Added note on Wu signatures (daily vs hourly data)

Author: RY4GIT

TOSSH_code/calculation_functions/calc_All.m

@@ -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

TOSSH_code/signature_functions/sig_EventGraphThresholds.m

@@ -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)
@@ -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;
@@ -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