A Probabilistic Approach to Characterizing Drought Using Satellite Gravimetry

• Published in: Water resources research Vol. 60; no. 8
• Main Authors: Saemian, Peyman, Tourian, Mohammad J., Elmi, Omid, Sneeuw, Nico, AghaKouchak, Amir
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 01.08.2024
• Subjects: Climate; Drought; Drought conditions; Drought index; Errors; GRACE; GRACE (experiment); GRACE Follow‐On; Gravimetry; Gravity; Groundwater; hydrological drought; Hydrology; Moisture content; Monte Carlo simulation; Probability theory; Recovery; Risk management; Rivers; Satellite orbits; Satellites; Soil moisture; Spacecraft recovery; Statistical analysis; stochastic process; Stochastic processes; Stochasticity; Surface water; total water storage; Uncertainty; Vegetation; Water storage
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1029/2023WR036873

In the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management. Plain Language Summary Total Water Storage (TWS) is defined as the sum of water stored as surface water (e.g., lakes and rivers), groundwater, soil moisture, snow, ice, and vegetation biomass. Since its launch in 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided unique TWS change measurements with many applications in hydrology, including characterizing drought events. Scientists have been using satellites like GRACE and its successor, GRACE‐FO, to understand drought by measuring the Total Water Storage Anomaly (TWSA). However, previous methods didn't consider uncertainties from satellite orbits, models, and data errors. This study offers a novel probabilistic approach for characterizing drought, Probabilistic Storage‐based Drought Index (PSDI), which acknowledges the uncertainties in the GRACE TWS change. We use simulations to create different drought scenarios, offering probabilities for each category instead of one fixed category. Comparing PSDI to traditional methods, we found that traditional methods tend to overestimate drought severity. We tested PSDI across different regions, and it consistently proved to be a reliable way to understand drought conditions, offering a more comprehensive perspective. Our probabilistic approach offers a more realistic view of TWS drought, making it suitable for adaptive strategies and risk management. Key Points A novel probabilistic framework is introduced to characterize drought using Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On observations and propagating their stochastics Our study suggests a tendency of deterministic approaches to overestimate storage‐based drought severity The probabilistic approach captures global hydrological droughts while delivering more realistic results suited for risk management