P. Suresh C. Rao’s research while affiliated with Purdue University and other places

We propose a systems model for urban population growth dynamics, disaggregated at the county scale, to explicitly acknowledge inter and intra-city movements. Spatial and temporal heterogeneity of cities are well captured by the model parameters estimated from empirical data for 2005–2019 domestic migration in the U.S. for 46 large cities. Model parameters are narrowly dispersed over time, and migration flows are well-reproduced using time-averaged values. The spatial distribution of population density within cities can be approximated by negative exponential functions, with exponents varying among cities, but invariant over the period considered. The analysis of the rank-shift dynamics for the 3100+ counties shows that the most and least dense counties have the lowest probability of shifting ranks, as expected for ‘closed’ systems. Using synthetic rank lists of different lengths, we find that counties shift ranks gradually via diffusive dynamics, similar to other complex systems.

Wetlands are vital components of landscapes that sustain a range of important ecosystem services. Understanding how wetland-rich landscapes - or wetlandscapes - will evolve under a changing climate and increasing anthropogenic encroachment is urgent. Wetlandscapes are highly heterogeneous, and scaling local modelling insights from individual instrumented wetlands to characterize landscape-scale dynamics has been a pervasive challenge. We investigate the potential to use water extent information from satellite imagery to calibrate landscape-scale process-based hydrological models. Applications to wetlandscapes in the Prairie Pothole Region (PPR) in North Dakota and the Texas Playa Lakes (TPL) shed light on two important trade-offs. First, in-situ monitoring provides accurate water extent information on an arbitrary subset of wetlands, whereas satellite imagery captures landscape-scale hydrological dynamics but suffers persistent water-detection challenges. Satellite imagery is a superior source of data for model calibration in sparsely monitored and spatially heterogeneous landscapes like the PPR, where the sampling uncertainty of monitored wetlands exceeds the water detection uncertainty of remote sensing. The two data sources are equivalent for more homogeneous landscapes like the TPL. The second tradeoff concerns the spatial resolution and temporal coverage of satellite imagery. In that regard, the 20 years of bi-weekly images captured by Landsat 7 provides unprecedented insights into the dynamic nature of the ecohydrological characteristics of wetlandscapes, such as seasonal and inter-annual changes of their metapopulation capacity. In the PPR, the amplitude of these dynamics far exceeds the bias introduced by Landsat's inability to capture ecologically important connectivity details due to its coarse spatial resolution compared to more recent imagery.

We show here that population growth, resolved at the county level, is spatially heterogeneous both among and within the U.S. metropolitan statistical areas. Our analysis of data for over 3,100 U.S. counties reveals that annual population flows, resulting from domestic migration during the 2015–2019 period, are much larger than natural demographic growth, and are primarily responsible for this heterogeneous growth. More precisely, we show that intra-city flows are generally along a negative population density gradient, while inter-city flows are concentrated in high-density core areas. Intra-city flows are anisotropic and generally directed towards external counties of cities, driving asymmetrical urban sprawl. Such domestic migration dynamics are also responsible for tempering local population shocks by redistributing inflows within a given city. This spill-over effect leads to a smoother population dynamics at the county level, in contrast to that observed at the city level. Understanding the spatial structure of domestic migration flows is a key ingredient for analyzing their drivers and consequences, thus representing a crucial knowledge for urban policy makers and planners.

We show here that population growth, resolved at the county level, is spatially heterogeneous both among and within the U.S. metropolitan statistical areas. Our analysis of data for over 3,100 U.S. counties reveals that annual population flows, resulting from domestic migration during the 2015 - 2019 period, are much larger than natural demographic growth, and are primarily responsible for this heterogeneous growth. More precisely, we show that intra-city flows are generally along a negative population density gradient, while inter-city flows are concentrated in high-density core areas. Intra-city flows are anisotropic and generally directed towards external counties of cities, driving asymmetrical urban sprawl. Such domestic migration dynamics are also responsible for tempering local population shocks by redistributing inflows within a given city. This "spill-over" effect leads to a smoother population dynamics at the county level, in contrast to that observed at the city level. Understanding the spatial structure of domestic migration flows is a key ingredient for analyzing their drivers and consequences, thus representing a crucial knowledge for urban policy makers and planners.

Rapid urbanization and climate change trends, intertwined with complex interactions of various social, economic, and political factors, have resulted in an increase in the frequency and intensity of disaster events. While regions around the world face urgent demands to prepare for, respond to, and to recover from such disasters, large-scale location data collected from mobile phone devices have opened up novel approaches to tackle these challenges. Mobile phone location data have enabled us to observe, estimate, and model human mobility dynamics at an unprecedented spatio-temporal granularity and scale. The COVID-19 pandemic, in particular, has spurred the use of mobile phone location data for pandemic and disaster management. However, there is a lack of a comprehensive review that synthesizes the last decade of work and case studies leveraging mobile phone location data for response to and recovery from natural hazards and epidemics. We address this gap by summarizing the existing work, and point to promising areas and future challenges for using mobile phone location data to support disaster response and recovery.

With rapid urbanization and increasing climate risks, enhancing the resilience of urban systems has never been more important. Despite the availability of massive datasets of human behavior (e.g., mobile phone data, satellite imagery), studies on disaster resilience have been limited to using static measures as proxies for resilience. However, static metrics have significant drawbacks such as their inability to capture the effects of compounding and accumulating disaster shocks; dynamic interdependencies of social, economic, and infrastructure systems; and critical transitions and regime shifts, which are essential components of the complex disaster resilience process. In this article, we argue that the disaster resilience literature needs to take the opportunities of big data and move toward a different research direction, which is to develop data-driven, dynamical complex systems models of disaster resilience. Data-driven complex systems modeling approaches could overcome the drawbacks of static measures and allow us to quantitatively model the dynamic recovery trajectories and intrinsic resilience characteristics of communities in a generic manner by leveraging large-scale and granular observations. This approach brings a paradigm shift in modeling the disaster resilience process and its linkage with the recovery process, paving the way to answering important questions for policy applications via counterfactual analysis and simulations.

Extreme heat continues to be a pressing challenge of the changing climate. The impacts of extreme heat manifest on two different spatio-temporal scales: (1) episodic continent-wide heatwaves (HWs) and (2) the city-scale urban heat island (UHI). As HWs are becoming more frequent, longer, and severe, they pose serious implications of increased public health risks at a city scale, and have adverse impacts on agricultural and terrestrial/aquatic ecosystems on the regional scale. Here we offer a fresh perspective of the HW as a forcing that invokes dynamic, heterogeneous, scale-dependent responses evident in inter and intra-urban heat islets. A numerical simulation of the 2018 European HW including the surface and air temperature-based UHIs of six urban agglomerations, with a high-resolution focus on Paris, serves as our case study. We find that the mean nighttime UHI intensities are reduced for inland cities but increased for coastal cities. Our examination of the heat islets reveals two major findings: (i) the HW homogenizes the intra-urban surface temperatures during the daytime (reduces variance), (ii) the HW impacts are most significant on the scale of large, spatially discontiguous extreme heat islets during nighttime. These results underscore the need to move beyond the prevalent HW-mean UHI intensity characterization and toward intra-urban heat islet analyses that aid targeted mitigation.

Structural and functional attributes across fractal river networks have been characterized by well-established and consistent hierarchical, Hortonian scaling patterns. In most of the global river basins, spatial patterns of human settlements also conform to similar hierarchical scaling. However, emergent spatial hierarchical patterns and scaling of heterogeneous anthropogenic nutrient loads over a river basin are less known. As a case study, we examined here a large intensely managed river basin in Germany (Weser River; 46K km2; 8M population). Archived data for point-/diffuse-sources of total Phosphorus (Ptot) input loads were combined with numerical and analytical model simulations of coupled hydrological and biogeochemical processes for in-stream Ptot removal at the network scale. We find that Ptot input loads scale exponentially over stream-orders, with the larger scaling constant for point-source loads from urban agglomerations compared to those for diffuse-source contributions from agricultural and forested areas. These differences in scaling patterns result from hierarchical self-organization of human settlements, and the associated clustering of large-scale, altered land-cover. Fraction of Ptot loads removed through in-stream biogeochemical processes also manifests Hortonian scaling, consistent with predictions of an analytical model. Our analyses show that while smaller streams are more efficient in Ptot removal, in larger streams the magnitude of Ptot loads removed is higher. These trends are consistent with inverse scaling of nutrient removal rate constant with mean discharge, and downstream clustering of larger cumulative input loads. Analyses of six nested sub-basins within the Weser River Basin also reveal similar scaling patterns. Our findings are useful for projecting likely water-quality spatial patterns in similar river basins in Germany, and Central Europe. Extensions and generalizations require further examination of diverse basins with archetype spatial heterogeneities in anthropogenic pressures and hydroclimatic settings.

Rapid urbanization and climate change trends are intertwined with complex interactions of various social, economic, and political factors. The increased trends of disaster risks have recently caused numerous events, ranging from unprecedented category 5 hurricanes in the Atlantic Ocean to the COVID-19 pandemic. While regions around the world face urgent demands to prepare for, respond to, and to recover from such disasters, large-scale location data collected from mobile phone devices have opened up novel approaches to tackle these challenges. Mobile phone location data have enabled us to observe, estimate, and model human mobility dynamics at an unprecedented spatio-temporal granularity and scale. The COVID-19 pandemic has spurred the use of mobile phone location data for pandemic and disaster response. However, there is a lack of a comprehensive review that synthesizes the last decade of work leveraging mobile phone location data and case studies of natural hazards and epidemics. We address this gap by summarizing the existing work, and pointing promising areas and future challenges for using data to support disaster response and recovery.

Cities are complex systems comprised of socioeconomic systems relying on critical services delivered by multiple physical infrastructure networks. Due to interdependencies between social and physical systems, disruptions caused by natural hazards may cascade across systems, amplifying the impact of disasters. Despite the increasing threat posed by climate change and rapid urban growth, how to design interdependencies between social and physical systems to achieve resilient cities have been largely unexplored. Here, we study the socio-physical interdependencies in urban systems and their effects on disaster recovery and resilience, using large-scale mobility data collected from Puerto Rico during Hurricane Maria. We find that as cities grow in scale and expand their centralized infrastructure systems, the recovery efficiency of critical services improves, however, curtails the self-reliance of socio-economic systems during crises. Results show that maintaining self-reliance among social systems could be key in developing resilient urban socio-physical systems for cities facing rapid urban growth.