Complex influences of meteorological drought time-scales on hydrological droughts in natural basins of the contiguous Unites States

Abstract

Con formato: Color de fuente: Texto 2 Con formato: Derecha Complex influences of meteorological drought timescales on hydrological droughts in natural basins of the contiguous Unites States

Global characterization of hydrological and meteorological droughts under future climate change: The importance of timescales, vegetation‐CO2feedbacks and changes to distribution functions

“…The rationale of this approach is that usable water resources like soil moisture, streamflow, reservoir storages, lake levels and groundwater show time‐lags in their response to climatic conditions (Changnon and Easterling, ). The times of these responses may vary strongly as a function of different physical and human factors (Lorenzo‐Lacruz et al ., ; Barker et al ., ; Yang et al ., ; Peña‐Gallardo et al ., ).…”

Unraveling the influence of atmospheric evaporative demand on drought and its response to climate change

“…Drought can be characterized based on impact information from socioeconomic sectors (Porter & Semenov, ), natural ecosystems (Bachmair, Tanguy, Hannaford, & Stahl, ; Camarero, Gazol, Sangüesa‐Barreda, Oliva, & Vicente‐Serrano, ), crops (Peña‐Gallardo et al, ; H. Wang et al, ), and hydrological systems (Barker, Hannaford, Chiverton, & Svensson, ; Peña‐Gallardo et al, ). Nevertheless, drought is more frequently quantified using meteorological variables that can be observed or modeled (Mukherjee et al, ).…”

Drought Propagation in Contiguous U.S. Watersheds: A Process‐Based Understanding of the Role of Climate and Watershed Properties

“…These studies can be categorized into two groups, i.e., those using statistical analysis and those using hydrologic modeling. Studies based on statistical analysis have used various statistical techniques such as correlation analysis (Barker et al, 2016;Haslinger et al, 2014;Huang et al, 2017;Jiang et al, 2015;Liu et al, 2019;Peña-Gallardo et al, 2019;Van Loon & Laaha, 2015;Wang et al, 2020;Wu et al, 2018;Xu et al, 2019;Yang et al, 2017) or machine learning techniques (Konapala & Mishra, 2020;Veettil & Mishra, 2020;Veettil et al, 2018) for identifying the climate and watershed properties which control drought propagation. The main advantage of the statistical approach is that it allows researchers to identify the dominant controlling factors from a large number of watershed and climate properties.…”

“…Similarly, Van Lanen et al (2013) studied the influence of soil and groundwater properties on drought propagation by changing soil and groundwater parameters for a fixed climate type in a conceptual hydrologic model. Previous studies on drought propagation in the contiguous United States (CONUS) have been conducted using statistical approaches (e.g., Konapala & Mishra, 2020;Peña-Gallardo et al, 2019;Veettil et al, 2018). These studies have focused on identifying the climate and watershed properties that control drought propagation in the CONUS watersheds; however, the patterns characterizing the drought propagation mechanisms across the CONUS, the differences, and their controls among different regions are not well understood.…”