About

Dr. Angela J. Rigden is an Assistant Professor in the Department of Earth System Science at the University of California, Irvine. Her research explores interactions between the biosphere and atmosphere using multi-scale data and models. Her most recent work focuses on understanding the implications of a changing water cycle on agriculture across the globe. Prior to her current position, she was a postdoctoral researcher at Harvard University in the Department of Earth and Planetary Sciences, supported by a fellowship from the Planetary Health Alliance. She completed her PhD at Boston University in the Department of Earth and Environment with a certificate in Biogeoscience. Her academic interests include hydrology, plant physiology, agronomy, remote sensing, and climate change.

Research topics

• Meteorology
• Ecology
• Geography
• Agronomy
• Environmental science
• Agricultural engineering
• Materials science
• Engineering
• Soil science
• Biology
• Geology
• Climatology
• Atmospheric sciences

Selected publications

• Apparent land-atmosphere coupling depends on where soil moisture is measured

  2026-05-07

  articleOpen access1st authorCorresponding

  Apparent land-atmosphere coupling can vary more strongly with soil moisture depth than with temporal scale. Using soil moisture from the wrong depth systematically biases inferred relation-

  PublisherOA PDFDOI

• Improved understanding of Okavango Delta inundation using GRACE-derived terrestrial water storage

  Environmental Research Water · 2025-11-06

  articleOpen access1st authorCorresponding

  Hydrologic contributions from the Angolan Highlands sustain the Okavango Delta, one of the world’s largest inland wetlands. Observations show a clear link between the timing, intensity, and seasonal distribution of precipitation in the Highlands and flood extent in the Delta. However, the role of upstream water storage remains poorly understood, largely due to limited and uneven hydrological data. Here, we combine satellite-derived terrestrial water storage anomalies from the NASA Gravity Recovery and Climate Experiment (GRACE) and follow-on (GRACE-FO) missions with precipitation from the Climate Hazards Center Infrared Precipitation with Station dataset to assess interannual drivers of maximum flood extent in the Okavango Delta from 2003–2024. We show that GRACE-based terrestrial water storage explains 62% of the variance in annual inundation extent (90% CI: 32%–83%), outperforming precipitation alone (52%, 90% CI: 18%–80%), and that combining both explanatory variables increases explained variance to 73% (90% CI: 52%–89%). Spatial analyses of terrestrial water storage identify the Angolan Highlands as the key source region to the Delta, consistent with prior hydrological and modeling studies. These findings provide the first demonstration, to our knowledge, of GRACE-based terrestrial water storage improving flood modeling in the Okavango Delta and motivate future work to better monitor water towers driven by both precipitation and subsurface aquifer contributions.

  PublisherDOI

• The climate econometrics toolkit

  Ecological Informatics · 2025-11-08

  articleOpen access

  Climate econometrics is a rapidly advancing field, driven by accelerating climate change, the growing availability of climate data, and new computational methods for climate projection and statistical analysis. Many climate econometrics articles use a similar workflow, yet little effort has been made to provide software toolkit support. The Climate Econometric Toolkit integrates aspects from each step of this workflow into a single software package, supporting: (1) aggregating gridded climate data, (2) constructing and evaluating econometric models, and (3) computing climate impacts. The toolkit is written in Python and provides both a programming and graphical user interface (GUI). We present the functionality of the toolkit, the results of a qualitative evaluation, and two applications based on replications of published papers in the field. The goals of this toolkit are to improve the quality of climate econometrics science by standardizing researchers’ treatment of critical statistical factors, reduce code duplication between articles, and ease the statistical burden on climate econometrics researchers. • The Climate Econometrics Toolkit provides missing software support for the field. • Supports tasks such as data aggregation, model development, and impact projection. • Open-source Python package, with both a programming and graphical user interface. • Researchers found the toolkit streamlined workflows for climate econometric analyses. • Functionality demonstrated by replication of published climate econometrics studies.

  PublisherDOI

• Food security in Southern Madagascar informed by satellite-based remote sensing data

  Research Square · 2025-08-20

  preprintOpen access
  PublisherOA PDFDOI

• Defining the agricultural wet season over Africa using satellite soil moisture

  2025-03-24

  preprintSenior author

  The wet season is typically defined based on daily precipitation accumulation, which represents water inputs but does not account for losses from evaporation, infiltration, and runoff. Here, we use soil moisture observations from the Soil Moisture Active Passive (SMAP) satellite to capture the year-to-year variations in the seasonal availability of soil moisture across Africa from 2016 to 2023 using an anomalous accumulation algorithm. Our analysis shows that soil moisture onset correlates more strongly (p < 0.01) with vegetation green-up than precipitation across African croplands with over 30% coverage. Additionally, soil moisture-based onsets capture small early wet season rainfall events that precipitation-based methods often misclassify as false onsets. However, in Southern Hemisphere woodlands, neither soil moisture nor precipitation fully explains vegetation variability, likely due to deep-rooted trees accessing moisture beyond SMAP’s detection limits. These findings highlight soil moisture as a valuable indicator for refining wet season definitions, particularly in agricultural regions.

  PublisherDOI

• Disparities in the impact of drought on agriculture across countries

  Scientific Reports · 2025-04-18 · 12 citations

  articleOpen access

  Over the last several decades, droughts driven by climate change have damaged agricultural production as the planet warms. It is crucial for the future of the global food supply to develop effective adaptation strategies. However, not all countries and regions are affected equally by drought. We fit a hierarchical Bayesian model with a dataset containing 60 years of country-level drought and agricultural productivity data to probabilistically identify the susceptibility of various countries and regions to drought. We find that regions such as Eastern Africa and Southern Asia are highly susceptible to drought, with each region exhibiting a >90% chance that drought has negatively affected agriculture, leading to estimated historical agricultural losses of >14%, while Eastern Asia is the most drought-resilient region, with only a 44% probability that drought has negatively affected agriculture in this region. The results of this study can help inform the allocation of future resources to enhance agricultural resilience in the most vulnerable regions. Additionally, they provide a foundation for case studies examining specific countries or regions that demonstrate notable resilience or susceptibility to drought.

  PublisherOA PDFDOI

• Defining the agricultural wet season in Africa using soil moisture from the Soil Moisture Active-Passive satellite

  2025-07-24

  preprintOpen accessSenior author

  The wet season is typically defined based on daily precipitation accumulation, which represents water inputs but does not account for losses from evaporation, infiltration, and runoff. Here, we estimate root-zone soil moisture using observations from the Soil Moisture Active Passive (SMAP) satellite to capture year-to-year variations in seasonal soil moisture availability across Africa from 2016 to 2023 using a cumulative anomaly algorithm. Our analysis shows that seasonal soil moisture timing correlates more strongly (p < 0.01) with seasonal vegetation timing than precipitation across African croplands with over 30% crop cover. Additionally, soil moisture-based onsets capture small early season rainfall events that precipitation-based methods misclassify as false onsets. However, in Southern Hemisphere woodlands, neither soil moisture nor precipitation fully explains vegetation variability, likely due to deep-rooted trees accessing moisture beyond SMAP’s detection limits. These findings highlight soil moisture as a valuable indicator for refining wet season definitions, particularly in agricultural regions.

  PublisherOA PDFDOI

• Cascading Spatial Scales in the Hydrological Cycle Over Africa

  Geophysical Research Letters · 2025-08-21

  articleOpen accessSenior author

  Abstract Characterizing hydrological variability is critical for water resource management in Africa. However, whether a spatial cascading link exists within the hydrological cycle remains poorly understood. Using satellite‐based precipitation, soil moisture (SM), and vegetation products during 2016–2023, we quantify and compare their spatial scales, defined as the distance over which a variable maintains similar temporal variations, across Africa. Results show spatial scales increase sequentially from precipitation to SM to vegetation. Spatial scales diverge from precipitation to the land surface, with precipitation scales having a weak positive correlation with SM scales and moderately negatively correlated with vegetation scales. Soil moisture and vegetation scales remain positively coupled. Regional analyses reveal stronger scale coupling in semi‐arid regions. Seasonally, precipitation–soil moisture scale correlation intensifies from onset to peak of the rainy season across all unimodal regimes, whereas SM‐vegetation scale coupling weakens slightly. Our study provides critical insights into land‐atmosphere interactions across Africa.

  PublisherOA PDFDOI

• Remotely Sensed High‐Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society

  Water Resources Research · 2025-05-01 · 17 citations

  articleOpen access

  Abstract This paper reviews the current state of high‐resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high‐resolution remote sensing using visible, near‐infrared, thermal‐infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio‐geo‐physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state‐of‐the‐science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open‐access, high‐resolution (3–30 m), sub‐daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data‐limited regions of the world, especially the Global South for socio‐economic development.

  PublisherDOI

• Defining the Agricultural Wet Season in Africa Using Soil Moisture From the Soil Moisture Active‐Passive Satellite

  Geophysical Research Letters · 2025-09-16

  articleOpen accessSenior author

  Abstract The wet season is commonly defined based on daily precipitation accumulation, which represents water inputs but does not account for losses from evaporation, infiltration, and runoff. Here, we estimate root‐zone soil moisture using observations from the Soil Moisture Active Passive (SMAP) satellite to capture year‐to‐year variations in seasonal soil moisture availability across Africa from 2016 to 2023 using a cumulative anomaly algorithm. Our analysis shows that seasonal soil moisture timing correlates more strongly (p 0.01) with seasonal vegetation timing than precipitation across African croplands with over 30% crop cover. Additionally, soil moisture‐based onsets capture small early season rainfall events that precipitation‐based methods misclassify as false onsets. However, in Southern Hemisphere woodlands, neither soil moisture nor precipitation fully explains vegetation variability, likely due to deep‐rooted trees accessing moisture beyond SMAP's detection limits. These findings highlight soil moisture as a valuable indicator for refining wet season definitions, particularly in agricultural regions.

  PublisherDOI

Frequent coauthors

• Peter Huybers

  Harvard University

  39 shared

• Guido D. Salvucci

  25 shared

• Kaighin A. McColl

  Harvard University

  15 shared

• Duo Chan

  University of Southampton

  14 shared

• Daniel J. Short Gianotti

  Parsons (United States)

  12 shared

• Dara Entekhabi

  Massachusetts Institute of Technology

  11 shared

• Dan Li

  11 shared

• Weilin Liao

  10 shared

Education

• PhD, Department of Earth and Environment

  Boston University

  2017

• B.S., Department of Biological and Environmental Engineering

  Cornell University

  2012