UC Berkeley

Wind energy is the leading renewable technology in the U.S., generating over 10% of utility-scale electricity in recent years. Rapid growth in wind energy installations has made modeling and prediction of atmospheric boundary layer (ABL) wind speeds and the associated turbulence critical for wind turbine siting, resource assessment, and operational power forecasting. A number of modeling challenges currently exist, such as representing the impact of terrain on wind turbine wakes and capturing small-scale turbulence in stably-stratified conditions. Many low-fidelity wind turbine simulation methods fail to incorporate topography and struggle to account for dynamic flow behavior. In this dissertation, results are presented using high-fidelity large-eddy simulation (LES), which captures the dynamic and turbulent behavior of ABL winds, providing a framework to simulate a wide variety of turbulent atmospheric phenomena with a wind turbine parameterization to understand turbine-airflow interactions.

First, high-resolution simulations of the 2017 Perdigão field campaign in Portugal are conducted. The Perdigão site consists of two parallel ridges with a wind turbine located on top of one of the ridges. Both convective and stable atmospheric conditions are simulated to understand how the wind turbine wake behaves in complex terrain in two representative flow regimes. For the convective case study, flow recirculation in the lee of the ridge occurred, thus deflecting the wake upwards. For the stable case study, the wake deflected downwards following the terrain due to a mountain wave that occurred. The vertical behavior of the wind turbine wake can be detrimental to downwind turbines; however, this vertical behavior is not accounted for in current wind farm design wake models. These case studies demonstrate the dependence of the wind turbine wake behavior on terrain-induced flow phenomena, which, in-turn, depend on the thermal stratification of the atmosphere.

The stable case study from Perdigão is then studied in more depth to better understand both the ambient and wind turbine wake turbulence characteristics. Novel derived measurements of the turbulence dissipation rate are available from the field campaign, providing an opportunity to further examine the spatial structure of turbulence predicted by the model. Additionally, in this study, the dynamic reconstruction model (DRM) LES turbulence closure is used to better represent smaller-scale turbulence. The DRM closure more accurately predicts turbulence metrics, including the turbulence dissipation rate, most notably upwind of the major topographic features. After the flow passes over the first ridge, the differences between the DRM and a standard eddy-viscosity closure are small close to the surface, although the DRM closure does better predict the turbulence dissipation rate in the upper atmosphere in this region. Because the DRM closure is not a standard eddy-viscosity closure, negative turbulence dissipation rate or the backscatter of energy from smaller scales to larger scales is predicted; however, backscatter cannot be derived from Perdigão measurements due to the experimental setup and analysis methods used, thus leaving validation of this aspect for future work.

Next, a range of idealized stable boundary layer (SBL) conditions are modeled in support of the American Wake Experiment (AWAKEN) field campaign to address: (1) the effect of wind turbines on SBL development, and (2) the effect of intermittent turbulence on wind farm performance. In weak SBL conditions, turbulence is continuous and easier to simulate. With the intermittent turbulence that occurs in strongly stable conditions, only the DRM closure can resolve realistic turbulence. For all SBL conditions simulated, the wind farm significantly impacts wind speeds and thermal structure well downwind (greater than 30 rotor diameters or 2.4 km) of the farm. Wind speeds in the wakes are reduced, and the increased mixing as a result of the wakes weakens the stable stratification in the boundary layer.

Finally, simulations are performed of a real case study of intermittent turbulence observed during the AWAKEN field campaign. The intermittent turbulence event is determined to be driven by a nocturnal mesoscale convective system (MCS). The MCS results in a cold pool, which radiates outwards as a density current. This density current perturbs the SBL, thus inducing gravity waves. The structure of the simulated gravity waves is found to be especially sensitive to the parameterization of cloud and precipitation processes (microphysics). The gravity waves have very strong effects on the flow in the upper atmosphere; however, closer to the surface where there is additional ambient turbulence and turbulence generated by wakes, the effect of the waves is more nuanced. Notably, the waves induce local wind direction variation, which leads to fluctuations in the power output as various turbines within the farm are subjected to the wakes of nearby turbines.

The findings presented in this dissertation provide insight into wind farm performance in a broad range of atmospheric conditions by incorporating both terrain effects and thermal stratification. Specifically, these conditions include dynamic turbulent phenomena that current wind farm design tools are unable to capture. The advances in this dissertation related to high-resolution LES reveal novel and complex relationships between wind turbines and the atmosphere that can significantly improve wind farm power predictions at large.

The enzyme rubisco (ribulose-1,5-bisphosphate carboxylase/oxygenase) catalyzes the majority of biological carbon fixation on Earth. Although the vast majority of rubiscos across the tree of life assemble as homo-oligomers, the globally predominant form I enzyme—found in plants, algae, and cyanobacteria—forms a unique hetero-oligomeric complex. The recent discovery of a homo-oligomeric sister group to form I rubisco (named form I′) has filled a key gap in our understanding of the enigmatic origins of the form I clade. However, to elucidate the series of molecular events leading to the evolution of form I rubisco, we must examine more distantly related sibling clades to contextualize the molecular features distinguishing form I and form I′ rubiscos. Here, we present a comparative structural study retracing the evolutionary history of rubisco that reveals a complex structural trajectory leading to the ultimate hetero-oligomerization of the form I clade. We structurally characterize the oligomeric states of deep-branching form Iα and I′′ rubiscos recently discovered from metagenomes, which represent key evolutionary intermediates preceding the form I clade. We further solve the structure of form I′′ rubisco, revealing the molecular determinants that likely primed the enzyme core for the transition from a homo-oligomer to a hetero-oligomer. Our findings yield new insight into the evolutionary trajectory underpinning the adoption and entrenchment of the prevalent assembly of form I rubisco, providing additional context when viewing the enzyme family through the broader lens of protein evolution.Proteins form oligomeric complexes with other proteins, enabling a diversity of biological functions. The acquisition of protein-protein interactions between binding partners is generally considered to be the primary driver of oligomerization. However, ligand binding is similarly capable of driving higher-order assembly, though how these interactions contribute to and constrain the evolution of protein complex formation is less well understood. Here, we demonstrate how protein oligomerization can emerge via ligand-protein interactions within an enzyme family. Through phylogenetic sampling of a unique clade of rubisco, we discover a range of oligomeric responses to ligand binding across the phylogeny and identify the structural features driving an observed dynamic shift in oligomeric state. We further identify the critical role of a metal cofactor in governing higher-order assembly. Our results describe the acquisition of a ligand-protein interaction that mediates oligomerization in rubisco, illustrating a means by which stochastic binding and entrenchment of a ligand enables an increase in complexity.

This dissertation shows how businesses including shipping companies, labor recruitment agents, and landed elites—“agents of globalization”—decisively shaped global mobility in an era of nominally “free” and unregulated migration. From South Carolina and Argentina to Saskatchewan and Australia, businesses tried desperately to court immigrants, resorting to subsidies and marketing campaigns, or, failing these, deceit and coercion. Recruiters were invariably aided by shipping companies, which agglomerated during the 1870s into a handful of globe-straddling, modern corporations like the British Cunard or the German Hamburg-America Line. Never simply neutral conduits of global interconnectivity, steamship lines indiscriminately fomented migration to countless destinations, which delivered profits far exceeding those on freight. Across Europe, meanwhile, the deluge of shipping agents and foreign recruiters provoked a backlash from commercial farmers, who had been buffeted by world markets and now faced a depleting labor force and rising wages. The net result was an international migration regime quite unlike our own. Ours is an age of chronic structural unemployment in the richest countries, in which liberalized immigration policies are consequently a hard sell. In 1900 by contrast the economies of the Americas, Australia, and New Zealand wrestled with an inexhaustible appetite for labor power. Simply put, even unskilled labor was a desperately sought commodity in a world awash in land and capital.

Data science is a fundamental tool in hydrology nowadays. The significance of data science lies in its ability to confront the multifaceted challenges posed by global warming, facilitating a deeper comprehension of hydrological processes, and enhancing the accuracy of runoff predictions. This dissertation embarks on a journey aimed at advancing our insights into hydrological processes, refining the physical-consistency of runoff predictions, and addressing the intricate task of forecasting hydrological behaviors in ungauged basins through the application of data science techniques. Comprising three main bodies of work, this dissertation unfolds a comprehensive exploration of these objectives. The first contribution (Chapter 2) centers on the synthesis of extensive hydrological datasets and subsequent analysis of hydrological trends under recent warming. Chapter 3 explores a physics-informed machine learning model designed for predicting streamflow tested across different scenarios. Lastly, the fourth chapter evaluates the potency of various watershed clustering mechanisms for predicting within ungauged basins (PUB).

Chapter 2 addresses a long-standing limitation in comparative hydrology: the scarcity of geographically extensive, inter-compatible monitoring data on comprehensive water balance stores and fluxes. These limitations have, for example, restricted comprehensive assessment of multiple dimensions of wetting and drying related to climate change and hampered understanding of why widespread changes in precipitation extremes are uncorrelated with changes in streamflow extremes. In this chapter, both the requirements of developing a new data synthesis product and using this data product to detect trends in the frequencies and magnitudes of a comprehensive set of hydroclimatic and hydrologic extremes are addressed. The Comprehensive Hydrologic Observatory Sensor Network (CHOSEN), a database encompassing hydroclimatic and hydrologic variables from 30 diverse study areas across the United States is introduced. And a reproducible data pipeline that ensures data quality and accessibility is developed. Analyzing the CHOSEN dataset, the hotspots of hydroclimatic extremes in regions like the Pacific Northwest, New England, Florida, and Alaska are uncovered. The analysis reveals regional coherence in extreme streamflow wetting and drying trends, shedding light on the complex interplay between climate-induced changes and hydrologic processes.

Chapter 3 is built upon the development of the CHOSEN dataset to create subsequent analyses and a new runoff prediction model. The challenge of a lack of interpretability and physical consistency in machine learning models used for streamflow prediction is confronted. To address this issue, a physics-informed long short-term memory (PILSTM) model is proposed, incorporating water balance restrictions for runoff prediction. A physical rainfall-runoff model is combined with the long short-term memory (LSTM) model, and it is applied to eight intensively-monitored watersheds in the United States, selected based on data length and hydroclimatic diversity. LSTM, physical, and PILSTM models are used under non-stationary scenarios and data-scarce situations. Results show that the PILSTM exhibits similar or better performance to the LSTM counterpart in terms of multiple metrics and under various scenarios. Additionally, based on the analysis of feature importance, it is shown that adding physical constraints could potentially guide machine learning models to generate predictions that are more consistent with known physical processes.

Chapter 4 explores the effectiveness of watershed clustering, a conventional practice in watershed regionalization, in combination with neural networks for predicting in ungauged basins. Traditionally, watershed clustering involves grouping basins with similar characteristics to facilitate knowledge transfer from monitored to ungauged basins within the same cluster. Recent advancements in data science, however, suggest that clustering may not be necessary. This study aims to investigate this matter and presents a comparative analysis of various watershed clustering methodologies. The concept is explored by directly integrating static watershed attributes into predictive models for streamflow (entity-aware LSTM). The analysis covers 415 sites from the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset. Results indicate that pre-clustering generally does not enhance the performance of entity-aware LSTM models for predicting in ungauged basins. Models incorporating clustering results either match or perform worse overall compared to global models that directly integrate clustering features as static inputs. Notably, among the different features used for clustering, hydrological signatures prove most effective in extracting information for use in the LSTM model.

Chapter 2 addresses crucial gaps in data availability, while the subsequent chapters explore novel approaches for forecasting streamflow across diverse scenarios and ungauged basins, leveraging the power of data science. In Chapter 3, the integration of physical and machine learning models is pursued, while Chapter 4 focuses on harnessing data science methodologies for predicting in ungauged basins. Collectively, these chapters offer an exploration of the intersection between data science and hydrology. This dissertation emphasizes the transformative potential of interdisciplinary strategies, which bridge data-driven insights with the dynamics of hydrological systems.

The word convivenza is used to describe peaceful ‘co-existence’ between diverse communities, particularly religious communities. The idea that convivenza is central to Sicilian identity has become commonplace in local discourse, and is often used to distinguish the island from mainland Italy and Europe. “Every Sicilian has Arab DNA,” I am often told during research in Sicily, “because Sicily was once an Islamic Emirate.” “While Jews were being persecuted in central Europe,” some argue, “they thrived in Sicily for centuries.” This narrative is neither new nor universally accepted – it is based on contested interpretations of Sicily’s past and present – but has acquired additional meanings in the twenty-first century, with Sicily now a point of arrival for migrants crossing the Mediterranean. Whereas numerous scholars have analyzed the contemporary effects of Américo Castro’s famous convivencia thesis in Spain (1948), the construction of a similar narrative in Sicily merits closer attention than it has thus far received. I focus on Palermo, Sicily’s regional capital, where this narrative is especially influential. I blend cultural studies and ethnography, both in-person and digital, to study the cultural memory of Palermo’s Jewish and Islamic past; its contemporary repercussions for the heritage sphere, and for the city’s religious landscape; and the role of the convivenza narrative in political rhetoric.

I first ask how the convivenza narrative encourages ‘rediscoveries’ of Palermo’s multi-religious heritage. I study how Palermo’s Jewish history has been emphasized in the past twenty years, over half a millennium after Sicily’s Jews were expelled in the late fifteenth century. I examine the emergence of Jewish ‘sites of memory’ in Palermo, including a medieval Jewish quarter; a pool that some believe was once a miqweh (ritual bath); and a former inquisitorial prison. I then turn my attention to two mosques and a planned future synagogue in Palermo, all located in former churches, arguing that heritage-based narratives are used to weave these into the contemporary city’s fabric. Finally, I analyze political messaging by one of the most vocal proponents of a ‘cosmopolitan’ Sicilian identity: Leoluca Orlando, Mayor of Palermo until 2022. I evaluate how Orlando uses history-based rhetoric to make pro-immigration arguments, framing Mediterranean ‘hospitality’ as an inherently Sicilian value. I build on studies of nostalgia, heritage, and cultural memory, critically examining the construction of a ‘cosmopolitan’ Mediterranean identity. I analyze how the stereotyped image of Sicily as ‘not quite European’ – once used within Italy to disparage Sicilians – is being celebrated by some in the twenty-first century, particularly by those who look at Italy’s anti-immigrant central government with trepidation.

The emergence and rapid spread of the severe acute respiratory syndrome coronavirus 2, SARS-CoV-2 (SCoV-2) caused catastrophic levels of morbidity in the world and presented the unmet need for treatments and drugs urgently. Small molecule therapeutics have tremen- dous potential to develop into antivirals and prevent the spread of infection. SCoV-2 genome encodes for 16 non-structural proteins (Nsps) that have been proven as potential target can- didates. Two vital proteins, Papain-like protease (PLpro) (Papain-like protease, from Nsp3) and Non-structural protein 15 (Nsp15) were chosen as therapeutic targets to combat the SCoV-2 virus. PLpro is an essential protein that cleaves the polyprotein into its individual proteins to form a replication-transcription complex for viral replication and synthesis. On another hand, Nsp15 is a crucial protein that evades the host immune response by cleaving the viral RNA, therefore, Nsp15 inhibition stimulates the protective response. Both nsps are highly conserved proteins and the development of drugs against them could also act as an initial step for future coronavirus pandemics. To find the covalent inhibitors of PLpro and Nsp15, the electrophile library was screened against these two proteins using their re- spective fluorescent-based, high-throughput screening assay. To find the inhibitors against the PLpro, a chemical library was screened against the recombinantly purified PLpro using a fluorescent-based high throughput screening assay. One compound, based on mercaptopy- rimidine inhibited PLpro invitro and SARS-CoV-2 viral replication in Vero E6 cells. This compound presented the first example of a thiol-targeted reversible covalent inhibitor of PLpro. In the case of Nsp15, an acrylamide-based electrophile library was screened to find cysteine-binding inhibitors. This discovered a new class of Nsp15 covalent inhibitors. This study not only discovered new covalent lead hits against crucial proteins from SCoV-2, but also a new platform to develop new potent drugs against coronaviruses for future pandemics.

This dissertation provides a description and analysis of the syntax of Kawahíva, a critically endangered Tupí-Guaraní language spoken by approximately 560 people in the Brazilian Amazon basin. The first part of this dissertation includes an overview of Kawahíva documentation and revitalization efforts, its phonology, and a comprehensive grammatical sketch of the language.

In the second half of this dissertation, I analyze two key topics of Kawahíva syntax: clause structure and relativization. In particular, I offer an analysis of VSO word order of Kawahíva clause structure. I argue that verb-initial word order in Kawahíva results from long head movement, a type of syntactic head movement (Harizanov and Gribanova 2019), similar to phrasal movement. This analysis is supported by the hallmarks of syntactic movement in Kawahíva verb movement, including interpretive semantic effects and nonlocality. I also show that two theoretical accounts of the V1 order – Remnant VP Movement and Head Movement – are insufficient to derive the V1 order in Kawahíva. This study is the first to demonstrate that a language can use long head movement as a general principle to create the verb-initial order.

The final chapter of the dissertation investigates relativization in Kawahíva. Several languages are documented with a relativization strategy where a clausal nominalization is used as an adnominal modifier. It follows from this that relativization in these languages is achieved by nominalization morphology itself, without any additional syntactic processes taking place (Comrie and Thompson 1985; Keenan 1985; Andrews 2007; Shibatani 2009). However, I argue that characterizing these structures as nominalizations in Kawahíva is insufficient and that there is additional clear evidence that the nominalized clause also involves a distinct operation, namely relativization. Evidence for this claim will come from showing that adnominal nominalizations exhibit the hallmark properties of relative clauses (and other extraction-based constructions) cross-linguistically: i) sensitivity to island effects and ii) the formation of a long-distance dependency between the gap in the nominalization and the filler (i.e., the modified noun).

Surveillance plays a key role in malaria control and elimination efforts by allowing for informed and effective allocation of often limited resources. Current methods for estimating malaria exposure are limited either by cost or accuracy. Serological data offers the potential to provide inexpensive and accurate estimates of exposure, but to date there is no consensus on which antibody responses are informative and how they can be interpreted. Previous studies have been limited to single age groups, transmission intensities, and geographic locations that are not generalizable across populations. This dissertation describes an investigation into serologic biomarkers of recent exposure to Plasmodium falciparum, the most deadly species causing malaria, using samples from 8 cohort studies representing diverse populations. Using an innovative approach that combined detailed individual-level exposure data, high-throughput screening of hundreds of antibody responses, and robust statistical methods, we were unable to identify a set of antibody responses predictive of recent exposure that was generalizable across settings. Although universal seromarkers may not exist, we found that accurate prediction of recent exposure was possible in a cohort of return travelers, suggesting the potential for seromarkers to be developed for specific settings, particularly those with limited cumulative exposure.

Climate change is altering physical environments and biotic communities globally. High-elevation mountain streams are particularly at risk because rising air temperatures can reduce snowpack and extend the duration of summer low flow, consequently altering a variety of abiotic variables. In turn, populations and communities exposed to environmental change can undergo shifts in phenology, fitness, and behavior–altering the ecosystem processes that these biota control. In this dissertation, I examined how climate change-induced low flows are impacting stream water temperature, invertebrate communities, and the mechanistic pathways through which low flow acts on communities. I achieved this with three complementary approaches. First, I tested the effects of earlier low flows on organismal phenology, community composition, and resulting ecosystem processes via a mesocosm experiment that simulated flow regimes expected under end-of-the-century climate projections. Second, I assessed spatiotemporal variation in thermal vulnerability to climate change in a mid-elevation stream network in the Sierra Nevada over an extreme drought year (2020–2021). Lastly, I investigated the abiotic and biotic pathways whereby drought alters invertebrate community composition and structure in a California Sierra Nevada watershed across nested spatio-temporal scales–from microhabitat to watershed, and over two decades. I found that extended low flows will likely have diverse abiotic and biotic ramifications on stream ecosystems, but the mechanisms behind these changes are complex and require deep understanding of the ecosystem context.In the summer of 2019, I experimentally examined how earlier snowmelt will alter the phenology of mountain stream organisms and ecosystem processes via outdoor mesocosm stream channels in the Eastern Sierra Nevada, California. Channels were assigned to three hydrograph treatments that simulated the current flow regime or a 3 to 6 week earlier return to summer baseflow conditions projected under regional climate change scenarios. I measured discharge, water temperature, primary production, benthic macroinvertebrate secondary production and phenology, macroinvertebrate emergence, and predatory behavior of a riparian bird. Water temperature increased under advanced low flow conditions, which may have played a role in biofilm production to respiration ratios declining by 32%. Additionally, the majority of the benthic and emergent invertebrate species explaining community dissimilarity changed in phenology as a consequence of the early low-flow treatment. Emergent flux pulses of the dominant insect group (Chironomidae) also nearly doubled in magnitude, benefitting riparian predators. One such riparian predator, the Brewer’s Blackbird, gained access to feed on benthic macroinvertebrates under the 6 week early low flow treatment that aligned with their nesting period. Changes in both invertebrate community structure (composition) and ecosystem processes were mostly fine-scale, and response diversity at the community level stabilized seasonally aggregated responses. My findings illustrate how climate change in mountain streams at the rain-to-snow transition is poised to alter the dynamics of stream food webs via fine-scale changes in phenology even when community structure and ecosystem processes appear stable over longer time periods. In 2020-2021, I deployed a nested array of high-frequency sensors and used advances in time-series models to examine spatiotemporal variation in thermal vulnerability. This work took place in Bull Creek, one of the Kings River Experimental Watersheds (KREW). Stream thermal sensitivity to atmospheric warming fluctuated strongly over the year and peaked in spring and summer. I found that spatially, the reach scale (~50 m) best captured variation in summer thermal regimes. Spatial variation in summer water temperature was driven firstly by upstream water temperature, with elevation, discharge, and conductivity as local correlates. Lastly, I combined the estimated summer thermal sensitivity and downscaled projections of summer air temperature to forecast end-of-the-century stream warming. I found that 25.5% of cold-water habitat may be lost under high-emissions scenario RCP 8.5 (or 7.9% under mitigated RCP 4.5). This estimated reduction suggests that up to 27.2% of stream macroinvertebrate biodiversity could be stressed or threatened in what was previously cold‑water habitat. My results support that thermal vulnerability in montane stream networks may be highly variable over space and time. Taking spatiotemporal variation into account is critical to understand how climate change will impact high mountain stream ecosystems through rising temperatures and shifts in precipitation. Stream low flows can alter communities via multiple environmental and biological mechanisms across time and space, but support is mixed as to which mechanisms are paramount and how spatial and temporal context determines their relative importance. I investigated the mechanisms whereby low flow alters stream invertebrate community composition and structure in high-mountain streams–across space and over time. To this end, I sampled aquatic macroinvertebrates from the same 60 sites in Bull Creek where temperature sensors were deployed in 2020, using a nested sampling design. Additionally, long-term data in four reaches were sampled 11 times from 2002 to 2023. The inspected abiotic mechanisms of drought (temperature, water velocity, and fine sediment) all explained variation in a similar percentage of taxa in the community (36.8% - 47.4%), but effects differed when examined spatially vs. temporally. Total spatial variance explained by abiotic mechanisms for each species had no relationship with its temporal counterpart. Biological mechanisms also differed across space and time; community dissimilarity across space was driven by differences in fine sediment causing species turnover (i.e., sensitive species being replaced by tolerant ones), while temporal dissimilarity was driven by differences in temperature and water velocity causing reordering (i.e., shifts in relative abundance). These results challenge the key assumption of ‘space-for-time’ substitution that underpins abundant research on climate change ecology. I contend that space-for-time substitution approaches may be inappropriate in mountain river studies because of their hierarchical structure, high temporal variability, and mechanisms operating distinctly across space and time. This dissertation provides evidence that climate change-induced low flows will alter Sierra Nevada stream ecosystems in a variety of ways. High elevation mountain streams will increasingly be affected by climate change, a threat that is not well understood despite extensive research efforts. My findings demonstrate that climate change effects are highly context dependent and examining them at the appropriate spatiotemporal scale is necessary to properly assess their impact. Notably, changing abiotic conditions due to extended low flows may benefit some ecosystem processes and taxa at the upper edge of their elevation range, potentially at the cost of coldwater specialists adapted to harsh high mountain streams. High response diversity of species in this dissertation ensured that ecosystem processes often remained stable even if many individual species responded to changing flow regimes. Advances in ecological models and methodology enable finer assessment of environments and communities than ever before, but my research shows that extensive data collection and knowledge of local natural history are necessary for these methods to be effective. This dissertation expands the field of drought ecology, which is of the utmost importance in a quickly changing world.

This dissertation employs a landscape-focused approach to examine practical craft objects, fine art, and architecture coated with Jōbōji lacquer that date from the late nineteenth century to the early 2000s. I address lacquered objects made from “Jōbōji lacquer” tree sap tapped from East Asian lacquer tree (Toxicodendron vernicifluum) forests in a remote, mountainous area of northern Japan that I refer to as the “Jōbōji lacquer landscape,” centered in the town of Jōbōji. I examine the dynamic expansions and contractions of Jōbōji lacquer as it is planted, harvested, purified, and rematerialized as craft objects and architectural complexes that serve as visual and material indicators of the modern Japanese nation-state. This landscape approach discloses site-specific responses to a shifting world of craft production; it reveals an ongoing struggle for survival—social, cultural, and biomaterial—of lacquer craft and the specific forest, community, and artisanal practices of Jōbōji. This struggle arose in the face of industrial modernity and its challenge to the value of the “handmade” and “traditional” crafts, mass-production of plastic wares that lowered demand for lacquerware, and the nation-state’s campaign to establish a national art canon and history, as well as a national aesthetics. I show how Meiji period (1868-1912) Jōbōji lacquer artisans conjoined the techniques and designs of Jōbōji lacquer with a nationalized lacquer tradition known as maki-e (“sprinkled picture”) in response to the technique’s promulgation by the Artists for the Imperial Household System, the Tokyo School of the Arts, and International Expositions in the 1880s and 1890s. In the 1930s and 1940s, Jōbōji lacquerware appears in magazines, periodicals, and exhibitions formulated by advocates of the Folk Crafts Movement (Mingei Undō), demonstrating how the aesthetic qualities of Jōbōji lacquer—simple designs, practicality, and the lively technique of urushi-e (“lacquer pictures”)—embodied the mingei theorist and collector Yanagi Muneyoshi’s (1889-1961) ideals of the “People’s Art.” In the postwar period, artists such as Koseki Rokuhei (1918-2011) utilized Jōbōji lacquer to execute the traditional maki-e lacquer technique, creating abstract sculptural forms that reveal the possibilities of lacquer material as a medium capable of abstract visual expression in the realm of contemporary “craft art” (kōgei bijutsu). Finally, the use of Jōbōji lacquer in the restoration of the exterior of the Yōmeimon (“Gate of Illuminating Sun”) at the Nikkō Tōshōgū shrine-temple complex—designated as a National Treasure and UNESCO World Heritage Site—points to the growing sense of national pride associated with the tapping of “authentic” Japanese lacquer, and demonstrates how the efforts lacquer sap collectors are now oriented toward national and global cultural heritage preservation and restoration.