Spatial and Temporal Pattern Assessment of Meteorological Drought in Tumakuru District of Karnataka during 1951-2019 using Standardized Precipitation Index

Abstract

Tumakuru district of Karnataka state is situated in the semiarid climatic region which is considered most sensitive area/zone in response to climate change. This study analyses the spatio-temporal changes of meteorological drought in the Tumakuru district during 1951-2019. To understand the spatial and temporal characteristics of drought, annual and seasonal drought trends were examined using standardized precipitation index. Based on three-seasons, pre-monsoon (January -May), southwest monsoon (June -September), northeast monsoon (October-December) the annual SPI were calculated. Characteristics, classification and the threshold value of drought classes were determined. The correlation between precipitation and SPI was similar and showed the strong correlation. The wettest (1962 & 2006), and driest years (1968,1954, 1965, & 1976) during the study were identified. The higher frequency distribution of the driest years addresses roughly 57% drought years detected for Madhugiri and Tumakuru (1) in SW-monsoon of the total years of study, in pre-monsoon Kunigal have 21.73%, and in NE-monsoon it was 50.72 % at Kunigal. In the district southwest monsoon is long spell rainy season which is contributing 54% of total rainfall and advantageous for early stage of Rabi and maturation of Kharif crops. Maximum evaporation from soil and vegetation measured by potential evaporation which is the criteria of the water consumption by crops. In the event of monsoon failure and very less rainfall during southwest monsoon, farmers could not able to sow new crops and existing rain-fed crops would be decimated.

Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland

“…According to results of drought mapping, the IDW method was the most useful for assessing drought. In turn, Kumar et al [ 68 ] chose the IDW method to analyze drought areas in India over 1951–2019 on the basis of SPI. The correlation between precipitation data and SPI was strong, and the results of mapping show that drought occurred mainly in southwest areas.…”

Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America

“…Several drought indices have been developed since the 1960s for monitoring and forecasting droughts based on individual or multiple meteorological or hydrological variables (e.g., rainfall, evapotranspiration, streamflow, groundwater level, and runoff) [ 18 ]. It is worth mentioning that among all drought indexes, the "standardized precipitation index (SPI)", proposed by [ 19 ], is broadly used to quantify drought across the world and receives considerable attention from researchers [ 5 , 20 , 21 ]. Based on SPI principles, several hydrological parameters have been derived and widely used to assess and monitor droughts, such as standardized streamflow index (SSI) [ 22 , 23 ], standardized hydrological drought index (SHDI) [ 24 ], standardized groundwater level index (SGI) [ 25 ], and standardized runoff index (SRI) [ 26 , 27 ].…”