The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

All key policy rates in the United States increased drastically in 2023. The Federal Funds target range was set to 4.5 to 4.75 percent in February, and it increased gradually to 5.25 to 5.5 percent by the end of the year. The interest rate on reserve balances (IORB Rate) increased from 4.65 percent to 5.4 percent, and the same trend could be seen with the Overnight Reverse Repo Facility Rate (ON RRP Rate), and the Standing Repo Facility Rate (SRF Rate). The sharp increase in all key policy rates was triggered by the growing inflation rate throughout 2023.

Increasing sea level rise poses a significant threat to some U.S. cities, including Grand Isla, Louisiana, with a rise rate of more than eight millimeters in 2024. In Alaska, much of the coast is seeing sea levels fall as the land pushes upward, no longer weighed down by glacial ice. Causes of sea level rise Greenhouse gas (GHG) emissions accumulate in the Earth’s atmosphere and trap solar radiation creating a warming effect. As a result, glaciers and ice sheets melt – in addition to the thermal expansion of seawater, causing the mean sea level to rise. If future GHG emissions are not cut down, sea levels could increase up to an additional 1.5 meters along the U.S. coastline by the end of the century. Impacts of the rising sea level in the U.S. By 2100, coastal shielding expenses are forecast to reach 300 billion U.S. dollars across the United States alone. Furthermore, several million residents will likely migrate further inland to avoid rising sea levels. This is especially concerning for Florida, which has one of the highest shares of homes at flood risk across the country.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Average daily rates for Airbnb and Vrbo listings have risen steadily in some of the top destinations for digital nomads.

This statistic presents the reaction of institutional investors to interest rate increases in 2016. The results of the survey carried out in October 2015 revealed that ** percent of the companies increased the use of alternatives as a result of interest rates increase.

The benchmark interest rate in Poland was last recorded at 5 percent. This dataset provides - Poland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Germany was last recorded at 4.50 percent. This dataset provides - Germany Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in New Zealand was last recorded at 3.25 percent. This dataset provides - New Zealand Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Hong Kong was last recorded at 4.75 percent. This dataset provides the latest reported value for - Hong Kong Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset tracks annual graduation rate from 2019 to 2022 for Da Vinci Rise High School vs. California and Da Vinci RISE High School District

Mortgage rates increased at a record pace in 2022, with the 10-year fixed mortgage rate doubling between March 2022 and December 2022. With inflation increasing, the Bank of England introduced several bank rate hikes, resulting in higher mortgage rates. In May 2025, the average 10-year fixed rate interest rate reached **** percent. As borrowing costs get higher, demand for housing is expected to decrease, leading to declining market sentiment and slower house price growth. How have the mortgage hikes affected the market? After surging in 2021, the number of residential properties sold declined in 2023, reaching just above *** million. Despite the number of transactions falling, this figure was higher than the period before the COVID-19 pandemic. The falling transaction volume also impacted mortgage borrowing. Between the first quarter of 2023 and the first quarter of 2024, the value of new mortgage loans fell year-on-year for five straight quarters in a row. How are higher mortgages affecting homebuyers? Homeowners with a mortgage loan usually lock in a fixed rate deal for two to ten years, meaning that after this period runs out, they need to renegotiate the terms of the loan. Many of the mortgages outstanding were taken out during the period of record-low mortgage rates and have since faced notable increases in their monthly repayment. About **** million homeowners are projected to see their deal expire by the end of 2026. About *** million of these loans are projected to experience a monthly payment increase of up to *** British pounds by 2026.

The US home loan market, a cornerstone of the American economy, is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This expansion is fueled by several key drivers. Low interest rates, particularly in the early part of the forecast period, have historically stimulated borrowing, making homeownership more accessible. A growing population, coupled with increasing urbanization and a persistent demand for housing in key metropolitan areas, further fuels this market's expansion. Government initiatives aimed at supporting homeownership, such as tax incentives and affordable housing programs, also play a significant role. The market is segmented by loan type (purchase, refinance, improvement), source (banks, HFCs), interest rate (fixed, floating), and loan tenure. While refinancing activity might fluctuate based on prevailing interest rates, the underlying demand for home purchases remains strong, particularly in regions with robust job markets and population growth. Competition among lenders, including major players like Rocket Mortgage, LoanDepot, and Wells Fargo, alongside regional and smaller banks, is fierce, resulting in innovative loan products and competitive pricing. However, the market is not without its challenges. Rising inflation and potential interest rate hikes pose a significant risk, potentially dampening demand and increasing borrowing costs. Stringent lending regulations and increased scrutiny of creditworthiness could restrict access to loans for some borrowers. Furthermore, fluctuations in the housing market itself, including supply chain disruptions impacting construction and material costs, can influence the overall growth trajectory. Despite these headwinds, the long-term outlook for the US home loan market remains positive, driven by the fundamental need for housing and ongoing economic expansion in select regions. The diverse segmentation of the market allows for a nuanced understanding of the specific growth drivers and challenges within each segment. For instance, the home improvement loan segment is expected to see strong growth driven by homeowners' increasing desire to upgrade their existing properties. Recent developments include: June 2023: Bank of America Corp has been adding consumer branches in four new U.S. states, it said on Tuesday, bringing its national footprint closer to rival JPMorgan Chase & Co. Bank of America will likely open new financial centers in Nebraska, Wisconsin, Alabama, and Louisiana as part of a four-year expansion across nine markets, including Louisville, Milwaukee, and New Orleans., July 2022: Rocket Mortgage entered the Canadian Market with the acquisition. The company expanded from offering home loans in Ontario at launch to now providing mortgages in every province, primarily from its headquarters in downtown Windsor. The Edison Financial team grew along with the company, starting with just four team members in early 2020 to more than 140 at present.. Key drivers for this market are: Increase in digitization in mortgage lending market, Increase in innovations in software designs to speed up the mortgage-application process. Potential restraints include: Increase in digitization in mortgage lending market, Increase in innovations in software designs to speed up the mortgage-application process. Notable trends are: Growth in Nonbank Lenders is Expected to Drive the Market.

Graph and download economic data for Net Percentage of Large Domestic Banks Increasing Spreads of Loan Rates Over Banks' Cost of Funds to Large and Middle-Market Firms (SUBLPDCILTSLGNQ) from Q2 1990 to Q2 2025 about funds, cost, large, spread, domestic, Net, percent, loans, banks, depository institutions, rate, and USA.

We used WARMER, a 1-D cohort model of wetland accretion (Swanson et al. 2014), which is based on Callaway et al. (1996), to examine SLR projections across each study site. Each cohort in the model represents the total organic and inorganic matter added to the soil column each year. WARMER calculates elevation changes relative to MSL based on projected changes in relative sea level, subsidence, inorganic sediment accumulation, aboveground and belowground organic matter productivity, compaction, and decay for a representative marsh area. Each cohort provides the mass of inorganic and organic matter accumulated at the surface in a single year as well as any subsequent belowground organic matter productivity (root growth) minus decay. Cohort density, a function of mineral, organic, and water content, is calculated at each time step to account for the decay of organic material and auto-compaction of the soil column. The change in relative elevation is then calculated as the difference between the change in modeled sea level and the change in height of the soil column, which was estimated as the sum of the volume of all cohorts over the unit area model domain. The total volume of an individual cohort is estimated as the sum of the mass of pore space water, sediment, and organic matter, divided by the cohort bulk density for each annual time step. Elevation is adjusted relative to sea level rise after each year of organic and inorganic input, compaction, and decomposition. We parameterized WARMER from the elevation, vegetation, and water level data collected at each site. We evaluated model outputs between 2010 and 2110 using marsh elevation zones defined above.Model inputs Sea-level rise scenariosIn WARMER, we incorporated a recent forecast for the Pacific coast which projects low, mid, and high SLR scenarios of 12, 64 and 142 cm by 2110, respectively (NRC 2012). We used the average annual SLR curve as the input function for the WARMER model. We assumed the difference between the maximum tidal height and minimum tidal height (tide range) remained constant through time, with only MSL changing annually.Inorganic matterThe annual sediment accretion rate is a function of inundation frequency and the mineral accumulation rates measured from 137Csdating of soil cores sampled across each site. For each site, we developed a continuous model of water level from the major harmonic constituents of a nearby NOAA tide gauge. This allowed a more accurate characterization of the full tidal regime as our water loggers were located above MLLW. Following Swanson et al. (2014), we assumed that inundation frequency was directly related to sediment mass accumulation; this simplifying assumption does not account for the potential feedback between biomass and sediment deposition and holds suspended sediment concentration and settling velocity constant. Sediment accretion, Ms,at a given elevation, z, is equal to, where f(z) is dimensionless inundation frequency as a function of elevation (z), and Sis the annual sediment accumulation rate in g cm-2 y-1.Organic matterWe used a unimodal functional shape to describe the relationship between elevation and organic matter (Morris et al. 2002), based on Atlantic coast work on Spartina alterniflora. Given that Pacific Northwest tidal marshes are dominated by other plant species, we developed site-specific, asymmetric unimodal relationships to characterize elevation-productivity relationships. We used Bezier curves to draw a unimodal parabola, anchored on the low elevation by MTL at the high elevation by the maximum observed water level from a nearby NOAA tide gauge. We determined the elevation of peak productivity by analyzing the Normalized Difference Vegetation Index (NDVI; (NIR - Red)/(NIR + Red)) from 2011 NAIP imagery (4 spectral bands, 1 m resolution; Tucker 1979) and our interpolated DEM. We then calibrated the amplitude of the unimodal function to the organic matter input rates (determined from sediment accumulation rates and the percent organic matter in the surface layer of the core) obtained from sediment cores across an elevation range at each site. The curves were truncated to zero below the lowest observed marsh elevation for each site from our vegetation surveys, reflecting the observed transition to unvegetated mudflat. The root-to-shoot ratio for each site was set to 1.95, the mean value from an inundation experiment conducted at Siletz in 2014 for Juncus balticusand Carex lyngbyei, two common high and low marsh species in the Pacific Northwest (C. Janousek et al., unpublished results). Compaction and decompositionCompaction and decomposition functions of WARMER followed Callaway et al. (1996). We determined sediment compaction by estimating a rate of decrease in porosity from the difference in measured porosity between the top 5 cm and the bottom 5 cm of each sediment core. We estimated the rate of decrease, r, in porosity of a given cohort as a function of the density of all of the material above that cohort.Following Swanson et al. (2014), we modeled decomposition as a three-tiered process where the youngest organic material, less than one year old, decomposed at the fastest rate; organic matter one to two years old decayed at a moderate rate; and organic matter greater than two years old decayed at the slowest rate. Decomposition also decreased exponentially with depth. We determined the percentage of refractory (insoluble) organic material from the organic content measured in the sediment cores. We used constants to parameterize the decomposition functions from Deverel et al. (2008). ImplementationFor each site, we ran WARMER at 37 initial elevations (every 10 cm from 0 to 360 cm, NAVD88). A two hundred year spin-up period for each model run was used to build an initial soil core. A constant rate of sea-level rise was chosen that the modeled elevation after 200 years was equal to the initial elevation. After the spin-up period, sea-level rose according to the scenario (+12, 63, or 142 cm by 2110). Linear interpolation was used to project model results every 10 years onto the continuous DEM developed from the RTK surveys. This raster contains data from Bandon marsh with the projection from the WARMER model for the year 2010 with a 63 cm sea-level rise rate.

Brazil's inflation rate and central bank interest rate have experienced significant fluctuations from 2018 to 2025, reflecting broader global economic trends. The country's inflation peaked at 12.13 percent in April 2020, followed by a gradual decline and subsequent rise, while the central bank adjusted its Selic rate in response to these economic dynamics. This pattern of volatility and monetary policy adjustments mirrors similar experiences in other major economies during the same period. Global context of inflation and interest rates Brazil's economic indicators align with the global trend of rising inflation and subsequent central bank responses observed in many countries. Like Brazil, other major economies such as the United States, United Kingdom, and European Union implemented aggressive rate hikes throughout 2022-2023 to combat inflationary pressures. However, a coordinated shift began in mid-2024, with many central banks initiating rate cuts. This global trend is reflected in Brazil's monetary policy decisions, as the country began reducing its Selic rate in August 2023 after maintaining it at 13.75 percent for several months. Comparison with other economies While Brazil's inflation rate reached 5.53 percent in April 2025, other major economies exhibited varying levels of inflationary pressure. For instance, China reported a deflationary rate of -0.1 percent, while Russia maintained a high inflation rate of 10.2 percent during the same period. The United Kingdom, which experienced similar volatility in its inflation rate, saw it peak at 9.6 percent in October 2022 before moderating to 2.6 percent by September 2024. These comparisons highlight the diverse economic conditions and policy responses across different countries, with Brazil's experience falling somewhere in the middle of this spectrum.

Summary: This dataset contains projections of coastal cliff-retreat rates and positions for future scenarios of sea-level rise (SLR). Projections were made using numerical and statistical models based on field observations such as historical cliff retreat rate, submarine slope, coastal cliff height, and mean annual wave power. Details: Cliff-retreat rate and position projections are for scenarios of 0, 0.5, 1, 1.5 and 2 meters of sea-level rise (SLR) by the year 2100. Projections were made at CoSMoS cross-shore transects(CST) spaced 100 m alongshore. Generally, projections were not made at transects where the sea cliff was armored or otherwise obstructed (for example, houses on the beach in front of the cliff, road between the beach and cliff), though some local exceptions apply where the obstruction was low enough to be easily overwashed. Spatial projections, such as those in the Google Earth KMZs, were made using a baseline sea-cliff edge from 2010.
Two process-based, numerical cliff-profile evolution models were used to make projections: a soft-rock model by Walkden and Hall (2005, 2011) and a hard-rock model by Trenhaile (2000, 2009, 2011). Both models relate breaking-wave height and period to rock erosion, and distribute erosion vertically over a tidal cycle. Model behavior includes a variable beach slope that varies with the prevailing wave climate, wave run-up (Stockdon and other, 2006), and wave set-up that raises the water level during big-wave events and allows waves to impact the sea cliff with greater efficacy and frequency.
The models were run on idealized cliff profiles extending from about 10 m water depth to 1 kilometer inland from the cliff edge. Profiles were extracted by overlaying the cross-shore transects on a high-resolution digital elevation model (DEM) covering the Southern California study area. Using aerial photography, the presence of a beach was recorded (yes or no) for all transects, and the cliff toe elevation (or beach/cliff junction) was digitized from the DEM profiles. Using historic cliff edge retreat rates by Hapke and Reid (2007), unknown coefficients within the cliff-profile models were calibrated using a Monte Carlo simulation (in other words, coefficients were tuned until the modeled mean retreat rate equaled the observed mean retreat rate for a given transect). This was successful for nearly 1,000 DEM profiles (about 45percent of all cliff transects). For those nearly 1,000 profiles, the profile models were run for two time periods: first, a period of 100–200 years using a historic rate of sea-level rise (2 mm/yr), and then for another 100 years using an accelerated mean rate of sea-level rise (5, 10, 15, or 20 mm/yr). The projected cliff-retreat rates are thus mean annual rates that represent the total retreat that occurred during this second time period.
Each of the resulting nearly 5,000 model runs had one dependent (predicted mean annual cliff retreat for a given sea-level rise scenario) and multiple semi-independent variables that determined the magnitude of the dependent variable (such as historic retreat rate, shore platform slope, cliff height, cliff-toe/beach height, cliff-face slope, mean annual wave power, beach slope). This information was used to train a statistical model called an Artificial Neural Network (ANN). The ANN iteratively maps the independent variables to the dependent variable using linear algebra and a weighting system that gives importance to the variables that most strongly influence future sea-cliff retreat. In the end, the trained ANN is a standalone model that has learned, and can reproduce, the process-based cliff-profile model behavior.
Independent tests between cliff profile model output and ANN output showed very good agreement (R-squared = 0.89 - 0.96; root-mean-square-error less than 0.1 m/yr). The trained ANN was then applied to each cross-shore transect, where observed independent variables such as sea cliff height, historic cliff retreat, mean wave power, shore platform slope, sea-level rise scenario, and cliff toe height were passed through the ANN to yield a prediction of future long-term cliff retreat rate. Two separate ANNs were trained: one to make predictions of the difference between future and historic cliff retreat rates (in other words, prediction = future cliff-retreat rate*historic cliff-retreat rate) and another to predict the mean trend, or acceleration, of cliff retreat as a function of sea-level rise (in other words, future cliff retreat = m*SLR + historic retreat rate, where the ANN predicts m). Training two separate ANNs allowed for two different predictions for each transect from the same training data.
Of the 2,117 cliff transects, there were 8 for which a prediction could not be made, likely because values of independent variables fell outside of t... Visit https://dataone.org/datasets/c50dc2df-589b-4168-957e-9ed177bd7dc7 for complete metadata about this dataset.

This data set includes R scripts & additional tables created by Lennart Gries for a master's thesis conducted at Johann Wolfgang Goethe-Universität Frankfurt am Main and Senckenberg Biodiversity- and Climate Research Centre (SBiK-F), being supervised by Prof. Susanne Fritz and Prof. Markus Pfenninger.

Temperature-dependent generation time data for ectotherm animals was collected from literature and incorporated into population models to predict a number of generations per year under different climatic scenarios. An increase in generations per year implies an increase of evolutionary rate for ectotherms with rising temperatures under stronger climate change scenario.

The statistic shows the inflation rate in India from 1987 to 2024, with projections up until 2030. The inflation rate is calculated using the price increase of a defined product basket. This product basket contains products and services, on which the average consumer spends money throughout the year. They include expenses for groceries, clothes, rent, power, telecommunications, recreational activities and raw materials (e.g. gas, oil), as well as federal fees and taxes. In 2024, the inflation rate in India was around 4.67 percent compared to the previous year. See figures on India's economic growth for additional information. India's inflation rate and economy Inflation is generally defined as the increase of prices of goods and services over a certain period of time, as opposed to deflation, which describes a decrease of these prices. Inflation is a significant economic indicator for a country. The inflation rate is the rate at which the general rise in the level of prices, goods and services in an economy occurs and how it affects the cost of living of those living in a particular country. It influences the interest rates paid on savings and mortgage rates but also has a bearing on levels of state pensions and benefits received. A 4 percent increase in the rate of inflation in 2011 for example would mean an individual would need to spend 4 percent more on the goods he was purchasing than he would have done in 2010. India’s inflation rate has been on the rise over the last decade. However, it has been decreasing slightly since 2010. India’s economy, however, has been doing quite well, with its GDP increasing steadily for years, and its national debt decreasing. The budget balance in relation to GDP is not looking too good, with the state deficit amounting to more than 9 percent of GDP.

This dataset contains projections of shoreline positions and uncertainty bands for future scenarios of sea-level rise. Projections were made using CoSMoS-COAST, a numerical model forced with global-to-local nested wave models and assimilated with lidar-derived shoreline vectors. Details: Projections of shoreline position in Southern California are made for scenarios of 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, and 5.0 meters of sea-level rise by the year 2100. Four datasets are available for different management conditions: shorelines are allowed to retreat unimpeded past urban tructures ("NO Hold the Line") or are limited to this urban boundary ("Hold the Line"), and shorelines are allowed to progress with projected increases in sediment ("Continued Nourishment") or with no projected increases ("No Nourishment"). Projections are made at CoSMoS Monitoring and Observation Points, which represent shore-normal transects spaced 100 m alongshore. The newly developed CoSMoS-COAST model solves a coupled set of partial differential equations that resembles conservation of sediment for the series of transects. The model is synthesized from several shoreline models in the scientific literature: One-line model formulations (Pelnard-Considere, 1956; Larson and others, 1997; Vitousek and Barnard, 2015) account for longshore transport, equilibrium shoreline-model formulations (Yates and others, 2009) account for wave-driven cross-shore transport, and equilibrium beach-profile formulations (Bruun, 1954; Davidson-Arnot, 2005; Anderson and others, 2015) account for long-term beach-profile adjustments due to sea-level rise. The model uses an extended Kalman filter data-assimilation method to improve the fit of the model to lidar-derived observed shoreline positions. As with previous studies (Hapke and others, 2006), the available shoreline data are spatially and temporally sparse. The data-assimilation method automatically adjusts model parameters and estimates the effects of unresolved processes such as natural and anthropogenic sediment supply. The data-assimilation method used in CoSMoS-COAST has been improved over the original method of Long and Plant (2012). The new method ensures that the coefficients of the equilibrium shoreline-change model retain their preferred sign. Without this improvement, the data-assimilation method was subject to instability. Data assimilation is performed only on days of the simulations where shoreline data are observed. For the shoreline projection period (2015–2100), no such data are available and thus no data-assimilation can be performed. Some of the model components are ignored for certain transects and geographic locations. For example, on small pocket beaches longshore transport is assumed negligible and, therefore, is not computed via the model. Generally, projections were not made at transects where the shoreline is armored and sandy beaches are not present. The formulations that comprise the shoreline model are only valid for sandy beaches. Furthermore, they become invalid as the beach becomes fully eroded and possibly undermines coastal infrastructure. Hence, we have specified a maximally eroded shoreline state that represents the interface of sandy beaches and coastal infrastructure (for example, roads, homes, buildings, sea-walls). If the beach erodes to this line, then it is not permitted to erode further. However, we note that the model can be run without specifying this unerodible line. The shoreline model uses a series of global-to-local nested wave models (such as WaveWatch III and SWAN) forced with Global Climate Model (GCM)-derived wind fields. Historical and projected time series of daily maximum wave height and corresponding wave period and direction from 1990 to 2100 force the shoreline model. The modeled wave predictions are a key input to the CoSMoS-COAST shoreline model because the calculation of both the longshore sediment-transport rate (obtained via the "CERC" equation developed by the Army Corp of Engineers; Shore Protection Manual, 1984) and equilibrium shoreline change (Yates and others, 2009) critically depends on the wave conditions. Notably, variations in nearshore wave angle can significantly affect the calculation of longshore transport. Thus, high-resolution modeling efforts to predict nearshore wave conditions are integral components of the shoreline modeling. Sea level vs. time curves are modeled as a quadratic function. Coefficients of the quadratic curves are obtained via three equations: (1) present sea level is assumed to be at zero elevation, (2) the present rate of sea-level rise is assumed to be 3 mm/yr, which is consistent with values observed at local tide gages, (3) future sea-level elevation at 2100 is either 0.93, 1.25, 1.5, 1.75, 2.0 or 5.0 m based on the scenarios considered. We note that sea level only affects the equilibrium-profile changes derived via the Anderson and others (2015) model. The model uses a forward Euler time-stepping method with a daily time step. The longshore sediment-transport term has the option of using a second-order, implicit time-stepping method (Vitousek and Barnard 2015). However, for these modeling efforts, the forward Euler time-stepping method is sufficient and does not violate numerical stability determined by the Courant-Friedrichs-Lewy CFL condition when using a daily time step on 100 m-spaced transects. The model is composed of numerous scripts and functions implemented in Matlab. The main modeling routines have approximately 1,000-plus lines of code. However, many other functions exist that are necessary to initialize and operate the model. Overall the entire shoreline-modeling system is estimated to have approximately 10,000 lines of code. The modeling system is computationally efficient in comparison to traditional coupled hydrodynamic-wave-morphology models like Delft3D. Century-scale simulations for the entire 400 km coast of Southern California take approximately 20–30 minutes of wall-clock time. This limited computational cost allows the possibility of applying ensemble prediction. Significant uncertainty is associated with the process noise of the model and unresolved coastal processes. This makes estimation of uncertainty difficult. The uncertainty bands predicted here represent 95 percent confidence bands associated with the modeled shoreline fluctuations. Unresolved processes are not accounted for in the uncertainty bands and could lead to significantly more uncertainty than reported in these predictions. These results should be considered preliminary. Although some QA/QC has been completed, the results will improve through time as 1) more shoreline data become available to the data-assimilation method, 2) the models are improved, and 3) ensemble wave-forcing is applied to the model. For more information on model details, data sources, and integration with other parts of the CoSMoS framework, see CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf).