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Suitability of distributions for standard precipitation and evapotranspiration index over meteorologically homogeneous zones of India

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TLDR
In this paper, the performance of a group of candidate probability distributions over seven meteorologically homogeneous zones and all over India using high resolution (0.25°) gridded daily precipitation data from India Meteorological Department (IMD).
Abstract
The Standardised Precipitation and Evapotranspiration Index (SPEI) became one of the popular drought indices in the context of increasing temperatures under global warming in recent periods. The SPEI is estimated by fitting a probability distribution for the difference between precipitation (P) and potential evapotranspiration (PET), which represents the climatic water balance. The choice of an inappropriate probability distribution may lead to bias in the index values leading to distorted drought severity. Till date, none of the studies have focused on the suitability of the probability distribution for SPEI over India. The objective of the present study is to compare and evaluate the performance of a group of candidate probability distributions over seven meteorologically homogeneous zones and all over India using high resolution (0.25°) gridded daily precipitation data from India Meteorological Department (IMD). The Kolmogorov–Smirnov (K–S) test was used to test the goodness-of-fit for (P–PET) and Akaike Information Criterion (AIC) was used to obtain the relative distribution rankings for each grid point. The results of the study suggest that Pearson type III distribution has performed better than other distributions, significantly for shorter time scales and slightly for longer time scales, for each meteorological homogeneous zone based on K–S test. Also, for shorter time scales, Pearson type III distribution has been observed to be significantly better based on AIC with 82.89% and 71.91% grid points for 3 and 6 months, respectively. However, the relative ranking by AIC revealed GEV distribution as the best fit for SPEI values all over India for longer time scales with total grid points as 50.26%, and 58.81% for 12- and 24-month time scales respectively. Pearson type III distribution for shorter time scales (3 and 6 months) and GEV distribution for longer time scales (12 and 24 months) have been identified as the best distributions for fitting SPEI for Indian case study. Comparison of GEV based SPEI with remote sensing-based drought severity index (DSI) for drought events indicated concordance for most of regions in India. Also, SPEI is evaluated to test its capability to represent seasonality and its performance has been compared with Standardised Precipitation Anomaly Index (SPAI) which is known to represent seasonality well.

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Citations
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High-Resolution Near Real-Time Drought Monitoring in South Asia

TL;DR: A high-resolution bias-corrected precipitation and temperature data that can be used to monitor near real-time drought conditions over South Asia and can effectively capture observed drought conditions as shown by the satellite-based drought estimates.
Journal ArticleDOI

A multi-scale daily SPEI dataset for drought characterization at observation stations over mainland China from 1961 to 2018

TL;DR: Wang et al. as mentioned in this paper developed a daily standardized precipitation evapotranspiration index (SPEI) dataset to overcome the shortcoming of the coarse temporal scale of monthly SPEI.
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Development of hydro-meteorological drought index under climate change - Semi-arid river basin of Peninsular India

TL;DR: In this paper, the authors proposed a new drought index that combines both meteorological and hydrological drought characteristics at catchment scale, which considers the hydrologically calibrated AET to account for the water use in addition to meteorological effect.
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Bayesian Network based modeling of regional rainfall from multiple local meteorological drivers

TL;DR: In this article, the conditional independence structure between regional monthly rainfall and several local meteorological drivers (probable predictors) to develop a parsimonious prediction model in the framework of Bayesian Networks (BN) was established.
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Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors

TL;DR: In this article, the authors explored the potential of the Deep Learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, relative humidity, evaporation, soil moisture and geopotential height.
References
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Journal ArticleDOI

A new look at the statistical model identification

TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Journal ArticleDOI

Natural evaporation from open water, bare soil and grass

TL;DR: It is shown that a satisfactory account can be given of open water evaporation at four widely spaced sites in America and Europe, the results for bare soil receive a reasonable check in India, and application of theresults for turf shows good agreement with estimates of evapolation from catchment areas in the British Isles.

The relationship of drought frequency and duration to time scales

TL;DR: The definition of drought has continually been a stumbling block for drought monitoring and analysis as mentioned in this paper, mainly related to the time period over which deficits accumulate and to the connection of the deficit in precipitation to deficits in usable water sources and the impacts that ensue.
Journal ArticleDOI

A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index

TL;DR: In this article, a new climatic drought index, the standardized precipitation evapotranspiration index (SPEI), is proposed, which combines multiscalar character with the capacity to include the effects of temperature variability on drought assessment.
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