Drought Assessment and Projection under Climate Change: A Case Study in the Middle and Lower Jinsha River Basin

Abstract

To gain an insight into the spatiotemporal variability of drought events and to assess trends under future climate change scenarios are fundamental for making sound mitigation and adaptation strategies. A new drought index, standardized supply-demand water index (SSDI), has been proposed in this research. The SSDI describes drought from the view of water supply-demand relations using a simple water balance model. It was used to assess historical drought events in the middle and lower Jinsha river basin (MLJRB) located in the southwest China and applied to address the drought conditions in the MLJRB under current and future climates. The results showed the following: (1) The average drought area during 2001 to 2011 reached up to 9.9 × 10 3 km 2 , accounting for 35.4% of the whole farmland area in the MLJRB, which was about twice as the drought area during 1961 to 2000. The region for greater drought severity with more drought events and longer duration was mainly distributed in Dali, Chuxiong, Kunming, and Yuxi. (2) For the period 2021 to 2050, total drought area was projected to increase by 43.2%. The drought-prone regions could move further towards the northwest of the MLJRB.

The Spatial-Temporal Variation Characteristics of Natural Vegetation Drought in the Yangtze River Source Region, China

“…The SSDI describes the degree of deviation between dry and wet conditions based on the difference between water supply and demand [ 30 ]. The algorithm of this index is similar to SPEI, but it defines drought in a broad term, considering water resources shortage instead of climatological factors.…”

“…The algorithm of this index is similar to SPEI, but it defines drought in a broad term, considering water resources shortage instead of climatological factors. Taking crop evapotranspiration and effective precipitation as input, the SSDI has been used to identify the agricultural drought [ 30 ]. In the same way, it can be used to quantify the natural vegetation drought conditions.…”

“…The bivariate return periods (T) of DD vs. DS, DD vs. DP and DS vs. DP for subregions I-V and the YRSR were estimated according to Equation (30) and (31), and results are illustrated in Figure 15. It can be found that various pairs of drought variables can result in the same return period.…”

Projection of Future Extreme Precipitation and Flood Changes of the Jinsha River Basin in China Based on CMIP5 Climate Models

“…The Jinsha River Basin (JRB, 90°30′–105°15′ E and 24°36′–35°44′ N) has a total area of 473,200 km 2 , which is about 26% of the total drainage area of the Yangtze River Basin [ 20 ]. The Jinsha River runs through four provinces and an autonomous region (namely, Qinghai, Tibet, Yunnan, Sichuan and Guizhou).…”

Probabilistic Projections of Hydrological Droughts Through Convection‐Permitting Climate Simulations and Multimodel Hydrological Predictions

“…A variety of drought indicators have been developed for assessing the hydrological drought over the past decades (Zargar et al, 2011), including Palmer Hydrological Drought Index (PHDI) (Karl, 1986), Surface Water Supply Index (SWSI) (Shafer, 1982), Reconnaissance Drought Index (RDI) (Tsakiris et al, 2007), Standardized Runoff Index (SRI) (Shukla & Wood, 2008), Streamflow Drought Index (SDI) (Nalbantis & Tsakiris, 2009), Standardized Streamflow Index (SSI) (Vicente-Serrano et al, 2011), and Standardized Supply-demand Water Index (SSDI) (Yuan et al, 2017). All these indices were mainly used to analyze historical hydrological drought regimes based on the observations from meteorological and hydrological stations.…”