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164 changes: 148 additions & 16 deletions 1_Download_Visualize.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -10,15 +10,12 @@ library(tidyverse)
library(GGally)

knitr::opts_chunk$set(echo = TRUE)
library(GGally)
```


# Data Acquisition

For this assignment we are going to be playing with annually
aggregated metrics of USGS flow data from the [CAMELS](https://ral.ucar.edu/solutions/products/camels) dataset. This dataset
has sparked a revolution in machine learning in hydrology.

For this assignment we are going to be playing with annually aggregated metrics of USGS flow data from the [CAMELS](https://ral.ucar.edu/solutions/products/camels) dataset. This dataset has sparked a revolution in machine learning in hydrology.

```{r}
if(!file.exists('data')){
Expand Down Expand Up @@ -60,10 +57,8 @@ download.file('https://gdex.ucar.edu/dataset/camels/file/camels_attributes_v2.0.

```


## Data org


```{r}
dat_files <- list.files('data',
full.names = T)
Expand All @@ -73,31 +68,49 @@ dat_files <- list.files('data',
climate <- read_delim(dat_files[1], delim = ';')

hydro <- read_delim('data/hydro.txt', delim = ';')
```

## Initial data viz
soil <- read_delim('data/soil.txt', delim = ';')

geol <- read_delim('data/geol.txt', delim = ';')
```

### Baseflow
## Initial data viz

### Baseflow

```{r}

ggplot(hydro, aes(x = baseflow_index,
y = q95)) +
#hydroclimate
climate_q <- inner_join(climate, hydro %>%
select(gauge_id, q95))
#baseflow
ggplot(hydro, aes(x=baseflow_index, y=q95))+
geom_point()


ggplot(climate_q, aes(x=p_mean, y=q95))+
geom_point()+
geom_smooth(method = "lm", se=F)

p_mean_mod <- lm(q95~p_mean, data=climate_q)
summary(p_mean_mod)
```

\*r\^2 \> 0.3

correlation ^2^ = r\^2

```{r}

Baseflow doesn't strongly control Q95 in a predictable way.
ggplot(hydro, aes(x = baseflow_index,y = q95)) +
geom_point()

```

Baseflow doesn't strongly control Q95 in a predictable way.

### Climate controls


```{r}

cq <- inner_join(climate, hydro %>%
Expand All @@ -113,7 +126,6 @@ p_mean_mod <- lm(q95 ~ p_mean, data = cq)

```


#### All at once

```{r}
Expand All @@ -138,17 +150,137 @@ ggplot(long_cq, aes(value,
scales = 'free')
```

The average precip (p_mean) controls 71% of the variation in 195, where every 1 mm/day increase in long-term average precip increases the q95 by 2.95 mm/day.
The average precip (p_mean) controls 71% of the variation in 195, where every 1 mm/day increase in long-term average precip increases the q95 by 2.95 mm/day.


# Assignment

## What are three controls on average runoff ratio?

```{r}
# Create a new data frame by joining climate and hydro data for analyzing runoff ratio
rr_data <- inner_join(climate, hydro %>%
select(gauge_id, runoff_ratio))

# Plot relationships between variables and runoff ratio
png(filename = 'big_rr_plot.png', width = 10, height = 8, units = 'in', res = 300)
rr_data %>%
select_if(is.numeric) %>%
ggpairs()
dev.off()

# Plot relationships between different variables and runoff ratio
ggplot(rr_data, aes(x = some_variable1, y = runoff_ratio)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Relationship between Some Variable 1 and Runoff Ratio")

ggplot(rr_data, aes(x = some_variable2, y = runoff_ratio)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Relationship between Some Variable 2 and Runoff Ratio")

ggplot(rr_data, aes(x = some_variable3, y = runoff_ratio)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Relationship between Some Variable 3 and Runoff Ratio")

# Model relationships and get summaries
p_mean_rr_mod <- lm(runoff_ratio ~ p_mean, data = rr_data)
summary(p_mean_rr_mod)

high_prec_rr_mod <- lm(runoff_ratio ~ high_prec_freq, data = rr_data)
summary(high_prec_rr_mod)

low_prec_rr_mod <- lm(runoff_ratio ~ low_prec_freq, data = rr_data)
summary(low_prec_rr_mod)

```
p_mean, high precipitation frequency and low precipitation frequency all have significant p value that they control avg runoff.

## What are three controls on baseflow_index?

```{r}
# Create a new data frame by joining climate and hydro data for analyzing baseflow index
bf_data <- inner_join(climate, hydro %>%
select(gauge_id, baseflow_index))

# Plot relationships between variables and baseflow index
png(filename = 'big_bf_plot.png', width = 10, height = 8, units = 'in', res = 300)
bf_data %>%
select_if(is.numeric) %>%
ggpairs()
dev.off()

# Plot relationships between different variables and baseflow index
ggplot(bf_data, aes(x = some_variable1, y = baseflow_index)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Relationship between Some Variable 1 and Baseflow Index")

ggplot(bf_data, aes(x = some_variable2, y = baseflow_index)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Relationship between Some Variable 2 and Baseflow Index")

ggplot(bf_data, aes(x = some_variable3, y = baseflow_index)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Relationship between Some Variable 3 and Baseflow Index")

# Model relationships and get summaries
frac_snow_bf_mod <- lm(baseflow_index ~ frac_snow, data = bf_data)
summary(frac_snow_bf_mod)

high_prec_freq_bf_mod <- lm(baseflow_index ~ high_prec_freq, data = bf_data)
summary(high_prec_freq_bf_mod)

low_prec_freq_bf_mod <- lm(baseflow_index ~ low_prec_freq, data = bf_data)
summary(low_prec_freq_bf_mod)

```

precip as snow, high precipitation frequency, and low precipitation frequency all have significant p value that they control baseflow index.
## What are three controls on mean flow?

```{r}

# Create a new data frame by joining climate and hydro data for analyzing mean flow
flow_data <- inner_join(climate, hydro %>%
select(gauge_id, q_mean))

# Plot relationships between variables and mean flow
png(filename = 'big_mean_flow_plot.png', width = 10, height = 8, units = 'in', res = 300)
flow_data %>%
select_if(is.numeric) %>%
ggpairs()
dev.off()

# Plot relationships between different variables and mean flow
ggplot(flow_data, aes(x = some_variable1, y = q_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Relationship between Some Variable 1 and Mean Flow")

ggplot(flow_data, aes(x = some_variable2, y = q_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Relationship between Some Variable 2 and Mean Flow")

ggplot(flow_data, aes(x = some_variable3, y = q_mean)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Relationship between Some Variable 3 and Mean Flow")

# Model relationships and get summaries
p_mean_q_mod <- lm(q_mean ~ p_mean, data = flow_data)
summary(p_mean_q_mod)

aridity_q_mod <- lm(q_mean ~ aridity, data = flow_data)
summary(aridity_q_mod)

low_prec_dur_q_mod <- lm(q_mean ~ low_prec_dur, data = flow_data)
summary(low_prec_dur_q_mod)
```

Fraction of mean precipitation, aridity, and low precipitation duration all have significant p value that they control mean flow.
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