CCExposure.Rmd

---
params:
  name:
    label: Park Name
    value: params$name
toc: true
toc_depth: 2
theme: united
output:
  word_document:
    reference_docx: template.docx
    df_print: paged
bibliography: ExposureReports.bib
csl: ecology.csl
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=TRUE)
```

```{r Initials, echo=FALSE, message=FALSE, warning=FALSE}

library(stars);library(dplyr);library(ggplot2);library(viridis);library(here);library(ggrepel);library(rlang);library(units);library(knitr);library(glue); library(gt); library(officer);library(officedown); library(abind); library(sf);library(shiny); library(here); library(data.table); library(lubridate); library(openxlsx); library(tidyr); library(raster);library(sp)


```

```{r values, include=FALSE}
# Run Appendix Script
SiteID = name
source("Appendix_Script.R", local = knitr::knit_global())
```
---
title: "`r LongName` Climate Futures Summary"
---
```{r figurename, echo=FALSE, fig.asp=1.15, out.width='8in'}
npsband <- paste0("nps-black-band.png")
cover1 <- paste0("exposure-report-photos/",params$name,"_cropped.jpg")
knitr::include_graphics(npsband)
knitr::include_graphics(cover1)
```

Prepared by the National Park Service Climate Change Response Program, 2024

U.S. Department of the Interior

\newpage

Recommended citation

Climate Change Response Program. 2024. `r LongName` climate futures summary. National Park Service. Fort Collins, CO.

Key messages

The climate has already changed in `r LongName`. Since 1970, the park warmed at a rapid rate of `r Exposure.Data$Tavg.rate.1970` °F per century.

The future won’t look like the past. All future projections in temperature are above the recent historical average (1979-2012).

There’s a range of ways the future climate may evolve. In 2050, average temperature projections range from +`r Exposure.Data$Future.DeltaTavg.min` to +`r Exposure.Data$Future.DeltaTavg.max` °F and precipitation from `r Exposure.Data$Future.DeltaPr.min` inches (`r Exposure.Data$Future.DeltaPr.min.percent`%) to +`r Exposure.Data$Future.DeltaPr.max` inches per year (+`r Exposure.Data$Future.DeltaPr.max.percent`%).

To account for this range, consider more than one future scenario. This report contains projections for `r LongName` for "`r CF1.Name`" and "`r CF2.Name`" climate futures and summarizes potential impacts to resources.

# Introduction

Rising temperatures, changing precipitation regimes, stronger storms, and other climatic changes are evident across America’s national parks. Effects include more severe wildland fires and floods, declining snowpack, melting glaciers, rising sea levels, intensifying drought, and increasing erosion. These effects can impact the natural, cultural, and built resources of our parks and impact opportunities to visit and recreate in these spaces. The pervasiveness of climate change impacts on resources, assets, operations, and human well-being means that all parks stand to benefit from recognizing and addressing climate change threats. The National Park Service (NPS) has developed guidance and resources to help parks incorporate climate considerations into their planning processes (https://www.nps.gov/subjects/climatechange/planning.htm).

The NPS [Planning for a Changing Climate](https://irma.nps.gov/DataStore/DownloadFile/662814) (@P4CC2021) guide emphasizes that climate-informed plans should:

1)  Develop forward-looking goals that consider future climatic conditions according to the climate projections.
2)  Consider more than one scenario of the future when developing management strategies and actions.

Successful climate change planning requires us to understand that we will need to adapt to some impacts from climate change. [Climate change adaptation]{custom-style="Emphasis"} is defined as "an adjustment in natural or human systems that moderates harm or harnesses beneficial opportunities." Timely, forward-looking adaptation can help conserve cultural and natural resources, develop climate change-ready infrastructure, safeguard human well-being, and foster a positive visitor experience.[^1]

[^1]: See [Climate Change Glossary](https://irma.nps.gov/DataStore/Reference/Profile/2287966) for full definition.

This [climate futures summary]{custom-style="Emphasis"} describes both recent changes in climate (historical trends) and plausible climate futures at `r park$UNIT_NAME` (`r params$name`). [Climate futures]{custom-style="Emphasis"} are derived from models and explain different ways that the future might evolve at `r params$name` due to climate change. Climate futures help parks make short- and long-term [decisions that avoid surprises]{custom-style="Emphasis"} and costly mistakes. The approach of selecting and planning around several climate futures is a practical response to the impossibility of precisely predicting greenhouse gas emissions and how our climate will respond to them. [Runyon et al. (2024)](https://www.nps.gov/subjects/climatechange/climatefutures.htm) includes an in-depth description of methods used for these analyses. Please read the Disclaimer for further information about interpreting climate future summaries. 

# Historical climate change

Temperatures at `r params$name` have already increased considerably (Figure 1). Average annual temperature increased at the rate of `r Exposure.Data$Tavg.rate.1900` °F per century from 1895-2022, and since the acceleration of anthropogenic climate changes around 1970, temperatures have increased at the rate of `r Exposure.Data$Tavg.rate.1970` °F per century. The hottest two years before 2000 were `r Exposure.Data$Tavg.Anomalies.1` and `r Exposure.Data$Tavg.Anomalies.2``r Exposure.Data$PercentTYearsCondition` Since 1970, daily minimum (night-time) temperatures have increased more rapidly than daily maximum (daytime) temperatures, meaning nights are warmer and the range between daytime and nighttime temperatures has diminished. 

Overall precipitation has `r Exposure.Data$Prcp.trend` since 1970 but remains highly variable. In addition to changes in average conditions, `r params$name` is also experiencing changes in extreme conditions. For example, there has been a `r Exposure.Data$ObservedPrcp`% increase in the amount of rain falling during heavy storms in the region since 1958 (Figure 2). This is because warm air can hold more water vapor, so as temperatures increase, precipitation will fall in more intense and severe events.

```{r, echo=FALSE}
fig1 <- paste0(Hist.data,params$name,"_Historical_Trends.png")
```

![[Figure 1.]{custom-style="Strong"} Historical trends in annual average temperature (upper plot) and annual total precipitation (lower plot) for `r params$name` from 1895-2022. Black points show yearly values while blue lines are linear regressions for the entire period record and red lines for 1970-2022 trends. Solid lines are significant trends (i.e., p\<0.05); dashed lines are not statistically significant.](`r fig1`){width="576"}

![[Figure 2.]{custom-style="Strong"} This map from the 5th National Climate Assessment (https://www.globalchange.gov/our-work/fifth-national-climate-assessment) shows regional changes in the amount of rain falling during heavy storms (heaviest 1% of events). For all regions in the contiguous US and Alaska, the percentage of rain falling during extreme precipitation events has increased since 1958.](TotalAnnualPrecip.png){width="480"}

# Projected climate change and related impacts

We examined 40 different climate model outputs to characterize the uncertainty about how climate change could affect `r params$name`. Relative to the 1979–2012 baseline period[^2] (see @Runyon2024 for details), all climate models project significant warming by 2050 with projected increases in average annual temperature ranging from +`r Exposure.Data$Future.DeltaTavg.min` °F to +`r Exposure.Data$Future.DeltaTavg.max` °F. `r Exposure.Data$PrcpModels` Given the range of these projections, it's clear that future conditions will not resemble the past, and planning for [more than one]{custom-style="Emphasis"} plausible future will best position the park to manage change. Thus, we identified two plausible, divergent climate futures (“`r CF1.Name`” and “`r CF2.Name`”) to capture relevant variation across climate models and greenhouse gas emissions scenarios (for more specifics on the process, see @Lawrence2021; @Runyon2024). These two climate futures represent different ways the climate might develop at `r params$name` through 2050 and are both plausible. Neither of these projections should be considered as "predictions"—we cannot know how the future will unfold. Considering both climate futures will best help the park prepare for future conditions despite climate uncertainty.  

It is important to note the naming of these futures is relative to one another, not the past. In other words, the `r CF1.Name` `r Exposure.Data$Name.Convention` `r CF2.Name` climate future.

[^2]: The historical baseline used to compare with climate futures is from 1979-2012, the period of record for the observational dataset in the downscaling process. Historical comparisons for climate futures use this baseline period, instead of 1895-2022 range reported above to avoid statistical errors. See Runyon et al. (2024) for details.

## Annual average temperature and precipitation projections

Average annual temperatures are projected to increase under both the `r CF1.Name` and `r CF2.Name` climate futures relative to the historical baseline period (1979-2012). Increases by 2050 under the `r CF1.Name` climate future are considerable (+`r Exposure.Data$DeltaTavg.CF1` °F) and increases under the `r CF2.Name` climate future are extreme (+`r Exposure.Data$DeltaTavg.CF2` °F) (Figure 3. See Appendix 1 for "Projected changes in climate metrics"). 

To put this in context, most historically hot years recorded at `r params$name` would be "average" temperature years in the `r CF1.Name` climate future. Average temperatures under the `r CF2.Name` climate future would generally exceed anything experienced at `r params$name` in recent history. These projections are consistent with temperature increases already observed due to climate change at `r params$name`.

Increases in average temperatures can result in a range of impacts on both human life and ecosystems. Rising temperatures can stress plants and animals not accustomed to living in warmer temperatures. Temperature increases can lead to intense heat waves that threaten human health, especially for vulnerable populations. Finally, rising temperatures often lead to worsening drought conditions and increased wildfire risk.

```{r, echo=FALSE}
fig3 <- paste0("data/",params$name,"/",CF_selected,"/figures/TavgF-Timeseries.png")
```

![[Figure 3.]{custom-style="Strong"} This graph compares observed average annual temperature at `r params$name` (°F) (1979-2022) with projected average annual temperature for the two climate futures over the period 2023-2099. The historically observed data is shown in gray, the `r CF1.Name` climate future in `r Exposure.Data$Colors`. The smooth line running through each projection is the 10-year running average for historical observations and future projections. Gray-shaded area represents the time period that was averaged (2035-2065) to summarize future projections for the year 2050 (Appendix 1).](`r fig3`){width="576"}

Average annual precipitation is projected to be highly variable. Projections under both climate futures indicate considerably drier and also wetter years than experienced historically. These projections are consistent with the scientific understanding that climate change will result in increased extremes in precipitation (@NCA5). This means that `r Exposure.Data$PrecipFutures`. Overall, our analysis projects `r ifelse(Exposure.Data$DeltaPrcp.CF1 >= 0, "+", "-")``r Exposure.Data$DeltaPrcp.CF1` inches in annual precipitation under the `r CF1.Name` climate future and `r ifelse(Exposure.Data$DeltaPrcp.CF2 >= 0, "+", "-")``r Exposure.Data$DeltaPrcp.CF2` inches under the `r CF2.Name` climate future relative to 1979-2012. 

Although average annual precipitation is projected to `r Exposure.Data$PrcpSeasons`, any changes in seasonal precipitation patterns could impact hydrologic systems, seasonal biology, and extreme events (see Appendix 1). Very dry intervals and warming can lead to drought conditions, affecting river levels, water availability, and ecosystem health. Very wet years and overall increases in annual precipitation can lead to flooding that may impact water quality, infrastructure, transportation routes, and more.

```{r, echo=FALSE}
fig4 <- paste0("data/",params$name,"/",CF_selected,"/figures/PrcpIn-Timeseries.png")
```

![[Figure 4.]{custom-style="Strong"} This graph compares observed average annual precipitation at `r params$name` (inches/year) (1979-2022) with projected average annual precipitation for the two climate futures over the period 2023-2099. The historically observed data is shown in gray, the `r CF1.Name` climate future in `r Exposure.Data$Colors`. The smooth line running through each projection is the 10-year running average for historical observations and future projections. The gray-shaded area represents the time period that was averaged (2035-2065) to summarize future projections for the year 2050 (Appendix 1).](`r fig4`){width="576"}

## Extreme Events

Average temperature and precipitation changes, particularly annual measures, can demonstrate direction and relative magnitude of change for each climate future, but on their own can be difficult to translate into resource impacts. Averages do not adequately capture variability and resulting changes in extremes or compound events that are often highly consequential for resources. Therefore, we summarize metrics of climate extremes below.[^3] 

[^3]: The caveat for these climate extremes is that the analyses necessarily evaluate events that occur rarely and, as such, are less frequently observed and are difficult to characterize. There is, therefore, a broader range in these projections than for temperature or precipitation averages.

### Extreme temperature

`r Exposure.Data$ExtremeTemps`

Extreme temperatures can also lead to infrastructure issues, such as accelerating the weathering of structures, stressing power grids and air conditioning systems, buckling and cracking roadways, and other secondary effects. Parks should consider adaptation principles in design, construction, and maintenance of infrastructure—for example, installing additional shade structures to reduce visitor exposure to extreme heat. 

```{r, echo=FALSE}
fig5 <- paste0("data/",params$name,"/",CF_selected,"/figures/",fig5code)
```

![[Figure 5.]{custom-style="Strong"} `r Exposure.Data$TempsFigures`](`r fig5`){width="480"}

### Extreme precipitation

Changes in extreme precipitation are characterized by two factors: frequency (how often extreme precipitation events occur) and intensity (amount of rainfall during an event). Although both intensity and frequency often change in the same direction, impactful consequences, like ground saturation or rapid runoff, can result from changes in either intensity or frequency alone.  

`r Exposure.Data$ExPrcTrend`. Figure 6 shows the number of days per year in which precipitation at `r params$name` is greater than or equal to `r Exposure.Data$HistPrecip99` inches in 24 hours, which is the historical (1979-2012) 99th-percentile event. This measure of extreme precipitation frequency shows that the number of days with extreme precipitation greater than the historical 99th percentile is projected to `r Exposure.Data$Prcp99thTrend`. The 24-hour period with the most precipitation observed historically (1979-2012) was `r Exposure.Data$Prcp.max.hist` inches. The largest rainfall event projected in the future 30-year period (2035-2065) is `r Exposure.Data$Future.PrcpIn.CF1` inches for the `r CF1.Name` climate future and `r Exposure.Data$Future.PrcpIn.CF2` inches for the `r CF2.Name` climate future. Note that the maximum 24-hour precipitation events for the 30-year period are `r Exposure.Data$Prcp24HrTrend`. This reflects the fact that climate change is projected to lead to more intense extremes in precipitation (@NCA5). Managers should consider potential impacts to infrastructure from flooding, disruptions to transportation routes, water quality impacts from stormwater runoff into freshwater systems, and the potential for landslides or mudslides. 

```{r, echo=FALSE}
fig6 <- paste0("data/",params$name,"/",CF_selected,"/figures/OverPrecip99-Annual-bar.png")
```

![[Figure 6.]{custom-style="Strong"} Average number of days annually with precipitation exceeding the 99th percentile (`r Exposure.Data$HistPrecip99` in.) of precipitation from the historical period 1979-2012 and for each climate future.](`r fig6`){width="576"}

### Drought
Exacerbated by more extreme precipitation regimes, hotter temperatures in both climate futures can rapidly evaporate surface water and increase drought risk. Figure 7 uses the Standardized Precipitation Evaporation Index (SPEI) to assess changing drought conditions at `r params$name` (@Runyon2024). SPEI factors in both precipitation and evapotranspiration, providing a measure of how much water is available in an area compared to what occurred historically. Positive SPEI values indicate wetter-than-average conditions, while negative values indicate drier-than-average conditions. The further the bars are from zero (positive or negative), the more extreme the conditions are.

```{r, echo=FALSE}
fig7 <- paste0("data/",params$name,"/",CF_selected,"/figures/SPEI-Panel.png")
```

![[Figure 7.]{custom-style="Strong"} Drought metrics for `r params$name`. Drought index (SPEI; units = standard deviations from historical) timeseries for each climate future. Below-average SPEI (black bars) indicates dry conditions. The dotted line (SPEI = -0.5) indicates drought conditions.](`r fig7`){width="576"}

Figure 8 complements the SPEI data by providing a more detailed view of projected drought characteristics in the region. Drought duration (left graph) refers to the length of time during which the area experiences continuous drought conditions, extending the impact on water availability in the area. The drought-free interval (middle graph, an alternative measure of drought frequency) represents periods between drought events when there is sufficient water availability. More years in a drought-free interval signify more extended periods of recovery from drought. Drought severity (right graph) reflects the intensity of drought conditions compounded by the duration of the drought period.

For `r params$name`, drought under a `r CF2.Name` climate future is projected to become `r Exposure.Data$DrtTrend.CF2`. For the `r CF1.Name` climate future, droughts are projected to become `r Exposure.Data$DrtCF1Duration` and `r Exposure.Data$DrtCF1Severity` severe, `r Exposure.Data$DrtGrammar.CF1` `r Exposure.Data$Drt.Severity.Length.CF1`.

`r Exposure.Data$Drt.Research` (@NCA5). Generally, the longer that an area experiences drought conditions, the more severe the drought becomes. More severe and prolonged droughts pose a risk to water supplies and both human and ecosystem health. Areas prone to wildfires may also experience more severe and higher-intensity fires, as drought can dry out vegetation and make it more likely to burn. Managers should prepare for potential impacts to water sources, plant species, and ecosystem health in the face of severe and prolonged drought.
``` {r message=FALSE, warning=FALSE, results='asis', echo=FALSE}
matching_indices_drought <- which(nps_boundary$UNIT_CODE == params$name)

if (length(matching_indices_drought) > 0) {
  unit_region <- nps_boundary$REGION[matching_indices_drought]
  unit_state <- nps_boundary$STATE[matching_indices_drought]

  if (unit_region == "Northeast" &&
      (Exposure.Data$DeltaPrcp.CF1 > 0 | Exposure.Data$DeltaPrcp.CF2 > 0) &&
      (Exposure.Data$Severity.CF2 | Exposure.Data$Severity.CF1)) {
    cat(readLines('Regional.Rmd')[c(37:38)], sep = '\n')
  } else if (unit_region == "Mountains" && unit_state %in% c("SD","WY","MT","ND","NE")) {
    cat(readLines('Regional.Rmd')[c(45:46)], sep = '\n')
  } else if (unit_region == "Pacific West" && unit_state %in% c("OR","WA","ID") && NPVuln$High.impact.summer.drought == 1) {
    cat(readLines('Regional.Rmd')[c(49:50)], sep = '\n')
  } else if (unit_region == "DesertSW" && unit_state %in% c("CA","NV","UT","CO","AZ","NM") && NPVuln$High.impact.summer.drought == 1) {
    cat(readLines('Regional.Rmd')[c(53:54)], sep = '\n')
  } else if (unit_region == "MW" && NPVuln$High.impact.summer.drought[which(NPVuln$code == params$name)] == 1) {
    cat(readLines('Regional.Rmd')[c(57:58)], sep = '\n')
  } else {
    cat(" ")
  }
} else {
  cat(" ")
}
```

```{r, echo=FALSE}
fig8 <- paste0("data/",params$name,"/",CF_selected,"/figures/Drought-characteristics-panel.png")
```

![[Figure 8.]{custom-style="Strong"} Drought metrics for `r params$name`. (Left) Average drought duration (years) historically (1979-2012) and under each climate future. (Middle) Average drought return interval (years), indicating the expected amount of time between periods of drought, historically and under each climate future. (Right) Average drought severity, a measure of drought intensity multiplied by duration, historically and under each climate future.](`r fig8`){width="576"}

### Plant-available water

Climatic water deficit—the difference between potential evapotranspiration and actual evapotranspiration—indicates the amount of additional water plants would use if it were available and it is often used to indicate landscape dryness. High climatic water deficit is also an indicator of increased fire risk and plant stress (@Thoma2020). We use a water balance model to determine climatic water deficit that accounts for the interactive effects of temperature, precipitation, and local characteristics (e.g., slope, aspect, soil profile).

The average annual climatic water deficit at `r params$name` is projected to `r Exposure.Data$WBtrend`  relative to the historical period (1979-2012) (`r CF1.Name`: +`r Exposure.Data$CF1.WBdelta` inches/year, `r CF2.Name`: +`r Exposure.Data$CF2.WBdelta` inches/year; Figure 9). Both climate futures project `r Exposure.Data$WBExtremes` Under the `r CF2.Name` climate future, `r Exposure.Data$wbCF2`

```{r, echo=FALSE}
fig9 <- paste0("data/",params$name,"/",CF_selected,"/figures/D.in-Timeseries.png")
```

![[Figure 9.]{custom-style="Strong"} This line graph compares observed climatic water deficit at `r params$name` (inches/year) (1979-2022) with projected climatic water deficit for the two climate futures over the period 2023-2099. The historical observation data is shown in gray, the `r CF1.Name` climate future in `r Exposure.Data$Colors`. The smooth line running through each projection is the 10-year running average for historical observations and future projections. Gray-shaded area represents the time period that was averaged (2035-2065) to summarize future projections for the year 2050.](`r fig9`){width="576"}

## Other Possible Climate-related Impacts

Other potential climate-related impacts that may occur at `r params$name` but cannot be represented by basic climate exposure metrics are described below. While these impacts are based on general literature review for the region and are not specific to `r params$name`, park staff should be aware of their potential threat and further study may be warranted.

```{r echo=FALSE, message=FALSE, warning=FALSE, results='asis'}
if (NPVuln$High.impact.projected.fire[which(NPVuln$code == params$name)] == 1 | NPVuln$High.impact.current.fire[which(NPVuln$code == params$name)] == 1){
  cat(readLines('Regional.Rmd')[c(131:134)], sep = '\n')
} else {
  cat(" ")
}
```

```{r, echo=FALSE, results='asis', message=FALSE, warning=FALSE}
matching_indices_fire <- which(ExpRegion$UNIT_CODE == params$name)
if (length(matching_indices_fire) > 0) {
  exp_rep <- ExpRegion$ExpRep[matching_indices_fire]
  exp_region <- ExpRegion$REGION[matching_indices_fire]
  exp_state <- ExpRegion$STATE[matching_indices_fire]
  
  if (exp_region == "IM" && NPVuln$High.impact.projected.fire[which(NPVuln$code == params$name)] == 1) {
    cat(readLines('Regional.Rmd')[c(63:64)], sep = '\n')
  } else if (exp_region == "PW" && nps_boundary$REGION[which(nps_boundary$UNIT_CODE == params$name)] == "Pacific West" && NPVuln$High.impact.projected.fire[which(NPVuln$code == params$name)] == 1) {
    cat(readLines('Regional.Rmd')[c(77:78)], sep = '\n')
  } else if (exp_rep == "DesertSW" && NPVuln$High.impact.projected.fire[which(NPVuln$code == params$name)] == 1) {
    cat(readLines('Regional.Rmd')[c(53:54)], sep = '\n')
  } else if (exp_region == "SE" && NPVuln$High.impact.projected.fire[which(NPVuln$code == params$name)] == 1) {
    cat(readLines('Regional.Rmd')[c(81:82)], sep = '\n')
  } else if (exp_state %in% c("NJ", "PA") && NPVuln$High.impact.projected.fire[which(NPVuln$code == params$name)] == 1) {
    cat(readLines('Regional.Rmd')[c(73:74)], sep = '\n')
  } else {
    cat(" ")
  }
} else {
  cat(" ")
}
```

### Invasive species and pests

Climate change can accelerate the introduction and spread of invasive species, and invasive species can amplify the effects of climate change (@Beaury2020). Extreme weather events may stress native species, create opportunities for invasive species movement, and alter mean conditions that affect species' life cycles and their ability to spread into new areas. Climate change can also affect the timing and efficacy of current invasive management treatments. Terrestrial and aquatic invasive species have the potential to cause direct damage to power, water, transportation, and building facilities (@Vissichelli2018) and impact natural and cultural resources. `r params$name` is in a region where climate change can favor invasive alien plant species in temperate zone ecosystems due to increased warmth, humidity, vegetation disturbances, and atmospheric carbon dioxide (@Davidson2011, @Hellmann2008, @Liu2017). Indirect climate change impacts may also affect facilities through forest insect pest invasions or fluctuating groundwater levels leading to increased vegetation stress, mortality, and treefall. The potential for increased invasive species should be prevented or mitigated through early detection and rapid response procedures. The [National Invasive Species Council](https://www.doi.gov/invasivespecies/early-detection-and-rapid-response) recently released EDRR tools and lessons learned and have additional resources for monitoring and managing invasive species and climate change. The [NPS Integrated Pest Management Program](https://www.nps.gov/orgs/1103/ipm.htm) also have resources available to help parks detect and prevent pests.
```{r echo=FALSE, message=FALSE, warning=FALSE, results='asis'}
if (ExpRegion$STATE[which(ExpRegion$UNIT_CODE == params$name)] %in% c("ME", "VT", "NH", "MA", "RI", "CT", "NY", "NJ", "PA", "DE", "MD", "VA", "WV", "OH", "MI", "WV", "NC", "SC", "GA", "FL", "AL", "TN", "KY", "IN", "MS", "LA", "TX", "AR", "MO", "IL", "WI", "MN", "IA", "MO", "OK", "KS", "NE")) {
  cat(readLines('Regional.Rmd')[c(137:138)], sep = '\n')
} else {
  cat(" ")
}
```


### Phenology

The timing of spring onset affects the seasonal life-history stages of plants throughout the national parks. Roughly three-quarters of parks (76%) are experiencing earlier spring onset than historical conditions, and this change is projected to reach all regions containing parks by mid-century (@Monahan2016). Earlier spring onset and longer growing seasons influenced by climate change will alter the phenological patterns of species that flower before or after peak summer heat, follow other temperature cues, or are driven by water availability. Spring timing can impact animals reliant on the rhythms of plant life stages (e.g., mismatches in plant-pollinator interactions), the timing of park operations, events, and visitor uses (e.g., road openings, flower festivals, and backcountry recreation), cascading effects on carbon cycling and other ecosystem processes, the risk of “false springs” that create devastating hard freezes and facilitate the spread of invasive species. 

```{r echo=FALSE, message=FALSE, warning=FALSE, results='asis'}
if (NPVuln$High.impact.sea.level.or.surge[which(NPVuln$code == params$name)] == 1 && nps_boundary$REGION[which(nps_boundary$UNIT_CODE == params$name)] == "Northeast") {
  cat(readLines('Regional.Rmd')[c(90:102)], sep = '\n')
} else if (NPVuln$High.impact.sea.level.or.surge[which(NPVuln$code == params$name)] == 1 && nps_boundary$REGION[which(nps_boundary$UNIT_CODE == params$name)] == "Pacific West") {
  cat(readLines('Regional.Rmd')[c(105:112)], sep = '\n')
} else if (NPVuln$High.impact.sea.level.or.surge[which(NPVuln$code == params$name)] == 1 && nps_boundary$STATE[which(nps_boundary$UNIT_CODE == params$name)] %in% c("TX","AL","LA","FL","MS","NC","SC","GA")) {
  cat(readLines('Regional.Rmd')[c(115:126)], sep = '\n')
} else {
  cat(" ")
}
```

# Adaptation planning: Address climate change impacts and implications

Concepts from [Planning for a Changing Climate](https://irma.nps.gov/DataStore/DownloadFile/662814){custom-style="Emphasis"} (@P4CC2021) can help address the above-described potential climate impacts in planning for `r params$name` and develop a robust climate change response, which will better protect park resources and assets today and for future generations. Potential climate impacts can be considered during goal setting, testing existing management activities, or identifying new climate change adaptation strategies. Below are some of the key principles from Planning for a Changing Climate, intended to inform a park's climate change response.

## Develop forward-looking goals that consider future climatic conditions

Adaptation planning looks to the future, which is likely to differ from the past, using climate projections to adopt forward-looking goals. The term 'goals' should be interpreted broadly, recognizing that different planning processes use different terms and approaches. For example, a General Management Plan or a Visitor Use Management Plan may focus on developing desired conditions, whereas a Resource Stewardship Strategy seeks to identify long- and short-term goals. Regardless of the specific terminology, adaptation planning offers an important opportunity to establish or adjust climate-informed goals which look to the future and seek to strike a balance between traditional aspirations and emerging realities. Furthermore, the information provided above can be used to identify climate implications for management strategies and actions that may flow from broader climate-informed goals.

[Putting it into action:]{custom-style="Strong"} This summary provides information about past, present, and potential future climate conditions for `r params$name`. The intent of this work is to enable managers to use climate information to develop new goals (e.g., desired conditions or trajectories) or reconsider existing management goals. If a goal clearly cannot be met under projected future conditions, it needs updating. Goals should acknowledge continuous change and the potential for unavoidable losses or ecological transformations.

The worksheet below (Table 1) is an example that can help to assess whether desired conditions, and/or management strategies and actions are feasible under different climate scenarios. Use this worksheet in conjunction with the climate information provided above to identify potential climate implications for desired conditions, goals, strategies, and/or actions and revise these as needed.

-   Adaptation planning offers an important opportunity to establish or adjust [desired conditions]{custom-style="Emphasis"} for the future and formulate [climate-informed goals]{custom-style="Emphasis"} that strike a balance between traditional aspirations and emerging realities.

-   If it is apparent that goals cannot be met under projected future conditions, goals will need to be updated or refined. Goals should increasingly acknowledge continuous change and the potential for unavoidable losses or transformations.

## Consider more than one scenario of the future

Adaptation planning considers [multiple scenarios]{custom-style="Emphasis"} of the future to account for uncertainty in the anticipated scope, magnitude, and effects of climate change. This structured approach allows planners and managers to (1) explore a variety of [plausible future conditions]{custom-style="Emphasis"}; (2) evaluate the implications of those conditions; and (3) identify a portfolio of possible management strategies. A table or exercise similar to Table 1 can be used to test existing (or develop new) management goals, strategies, or other plan elements. See Schuurman et al. (2019) for examples.

[Putting it into action:]{custom-style="Strong"} This summary provides two climate scenarios ("`r CF1.Name`" and "`r CF2.Name`") that represent plausible future conditions for `r params$name`. Under each future, managers can identify what strategies and actions can reduce risk and enable the park to meet its goals. You might also consider what strategies and actions can take advantage of possible opportunities under each scenario.

1.  Start by brainstorming an array of strategies that could address important climate risks. Consult existing sources of climate-informed management strategies and actions, which might be found in other planning documents like a Resource Stewardship Strategy, Natural Resource Condition Assessment, Cultural Resource Stewardship Assessment, climate-friendly park plan, or vulnerability assessments.

2.  Next, decide how you will compare and evaluate strategies to decide which to select and include in the plan. Strategies and actions could be evaluated based on their effectiveness across multiple scenarios, effectiveness in extreme scenarios, how they align with park management goals, and/or their feasibility.

[Table 1.]{custom-style="Strong"} Example worksheet that can be used to assess if desired conditions/goals are feasible under different climate futures.

```{r echo=FALSE, message=FALSE, warning=FALSE}
library(flextable)

if(CF1.Name == "Warm Wet"){
my_data <- data.frame(
  "Resource/ Asset/ Value" = c("Wetlands"),
  "Desired Condition" = c("No net loss of wet meadows and fens"),
  "How will climate change affect this desired condition under the Warm Wet scenario?" = c("Warmer conditions could lead to smaller or fully-dried and transformed wetlands. Under wetter conditions, some wetlands could remain stable or potentially expand."),
  "Is the Desired Condition achievable under the Warm Wet scenario?" = c("No net loss is probably not feasible, but minimal loss might be possible."),
  "How will climate change affect this desired condition under the Hot Dry scenario?" = c("No practical way to keep water in all meadows. Marginal wetlands will convert to uplands. All wetlands could periodically dry out under extreme drought conditions."),
  "Is the Desired Condition achievable under the Hot Dry scenario?" = c("No. No net loss is not attainable."),
  "Revise Desired Condition? Remove Desired Condition?" = c("Revision: Minimize loss of high value wetlands.")
)

ft <- flextable(my_data)

header_labels <- c("Resource/ Asset/ Value", "Draft Desired Condition", "How will climate change affect this desired condition under the Warm Wet scenario?", "Is the Desired Condition achievable under the Warm Wet scenario?", "How will climate change affect this desired condition under the Hot Dry scenario?", "Is the Desired Condition achievable under the Hot Dry scenario?", "Revise Desired Condition? Remove Desired Condition?")
ft <- flextable::set_header_labels(ft, values = header_labels)

ft <- flextable::set_table_properties(ft, width = 1, layout = "autofit") %>%
  flextable::border_outer() %>%
  flextable::border_inner_h() %>%
  flextable::border_inner_v()

ft <- bg(ft, bg = "#4E6543", part = "header")
ft <- color(ft, color = "#f2f2f2", part = "header")
ft <- bold(ft, bold = TRUE, part = "header")
ft <- fontsize(ft, size = 9, part = "all")
} else {
  my_data <- data.frame(
  "Resource/ Asset/ Value" = c("Wetlands"),
  "Draft Desired Condition" = c("No net loss of wet meadows and fens"),
  "How will climate change affect this desired condition under the Warm Dry scenario?" = c("Marginal wetlands may convert to uplands due to warmer conditions. Some wetlands could expand or remain stable under wetter conditions."),
  "Is the Desired Condition achievable under the Warm Dry scenario?" = c("No net loss is probably not feasible, but minimal loss might be possible."),
  "How will climate change affect this desired condition under the Hot Wet scenario?" = c("No practical way to keep water in all meadows. Marginal wetlands will convert to uplands. All wetlands could periodically dry under extreme drought conditions."),
  "Is the Desired Condition achievable under the Hot Wet scenario?" = c("No. No net loss is not attainable."),
  "Revise Desired Condition? Remove Desired Condition?" = c("Revision: Minimize loss of high value wetlands.")
)

ft <- flextable(my_data)

header_labels <- c("Resource/ Asset/ Value", "Draft Desired Condition", "How will climate change affect this desired condition under the Warm Dry scenario?", "Is the Desired Condition achievable under the Warm Dry scenario?", "How will climate change affect this desired condition under the Hot Wet scenario?", "Is the Desired Condition achievable under the Hot Wet scenario?", "Revise Desired Condition? Remove Desired Condition?")
ft <- flextable::set_header_labels(ft, values = header_labels)

ft <- flextable::set_table_properties(ft, width = 1, layout = "autofit") %>%
  flextable::border_outer() %>%
  flextable::border_inner_h() %>%
  flextable::border_inner_v()

ft <- bg(ft, bg = "#4E6543", part = "header")
ft <- color(ft, color = "#f2f2f2", part = "header")
ft <- bold(ft, bold = TRUE, part = "header")
ft <- fontsize(ft, size = 9, part = "all")
}


ft
```

This climate futures summary includes a concise summary of key climate trends, projections, impacts, and planning concepts to empower parks in managing climate change and adapting to an uncertain future. Browse the [NPS Climate Change Response Program](https://www.nps.gov/orgs/ccrp/index.htm) website to find additional information about climate assessments, climate adaptation by parks, and more. 

\newpage

# Disclaimer 

This series of park-specific climate future summaries were developed to provide information that can serve as cursory identification of vulnerabilities or “red flag checks” for areas that may require further scoping. Information provided by the climate future summaries is widely used by the NPS and partners in many routine planning processes. For example, an assessment of historical and future climate exposure is foundational for climate change vulnerability assessments, scenario-based climate change adaptation, and basic evaluations of proposed infrastructure projects and other climate-sensitive planning. However, climate futures should be interpreted as representations of future uncertainty, rather than predictive forecasts.  

The climate future summaries described here are, in general, most appropriately used as a coarse filter or initial climate assessment that can identify concerns that warrant a more detailed assessment. These summaries use a standardized approach that is not tailored to site-specific issues or climate sensitivities. A more detailed and site-specific climate assessment is required for evaluations of, e.g., requirements of major infrastructure or resource projects that may be highly consequential. 

See [Runyon et al. (2024)](https://www.nps.gov/subjects/climatechange/climatefutures.htm) for details on data, methods, further discussion on scope and limitations, and FAQ section. 

\newpage

# References

<div id="refs">



</div>

\newpage

# Appendix 1. Climate futures table

[Table A1.]{custom-style="Strong"} Projected changes in climate metrics presented in this report. Change values are the difference between the climate metric's future-period (2035-2065) average and the historical-period (1979-2012) average.

| Climate Metric                                                | Historical                          | `r CF1.Name`                           | `r CF2.Name`                      |
|-------------------|------------------|------------------|------------------|
| Change in annual average temperature (°F)   | `r D_Annual$TavgF[1]`   | `r ifelse(Exposure.Data$DeltaTavg.CF1 >= 0, "+", "")``r Exposure.Data$DeltaTavg.CF1`   | `r ifelse(Exposure.Data$DeltaTavg.CF2 >= 0, "+", "")``r Exposure.Data$DeltaTavg.CF2`           |
| Change in average winter (DJF) temperature (°F)   | `r Exposure.Data$TempWinterHist`   | `r ifelse(Exposure.Data$DTempWinterCF1 >= 0, "+", "")``r Exposure.Data$DTempWinterCF1`   | `r ifelse(Exposure.Data$DTempWinterCF2 >= 0, "+", "")``r Exposure.Data$DTempWinterCF2`           |
| Change in average spring (MAM) temperature (°F)   | `r Exposure.Data$TempSpringHist`   | `r ifelse(Exposure.Data$DTempSpringCF1 >= 0, "+", "")``r Exposure.Data$DTempSpringCF1`   | `r ifelse(Exposure.Data$DTempSpringCF2 >= 0, "+", "")``r Exposure.Data$DTempSpringCF2`           |
| Change in average summer (JJA) temperature (°F)   | `r Exposure.Data$TempSummerHist`   | `r ifelse(Exposure.Data$DTempSummerCF1 >= 0, "+", "")``r Exposure.Data$DTempSummerCF1`   | `r ifelse(Exposure.Data$DTempSummerCF2 >= 0, "+", "")``r Exposure.Data$DTempSummerCF2`           |
| Change in average fall (SON) temperature (°F)   | `r Exposure.Data$TempFallHist`   | `r ifelse(Exposure.Data$DTempFallCF1 >= 0, "+", "")``r Exposure.Data$DTempFallCF1`   | `r ifelse(Exposure.Data$DTempFallCF2 >= 0, "+", "")``r Exposure.Data$DTempFallCF2`           |
| Change in annual precipitation (in)         | `r D_Annual$PrcpIn[1]`   | `r ifelse(Exposure.Data$DeltaPrcp.CF1 >= 0, "+", "")``r Exposure.Data$DeltaPrcp.CF1`   | `r ifelse(Exposure.Data$DeltaPrcp.CF2 >= 0, "+", "")``r Exposure.Data$DeltaPrcp.CF2`         |
| Change in average winter (DJF) precipitation (total in)   | `r Exposure.Data$PrcpWinterHist`   | `r ifelse(Exposure.Data$DPrcpWinterCF1 >= 0, "+", "")``r Exposure.Data$DPrcpWinterCF1`   | `r ifelse(Exposure.Data$DPrcpWinterCF2 >= 0, "+", "")``r Exposure.Data$DPrcpWinterCF2`           |
| Change in average spring (MAM) precipitation (total in)   | `r Exposure.Data$PrcpSpringHist`   | `r ifelse(Exposure.Data$DPrcpSpringCF1 >= 0, "+", "")``r Exposure.Data$DPrcpSpringCF1`   | `r ifelse(Exposure.Data$DPrcpSpringCF2 >= 0, "+", "")``r Exposure.Data$DPrcpSpringCF2`           |
| Change in average summer (JJA) precipitation (total in)   | `r Exposure.Data$PrcpSummerHist`   | `r ifelse(Exposure.Data$DPrcpSummerCF1 >= 0, "+", "")``r Exposure.Data$DPrcpSummerCF1`   | `r ifelse(Exposure.Data$DPrcpSummerCF2 >= 0, "+", "")``r Exposure.Data$DPrcpSummerCF2`           |
| Change in average fall (SON) precipitation (total in)   | `r Exposure.Data$PrcpFallHist`   | `r ifelse(Exposure.Data$DPrcpFallCF1 >= 0, "+", "")``r Exposure.Data$DPrcpFallCF1`   | `r ifelse(Exposure.Data$DPrcpFallCF2 >= 0, "+", "")``r Exposure.Data$DPrcpFallCF2`           |
| Change in days / \> 99th Tmax / year (number of days)        | `r D_Annual$Tmax99[1]`            | `r ifelse(D_Annual$Tmax99[2] >= 0, "+", "")``r D_Annual$Tmax99[2]`            | `r ifelse(D_Annual$Tmax99[3] >= 0, "+", "")``r D_Annual$Tmax99[3]`          |
| Change in days with 'dangerous' heat index (number of days)  | `r D_Annual$HI.Dan[1]`      | `r ifelse(Exposure.Data$HI.Dan.CF1 >= 0, "+", "")``r Exposure.Data$HI.Dan.CF1`      | `r ifelse(Exposure.Data$HI.Dan.CF2 >= 0, "+", "")``r Exposure.Data$HI.Dan.CF2`          |
| Change in largest rainfall event (in/day) | `r Exposure.Data$Prcp.max.hist` | `r ifelse(Exposure.Data$PrcpChangeCF1 >= 0, "+", "")``r Exposure.Data$PrcpChangeCF1` | `r ifelse(Exposure.Data$PrcpChangeCF2 >= 0, "+", "")``r Exposure.Data$PrcpChangeCF2`        |
| Change in days \> 99th precipitation / year (number of days) | `r D_Annual$OverPrecip99[1]`      | `r ifelse(D_Annual$OverPrecip99[2] >= 0, "+", "")``r D_Annual$OverPrecip99[2]`      | `r ifelse(D_Annual$OverPrecip99[3] >= 0, "+", "")``r D_Annual$OverPrecip99[3]`    |
| Change in average drought duration (years)                    | `r Drought.char$Duration[1]`      | `r ifelse(Exposure.Data$DrtDurChangeCF1 >= 0, "+", "")``r Exposure.Data$DrtDurChangeCF1`      | `r ifelse(Exposure.Data$DrtDurChangeCF2 >= 0, "+", "")``r Exposure.Data$DrtDurChangeCF2`    |
| Change in drought-free interval (years)                       | `r Drought.char$Drt.Free[1]`      | `r ifelse(Exposure.Data$DrtFreeChangeCF1 >= 0, "+", "")``r Exposure.Data$DrtFreeChangeCF1`      | `r ifelse(Exposure.Data$DrtFreeChangeCF2 >= 0, "+", "")``r Exposure.Data$DrtFreeChangeCF2`    |
| Change in drought severity                            | `r Drought.char$Severity[1]`      | `r ifelse(Exposure.Data$DrtSevChangeCF1 >= 0, "+", "")``r Exposure.Data$DrtSevChangeCF1`      | `r ifelse(Exposure.Data$DrtSevChangeCF2 >= 0, "+", "")``r Exposure.Data$DrtSevChangeCF2`    |
| Change in annual average water deficit (in/yr)      | `r Exposure.Data$Hist.meanWB`     | `r ifelse(Exposure.Data$CF1.WBdelta >= 0, "+", "")``r Exposure.Data$CF1.WBdelta`     | `r ifelse(Exposure.Data$CF2.WBdelta >= 0, "+", "")``r Exposure.Data$CF2.WBdelta`   |
| Change in annual actual evapotranspiration (in/yr)  | `r Exposure.Data$AET3`            | `r ifelse(Exposure.Data$AET1 >= 0, "+", "")``r Exposure.Data$AET1`            | `r ifelse(Exposure.Data$AET2 >= 0, "+", "")``r Exposure.Data$AET2`          |

------------------------------------------------------------------------