Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies

Citation: Nandgude, N.; Singh, T.P.;
Nandgude, S.; Tiwari, M. Drought
Prediction: A Comprehensive Review
of Different Drought Prediction
Models and Adopted Technologies.
Sustainability 2023, 15, 11684.
https://doi.org/10.3390/su151511684
Academic Editor: Steve W. Lyon
Received: 4 June 2023
Revised: 24 July 2023
Accepted: 26 July 2023
Published: 28 July 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).sustainability
Review
Drought Prediction: A Comprehensive Review of Different
Drought Prediction Models and Adopted Technologies
Neeta Nandgude 1 , T. P. Singh 1,*, Sachin Nandgude 2 and Mukesh Tiwari 3
1 Symbiosis Institute of Geo-Informatics, Symbiosis International (Deemed University), Pune 411016, India;
[e-mail]
2 Department of Soil and Water Conservation Engineering, Mahatma Phule Krishi Vidyapeeth,
Rahuri 413722, India; [e-mail]
3 Department of Soil and Water Conservation Engineering, College of Agriculture Engineering and Technology,
Anand Agriculture University, Godhra 389001, India; [e-mail]
* Correspondence: [e-mail]
Abstract: Precipitation deficit conditions and temperature anomalies are responsible for the occur-
rence of various types of natural disasters that cause tremendous loss of human life and economy
of the country. Out of all natural disasters, drought is one of the most recurring and complex phe-
nomenons. Prediction of the onset of drought poses significant challenges to societies worldwide.
Drought occurrences occur across the world due to a variety of hydro-meteorological causes and
anomalies in sea surface temperature. This article aims to provide a comprehensive overview of the
fundamental concepts and characteristics of drought, its complex nature, and the various factors
that influence drought, drought indicators, and advanced drought prediction models. An extensive
survey is presented in the different drought prediction models employed in the literature, ranging
from statistical approaches to machine learning and deep learning models. It has been found that
advanced techniques like machine learning and deep learning models outperform traditional models
by improving drought prediction accuracy. This review article critically examines the advancements
in technology that have facilitated improved drought prediction, identifies the key challenges and
opportunities in the field of drought prediction, and identifies the key trends and topics that are
likely to give new directions to the future of drought prediction research. It explores the integration
of remote sensing data, meteorological observations, hydrological modeling, and climate indices for
enhanced accuracy. Under the frequently changing climate conditions, this comprehensive review
provides a valuable resource for researchers, practitioners, and policymakers engaged in drought
prediction and management and fosters a deeper understanding of their capabilities and limitations.
This article paves the way for more accurate and effective drought prediction strategies, contributing
to improved resilience and sustainable development in drought-prone regions.
Keywords: spatial data; drought prediction; machine learning; deep learning
1. Introduction
Variability in climatic conditions adversely impacts the global ecosystem. Precipitation
deficit conditions and anomalies in high temperatures are the most dreadful natural events.
Precipitation and land surface temperature anomalies are majorly responsible for the
occurrences of extreme weather and climatic events like droughts and heat waves. These
events adversely affect ecological, environmental, and socio-economic aspects around the
world. In recent years, climate change can lead to more dangerous and frequently occurring
natural calamities such as droughts, storms, floods, wildfires, and desertification. Out
of the total economic damage across the globe, 22% is due to these natural disasters [1 ].
Out of all natural hazards, drought is the most dreadful natural hazard [2 ,3]. Due to the
multiple climatic and meteorological driving factors [4 ] at different spatial or temporal
scales, drought has become the most complex phenomenon [5].
Sustainability 2023, 15, 11684. https://doi.org/10.3390/su151511684 https://www.mdpi.com/journal/sustainability

Sustainability 2023, 15, 11684 2 of 19
Drought is one of the deadliest natural phenomena that has devastating effects on
ecosystems and communities. Drought prediction models are essential for preparing for
and mitigating the impacts of drought and, subsequently, contingency planning. Early
drought prediction at local as well as global levels is becoming essential. Drought prediction
study requires a huge dataset at temporal and spatial scales, a selection of appropriate
models based on the available data, and computing resources. Most of the time, data
unavailability and the requirement of huge computing resources become key issues in
drought prediction research. So, this paper aims to highlight key aspects of drought
prediction research.
Drought: A Complex Phenomenon
Wilhite and Glantz [5 ] classified droughts into different types, like meteorological,
hydrological, agriculture, and socio-economic droughts [6, 7], which are shown in Figure 1.
Meteorological drought occurs due to deficit rainfall for a longer period, and it becomes
a key for the occurrence of other types of droughts [8]. Less precipitation, the rise in
temperature, increase in the evaporation rate, and reduction in soil moisture are key
indicators of agriculture drought triggers [ 9, 10 ]. Declination of stream-flow and reduction
in water within water bodies causes hydrological droughts [ 11 ], which later impacts the
social and economic aspects of society and causes socio-economic drought [12]. Many
studies revealed that drought events lead to reduction in total agriculture productivity,
uncontrollable socioeconomic losses, and ecosystem degradation [13,14]. Due to the multiple climatic and meteorological driving factors [4] at different spatial ortemporal scales, drought has become the most complex phenomenon [5].Drought is one of the deadliest natural phenomena that has devastating effects onecosystems and communities. Drought prediction models are essential for preparing forand mitigating the impacts of drought and, subsequently, contingency planning. Earlydrought prediction at local as well as global levels is becoming essential. Drought predic-tion study requires a huge dataset at temporal and spatial scales, a selection of appropriatemodels based on the available data, and computing resources. Most of the time, data un-availability and the requirement of huge computing resources become key issues indrought prediction research. So, this paper aims to highlight key aspects of drought pre-diction research.Drought: A Complex PhenomenonWilhite and Glantz [5] classified droughts into different types, like meteorological,hydrological, agriculture, and socio-economic droughts [6,7], which are shown in Figure1. Meteorological drought occurs due to deficit rainfall for a longer period, and it becomesa key for the occurrence of other types of droughts [8]. Less precipitation, the rise in tem-perature, increase in the evaporation rate, and reduction in soil moisture are key indica-tors of agriculture drought triggers [9,10]. Declination of stream-flow and reduction inwater within water bodies causes hydrological droughts [11], which later impacts the so-cial and economic aspects of society and causes socio-economic drought [12]. Many stud-ies revealed that drought events lead to reduction in total agriculture productivity, un-controllable socioeconomic losses, and ecosystem degradation [13,14]. Figure 1. Drought types and its propagation.Due to the complex nature of drought, its prediction has become a key challenge infront of researchers, planners, and decision and policymakers. The identification and as-sessment of drought-driving risk factors for planning and mitigation is becoming a vitalissue across the globe [15,16].The objective of this article is to offer a comprehensive overview of the key principlesand features of drought, highlighting its complex nature, the major driving factors thatcontribute to its occurrence, and the drought indicators used to measure drought. A thor-ough examination is conducted on a variety of drought prediction models found in theexisting literature, encompassing statistical approaches as well as machine learning anddeep learning models.This critical review article analyzes the technological advancements that have con-tributed to enhanced drought prediction, evaluates the main challenges and opportunitiesin this field, and identifies the emerging trends and topics that will shape the future ofdrought prediction research. It investigates the integration of remote sensing data, mete-orological observations, hydrological modeling, and climate indices to improve predictionaccuracy. By offering a comprehensive overview, this review serves as a valuable resourcefor researchers, practitioners, and policymakers involved in drought prediction and man-agement. The review will conclude by summarizing the current state of knowledge, aswell as challenging and identifying areas for future research. MeteorologicalDrought •Lessprecipitation•Hightemperature AgriculturalDrought •Temperature rise•Soil moisturereduction•Increased evapo-transpiration rate HydrologicalDrought •Stream flowdeclination•Low water levels inwater bodies Socio-EconomicDrought •Food insecurity•Water scarcity•Health issues
Figure 1. Drought types and its propagation.
Due to the complex nature of drought, its prediction has become a key challenge
in front of researchers, planners, and decision and policymakers. The identification and
assessment of drought-driving risk factors for planning and mitigation is becoming a vital
issue across the globe [15,16].
The objective of this article is to offer a comprehensive overview of the key principles
and features of drought, highlighting its complex nature, the major driving factors that
contribute to its occurrence, and the drought indicators used to measure drought. A
thorough examination is conducted on a variety of drought prediction models found in the
existing literature, encompassing statistical approaches as well as machine learning and
deep learning models.
This critical review article analyzes the technological advancements that have con-
tributed to enhanced drought prediction, evaluates the main challenges and opportunities
in this field, and identifies the emerging trends and topics that will shape the future of
drought prediction research. It investigates the integration of remote sensing data, meteo-
rological observations, hydrological modeling, and climate indices to improve prediction
accuracy. By offering a comprehensive overview, this review serves as a valuable resource
for researchers, practitioners, and policymakers involved in drought prediction and man-
agement. The review will conclude by summarizing the current state of knowledge, as well
as challenging and identifying areas for future research.

Sustainability 2023, 15, 11684 3 of 19
2. Methodology
This study provides a comprehensive analysis of the existing literature related to the
anticipation of drought through the utilization of advanced drought prediction methods.
The detailed methodology adopted for this article is presented in Figure 2. The research
involved an extensive search in various databases, including the Web of Science (WoS) Core
Collection Database, online journal databases, Google Scholar, and the Google search engine.
Several relevant keywords were employed during the search, such as drought prediction,
precipitation, temperature, ENSO, IOD, machine learning, deep learning, climatic indices,
and related terms. Various combinations of these keywords were also utilized, including
drought prediction + machine learning + ENSO + IOD, drought prediction + deep learning,
drought prediction + ENSO + IOD, and drought prediction + climatic indices. Search results
listed a total of 1110 research articles based on various keyword combinations. These papers
focus on application areas like agriculture, hydrology, climate change, natural hazards,
water resources, and the environment. The search was limited to studies published in the
last 20 years to ensure comprehensive coverage of recent research. involved an extensive search in various databases, including the Web of Science (WoS)Core Collection Database, online journal databases, Google Scholar, and the Googlesearch engine. Several relevant keywords were employed during the search, such asdrought prediction, precipitation, temperature, ENSO, IOD, machine learning, deeplearning, climatic indices, and related terms. Various combinations of these keywordswere also utilized, including drought prediction + machine learning + ENSO + IOD,drought prediction + deep learning, drought prediction + ENSO + IOD, and drought pre-diction + climatic indices. Search results listed a total of 1110 research articles based onvarious keyword combinations. These papers focus on application areas like agriculture,hydrology, climate change, natural hazards, water resources, and the environment. Thesearch was limited to studies published in the last 20 years to ensure comprehensive cov-erage of recent research.Research articles were screened based on the titles and abstracts of the identified ar-ticles for relevance. The inclusion criteria were studies that focused on various droughtprediction approaches, adopted technologies, and prediction accuracy. Discrepancies instudy selection were resolved through discussion and consensus. Studies focusing on spe-cific regions or using different methodologies were included to capture the diversity ofthe perspectives on drought. This paper restricted search results based on the publicationyear, a journal impact factor above two, the drought type, and the methodology adopted.Based on the above said criteria, a total of 131 papers were selected for this review paperto highlight drought prediction research. Figure 2. Methodology adopted for comprehensive review of drought. Conclusions Selected 131 papers were carefully examined for Comprehensive review of drought and technologies adopted for its prediction Inclusion criteria Selection of articles was based on Drought type, publication year, research area, Impact factor of journal, technology adopted and relevence of paper Search results Drought prediction + Machine learning+ ENSO+IOD: 1110 papers Keyword combinations Drought prediction + Machine learning + ENSO + IOD, Drought prediction + Deep learning, Drought prediction + ENSO + IOD, and Drought prediction + Climatic indices Keywords used Drought prediction, ENSO, IOD, Machine learning, Deep learning, Climatic indices Databases used The Web of Science (WoS), Scopus, Online journal databases, Google Scholar, Google search engine
Figure 2. Methodology adopted for comprehensive review of drought.
Research articles were screened based on the titles and abstracts of the identified
articles for relevance. The inclusion criteria were studies that focused on various drought
prediction approaches, adopted technologies, and prediction accuracy. Discrepancies in
study selection were resolved through discussion and consensus. Studies focusing on
specific regions or using different methodologies were included to capture the diversity of
the perspectives on drought. This paper restricted search results based on the publication
year, a journal impact factor above two, the drought type, and the methodology adopted.
Based on the above said criteria, a total of 131 papers were selected for this review paper to
highlight drought prediction research.

Sustainability 2023, 15, 11684 4 of 19
3. Drought Modeling Components
Drought is an environmental catastrophe characterized by an extended period of
unusually low precipitation, leading to water scarcity and posing risks to agriculture,
livestock, and ecosystems. There are several parameters like precipitation, soil moisture,
stream-flow, evapotranspiration, temperature, wind, relative humidity, and vegetation
health which can help to gain insights into the severity and duration of drought events.
Drought studies require climatic, remote sensing (RS), hydrologic, and atmospheric
data. The data can be acquired from various ground observation stations, earth observation
satellites, open linked data, sensors, and the Internet of Things (IoT). So, the data is
usually extracted from heterogeneous data sources in a heterogeneous format. It may
be continuous, discrete, images, texts, videos, etc. [17 ]. As earth observation systems
continue to advance rapidly, remote sensing (RS) systems and climate sensing systems offer
a wide range of high-resolution earth observations encompassing spatial, temporal, and
radiometric measurements [ 18 –20 ]. Numerous efforts have been made to comprehend the
connection between drought and its influencing factors and to enhance the precision of
drought prediction [21– 31 ]. Improving the accuracy of drought forecasting models requires
an understanding of the broad range connections, or tele-connections, between climatic
indices and variables indicating drought occurrence such as precipitation, vegetation,
and temperature.
3.1. Hydro-Meteorological Parameters
Hydrological and meteorological parameters play an important role in predicting
drought. The key parameters that are used for drought prediction are: (i) Precipitation: it is
the amount of rain or snowfall that occurs in a particular region. Low levels of precipitation
can indicate the onset of drought conditions [7]. (ii)Temperature: Elevated temperatures
have the potential to accelerate evapotranspiration, resulting in reduced soil moisture and
ultimately leading to the onset of drought conditions [32]. (iii) Evapotranspiration: It is
the mechanism by which water is moved from the surface of the Earth to the atmosphere
through evaporation and plant transpiration. High rates of evapotranspiration can cause
soil moisture deficits, leading to an increased risk of drought [ 33 ]. (iv) Soil Moisture:
Soil moisture is the water content in the soil. A lack of soil moisture can cause drought
conditions. Soil moisture is influenced by precipitation, evapotranspiration, and other
factors [34]. (v) Stream-flow: Stream-flow is the amount of water that flows in a stream or
river. A decrease in stream-flow over a prolonged period can indicate hydrological drought
conditions [ 35 ]. (vi) Groundwater level: The groundwater level is the depth at which water
is found below the ground surface. A decrease in the groundwater level over a prolonged
period can indicate drought conditions [36 ]. (vii) Atmospheric conditions: These include
factors that can influence precipitation patterns and the initiation of drought conditions,
such as atmospheric pressure, humidity, wind speed, and direction [37–39].
In order to anticipate the likelihood and severity of drought conditions, drought predic-
tion models often combine these hydrological and meteorological information. Researchers
and decision-makers can create efficient strategies for reducing the effects of drought on
agriculture, water resources, and other crucial sectors by tracking these characteristics
over time.
3.2. Climatological Parameters
Oceanic atmospheric parameters can significantly affect drought conditions. One of
the most important factors is the El Niño–Southern Oscillation (ENSO), which is a natural
climate phenomenon characterized by the periodic warming and cooling of the equatorial
Pacific Ocean. During El Niño events, the sea surface temperatures in the central and
eastern Pacific Ocean rise, resulting in changes in atmospheric circulation patterns that can
alter rainfall patterns around the world. The patterns of ENSO phase variation can lead
to large variations in precipitation in the tropical Pacific Ocean [40]. Due to the potential
tele-connections, ENSO is considered to be an important driver that can be used to predict