The frequency and severity of global drought-induced impacts have led to raising awareness of the need for improved river management. Although academic publications on drought have proliferated, a systematic review of literature has not yet been conducted to identify trends, themes, key topics, and authorships. This study aims to evaluate the scientific evidence for the hydrological drought characteristics and the methodologies by performing as a framework. This systematic review performed three-stage screening of literature review for current applicable hydrological drought studies that have been conducted since the year of 2000 concerning methodologies, literature research gaps, and trends, and contribute to future studies. The analysis shows the increasing trends of research and publications in the hydrological drought assessment. The primary research themes are hydrological drought is drought severity, drought vulnerability, and drought forecast. Despite the current research findings, spatial and temporal variability, low flow analysis and regional modelling are the most important to encourage a holistic approach and international collaborations. The finding identified the shortcomings of most research, which are the use of non-standardized methodological and distinct sample sizes, resulting in data summary challenges and unrealistic comparisons.

Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is the Langat River Basin, located in the central part of peninsular Malaysia. The analysis was done using rainfall and water level data over 30 years, from 1986 to 2016. Both of the indices were calculated in monthly scale, and two neural network-based models and two wavelet-based artificial neural network (W-ANN) models were developed for monthly droughts. The performance of the SIAP and SWSI models, in terms of the correlation coefficient (R), was 0.899 and 0.968, respectively. The application of a wavelet for preprocessing the raw data in the developed W-ANN models achieved higher correlation coefficients for most of the scenarios. This proves that the created model can predict meteorological and hydrological droughts very close to the observed values. Overall, this study helps us to understand the history of drought conditions over the past 30 years in the Langat River Basin. It further helps us to forecast drought and to assist in water resource management.