Environmental Conditions During Fall Moose Hunting Seasons, Alaska, 2000-2016

By gerald · November 28, 2025

Insights on daily and annual air temperature, river water level, and leaf drop dates coincident with the moose (Alces alces) hunting season (September)…

The dataset covers a geographic area in western Alaska, near the Yukon-Kuskokwim Delta region. The boundaries extend from 64.55°N to 64.93°N latitude (a span of about 42 kilometers north to south) and from 156.67°W to 158.53°W longitude (approximately 88.5 kilometers east to west).

Hunting Week 2 experiences the most extreme temperature variations between maximum and minimum temperatures. The analysis calculated a variation score for each hunting week by combining the standard deviation of high temperatures, low temperatures, and the average daily temperature range.

Based on the analysis of temperature and water level conditions across all months, September emerges as the best month for hunting. This month provides the optimal combination of average temperature and water level conditions when both factors are weighted equally.

Yes, leaf drop timing significantly affects hunting conditions across different hunting weeks. The analysis reveals that early leaf drop periods are actually associated with warmer temperatures (averaging 9.45°C), while late leaf drop periods tend to be cooler (averaging 6.50°C). This represents a temperature difference of about 3°C between the two timing categories.

I've identified the days with the largest temperature differences between maximum and minimum temperatures, which significantly impact hunter comfort. The analysis shows which specific dates experienced the most dramatic temperature swings throughout the day, requiring hunters to adapt their clothing and gear accordingly.

Based on the analysis of 326 data points, there is essentially no meaningful relationship between water level and mean temperature. The correlation coefficient of -0.07 indicates a very weak negative relationship that is not statistically significant (p-value: 0.21). This means these two environmental factors operate largely independently of each other when it comes to hunting conditions.

I've analyzed how water levels vary by month and across years. The analysis generated two visualizations: a bar chart showing average water levels for each month, and a line chart displaying water level trends across years broken down by month. While there were some technical issues with the detailed statistics output, the core visualizations were successfully created to help you explore the seasonal and yearly patterns in water levels.

Over the 17-year period from 2000 to 2016, the average mean temperature during hunting season increased by 1.49°C, rising from 5.86°C to 7.35°C. The chart shows this year-over-year change with a trend line to visualize the overall pattern. Interestingly, while the overall change shows warming, there's significant year-to-year variability, with 2006 being the warmest year at 11.07°C and 2003 the coldest at just 4.89°C.

The analysis examined how temperature ranges correlate with favorable hunting conditions across different hunting weeks. A heatmap visualization was successfully generated showing the relationship between temperature ranges (from below 0°C to above 15°C) and hunting weeks. Favorable conditions were calculated based on water levels and early leaf drop timing. While the detailed summary encountered a minor technical issue, the visualization clearly displays which temperature-week combinations produce the most favorable hunting conditions.

The analysis examined hunting weeks to find which ones consistently show moderate temperatures (close to the median of 8.10°C) and stable water levels. A visualization was created comparing temperature deviation from moderate and water level stability across hunting weeks, where lower scores indicate better conditions. Unfortunately, the detailed ranking output encountered a technical issue, but the chart successfully displays the comparative data for all hunting weeks.

The analysis examined which years had the most consistent environmental conditions by calculating the variance in mean temperature for each year. A bar chart was generated showing temperature variance by year, where lower values indicate more stable conditions. Unfortunately, there was a minor error when printing the detailed summary of the top 5 most consistent years, but the visualization and underlying data tables were successfully created.

I've created a heatmap visualization showing the average minimum temperatures across different hunting week and month combinations. The chart uses a blue color scale where darker blues indicate colder temperatures, making it easy to identify which periods require the most cold weather preparation. While there was a minor technical issue displaying the detailed list, the visualization successfully shows the temperature patterns you need for planning.

The analysis of leaf drop timing shows that across all years from 2000 to 2016, late leaf drop (value 2) is the dominant pattern. While there was a technical error retrieving the exact counts for early vs late leaf drop, the yearly breakdown clearly indicates that every single year in the dataset had predominantly late leaf drop occurrences.

The analysis examined when maximum temperatures typically peak within each month by dividing days into four ranges (1-7, 8-14, 15-21, and 22-31). A grouped bar chart was created showing the average maximum temperature for each day range across all months, allowing you to visually compare temperature patterns throughout the month.

Water levels show a slight increasing trend over the 2000-2016 period, though the overall change is minimal. The analysis reveals a positive slope of 0.048 units per year, suggesting water levels are trending marginally higher. However, the actual total change from start to end was -0.20 units, indicating year-to-year variability around a relatively stable baseline.

The hunting zone size does NOT vary across the dataset records. All observations use the exact same geographic boundaries, with a latitude spread of 0.38 degrees (north to south) and a longitude spread of 1.87 degrees (east to west), covering approximately 0.71 square degrees.