significantly, but increased greatly in the bare land and sparse vegetation coverage areas in the northern part of the plateau. Such changes are believed to be conducive for ecological restoration. Most importantly, in the southern foothills of the Himalayas, many light precipitation days have turned into moderate and heavy precipitation days, and a significant increase in annual precipitation has also been noted, which are likely to cause natural disasters considering the local topographical conditions, such as landslides and mudslides. Highlights In QTP, SVIME in May is closer to summer mode, indicating monsoon beginning have advanced to May. Extreme precipitation significantly influences precipitation variability. Daily SVIAE decreases in grassland/barren/sparsely vegetated land, good for ecology. Light rain days increased in bare land/sparse vegetation areas, benefiting vegetation restoration. In southern Himalayas, light rain changed to moderate/heavy rain, leading to natural disasters.

“…Zhang et al . (2016) studied the spatial and temporal variability of precipitation in the Huai River basin based on information entropy theory, and showed that the precipitation decreased from north to south. Wang et al .…”

Section: Introductionmentioning

confidence: 99%

“…Feng et al (2011) defined meteorological information entropy based on information entropy theory, and used it to assess uncertainty of the global temperature field under various spatial and temporal scales. Zhang et al (2016) studied the spatial and temporal variability of precipitation in the Huai River basin based on information entropy theory, and showed that the precipitation decreased from north to south. Wang et al (2019) found that, in Chengdu, the information entropy of precipitation in summer is greater than that in spring and autumn, and it decreases from west to east in space.…”

Section: Introductionmentioning

confidence: 99%

Information entropy‐based projection of temperature variability under the warming trend in China

Sun

Zhang

Yao

et al. 2022

Quantifying the predictability of temperature in the future is a priority to inform policy and adaptation planning. Based on information entropy theory, this study uses global climate models within the Coupled Model Intercomparison Project Phase 6 (CMIP6) to project spatiotemporal changes and variability of temperature in China in the 21st century under different shared socio-economic pathway scenarios (SSP2-4.5 and SSP5-8.5). Resultsshow that the multimodel ensembled values can accurately capture the spatiotemporal pattern of temperature in China. The warming rates of annual mean temperature in China under SSP2-4.5, SSP5-8.5 scenarios are 0.42 and 0.61 CÁdecade −1 in the near-term (2021-2050), 0.20 and 0.87 CÁdecade −1 in the long-term (2071-2100), respectively. Warming is found to be the greatest in winter. Northeast, northwest, and the first region of southwest are anticipated to increase particularly rapidly, with larger increases than the rest of China.The intrayear variability of temperature (the variability of monthly mean temperature within the year or different seasons) in China shows obvious latitudinal differences. The variability of monthly mean temperature exhibits a decreasing trend in parts of northeast and the first region of southwest, and an increasing trend in northwest and parts of north. This is mainly resultant from the variability of monthly mean temperatures within spring and autumn. And this trend is more significant under the SSP5-8.5 scenario than that under the SSP2-4.5 scenario. The interannual variability of temperature (the variability of annual or seasonal mean temperature) in China shows significant regional difference, and two obvious regions with extreme variability (northeast and the first region of southwest). The trend of variability increases and then decreases under the SSP2-4.5 scenario, whereas it keeps increasing under the SSP5-8.5 scenario. This study helps to understand the spatiotemporal characteristics of temperature variability in China in the 21st century.