Advances in Quantification and Modeling of Hydrological Droughts

Dear Colleagues,

Drought constitutes an important part of the scientific field of hydrology and water resources. Drought is considered one of the major natural hazards, with significant adverse impacts on the environment and consequently on a variety of economic sectors across the world. Because of the wide spatial and temporal diversity of droughts, there exists no universally accepted definition. Currently, drought draws worldwide attention primarily because of climate change, among other causative factors. The quantification of drought encompasses the study of deficiencies in precipitation, streamflow, water storage in surface waters, groundwater storage and soil water content. Accordingly, three major types of droughts are recognized: meteorological, hydrological and agricultural.

A hydrologic drought refers to a period of low flows in rivers, low water levels in surface reservoirs, ponds, wetlands, aquifers, etc., that lasts long enough to cause noticeable damage to sectors such as municipal water supply, agriculture industry, hydropower generation and navigation. The recent California drought and almost irreversible water levels in Lake Mead are grim reminders of this form of drought. The major cause of hydrologic droughts is a deficiency in precipitation (termed meteorological droughts), which translates into a deficiency in the aforesaid water bodies. The quantification of hydrological drought parameters (namely initiation and termination points on the time scale, duration, magnitude, intensity, frequency and regional spread) is crucial for planning suitable strategies for mitigation of drought impacts and management of water resources in a region. The role of hydrologic drought indices in the assessment of the severity of a drought is of paramount importance, although no universal index has yet been adopted. In addition, the quantification of drought magnitude and duration is an essential aspect of the sizing, operation and management of reservoirs for equitable distribution of water resources to multiple users during extended drought periods.

The other major component of hydrological drought analysis is modelling for prediction and forecasting. These components assume special significance in the wake of climate change. In particular, forecasting is more important as it deals with the warning signals of future droughts, which are likely to impact the availability of water for multiple uses in society.

The present Issue will focus on the above aspects of hydrological droughts, and papers are solicited which address the quantification of hydrological droughts based on the historical data, choice of suitable drought indices, stochastic characteristic of drought parameters, frequency and time domain analyses using traditional and machine learning algorithms (ANN, artificial intelligence, support vector regression, discrete wavelet transform, etc.) for prediction and forecasting. Papers emphasizing the role of climate change in the worsening scenario of hydrological droughts are encouraged, and case studies highlighting the applications of recent analytical and machine-learning-based modelling tools and methodologies shall be given prominence.

Prof. Dr. Umed S. Panu
Dr. Tribeni C. Sharma
Guest Editors

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