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Added note on Wu signatures (daily vs hourly data) #15

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Merged
RY4GIT merged 1 commit into from
Sep 29, 2025
Merged

Added note on Wu signatures (daily vs hourly data) #15

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1 change: 1 addition & 0 deletions TOSSH_code/calculation_functions/calc_All.m
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Original file line number Diff line number Diff line change
Expand Up @@ -242,6 +242,7 @@
SE_thresh_signif(i),SE_thresh(i),SE_slope(i),Storage_thresh(i), ...
Storage_thresh_signif(i),min_Qf_perc(i),R_Pvol_RC(i),R_Pint_RC(i),~,EventGraph_error_str(i)] ...
= sig_EventGraphThresholds(Q_mat{i},t_mat{i},P_mat{i},'plot_results',plot_results);
% For R_Pint_RC, if the data is daily, use sig_EventGraphThresholds_hourlyfrac.m instead
end

% add results to struct array
Expand Down
44 changes: 34 additions & 10 deletions TOSSH_code/signature_functions/sig_EventGraphThresholds.m
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Expand Up @@ -68,17 +68,20 @@
% Indicates impermeable area contribution (qualitative description in
% Becker and McDonnell, 1998).
%
% Event signatures from Wu et al., (2021); translated to Matlab in
% Bolotin and McMillan (2025).
% R_Pvol_RC: Pearson correlation beween total precipitation vs. normalized quick flow
% Event signatures from Wu et al., (2021); translated to Matlab in Bolotin and McMillan (2025).
%
% *** Note: This signature, especially R_Pint_RC, is reliable only for
% sub-daily temporal resolution (hourly data is used in Wu et al., 2021).
% If you are using daily data, we recommend using sig_EventGraphThresholds_hourlyfrac.m instead. ***
%
% R_Pvol_RC: Pearson correlation between total precipitation vs. normalized quick flow
% (equivalent to event runoff coefficient = (quickflow volume / total P))
% Related to stormflow processes which are sensitive to rainfall volume,
% for example, SSF2, SOF, SSF1, and GWF. Called "SE_correlation" in
% Bolotin and McMillan (2025).
% R_Pint_RC: Pearson correlation beween average precipitation intensity vs. normalized quick flow
% for example, SSF2, SOF, SSF1, and GWF. Called "SE_correlation" in Bolotin and McMillan (2025).
%
% R_Pint_RC: Pearson correlation beween maximum precipitation intensity vs. normalized quick flow
% Related to Stormflow processes which are sensitive to rainfall
% intensity, for example, HOF. Called "IE_correlation" in
% Bolotin and McMillan (2025).
% intensity, for example, HOF. Called "IE_correlation" in Bolotin and McMillan (2025).
%
% error_flag: 0 (no error), 1 (warning), 2 (error in data check), 3
% (error in signature calculation)
Expand Down Expand Up @@ -401,9 +404,10 @@
large_events = and(start_ratio < bfi, end_ratio < bfi);

if sum(large_events)>2
%Spearman rank Corr of total P and average intensity with RC
%Spearman rank Corr of total P (event_array(:,1)) and maximum intensity
%(event_array(:,3)) with runoff coefficient per storm event
[R_Pvol_RC,~] = corr(event_array(large_events,1), rcq(large_events), 'Type', 'Spearman');
[R_Pint_RC,~] = corr(event_array(large_events,2), rcq(large_events), 'Type', 'Spearman');
[R_Pint_RC,~] = corr(event_array(large_events,3), rcq(large_events), 'Type', 'Spearman');
else
R_Pvol_RC = NaN;
R_Pint_RC = NaN;
Expand Down Expand Up @@ -499,4 +503,24 @@
title('Quickflow vs intensity per season') % Wrede 2015
fig_handles.EventGraphThresholdsSeasons = fig2;


% plot Spearman rank correlation of total P and max intensity against
fig3 = figure('Position',[100 100 700 300]);
subplot(1,2,1)
title('R (Pvol, RC)')
hold on
scatter(event_array(large_events,1), rcq(large_events), 'filled','MarkerFaceAlpha',0.5)
xlabel('Event total [mm]')
ylabel('Event runoff coeff. [-]')
legend(['R_{Pvol\_RC} = ', num2str(round(R_Pvol_RC,2))], 'Location', 'best')

subplot(1,2,2)
title('R (Pint, RC)')
hold on
scatter(event_array(large_events,3), rcq(large_events), 'filled','MarkerFaceAlpha',0.5)
xlabel('Event max intensity [mm/timestep]')
ylabel('Event runoff coeff. [-]')
legend(['R_{Pint\_RC} = ', num2str(round(R_Pint_RC,2))], 'Location', 'best')

fig_handles.EventGraphThresholds = fig3;
end
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