Open AccessNovel Drought Hazard Monitoring Framework for Decision Support Under Data Scarcity
Author(s) -
R. A. Real-Rangel,
Adrián PedrozoAcuña,
José Agustín Breña-Naranjo,
Víctor H. Alcocer-Yamanaka
Publication year - 2018
Publication title -
epic series in engineering
Language(s) - English
Resource type - Conference proceedings
ISSN - 2516-2330
DOI - 10.29007/1l5w
Subject(s) - scarcity , univariate , warning system , water scarcity , streamflow , environmental science , hazard , multivariate statistics , environmental resource management , water resources , climatology , data assimilation , water resource management , geography , computer science , meteorology , drainage basin , cartography , ecology , geology , telecommunications , machine learning , economics , biology , microeconomics
Droughts are among the weather-related disasters, which affects most people around the world. Its large spatial extent and slowly, creeping onset, makes it difficult to define its start and end. Indeed, monitoring and early warning systems for drought hazards are recognized as critical activities of risk governance. Nevertheless, in many regions of the world, the scarcity of direct observations of climatological and hydrological variables, hinder an adequate follow-up and investigation of this phenomenon. This paper introduces a novel framework to generate drought hazard maps and time series, at national and regional levels, based on univariate and multivariate standardized drought indices. Notably, we utilize freely and globally available, gridded datasets of hydrological variables derived from remote sensing and data assimilation systems (e.g., rainfall, soil moisture, streamflow), which are verified against in situ measurements. A good performance of the framework is documented through the comparison of results against observed drought events in Mexico. This paves the road towards its use in other regions of the world, where data scarcity is an issue for drought monitoring activities.