Maintenance/1.0.x

.gitignore

@@ -71,4 +71,5 @@ tmp*
 
 # Documentation files
 doc/source/api_reference/**/smash
-doc/*-dataset
+doc/*-dataset
+doc/*~

Makefile

@@ -41,7 +41,7 @@ test-coverage:
 
 #% Generate baseline for test with args (see argparser in gen_baseline.py)
 test-baseline:
-	cd smash/tests ; python3 gen_baseline.py
+	cd smash/tests ; python3 generate_baseline.py
 
 #% Format Python files with ruff and Fortran files with fprettify
 format:

doc/source/_static/bib/references.bib

@@ -1258,4 +1258,14 @@ @article{todini1996arno
   abstract = {This paper describes in detail a semi-distributed conceptual rainfall-runoff model known as the ARNO model, which is now in widespread use both in land-surface-atmosphere process research and as an operational flood forecasting tool on several catchments in different parts of the world. The model, which derives its name from its first application to the Arno River, incorporates the concepts of a spatial probability distribution of soil moisture capacity and of dynamically varying saturated contributing areas. The ARNO model is characterized by two main components: the first and most important component represents the soil moisture balance, and the second describes the transfer of runoff to the outlet of the basin. The relevance of the soil component emerges from the highly nonlinear mechanism with which the soil moisture content and its distribution controls the dynamically varying size of the saturated areas mainly responsible for a direct conversion of rainfall into runoff. The second component describes the way in which runoff is routed and transferred along the hillslopes to the drainage channels and along the channel network to the outlet of the basin. Additional components, such as the evapotranspiration, snowmelt and groundwater modules, are also described. A discussion on the advantages of the model, calibration requirements and techniques is also presented, together with the physical interpretation of model parameters. Finally, after describing the original calibration of the ARNO model on the Arno basin, and a comparison with several conceptual models, recent applications of the ARNO model, as part of a real-time flood forecasting system, as a tool for investigating land use changes and as an interesting approach to the evaluation of land-surface-atmosphere interactions at general circulation model (GCM) scale, are illustrated.}
 }
 
+
+@book{monnier2021coursevariational,
+  title={Data Assimilation - Inverse Problems, Assimilation, Control, Learning},
+  author={Monnier, Jerome},
+  year={2024},
+  publisher={INSA Toulouse},
+  url = {https://www.math.univ-toulouse.fr/~jmonnie/Enseignement/CourseVDA.pdf}
+}
+
+
 @Comment{jabref-meta: databaseType:bibtex;}

doc/source/_static/css/smash.css

@@ -104,6 +104,10 @@ h4 {
   background-color: transparent !important;
 }
 
+details.sd-dropdown {
+    box-shadow: none !important;
+}
+
 .bolditalic {
   font-weight: bold;
   font-style: italic;
@@ -116,7 +120,7 @@ html[data-theme=dark] .sd-card {
 
 html[data-theme=dark] .sd-card .sd-card-header {
   background-color: transparent;
-  color: #150458 !important;
+  color: #F6F1F1 !important;
 }
 
 html[data-theme=dark] .sd-card .sd-card-footer {