A novel generalized combinative procedure for Multi-Scalar standardized drought Indices-The long average weighted joint aggregative criterion

Abstract

Drought hazards have complex climatic and spatio-temporal features. Therefore, its accurate monitoring is a challenging task in hydrological research. In recent, the use of standardized drought indices for drought monitoring is common in practice. However, the existence of several drought indices creates chaotic problems in data mining and decision making. This article presents a new weighting scheme for combining multiple drought indices. We propagated steady-state probabilities of Markov chain as weights in the Probabilistic Weighted Joint Aggregative Index (PWJADI) criterion. Hence, to aggregate drought characterization two or more indices, averaged long term behavior of drought classification states observed in the individual drought index is considered as a weighting characteristic. The proposed algorithm is rather general and can be used for any standardized type of drought indices. The new procedure is named as Long Averaged Weighted Joint Aggregative Criterion (LAWJAC). In this research, we focused on the three multi-scalar drought indices namely, Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Standardized Precipitation Temperature Index (SPTI). The selection of these indices is due to their similar computational procedures. In the evolution of LAWJAC, three meteorological stations of the Northern Area of Pakistan are considered. A comparison of LAWJAC is made with PWJADI. Results show significant deviations between existing and proposed methods. By the rationale of the proposed algorithm, these deviations strongly advocate the use of LAWJAC for more accuracy in drought characterization.

A novel spatially weighted accumulative procedure for regional drought monitoring

“…For example, Astore station has a SW condition, Bunji has ND, Gupis has SW while Chilas, Gilgit and Skardu have SD, ED and ND, respectively. For this scenario, the classes in each index (SPI, SPEI and SPTI) at different time scales were weighted by transient probabilities and steady-state probabilities (Ali et al, , 2020. Where the classes which received maximum weights among the indices concerning time scale and station had to select for their indices.…”

Characterization of Meteorological Drought Using Monte Carlo Feature Selection and Steady‐State Probabilities

“…Recently, Niaz et al [97] proposed a weighting scheme based on steady-state probabilities for selecting classes among the three indices (SPI, SPEI, and SPI). ese indices are correlated for a one-month time scale and present similar information for the six stations in the northern areas [85,96,117]. e mathematical detail of the weighting scheme is available in [76].…”

On The Reduction of Inaccuracies in Drought Monitoring - A Novel Blended Procedure for Standardized Type Drought Indicators

“…Under certain circumstances, some recent developments include Seasonally Combinative Regional Drought Indicator (SCRDI) (Ali et al, 2020c), Regionally Improved Weighted Standardized Drought Index (RIWSDI) (Jiang et al, 2020), Multi-Scalar Aggregative Standardized Precipitation Temperature Index (MASPTI) (Ali et al, 2020b), Probabilistic Weighted Joint Aggregative Index (PWJADI) (Ali et al, 2019a) and Long Averaged Weighted Joint Aggregative Criterion (LAWJAC) (Ali et al, 2020a).…”