Research papersDevelopment of hydro-meteorological drought index under climate change – Semi-arid river basin of Peninsular India
Introduction
Drought is one of the most widespread and slowly developing natural hazard due to the lack of water availability in terms of precipitation and consequent shortage of streamflow and soil moisture affecting socioeconomics (Aadhar and Mishra, 2017, AghaKouchak et al., 2015, Dai, 2011). It corresponds to the failure of spatial and temporal precipitation (meteorological drought), inadequate streamflows (hydrological drought), decrease in soil moisture and crop yields (agricultural drought), therefore consequent impact on ecosystem and socioeconomic activities of the human being (socio-economic drought) (Wilhite and Glantz, 1985). Among these, the most widely used drought indices at regional scale water resources management are meteorological and hydrological to characterise and compare drought severity, frequency and duration (Marcos-Garcia et al., 2017). A meteorological drought index accounts for the deviation of climatological variables (precipitation and Potential Evapotranspiration (PET)) in a given year from the normal conditions [e.g. Standardized Precipitation Index (SPI)] by (McKee et al., 1993); Standardized Precipitation Evapotranspiration (SPEI) developed by (Vicente Serrano et al., 2010). Nevertheless, none of these meteorological indices can consider the effect of evapotranspiration flux based on actual water availabilities in the drought estimation. Furthermore, such meteorological droughts are independent of actual water availabilities, land use and vegetation in the drought estimation at catchment scale. A drought assessment solely based on meteorological aspects without considering deficits in hydrological cycle will not be sufficient for the regional water resources management decision under climate change (Oloruntade et al., 2017). Whereas, hydrological drought assessment is based on the fall of streamflow and water storages below long-term mean levels [e.g. Standardized Runoff Index (SRI), Shukla and Woods (2008)]. Implementation of such hydrological drought assessments are limited for ungauged basins (Loon et al., 2019). Furthermore, hydrological drought assessment entirely based on below normal streamflow may mislead due to the human influenced regulated flows due to diversions, water transfers and instream abstractions (Lanen et al., 2013). Traditionally, drought assessment studies were exclusively based on either meteorological or hydrological aspects without considering the combined effect of climatological deviations and acute water shortages at river basin scales. To this end, drought assessment studies have evolved by integrating various meteorological (e.g. SPI) and hydrological drought indicators (e.g. SRI) in a unique manner of aggregation to develop composite drought indices (Shah and Mishra, 2020, Wang et al., 2020). These composite drought indices can combine the individual drought indicators either statistically (e.g. copula based composite drought index, (Shah and Mishra, 2020, Wang et al., 2020) or simple weighting (entropy weighted drought index, Waseem et al., 2015). However, to understand the complexity and time lags (Yang et al., 2017b) between meteorological and hydrological drought indices, it is very important to structure the drought indices with an integration of most prominent hydro-meteorological variables to retain the dependence between these variables. Given the limitations of meteorological and hydrological drought indices as individual, a drought index which will synthesize the hydro-meteorological information can be more reliable in the context of operational drought management at river basin scale under climate change. A comprehensive hydro-meteorological drought index combining major hydrological variables such as precipitation, PET, AET and runoff (R) simultaneously to characterise the meteorological and hydrological drought will be more promising for efficient drought management at catchment scale..
The Standardized Precipitation Evapotranspiration Index (SPEI) has become popular meteorological drought index due to the inclusion of atmospheric climate demand as the difference (P-PET) between Precipitation (P) and Potential Evapotranspiration (PET) (Vicente Serrano et al., 2010). Even though, SPEI has proven to be more reliable measure than Standardized Precipitation Index (SPI) (e.g. Tirivarombo et al., 2018), as it includes PET in addition to P, it cannot account for the actual water availability of a region. Moreover, (P-PET) is energy based atmospheric water demand and do not account for the effects of regional land surface changes and actual moisture availability, which is the difference (P-AET) between P and Actual Evapotranspiration (AET). Also, PET is the maximum possible moisture loss limited only by the energy endowment or it is the energy-driven ET (Shelton, 2008). Whereas, AET represents the transfer of moisture from the surface to the atmosphere in response to both the energy demand and available moisture supply and can be a promising variable in the drought estimation (Liu et al., 2017). Inclusion of AET in meteorological drought indices, such as SPEI, which is a prominent hydrological variable, can represent hydro-meteorological drought indicator. The present study aimed at inclusion of most prominent hydrological variables of P, PET, AET and R in the drought formulation to develop a hydro-meteorological drought indicator which can work accurately to define both meteorological and hydrological aspects together at catchment scale.
The conventional approaches to estimate AET at river basin scales is based on data intensive macro scale distributed hydrological simulation models and water balance methods expressed as AET = P-R, at annual time scale (Hamel and Guswa, 2015). Alternatively, several parametric models have been developed for estimating AET, with operational meteorological variables of precipitation and temperatures as inputs (e.g. Zhang et al., 2004). Such parametric models are based on the assumption that AET is limited by precipitation under very dry conditions and limited by PET under very wet conditions (e.g. Budyko, 1974). However, these parametric models of AET are purely based on region-specific climate considering P and PET and limited to represent variability of evapotranspiration under water uses (Asokan et al., 2010). In this context, time-invariant model parameters were estimated at catchment scale with consideration of closure of water-balance by (Asokan et al., 2010, Jarsjö et al., 2008). However, time-invariant catchment scale parameters are limited to capture temporal variability of water-energy balance variables, instead, dynamic model parameters accounting for the variations of P, PET and R under climate signals with the closure of water balance can be more promising (e.g. Rehana et al., 2020c). Application of such hydrological calibration models on the AET estimates can account for the variability of P, PET and R at catchment scale. Inclusion of such hydrological induced AET in the drought estimation can account for the catchment-scale hydro-meteorological aspects. The present study proposed a modelling framework to include hydrologically calibrated AET estimates in the formulation of SPEI to develop a new hydro-meteorological drought monitoring index, Standardized Precipitation Actual Evapotranspiration Index (SPAEI). It can be noted that, the parametric AET model adopted in the present study is based on Budyko formulation, which is suitable for long-term basin average scale and large catchments (Gunkel and Lange, 2017). Therefore, the study mainly focused on 12-month time scale (annual drought) characterization to avoid the use of short-term soil moisture storages (Donohue et al., 2007). The proposed drought index of SPAEI consider the joint effect of meteorological and actual water budget, and has a potential to evaluate the effects of climate and hydrological changes. In order to study the impacts of climate variability on drought characteristics the study adopted statistical downscaling model-based projections of precipitation and temperature based on General Circulation Model (GCM) outputs. The study compared the newly proposed hydro-meteorological drought index of SPAEI with meteorological drought index of SPEI and hydrological drought index of Standardized Runoff Index (SRI) for current and projected scenarios. The proposed drought index was tested on a semi-arid river basin of peninsular India, Krishna River Basin (KRB).
Section snippets
Case study and data
Krishna River Basin (KRB) is the fifth largest river basin in India occupying an area of 2, 58, 948 km2 which is 8% of the total geographical area of the country within the range 73o17′–81o9′E and 13o10′-19o22′ N (Fig. 1). Most of the basin is covered by semi-arid climate with annual average precipitation as 784 mm, of which approximately 90% occurs during the South West Monsoon from June to October (http://indiawris.nrsc.gov.in/wrpinfo/?title=Krishna). The KRB is with semi-arid climate with
Results
The first part of the study tested the performance of the developed hydro-meteorological drought index in capturing the meteorological and hydrological drought characteristics. The second part of the study has focused on quantification of climate change impacts on the proposed hydro-meteorological drought index under precipitation and temperature projections based on statistical downscaling model. More specifically, the study compared the drought characteristics of areal extent, frequency,
Discussion
The study aimed to include AET in the drought characterisation along with precipitation at catchment scale to represent both hydrological and meteorological aspects combinedly. Whereas, AET is a complex hydrological variable to estimate in comparison to other forms of hydrological variables such as precipitation, runoff and PET. There are various advanced state-of-the-art satellite based remote sensing observations of AET which are available at various spatial and temporal resolutions. However,
Conclusions
The following conclusions are derived from this study:
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The SPAEIHydro can provide more insight in capturing the severe and extreme drought characteristics at catchment scales compared the SPEI due to the inclusion of hydrologically induced AET in the drought characterizing instead of PET.
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There is an average increase of precipitation (temperature) with about 3.38% (0.59 °C), 4.2% (0.37 °C) and 4.1% (0.32 °C) with BCCCSM, CanESM and MIROC models respectively over KRB for the future scenarios of
CRediT authorship contribution statement
S. Rehana: Conceptualization, Methodology, Writing - original draft, Investigation, Funding acquisition, Supervision. G. Sireesha Naidu: Data curation, Software, Visualization.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The research work presented in the paper is funded by Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India through Start-up Grant for Young Scientists (YSS) Project no. YSS/2015/002111 to Dr. S. Rehana. We sincerely acknowledge the India Meteorological Department (IMD) for providing the gridded observations of precipitation and temperatures and details of data are given in the website at
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