Fix broken links

Author: ConnectedSystems

docs/src/examples/calibration/calibration.md

@@ -134,13 +134,13 @@ temporal_cross_section(sim_dates, calib_data[:, "406000"], sn2[3].level)
 
 The last two lines produces the plots below
 
-![](../assets/calibrated_example.png)
+![](../../assets/calibrated_example.png)
 
 The `quickplot()` function creates the figure displayed above which shows dam levels on the
 left (observed and modelled) with a [Q-Q plot](https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot)
 on the right.
 
-![](../assets/temporal_xsection_historic_calibrated.png)
+![](../../assets/temporal_xsection_historic_calibrated.png)
 
 The above shows a "cross-section" of model predictions for each month-day across simulation
 time. It is useful to gain an understanding on when models may underperform and give a

docs/src/examples/calibration/custom_calibration.md

@@ -153,8 +153,8 @@ gr4j_qp = quickplot(burn_obs, gr4j_node.outflow[burn_in:end], climate; label="We
 # savefig(gr4j_qp, "custom_calibration_gr4j.png")
 ```
 
-![](../assets/default_calibration_gr4j.png)
-![](../assets/custom_calibration_gr4j.png)
+![](../../assets/default_calibration_gr4j.png)
+![](../../assets/custom_calibration_gr4j.png)
 
 The results show improved performance under low-flow conditions, at the expense of
 mid-to-high flow conditions.
@@ -260,7 +260,7 @@ ensemble_qp = quickplot(burn_obs, ensemble.outflow[burn_in:end], climate; label=
 # savefig(ensemble_qp, "custom_calibration_ensemble.png")
 ```
 
-![](../assets/custom_calibration_ensemble.png)
+![](../../assets/custom_calibration_ensemble.png)
 
 Here we see the weighted ensemble retains much of the performance characteristics under
 a variety of conditions.

docs/src/examples/ensembles/weighted_ensembles.md

@@ -84,7 +84,7 @@ flows and high flows, whereas GR4J had a tendency to overestimate low flows.
 The weighted ensemble combined characteristics of both, with a tendency to overestimate
 low flows as with GR4J.
 
-![](../assets/ensemble_model_comparison_quickplots.png)
+![](../../assets/ensemble_model_comparison_quickplots.png)
 
 Comparing the temporal cross section:
 
@@ -98,7 +98,7 @@ plot(ihacres_xs, gr4j_xs, ensemble_xs; layout=(3, 1), size=(800, 1200))
 
 A reduction in the median error can be seen with extreme errors reduced somewhat.
 
-![](../assets/ensemble_xsection.png)
+![](../../assets/ensemble_xsection.png)
 
 The median error can then be applied to modelled streamflow (on a month-day basis) as a
 form of bias correction.
@@ -123,7 +123,7 @@ While the median error has increased, its variance has reduced significantly. At
 time, performance at the 75 and 95% CI remain steady relative to the original weighted
 ensemble results.
 
-![](../assets/ensemble_bias_corrected.png)
+![](../../assets/ensemble_bias_corrected.png)
 
 This ensemble approach may be improved further by:
 

docs/src/examples/evaluation/model_comparison.md

@@ -82,4 +82,4 @@ display(combined_plot)
 savefig("multi_model_comparison.png")
 ```
 
-![](../assets/multi_model_comparison.png)
+![](../../assets/multi_model_comparison.png)

docs/src/examples/evaluation/simple_multisystem.md

@@ -125,4 +125,4 @@ display(f)
 Streamfall.temporal_cross_section(sim_dates, calib_data[:, "406000"], sn[2].level)
 ```
 
-![](../assets/simple_water_demand.png)
+![](../../assets/simple_water_demand.png)

docs/src/examples/evaluation/simple_showcase.md

@@ -67,7 +67,7 @@ The `quickplot()` function creates the figure displayed above which shows dam le
 left (observed and modelled) with a [Q-Q plot](https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot)
 on the right.
 
-![](../assets/calibrated_example.png)
+![](../../assets/calibrated_example.png)
 
 ```julia
 sim_dates = Streamfall.timesteps(climate)
@@ -86,4 +86,4 @@ Here, we see that while performance is generally good (mean of Median Error is n
 the model can under-estimate dam levels in late-April to May and displays a tendency to
 over-estimate dam levels between January and June, relative to other times.
 
-![](../assets/temporal_xsection_historic_calibrated.png)
+![](../../assets/temporal_xsection_historic_calibrated.png)