Regional drought assessment using improved precipitation records under auxiliary information - He Jiang (Tellus: Series A, Dynamic Meteorology and Oceanography, 2020)
Changes in the climate and weather conditions, as well as rising earth’s average temperature are likely to escalate deterioration of global drought occurrence. Drought is considered an interwoven natural disaster composed by a number of different factors, as for example agricultural, meteorological or hydrological. Hydrological drought estimation with regional accuracy is the most problematic and challenging issue. In order to monitor and characterize drought conditions, using Standardized Drought Indices (SDI) is recently the most frequently used practice. In this research article, we suggest an improved hydrological drought index that incorporates upgraded monthly rainfall estimation records, which play an important role in defining regional drought conditions, with regard to the global temperature rise. Rainfall is highly changeable even at a low distance and therefore should be also considered in precipitation estimation records because temporal rainfall records play a significant role in determining long-term rainfall shortages. Thus, the integration of regional aspect to the amount of rainfall is essential for accurate regional drought assessment. This research article proposes adding auxiliary data such as regional weights in order to make monthly rainfall records more accurate in relation to the dependency characteristics of temperature and rainfall records under regression and product estimation settings. Subsequently, we propose an innovative method of hydrological drought evaluation, a so-called Regionally Improved Weighted Standardized Drought Index (RIWSDI). We evaluated hydrological drought with the usage of RIWSDI at seven various meteorological regions situated in climatologically different areas in Pakistan. We assessed and compared the results using RIWSDI, Standardized Precipitation Index (SPI) on 3 and 12-month interval period on the basis of Pearson correlation. Under both parametric and non-parametric standardization, we discovered that there is a high positive correlation between RIWSDI and current methodology (SPI). To sum up, we proved that the upgraded estimations of rainfall are able to improve systems for monitoring droughts.