Beth Tellman

Understanding what and who shapes urban growth and how it unfolds in the Global South is critical to ensure theories and predictive models of urban expansion and, consequently, global environmental change, are salient to contemporary contexts. Much of the urban expansion in the Global South occurs in informal settlements where urbanization is not officially permitted and residents often lack access to basic services and infrastructure. As a result, formalizing settlements becomes an asset for political transactions where politicians provide titles and improve conditions in exchange for residents’ votes. Yet the cumulative effect of these political transactions on land change remains unknown, rendering electoral dynamics absent from land system science models of urbanization. Here we use panel regression to examine the influence of electoral cycles and voting patterns on urban expansion and formalization of property titles from 1997–2015 in Mexico City. We find the distribution of land titles to residents increases in the months leading up to local elections, and more titles are given to core voting neighborhoods of the historically dominant party. Furthermore, urban built-up area increases in districts with higher electoral competition. These results demonstrate that electoral politics influence when and where the legal city boundary expands, who can access secure tenure, and potentially which settlements grow. These results imply sociopolitical dynamics shape urban sustainability. Incorporating quantified links between political dynamics and urban expansion could improve models of future urban growth and identify relevant levers to improve urban sustainability.

NASA's Global Flood Product has provided near real-time daily flood maps since 2012 using the Moderate Resolution Imaging Spectroradiometer (MODIS). With MODIS's impending retirement, NASA introduced in 2025 a successor flood product using the Visible Infrared Imaging Radiometer Suite (VIIRS). To enhance the VIIRS flood product using newer AI methods, we developed VIIRS Machine Learning Fractional Water Detection (ML-FWD): a deep learning algorithm that estimates fractional water extent from 375 m surface reflectance imagery. Trained on over 95,000 global water observations from the Dynamic World Sentinel-2 land-cover classification, ML-FWD achieves a test R2 of 0.83 with minimal bias (mean error of -0.6% water extent) and consistent performance across 14 global biomes. ML-FWD outperforms existing VIIRS algorithms from NASA and NOAA by 0.27 and 0.12 in absolute R2 when tested across 100 flood events—a 141% and 35% relative improvement, respectively. ML-FWD also outperforms the existing NASA algorithm on expert visual inspection of 19 scenarios that commonly result in false water detections. NASA is considering deploying ML-FWD in near real-time, potentially paving the way for the broader adoption of ML by public agencies for routinely generated global earth science datasets. Our study demonstrates that combining deep learning and medium-resolution observations improves daily flood extent estimates from moderate-resolution sensors such as VIIRS, critical for flood monitoring, response, and relief across a range of sectors.

El libro Desigualdad en el acceso al agua en los hogares de México examina la desigualdad en el acceso al agua en los hogares de México desde un enfoque interdisciplinario que articula economía, política pública, salud y derechos humanos. A partir de evidencia empírica y análisis cuantitativos y espaciales, los trece capítulos que integran la obra muestran que la cobertura oculta profundas brechas en dimensiones clave del derecho humano al agua: disponibilidad, calidad, accesibilidad, continuidad y asequibilidad. Los estudios documentan que la provisión del servicio es sistemáticamente desigual y afecta con mayor intensidad a hogares de bajos ingresos, comunidades rurales aisladas, asentamientos informales y zonas urbanas periféricas, donde la intermitencia, la mala calidad del agua y los altos costos relativos son condiciones recurrentes. Asimismo, se analizan los vínculos entre exclusión hídrica, mortalidad infantil, arreglos institucionales, esquemas tarifarios y patrones de financiamiento público, evidenciando sesgos regresivos en la asignación de recursos. La obra concluye que, si bien se presenta un gran avance en el acceso al agua en México con el paso de los años, la desigualdad hídrica es un fenómeno estructural estrechamente asociado a la desigualdad socioeconómica, y plantea la necesidad de reorientar la política hídrica hacia diagnósticos integrales, criterios de equidad y soluciones técnicas y comunitarias contextualizadas.

Despite increasing adoption of earth observations data to inform disaster response and recovery, deciding which measurements to use—and how—remains an open question. An increasing number of flood insurance programs have been using observable proxies—or indices—to activate payouts. However, convincing evaluation of important design features, including choice of index data, are lacking. This study investigates five potential flood data sets at national and regional scales in a simulated index‐based insurance program in Bangladesh: gridded precipitation, river‐height and modeled inundation from the national flood agency, and two satellite data sets of surface‐water‐extent (one state‐of‐practice, the other state‐of‐the‐art). We demonstrate that data choice determines the accuracy and timeliness of indexed payouts, as well as the uncertainty associated with their likelihood, which influences program costs. For example, while river‐height and satellite water‐extent indices activated payouts during the two worst floods in the 20‐year study period (2004 and 2007), the precipitation‐index activated for just one of them. Furthermore, our state‐of‐the‐art satellite index activated on average 1 week earlier and with 21% lower uncertainty than the satellite‐index used in practice. We propose that practitioners leverage the divergence‐of‐evidence among multiple data sets to identify regions where there is lower confidence in making accurate and timely payouts, which can help inform additional programming such as back‐up payout mechanisms. Beyond insights for practitioners leveraging insurance to protect Bangladeshi communities threatened by extreme monsoon floods, this work offers techniques to assess the sensitivity of indexed programs to different data and scales in other flood‐prone regions.

Despite increasing adoption of earth observations data to inform disaster response and recovery, deciding which measurements to use - and how - remains an open question. An increasing number of flood insurance programs have been using observable proxies - or indices - to activate payouts. However, convincing evaluation of important design features, including choice of index data, are lacking. This study investigates five potential flood datasets at national and regional scales in a simulated index-based insurance program in Bangladesh: gridded precipitation, river-height and modeled inundation from the national flood agency, and two satellite datasets of surface-water-extent (one state-of-practice, the other state-of-the-art). We demonstrate that data choice determines the accuracy and timeliness of indexed payouts, as well as the uncertainty associated with their likelihood, which influences the program’s costliness. For instance, while river-height and satellite water-extent indices activated payouts during the two worst floods in the 20-year study period (2004 and 2007), the precipitation-index activated for just one of them. Furthermore, our state-of-the-art satellite index activated on average one week earlier and with 18% lower uncertainty than the satellite-index used in practice. We propose that practitioners leverage the divergence-of-evidence among multiple datasets to identify regions where there is lower confidence in making accurate and timely payouts, which can help inform additional programming such as back-up payout mechanisms. Beyond insights for practitioners leveraging insurance to protect Bangladeshi communities threatened by extreme monsoon floods, this work offers techniques to assess the sensitivity of indexed programs to different data and scales in other flood-vulnerable regions.

We assess how much of Central America is likely to be agriculturally suitable for cultivating coca (Erythroxylum spp), the main ingredient in cocaine. Since 2017, organized criminal groups (not smallholders) have been establishing coca plantations in Central America for cocaine production. This has broken South America’s long monopoly on coca leaf production for the global cocaine trade and raised concerns about future expansion in the isthmus. Yet it is not clear how much of Central America has suitable biophysical characteristics for a crop domesticated in, and long associated with the Andean region. We combine geo-located data from coca cultivation locations in Colombia with reported coca sites in Central America to model the soil, climate, and topography of Central American landscapes that might be suitable for coca production under standard management practices. We find that 47% of northern Central America (Honduras, Guatemala, and Belize) has biophysical characteristics that appear highly suitable for coca-growing, while most of southern Central America does not. Biophysical factors, then, are unlikely to constrain coca’s spread in northern Central America. Whether or not the crop is more widely planted will depend on complex and multi-scalar social, economic, and political factors. Among them is whether Central American countries and their allies will continue to prioritize militarized approaches to the drug trade through coca eradication and drug interdiction, which are likely to induce further expansion, not contain it. Novel approaches to the drug trade will be required to avert this outcome.

Floods impact communities worldwide, resulting in loss of life, damaged infrastructure and natural assets, and threatened livelihoods. Climate change and urban development in flood‐prone areas will continue to worsen flood‐related losses, increasing the urgency for effective tools to monitor recovery. Many Earth Observation (EO) applications exist for flood‐hazard monitoring and provide insights on location, timing, and extent in near real‐time and historically to estimate flood risk. Less attention has been paid to flood recovery, even though differing recovery rates and outcomes can have immediate and enduring distributional effects within communities. EO data are uniquely positioned to monitor post‐flood recovery and inform policy on hazard mitigation and adaptation but remain underutilized. We encourage the EO and flood research community to refocus on developing flood recovery applications to address growing risk. Translation of EO insights on flood recovery among flood‐affected communities and decision‐makers is necessary to address underlying social vulnerabilities that exacerbate inequitable recovery outcomes and advocate for redressing injustices where disparate recovery is observed. We identify an unequivocal need for EO to move beyond mapping flood hazard and exposure toward post‐flood recovery monitoring to inform recovery across geographic contexts. This commentary proposes a framework for remote sensing scientists to engage community‐based partners to integrate EO with non‐EO data to advance flood recovery monitoring, characterize inequitable recovery, redistribute resources to mitigate inequities, and support risk reduction of future floods.

Floods impact communities worldwide, resulting in an estimated $651 billion (USD) in damages, countless fatalities, and threatened livelihoods over the last two decades alone. Climate change and urban development in flood-prone areas will continue to worsen flood-related losses increasing the urgency for effective tools to monitor recovery. Many Earth Observation (EO) applications exist for flood-hazard monitoring and provide insights on location, timing, and extent in near real-time and historically to estimate flood risk. Less attention has been paid to flood recovery, even though differing recovery rates and outcomes can have immediate and enduring effects within communities. Here, we define post-flood recovery as a change in land cover types, conditions, or land surface features in the days, weeks, months, or years following a flood event. EO data are uniquely positioned to monitor post-flood recovery and inform policy on hazard mitigation and adaptation but remain underutilized. We urge the EO and flood research community to renew focus on developing flood recovery applications to address growing flood risk. Both methodological innovations and translation of EO insights on flood recovery among flood-affected communities and decision-makers are necessary to address underlying vulnerabilities in social systems that exacerbate flooding. We identify an unequivocal need for EO to move beyond hazard mapping to post-flood recovery monitoring to inform recovery across geographic contexts. This commentary proposes a framework to use EO to advance flood recovery monitoring, characterize inequitable recovery, redistribute resources to mitigate inequities, and support risk reduction of future floods.