This article reviews recent literature on drought of the last millennium, followed by an update on global aridity changes from 1950 to 2008. Projected future aridity is presented based on recent studies and our analysis of model simulations. Dry periods lasting for years to decades have occurred many times during the last millennium over, for example, North America, West Africa, and East Asia. These droughts were likely triggered by anomalous tropical sea surface temperatures (SSTs), with La Ni˜ na-like SST anomalies leading to drought in North America, and El-Ni˜ no-like SSTs causing drought in East China. Over Africa, the southward shift of the warmest SSTs in the Atlantic and warming in the Indian Ocean are responsible for the recent Sahel droughts. Local feedbacks may enhance and prolong drought. Global aridity has increased substantially since the 1970s due to recent drying over Africa, southern Europe, East and South Asia, and eastern Australia. Although El Ni˜ no-Southern Oscillation (ENSO), tropical Atlantic SSTs, and Asian monsoons have played a large role in the recent drying, recent warming has increased atmospheric moisture demand and likely altered atmospheric circulation patterns, both contributing to the drying. Climate models project increased aridity in the 21 st century over most of Africa, southern Europe and the Middle East, most of the Americas, Australia, and Southeast Asia. Regions like the United States have avoided prolonged droughts during the last 50 years due to natural climate variations, but might see persistent droughts in the next 20–50 years. Future efforts to predict drought will depend on models’ ability to predict tropical SSTs. 2010 JohnWiley &Sons,Ltd.WIREs Clim Change2010 DOI:10.1002/wcc.81

Drought under Global Warming: A Review

Drought in South Asia affect food and water security and pose challenges for millions of people. For policy-making, planning, and management of water resources at sub-basin or administrative levels, high-resolution datasets of precipitation and air temperature are required in near-real time. We develop a high-resolution (0.05°) bias-corrected precipitation and temperature data that can be used to monitor near real-time drought conditions over South Asia. Moreover, the dataset can be used to monitor climatic extremes (heat and cold waves, dry and wet anomalies) in South Asia. A distribution mapping method was applied to correct bias in precipitation and air temperature, which performed well compared to the other bias correction method based on linear scaling. Bias-corrected precipitation and temperature data were used to estimate Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to assess the historical and current drought conditions in South Asia. We evaluated drought severity and extent against the satellite-based Normalized Difference Vegetation Index (NDVI) anomalies and satellite-driven Drought Severity Index (DSI) at 0.05°. The bias-corrected high-resolution data can effectively capture observed drought conditions as shown by the satellite-based drought estimates. High resolution near real-time dataset can provide valuable information for decision-making at district and sub-basin levels. Machine-accessible metadata file describing the reported data (ISA-Tab format)

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

 The monthly standardized precipitation evapotranspiration index (SPEI) can
be used to monitor and assess drought characteristics with 1-month or
longer drought duration Based on data from 1961 to 2018 at 427
meteorological stations across mainland China, we developed a daily SPEI
dataset to overcome the shortcoming of the coarse temporal scale of monthly
SPEI Our dataset not only can be used to identify the start and end dates
of drought events, but also can be used to investigate the meteorological,
agricultural, hydrological, and socioeconomic droughts with a different timescales In the present study, the SPEI data with 3-month (about 90 d)
timescale were taken as a demonstration example to analyze spatial distribution
and temporal changes in drought conditions for mainland China The SPEI
data with a 3-month (about 90 d) timescale showed no obvious intensifying
trends in terms of severity, duration, and frequency of drought events from
1961 to 2018 Our drought dataset serves as a unique resource with daily
resolution to a variety of research communities including meteorology,
geography, and natural hazard studies The daily SPEI dataset developed is
free, open, and publicly available from this study The dataset
with daily SPEI is publicly available via the figshare portal (Wang et al,
2020c), with https://doiorg/106084/m9figshare12568280  Highlights A multi-scale daily SPEI dataset was developed across mainland China
from 1961 to 2018
The daily SPEI dataset can be used to identify the start and end days of the
drought event
The developed daily SPEI dataset in this study is free, open, and
publicly available

/pdf/a-multi-scale-daily-spei-dataset-for-drought-24yk06ow32.pdf

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

We develop a modelling methodology and investigate how spatial data on catchment surface characteristics may be used for quantifying relative coastal discharge contributions of streams and diffuse submarine groundwater discharge along the coastlines of unmonitored near-coastal catchment areas. The methodology is applied to two small Swedish coastal catchments areas, considering also two different ways to estimate evapotranspiration and its spatial variation within the catchments. Despite considerable differences between the characteristics of the two catchment areas, their relative coastal distribution results are similar, with about 80% of the total coastal discharge occurring through focused flows in visible and permanent streams, whereas the remaining 20% is diffuse and may occur through submarine groundwater discharge (SGD), small transient streams or both at different points in time. Evapotranspiration differs between the two used models, which results in relatively uncertain local flux values within the diffuse flow fields. The large focused stream flows and the mean values and total sums of diffuse flows (such as SGD) along some considerable coastline length, however, are constrained by the catchment-scale hydrological balance and considerably more robust and certain to estimate than the spatially variable small local fluxes within the diffuse flow fields.

Spatial distribution of unmonitored inland water discharges to the sea

Understanding responses to climate-related water scarcity in Africa.

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.

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

Understanding the drought characteristics is critical for water resources management in water stressed countries such as India. Previous studies evaluating drought assessments over India considered precipitation (P) and potential evapotranspiration (PET) as drivers using standardized precipitation‐evapotranspiration index (SPEI). The suitability of actual evapotranspiration (AET), which accounts for both water and energy based evaporative demands, in drought characterization is limited. In this study, SPEI is restructured with AET to characterize the regional drought over water and energy limited regions as standardized precipitation actual evapotranspiration index (SPAEI). For this, AET estimated based on Budyko framework and remote sensing‐based AET data has been used. The original formulation of SPEI is limited toward capturing the seasonality present in P and PET. The SPEI is restructured to account for the water availability deficit in the drought assessment rather than the actual atmospheric water demand in a given period to capture the strong seasonality of rainfall. The study compared the drought characteristics with both PET and AET for various meteorological homogeneous zones of India, which are characterized as water‐limited (Central, North, West, South, and Jammu and Kashmir, J&K) and energy‐limited (Northeast and Northeast hills) zones. Overall, the proposed new drought index based on AET can be promising toward drought intensity, extreme drought areal extents, shorter‐time scale drought frequencies, and longer‐time scale drought durations for water‐limited zones. The proposed drought indices based on AET can be robust for the drought assessment under consideration of water energy along with land and vegetation variability and can provide more insights for water‐limited regions.

Characterization of Regional Drought Over Water and Energy Limited Zones of India Using Potential and Actual Evapotranspiration

Parametric models of actual evapotranspiration (AET) based on precipitation (P) and potential evapotranspiration (PET) are region-specific and purely climate-induced and limited to represent the hydrological water balances. Basin-averaged model parameters considering P, AET, and runoff (R) using a machine learning algorithm, ensemble regression model, is proposed. Hydrologically calibrated model parameters allowed the study of AET under alterations of water use for current and for future scenarios under climate change. The effect of climate, water, and land use changes on AET was studied for the post-change period of 2004–2014 compared to pre-change period of 1965–2003 over Krishna river basin (KRB), India. The AET has increased under climate and water use changes while there is both increase and decreases of AET under land use changes for post-change period compared to pre-change period over the basin. Severe water shortages were estimated under pronounced increase of temperature (1.29 C) compared to precipitation increase (2.19%) based on Coordinated Regional Downscaling Experiment (CORDEX) projections for the period 2021–2060. Hydrologically induced AET changes were more pronounced than climate for current climate; whereas climateinduced AET changes were found to be more prominent for projected climate signals over the basin. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/ licenses/by-nc-nd/4.0/). doi: 10.2166/wcc.2020.173 om http://iwaponline.com/jwcc/article-pdf/12/2/502/865560/jwc0120502.pdf er 2021 S. Rehana (corresponding author) G. Sireesha Naidu N. T. Monish U. Sowjanya Lab for Spatial Informatics, International Institute of Information Technology, Hyderabad 500032, India E-mail: rehana.s@iiit.ac.in

Modeling hydro-climatic changes of evapotranspiration over a semi-arid river basin of India

Investigations on carbonization operating conditions of ANSYS customized kiln for charcoal production from Prosopis juliflora biomass and ANN model prediction for optimized operating conditions

N. T. Monish

Papers

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

Characterization of Regional Drought Over Water and Energy Limited Zones of India Using Potential and Actual Evapotranspiration

Modeling hydro-climatic changes of evapotranspiration over a semi-arid river basin of India

Investigations on carbonization operating conditions of ANSYS customized kiln for charcoal production from Prosopis juliflora biomass and ANN model prediction for optimized operating conditions