Spatio-temporal variation of the wet-dry conditions from 1961 to 2015 in China

Han

et al. 2019

Water

Our knowledge of the similarities and differences in ecological systems is vital to understanding the co-evolution of ecological factors. This study proposes a multi-dimensional hydro-climatic similarity and classification framework based on Budyko theory. The framework employs the dryness index (DI), evaporative index (EI), and an empirical parameter (ω) to further sub-divide four climatic zones (humid, semi-humid, semi-arid, and arid zones) in terms of DI. A criterion that define the similarities between stations is proposed to verify the classification to obtain optimal results. This method is applied to Mainland China, and 637 stations are adopted for continental-scale classification experiments. The point cloud of the Budyko curve for all the stations in Mainland China is plotted. We find that the hydrothermal conditions of the vertically distributed stations on the Budyko curve can be quite different in the same climatic zone when DI < 4.0. The higher the vertical locations of the stations on the Budyko curve are, the drier and colder the climates and corresponding natural landscapes. Under the proposed hydro-climatic classification framework, the four climatic zones are further divided into 17 sub-regions, and the hydrothermal conditions for each sub-region are discussed. The results suggest that regional differences of long-term water balance are resulted by not only mean annual hydrothermal factors and catchment forms but also annual distribution of hydrothermal factors. Our framework can provide hydrologically-based classification across continental scale and, thus, provide a profound understanding of hydrothermal conditions of continental-scale hydrological cycles.

Section: General Classification Resultssupporting

confidence: 72%

A Multi-Dimensional Hydro-Climatic Similarity and Classification Framework Based on Budyko Theory for Continental-Scale Applications in China

Han

et al. 2019

Water

“…The scPDSI anomaly showed a significant increasing trend with the slope value of 0.024/yr (R = 0.347, Z c = 2.54), indicating the YZR basin was, overall, becoming wetter during the study period. Yuan et al (2017) used the ration of potential evapotranspiration to precipitation to detect spatial and temporal variations of the wet-dry condition over China, and concluded that the degrees of wetness in the Qinghai-Tibet Plateau had substantially increased from 1961 to 2015 [85]. Owing to the better capability of the LOWESS method to capture the intrinsic variation trend by smoothing time series, an obvious climate transition period from wet to dry during the mid 1990s was detected (brown line in Figure 2).…”

Section: Temporal Variation Analysismentioning

confidence: 99%

Spatiotemporal Variation of Drought and Associated Multi-Scale Response to Climate Change over the Yarlung Zangbo River Basin of Qinghai–Tibet Plateau, China

Shan

et al. 2019

Remote Sensing

Drought is one of the most widespread and threatening natural disasters in the world, which has terrible impacts on agricultural irrigation and production, ecological environment, and socioeconomic development. As a critical ecologically fragile area located in southwest China, the Yarlung Zangbo River (YZR) basin is sensitive and vulnerable to climate change and human activities. Hence, this study focused on the YZR basin and attempted to investigate the spatiotemporal variations of drought and associated multi-scale response to climate change based on the scPDSI (self-calibrating Palmer drought severity index) and CRU (climate research unit) data. Results showed that: (1) The YZR basin has experienced an overall wetting process from 1956 to 2015, while a distinct transition period in the mid 1990s (from wet to dry) was detected by multiple statistical methods. (2) Considering the spatial variation of the scPDSI, areas showing the significantly wetting process with increasing scPDSI values were mostly located in the arid upstream and midstream regions, which accounted for over 48% area of the YZR basin, while areas exhibiting the drying tendency with decreasing scPDSI values were mainly concentrated in the humid southern part of the YZR basin, dominating the transition period from wet to dry, to which more attention should be paid. (3) By using the EEMD (ensemble empirical mode decomposition) method, the scPDSI over the YZR basin showed quasi-3-year and quasi-9-year cycles at the inter-annual scale, while quasi-15-year and quasi-56-year cycles were detected at the inter-decadal scale. The reconstructed inter-annual scale showed a better capability to represent the abrupt change characteristic of drought, which was also more influential to the original time series with a variance contribution of 55.3%, while the inter-decadal scale could be used to portray the long-term drought variation process with a relative lower variance contribution of 29.1%. (4) The multi-scale response of drought to climate change indicated that changes of precipitation (PRE) and diurnal temperature range (DTR) were the major driving factors in the drought variation at different time scales. Compared with potential evapotranspiration (PET), DTR was a much more important climate factor associated with drought variations by altering the energy balance, which is more obvious over the YZR basin distributed with extensive snow cover and glaciers. These findings could provide important implications for ecological environment protection and sustainable socioeconomic development in the YZR basin and other high mountain regions.

“…Based on this indicator, the dryness is always divided into five subtypes: hyper‐arid (AI < 0.05), arid (0.05 < AI < 0.2), semi‐arid (0.2 ≤ AI < 0.5), sub‐humid (0.5 ≤ AI < 0.75) and humid (AI > 0.75) (Middleton and Thomas, ; Fu and Feng, ; Huang et al ., ). It should also be noted that the ratio of PRE and PET is also known as humidity index (Yin et al ., ; Yuan et al ., ).…”

Section: Methodsmentioning

confidence: 97%

“…For example, Yuan et al . () indicated that precipitation dominated changes in the AI through linear correlation analysis in southern China, whereas Park et al . () suggested that relative humidity (RH) contributed the most to AI changes using multiple linear regression models.…”

Section: Introductionmentioning

confidence: 99%

Changes in aridity and its driving factors in China during 1961–2016

Intl Journal of Climatology

Wang

You

et al. 2018

Understanding the changes in aridity is crucial because of the substantial impacts it can have on human society. However, realistic aridity changes and their underlying climatic mechanisms demonstrate large uncertainties in China. Here we adopted the aridity index (AI) to assess the aridity changes in China, and we quantified the relative contributions of various climatic drivers (the precipitation, net radiation, wind speed, relative humidity, and air temperature) to AI changes based on the station data during 1961–2016. Our results showed that the arid/semi‐arid northwest China and Tibet Plateau experienced wetting trends, while western sub‐humid north and northeast China and western humid south China exhibited drying trends during 1961–2016. The dominant climatic drivers involved in the changing AI trends changed from arid to humid regions. In arid northwest China, increasing precipitation and decreasing wind speeds contributed the most to the wetting trends (0.024 and 0.017 in 56 years, respectively). In the semi‐arid Tibet Plateau, increasing precipitation dominated the wetting trends (0.045 in 56 years). However, in sub‐humid/humid north and northeast China and south China, decreasing wind speed and net radiation dominated the annual AI changes (0.061 and 0.092 in 56 years, respectively). In addition, AI of northwest China and the Tibet Plateau exhibited a significant abrupt change in 1987 and exhibited wetter states afterwards, which could be caused by the abrupt increase in precipitation in 1987. This study could improve current understanding of the aridity changes in China and help adapt its potential impacts.