GEOG 566






         Advanced spatial statistics and GIScience

June 4, 2018

Shift in the lag of GPP with soil water content at a C4 grassland site

Filed under: 2018,Exercise/Tutorial 3 2018 @ 4:48 pm

1. Question asked:

How is soil water content related to gross primary production (GPP) and the light use efficiency of photosynthesis (LUE) of a natural grassland? How do these relationships differ between C3- and C4-dominated grasslands? Specifically, how does interannual variation among water years?

2. Tool / approach used:

To answer this question, I calculated cross-correlation of GPP and LUE with soil water content (SWC). I then compare the maximum cross-correlation, as well as comparing the timing of the lag at which the maximum cross-correlation occurs. I performed these calculation separately for two sites, and for water years.  Water years are measured from October to September. Water years, in contrast to calendar years, are a more biologically meaningful variable for examining the relationship between plant growth and water variables.

My data are derived from eddy covariance flux tower locations in natural grasslands in Eastern Kansas. Soil water content, like other eddy covariance flux data, is recorded at 30-minute intervals. Here, I have aggregated flux and environmental data to daily sums and averages.

3. Steps followed to complete analysis

Data prep and selection

– Because SWC data are most consistently available at both sites between 2009-2014, I limited my analysis to data collected between those years. I further limited the data to 2010-2013 due to data availability.

– I also initially examined the water use efficiency (WUE) of production, but WUE, calculated as GPP / evapotranspiration (ET), has distinct dynamics from GPP or LUE and does not appear to be strongly cross-correlated with soil water content, so I have omitted it from this analysis.

Plotting GPP and Light Use Efficiency (LUE) against soil water content, for each water year, helps, Visualizing inital lag correlation between the variables. Here, it seems evident that the water year captures the temporal extent of the relationship between production and soil water content, and you can perceive a hump in soil water content (dashed line) that precedes a hump in GPP and LUE at each site, in each year.

Visual assessment of additional environmental variables that may be related to production

This plot shows the annual timecourse of cumulative precipitation and GPP, for water years from 2010-2013. This was an attempt to investigate more closely the environmental conditions that might be driving thesoil water content and its relationship with production. However, the cumulative annual precipitation, calculated as the rolling sum of precipitation measured at the flux tower over the course of a water year, shows very low values of precipitation in 2012 and 2013 at KFS and Konza, respectively. This suggests that there was an error in the collection of the precipitation data. As I proceed with this analysis, I will substitute [DayMet](https://daymet.ornl.gov/) climate data for the site-level flux data, which will sacrifice spatial resolution but should improve data quality.

Analytical Methods

I used the ccf() function to calculate cross-correlation between GPP and SWC, and between LUE and SWC. First, I calculated the cross-correlation of GPP and LUE, with soil water content, at each site separately, for all years from 2010-2013.

However, I’m most interested in comparing patterns of cross-correlation among years, particularly with regard to the timing of peak cross-correlation in 2012, a drought year. Does the drought year have a distinct pattern of cross-correlation between GPP, LUE, and soil water content than a non-drought year?

I then used the ccf() function to calculate cross-correlation of GPP and LUE with soil water content at each site, for each year from 2010-2013 in order to assess interannual variability in cross-correlation between GPP and LUE with soil water content. In order to visualize trends both between sites and between years, I separately plotted each site, by year– as well as each year, by site.

Lastly, to better visualize the shift in timing and magnitude of cross-correlation at each site, I extracting the date and value of the maximum and minimum cross-correlation and plotted these values for each year.

4. Results obtained

Cross-correlation of production with soil water content, for all years

First, I calculated cross-correlation between GPP and SWC, and between LUE and SWC, for all years at both sites. Thus, this is the “average” cross-correlation between production and soil water content, which has a peak maximum cross-correlation at a lag of about 100 days for both GPP and LUE. However, I’m most interested in looking at how interannual variation in climate affects plant production.

Cross-correlation between production and soil water content, by site and water year

When we examine how cross-correlation between GPP and soil water content varies between sites and between years, we see a clear shift at Konza Praire, the C4 grassland in 2012. This shift indicates that GPP and LUE are peaking earlier in relation to soil water content: with a lag of about 30 days, as opposed to 100 days at the Kansas Field Station, and at Konza in previous years.

Because 2013 followed the severe drought year, 2012, this shift suggests that the C4 grasses at Konza may be more flexible in shifting the timing of their water use in response to drought or other irregular moisture conditions.

To visualize this further, I also extracted the value and lag date of the maxima and minima of the cross-correlation function to visualize how these points change over time at each year.

Interannual variation in the timing and value of maximum cross-correlation

Plotting the date of the maximum cross-correlation, as it changes over years at each site, also helps visualize the divergence in the timing of the relationship between the production indices and soil water content.

5. Critique of the method

While this method is useful in assessing which variables are cross-correlated, and when those relationships peak, it does not clarify which environmental variables are controlling the production dynamics that I am seeing. In order to determine whether soil moisture content, or another variable (like cumulative precipitation, air temperature, soil temperature, a drought severity index, or others) is more responsible for the production dynamics, I will need to build and compare linear mixed models that predict GPP and LUE, from a suite of environmental variables, for sites at different years. This will help me assess which environmental variables may be more or less important in certain years, and better quantify their relationships with production.

This task will be made more complex as the timing of the relationships, where cross-correlation is a strong factor, change over time. So, it may be difficult to construct an appropriate linear mixed model with the most influential lag variable.

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