Anthropogenic global warming caused by increased atmospheric carbon forcing is expected to cause a decrease in peak snow water equivalent (SWE), shift the timing of snowmelt to earlier in the year, and lead to slower melt rates in the mountains of the Western United States. High-elevation forests in mountainous terrain represent a critical carbon sink. Understanding the ecohydrology of subalpine forests is crucial for assessing the health of these sinks. The Niwot Ridge Long Term Ecological Research station, located at 3000 m amsl in the southern Rocky Mountains of Colorado, receives just over 1 m of annual precipitation mostly as snow, supporting a persistent seasonal snowpack in alpine and subalpine ecosystems. Previous studies show that longer growing season length is correlated with shallower snowpack, earlier spring onset and reduced net CO2 uptake. Co-located sensors provide over 20 years of continuous SWE and eddy covariance (EC) data, allowing for robust direct comparison of snow and carbon phenomena in a high-elevation catchment. Linear regression and time series analysis was performed on snowmelt, meteorological, phenological and ecosystem productivity variables. Peak productivity is correlated with peak SWE (R2=0.54) and further correlated with snowmelt disappearance (R2=0.38) and the timing of spring growth onset (R2=0.30). Timing of both peak productivity and spring growth onset are correlated with snowmelt and meteorological variables. A multivariable regression of meteorological variables, timing of spring growth onset, a temporal trend, and snowmelt rate and explains 94% of interannual variability in the timing of peak forest productivity. These results develop support and introduce new evidence for the existing studies of Niwot Ridge ecohydrology. Future work will investigate the meteorological and hydrological record extending back to 1979 and the long-term trends in snowmelt and forest productivity.

A large-scale seafloor observation network, known as S-net, has been operated by National Research Institute for Earth Science and Disaster Resilience (NIED) in the Japan Trench area since 2016. The main objective of the observation is to provide reliable earthquake and tsunami early warnings. The network consists of 150 in-line-type observatories with sensors housed in cylindrical pressure vessels. The stations and cables were buried about one meter beneath the seafloor in the region of water depth less than 1500 m while they were sited on the seafloor in the deeper area. Previous studies have shown that the S-net sensors rotate after some threshold peak ground accelerations due to poor coupling between the sensor house and sediment, resulting in large rotational noises. Another serious concern at the S-net sites is the amplification effect of sediments. To minimize the various effects, researchers have devised equations using the displacement amplitude of vertical component of motions to estimate the magnitude for Japan Meteorological Agency earthquake early warning (Hayashimoto et al. 2019). The vertical motions are affected as well by the presence of water layer in the oceanic area. Thus, the analysis of seafloor records is challenging due to the multiply effects of sediments and water layer. In this meeting, we report the results of analyses of strong-motion records at the ground in the Kanto Basin and adjoining areas and the S-net sites from nearby moderate to strong offshore earthquakes. Strong motions at ground and borehole sites were taken from K-NET/KiK-net and MeSO-net, respectively. We have found that the horizontal PGAs and PGVs at the S-net sites were, on average, similar to or larger than those at the ground and borehole sites at equal distances. In contrast, the vertical PGAs and PGVs at the S-net sites tended to be smaller than those at the ground sites for the S wave. Notably, the PGAs and PGVs for the P-wave parts on the vertical records of S-net were smaller than those at the ground sites. A portion of the results was published in Dhakal and Kunugi (2021). We are now analyzing a larger dataset and report the detailed results elsewhere. This study was supported by the Advanced Earthquake and Tsunami Forecasting Technologies Project of NIED and JSPS KAKENHI Grant Number JP20K05055.

We give a preliminary report on results of detecting low-frequency earthquakes (LFs) occurring at Mt. Fuji, Japan, using the matched filter method (MF method: e.g., Peng & Zhao, 2009). LFs have been observed in the depth 10-25km beneath Mt. Fuji (Hamada, 1981; Ukawa et al, 2005). These LFs seem to occur at an almost constant rate at all times, but it may become remarkably active as in the fall of 2000 (Yoshida et al., 2006). Because it is considered that the activity of LFs is associated with behavior of magmatic fluid at depth (e.g., Nakamichi et al., 2003), an investigation into the relationship between LFs and volcanic activity (e.g., Harada et al., 2010) is important. Understanding of the details of LFs activity is the first step for this investigation. Here, a system using the MF method for detecting LFs at Hakone volcano, Japan (Yukutake, 2017; Yukutake et al., 2019), was modified to be applicable to the detection of LFs at Mt. Fuji. Then, this was applied to continuous seismic record at seismic stations around Mt. Fuji during the period of 2012-2020. Next, the template waveforms of LFs were prepared on the basis of the earthquake catalog maintained by the Japan Meteorological Agency (JMA). Then, the cross-correlation analysis was conducted between the template waveforms and the seismic records. Finally, a catalog of LFs, obtained by using the MF method, was created. Using this catalog, we confirmed that LFs in 2012-2020 occurred at an almost constant rate, and that this is also true for LFs included in the JMA catalog. However, our case shows that LFs occurred at a rate of about 1,250 per year, which is about 10 times higher than that shown for the JMA case (a rate of about 125 per year). It was also confirmed that the larger LFs tend to have fewer numbers and smaller LFs tend to have more numbers, again a feature found by using the JMA catalog. Our research is underway, and tackling challenges such as selection of appropriate template waveforms of LFs, correction of magnitude estimate, and extension of analysis period will improve our results, which will be reported in the presentation.

The impacts of global change especially the recent climate-related extremes such as floods and droughts reveal significant vulnerability and exposure of freshwater ecosystems and related human systems to current climate variability. However, the effects of the extreme drought in the Okavango Delta system are not well understood and documented. Therefore, the objective of this use case was to apply the products from Digital Earth Africa namely: the Water Observation from Space (WOfS) derived from Landsat, vegetation cover baseline derived from Sentinel 2 data; and data from the meteorological agencies such as rainfall and measured river discharge data to evaluate the effects of drought in the Okavango Delta wetland system in relation to its upstream areas in Angola. In particular, we used the 2019 drought as a case study to assess inundation extent and vegetation cover dynamics with an emphasis on floodplain and dryland vegetation. Our preliminary results reveal that the Okavango Delta permanent marshes are resilient to drought, whereas seasonal floodplains are susceptible to drought. Further, we discovered that the geospatial location of floodplains has a direct effect on the timing of desiccation, with the western tributaries that flow into Lake Ngami and Thamalakane River being the last to dry out due to drought. In addition, we found that the drought phenomenon in the Cubango-Okavango River Basin region started earlier than 2019 spanning over a period of 5 years; with 2018 as the year when the wetland system reached a minimum threshold for a tipping point triggered by the 2019 drought. In addition, the results contribute to the development of large-scale drought risk information and products for the Cubango- Okavango River Basin with a major focus in the Okavango Delta. Further, this use case provides recent baseline information on the effects of drought on vegetation cover and river flows in the Okavango Delta system at a landscape approach, which are essential elements for making informed science-based decisions on climate risks management and Sustainable Development Goals (SDGs) by relevant authorities in the Okavango Delta and the whole of Cubango-Okavango River Basin. In conclusion, this use case will be upscaled to other transboundary river basins in the Southern Africa Development Community.

Near-Earth asteroids and meteoroids constitute various levels of impact danger to our planet. On the one end, billions of events associated with small-sized meteoroids have resulted in trivial effects. On the other end, the occurrences of large-sized asteroidal collisions that can cause mass extinctions and may wipe out the modern human civilization are extremely rare. In addition, large near-Earth asteroids are being monitored constantly for accurate and precise predictions of potential hazardous visits to our planet. However, small asteroids and large meteoroids can still often go under the radar and cause bolide explosions with potential of significant damage to communities on the ground. To facilitate management of bolide hazard, a number of scholarly works have been dedicated to estimation of frequencies of bolide events from a global perspective for planetary defense and mitigation. Nevertheless, few of the existing bolide frequency models were developed for local hazard management. In this presentation, the author introduces two recently developed frequency models for local management of bolide hazard. The first one, called the Dome model, computes the expected frequency of bolide explosions within a dome-shaped volume around a location. The second one, called the Coffee Cup model, is for a column-shaped volume above an area. Both models are based on empirical calibrations with historical data on energy, latitude, altitude, and frequency of bolide events. The modeling results indicate a linearly decreasing trend of frequency of bolide events from south to north latitudinally around the globe. The presented models can be applied to any location or area on Earth, including the entire surface of the planet.

Floods are convincingly the most frequent and widespread natural hazard across the world. With an ample amount of literature forecasting increase in its frequency and magnitude further in the future, highly credible and efficient algorithms and tools are crucial for real-time flood monitoring. In this study, a highly efficient tool, Multi-Mission Flood Mapper, has been developed to delineate flood inundation extent without any human intervention from SAR images captured by multiple microwave SAR satellite missions, including ALOS PALSAR CEOS, ALOS 2 CEOS, COSMO-SkyMed, ENVISAT ASAR, ERS 1/2 CEOS, ERS 1/2 SAR(.E1, .E2), ICEYE, JERS CEOS, KOMPSAT-5, PAZ, RADARSAT-1 & -2, RCM, SAOCOM, SeaSat, Sentinel-1, TerraSAR-X, and TanDEM-X. The efficacy of the developed tool is assessed by performing a test on a significant number of flood events in India having diverse flooding patterns and landforms. To manifest the performance of the tool, the step-by-step processing at the backend of the tool is discussed in detail in this study by taking a flood event along the Ganga River in India as a case study. The algorithm of the tool includes various processing steps: pre-processing that incorporate applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion; thematic analysis that involves multi-segmentation and Otsu’s thresholding techniques; post-processing that consists of the elimination of hill shadows, applying majority filter, and masking out permanent water bodies. Thus derived flood inundation layer is observed to be highly accurate compared to the master image. The total time taken by the tool for processing is about 4 minutes for the given image. The developed tool would be beneficial for rapid flood inundation map generation on a timely basis for flood monitoring and relief management during a disaster. In addition, the flood inundation layers can also be used for calibration/validation of hydrological/hydraulic models, geospatial planning, and generating flood hazard maps. Also, the Multi-Mission Flood Mapper tool is facilitated with a user-friendly Graphical User Interface (GUI), making it look simple and easy to use.

Water management practices in cities around the world are faced with growing social and environmental pressures. Unfortunately, the linear “take-make-waste” approach, previously recognized as the most conclusive practice to address water-related issues, has been found to be unsustainable due to its dependence on the limited availability of energy and resources. It is, therefore, necessary to change the current linear approach dominant in most cities across the world to one that utilizes a high degree of reuse and recycling that is known as “One Water”. The goal of this study is to evaluate a series of expert interviews that were conducted with utilities across the US and Canada to gain insights into implementing One Water principles. Interpreting several interviews is the key step to provide water managers with an understanding of the perspective and required actions towards transitions in urban water management. The results indicated that although several pressures were described in the expert interviews responses, climate change was the most frequently described pressure, followed by water quality impairments and population growth. Moreover, it has been identified that the studied cities have implemented several strategies such as green infrastructure, recycled water, desalination, and stormwater management to achieve this holistic approach. The thematic analysis revealed that all cities demonstrated the importance of cultural change to break down silos and support various technological solutions. Further investigations revealed that cities encounter several barriers that inhibit the One Water transition. One of the most frequently discussed barriers was related to financial challenges in most cities, especially in light of the pandemic when substantial cities lost their revenue. In addition to the financial challenges, lack of regulatory process and framework, institutional barriers for expanding One Water strategies, short-term thinking, lack of collaboration, community resistance to change, lack of public support, and water rights were mentioned by participants as the top barriers.

Since the start of production in 1968 in the Groningen gas field (Netherlands) considerable land subsidence (>30 cm) has occurred above the field. Variability in reservoir compaction has led to earthquakes on reactivated Mesozoic age reservoir faults. Even though the impact of this seismicity (MW ≤3.6) on society has been large, due to substantial structural damage to buildings, surface deformation induced by the co-seismic slip has been too small to detect using geodetic data. It is possible that differential compaction across faults is not only accommodated by seismic slip, but also by aseismic slip (e.g., creep). Aseismically slipping reservoir faults would relax the stresses in the reservoir and, thus, reduce the severity of the seismicity. In this study we explore the potential occurrence of aseismic slip on the reservoir faults. We perform a sensitivity analysis to investigate whether aseismic slip on the different reservoir faults has the capacity to produce detectable surface signals. We use the analytical Okada (1992) model of slip on a discrete dislocation in a uniform elastic half-space to simulate the deformations originating from slip on a wide range of fault geometries, representing the variability in the field. Unsurprisingly, laterally extensive faults with strong compaction contrasts across them (large differential slip magnitudes) produce the largest surface signals. To determine which potentially aseismically slipping faults produce surface signals that could be detectable in persistent scatterer InSAR time series, we analyze the surface patterns for large differential displacements across large length scales, since InSAR observations are most sensitive to spatially extensive patterns with high spatial gradients. We use the results of the sensitivity analysis to guide our search for patterns originating from aseismically slipping reservoir faults in PS-InSAR time series data of the Groningen area. First results show that these specific patterns are rare, indicating that the amount of aseismic slip is limited. For faults lacking surface signals related aseismic slip, the results of sensitivity analysis are used to determine upper limits for the aseismic differential slip magnitudes.

The frequency, size, and intensity of wildfires in California have increased substantially in recent years, leading to widespread mandatory evacuations affecting millions of residents. However, because evacuation orders are implemented by local agencies, there is limited quantitative evidence on the scope of evacuations statewide. In order to improve the understanding of wildfire evacuations, we assembled information on historical evacuation orders for two distinct wildfire-prone regions --- Fresno and Sonoma county --- in California. This data was used to understand how the frequency and extent of evacuations have changed over time. We then combined this information with census data to characterize which populations have been most affected by evacuation orders. Ultimately, our work aims to quantify this important element of wildfire impacts in key regions around California. Collectively, it provides a starting point for a public database of evacuation orders that could be used by researchers and policymakers to better understand dynamics and improve decision-making around wildfire evacuations.

Earthquakes are among the most destructive natural disasters, resulting in a huge number of fatalities and economic losses all over the world. For regions with limited number of seismic networks or for regions where there is a seismic gap to produce large magnitude and destructive earthquakes, ground motion simulation approaches provide alternative region-specific time histories for potential events. Region-specific simulations require modeling and calibration of input parameters in terms of source, path and site effects. Verification of these parameters is a challenging task and can be accomplished through comparing the real time histories of the past events against the simulated data. In this study, the recorded time histories of the 9th of July 1998 Faial earthquake (Mw=6.2) at available stations are simulated with the stochastic finite-fault ground motion simulation approach based on a dynamic corner frequency concept. For ground motion simulations, alternative region-specific source, path and site models are employed and tested. The best model is proposed through goodness of fit score which is evaluated through the complementary error function in terms of various seismological parameters between the real and simulated record sets. These seismological parameters include peak ground acceleration peak ground velocity, the ratio of peak ground velocity to peak ground acceleration, Arias intensity, cumulative absolute velocity, acceleration spectrum intensity, modified acceleration spectrum intensity for the period range of 0.1 to 2.5 seconds, velocity spectrum intensity, Housner intensity, significant duration, bracketed duration, Fourier amplitude spectra within the frequency range of 0.1 to 25 Hz and pseudo response spectra within the period range of 0 to 4 seconds. The simulation results demonstrate that for the event of interest the input parameters are verified, and the fits between the real and simulated time histories are satisfactory. Keywords: The 1998 Faial Earthquake, Azores- North Atlantic, Stochastic finite-fault Ground Motion Simulation method, Goodness of Fit Score

Climate-induced episodes of extensive tree mortality worldwide are leading to abrupt changes in forest carbon stocks. A severe frost in early February 2011 triggered widespread tree mortality in the lowland tropical dry forest (TDF) of northwestern Mexico. The studied landscape in southern Sonora is composed by a patchy matrix dominated by mature, secondary (originated in abandoned fields), and active agricultural fields. In this forest, we used allometric equations to assessed frost-induced changes in aboveground biomass (AGB) stocks in mature and secondary tropical dry forests. For AGB estimations we used 48 1-ha plots (24 plots per forest type) distributed within four distant subareas in our 83 230 ha study area. On each plot, we recorded all live/dead individuals, and a total of 11 205 woody plants were registered, of which 7 137 (with at least a stem DBH > 2.5 cm) were likely present before the frost, and the remaining smaller ones were considered as new recruits regenerated from seeds (4,068 individuals). From those plants present before the frost, 26 842 and 8 059 were live and dead stems, and 1 222 were dead individuals. All registered live and dead stems accounted for a total of 273.4 Mg of AGB in our study plots (4.8 ha). From this amount, 57.3 Mg was necromass (dead stems). Interestingly, only two out of a total of 74 registered species contributed with ca. 80% of this necromass. These highly sensitive species are the tree legumes Lysioma divaricatum and Acacia cochliacantha. On average, AGB in the studied mature and secondary TDF was 64.3 and 49.6 Mg ha-1, respectively. The corresponding necromass for these forests was 10.9 and 13 Mg ha, respectively. The 2011 frost induced a greater change from live biomass to necromass in secondary than mature forests, 26.2% and 16.9%, respectively, which can be explained by the higher abundance of individuals from sensitive species in secondary forests. Our results suggest that climate-induced shifts in carbon stocks are linked to previous land-use changes in tropical dry forests. Restoration plans of these degraded lands should consider the vulnerability of tropical dry forest species to climate extremes.

UCSC GEOPATHS is an NSF-supported initiative to improve undergraduate success in the geosciences, driven by a desire to broaden academic engagement. One component of the program is a funded undergraduate summer program that provides authentic, professional experiences – across all employment sectors – to increase commitment in the geoscience pipeline. Many hydrologic basins rely on groundwater to supply domestic, municipal, and agricultural demand, but resources are increasingly stressed by rising demand, changes in land use, and a shifting climate. Consequences of groundwater overdraft include drying surface water systems, land subsidence, and seawater intrusion. Managed aquifer recharge (MAR) can help improve groundwater resources by increasing infiltration of excess surface water. We are part of a research team assessing hydrologic conditions during MAR on an active vineyard in Central California, through diversion of high flows from an adjacent river, a strategy known as “flood-MAR.” Our team collected soil samples from the upper 100 cm below ground surface at 24 locations across the 785-acre field site. We analyzed samples for soil texture at 10-cm spacing using a particle size analyzer based on laser light scattering. Preliminary analysis of fractions of sand, silt, and clay-sized particles indicate some lateral continuity from site to site. The northern part of the field area appears to be finer grained, on average, consistent with regional soil maps, but there is also considerable variability with depth. These data will be used to assess variations in expected infiltration rates by combining soil texture (to estimate infiltration capacity) and potential flood and saturation depths (to bracket vertical head gradients). Studies of this kind are helpful for assessing the efficacy of flood-MAR as a strategy to improve groundwater supplies and quality.

Despite extensive research into the transport and fate of oceanic microplastics (MP, <5mm in size), there is comparatively little focus on river systems considered to be pathways for these contaminants. The Savannah River, forming the border between Georgia and South Carolina, provides a unique location to study MP pollution along a variably industrialized river system terminating in the Atlantic Ocean. We investigated spatial variations in MP concentrations along the Savannah River to better understand their transport and deposition in rural to highly developed fluvial systems. Samples of riverbank sediment and suspended particles captured by a <80μm plankton net were collected along a 115 km reach of the river extending from just below the Strom Thurmond Dam to 25km downstream of Augusta, GA. Laboratory MP separation followed NOAA guidelines with a heavy liquid float-sink separation technique and wet peroxide oxidation treatment. Visually identified MPs were counted and photographed using a stereomicroscope; a subset of particles from each sample were examined using a Horiba XploRa Plus confocal microscope system. Average MP concentrations were measured at 3.1 (range: 1.5-4.6) particles/cubic meter in water and 16.8 (range: 6.2-27.4) particles/kg sediment and primarily composed of polyester fibers and polypropylene pellets. Comparison of MP concentrations between sediment samples from the upper bank and water margin suggests that MP particle deposition is dependent on river stage. Preliminary results further indicate that there is no observable relationship between increasing drainage area and MP concentration, suggesting that concentration may be dependent on localized anthropogenic sources rather than cumulative upstream contributions. Measured concentrations of MP in bank sediment in the upper reaches of the Savannah River are an order-of magnitude less than published concentrations at the river’s mouth collected over the same sampled cross-sectional area, suggesting tidal action exerts a significant control on MP pollution in coastal and near coastal areas. Future work will focus on quantifying the predicted role of tidally dominated systems in concentrating microplastics around river mouths and identifying river reaches with highly concentrated MP particles for targeted remediation.

Seismic noise correlation is one of the most used tools to know the earth's structure in the last decade. In this study, we used autocorrelation to determine the presence of underground mines by extracting the normal seismic response in transmission between the ground surface and the cavity roof. The experiments are carried out in the urban environment of the Mexico City western zone, where a high risk of mines collapse subsists. For this, we use ambient noise recorded for 30 min in vertical 4.5 Hz geophone arrays. We obtain zero offset sections of power spectra density from the stacking of autocorrelations in 4 s time windows. The results are compared with GPR, ERT, and seismic refraction studies. We observe that surface cavities such as drainpipe systems are present at frequencies greater than 30 Hz. Between 10 and 30 Hz, the seismic response is produced by resonances associated with cavities that can be delimited laterally by spectral maxima and whose presence agrees with discontinuities on radargrams. The mine roof depth is related to half-wavelength and the compression wave velocity of the surface layer determined by seismic refraction. The autocorrelation method does not determine the shape or vertical extent of the cavity, which is well resolved by the high resistivity values of the ERT method. However, low spectral amplitudes are observed on saturated materials where the electromagnetic wave is noisy and low resistivity values are resolved.

The tandem rise in satellite-based observations and computing power has changed the way we (can) see rivers across the Earth’s surface. Global datasets of river and river network characteristics at unprecedented resolutions are becoming common enough that the sheer amount of available information presents problems itself. Fully exploiting this new knowledge requires linking these geospatial datasets to each other within the context of a river network. In order to cope with this wealth of information, we are developing Veins of the Earth (VotE), a flexible system designed to synthesize knowledge about rivers and their networks into an adaptable and readily-usable form. VotE is not itself a dataset, but rather a database of relationships linking existing datasets that allows for rapid comparison and exports of river networks at arbitrary resolutions. VotE’s underlying river network (and drainage basins) is extracted from MERIT-Hydro. We link within VotE a newly-compiled dam dataset, streamflow gages from the GRDC, and published global river network datasets characterizing river widths, slopes, and intermittency. We highlight VotE’s utility with a demonstration of how vector-based river networks can be exported at any requested resolution, a global comparison of river widths from three independent datasets, and an example of computing watershed characteristics by coupling VotE to Google Earth Engine. Future efforts will focus on including real-time datasets such as SWOT river discharges and ReaLSAT reservoir areas.

An accurate estimation of the shale permeability is essential to understand heterogeneous organic-rich shale reservoir rocks and predict the complexity of pore fluid transport in the rocks. However, predicting the matrix permeability by traditional models is still challenging because they require information often measured from core measurements. First, Kozeny’s equation (Kozeny, 1927) uses porosity and specific surface area of solid grains. However, it is difficult to characterize the specific surface area values or grain sizes from the logs. Second, Herron’s method (Herron, 1987) has been used for predicting permeability based on the mineral contents provided by well log data in conventional sandstone reservoirs. However, the predictive accuracy is low due to the different pore network structures of the shales. In this study, we estimate shale matrix permeability by a combined exploratory data analysis (EDA) and nonlinear regression estimation from the wireline logs. First, we conduct a bivariate correlation analysis for permeability and rock properties in core measurements. According to the correlation and Shapley value sensitivity test, we find that permeability change has a significant effect on the variation in porosity. Also, we investigate a nonlinear behavior between porosity and permeability. Second, we derive a nonlinear polylogarithmic estimation function of porosity to permeability, comparing it to the multivariate linear regression of porosity and clay volume fraction. As a result, a cubic logarithmic function of porosity significantly improves the fitting performance of the permeability values, better than the traditional methods. Moreover, we generate the permeability logs from the calibrated porosity logs, and they imply better shale permeability prediction as well. Since we can invert the porosity distribution from seismic data, this approach can provide a more accurate permeability estimation and reliable fluid flow modeling for shale and mudrock.

The spatiotemporal patterns of precipitation are critical for understanding the underlying mechanism of many hydrological and climate phenomena. Over the last decade, applications of the complex network theory as a data-driven technique has contributed significantly to study the intricate relationship between many variable in a compact way. In our work, we conduct a study to compare an extreme precipitation pattern in Ganga River Basin, by constructing the networks using two nonlinear methods - event synchronization (ES) and edit distance (ED). Event synchronization has been frequently used to measure the synchronicity between the climate extremes like extreme precipitation by calculating the number of synchronized events between two events like time series. Edit distance measures the similarity/dissimilarity between the events by reducing the number of operations required to convert one segment to another, that consider the events’ occurrence and amplitude. Here, we compare the extreme precipitation patterns obtained from both network construction methods based on different network’s characteristics. We used degree to understand network topology and identify important nodes in the networks. We also attempted to quantify the impact of precipitation seasonality and topography on extreme events. The study outcomes suggested that the degree is decreased in the southwest to the northwest direction and the timing of peak precipitation influences it. We also found an inverse relationship between elevation and timing of peak precipitation exists and the lower elevation greatly influences the connectivity of the stations. The study highlights that Edit distance better captures the network’s topology without getting affected by artificial boundaries.

Concerns about water security often inform climate risk-related decisions made by environmentally focused investors (Porritt, 2001; Stern, 2006). Yet, potential liabilities for damage caused by extreme flood and drought events linked to global warming present risks that are not always reflected in share prices (Krosinsky et al., 2012). Considering the highly destructive nature of such events, we query whether companies, or specific sectors, could and should be held at least partially liable for their emission-releasing business activities. Recent articles (Rayer & Millar, 2018; Rayer et al., 2020) estimate that under a hypothetical climate liability regime, North Atlantic hurricane seasons might increasingly generate 1-2% losses on market capitalizations (or share prices) for the top seven carbon-emitting, publicly listed companies. In this paper, we extend the concept of the climate liability regime to estimate the impact of global flood- and drought-related damages on the share prices of nine fossil-fuel firms (including the seven mentioned by Rayer et al. (2020)). Following Rayer et al. (2020), we use incremental climate impacts and historical corporate emissions to estimate that climate change-related global flood and drought damages for the period of 2012 to 2016 amount to approximately 2-3% of the top nine carbon-emitting companies’ market capitalizations. We also include a discussion of moral responsibility and the proportion of obligations between producers and users. Quantifying impacts from extreme weather events increases salience and serves as an example of how science can identify and address the important business questions, pertinent to both investors and companies, that arise from a changing climate. References Krosinsky, C., Robins, N., & Viederman, S. (2012). Evolutions in sustainable investing. John Wiley & Sons. Porritt, J. (2001). The world in context. HRH The Prince of Wales’ Business and the Environment Programme, Cambridge. Rayer, Q. G., & Millar, R. J. (2018). Investing in Extreme Weather Conditions. Citywire Wealth Manager®, (429) 36. Rayer, Q., Pfleiderer, P., & Haustein, K. (2020). Global Warming and Extreme Weather Investment Risks. Palgrave Macmillan. https://doi.org/10.1007/978-3-030-38858-4_3 Stern, N. (2006). Stern Review executive summary. London.