Update literature

docs/paper/paper.bib

@@ -206,38 +206,13 @@ @ARTICLE{Reyes-Silva2022-pr
   doi = "10.3390/w15010046"
 }
 
-@UNPUBLISHED{Dobson2024-dv,
-  title    = "{SWMManywhere}: A global-scale workflow for generation and
-              sensitivity analysis of synthetic urban drainage models",
-  author   = "Dobson, Barnaby and Jovanovic, Tijana and Alonso-{\'A}lvarez,
-              Diego and Chegini, Taher",
-  abstract = "Continual improvements in publicly available global geospatial
-              datasets provide an opportunity for deriving urban drainage
-              networks and simulation models of these networks (UDMs)
-              worldwide. We present SWMManywhere, which leverages such datasets
-              for generating synthetic UDMs and creating a Storm Water
-              Management Model for any urban area globally. SWMManywhere's
-              highly modular and parameterised approach enables significant
-              customisation to explore hydraulicly feasible network
-              configurations. Key novelties of our workflow are in network
-              topology derivation that accounts for combined effects of
-              impervious area and pipe slope. We assess SWMManywhere by
-              comparing pluvial flooding, drainage network outflows, and design
-              with known networks. The results demonstrate high quality
-              simulations are achievable with a synthetic approach even for
-              large networks. Our extensive sensitivity analysis shows that the
-              locations of manholes, outfalls, and underlying street network
-              are the most sensitive parameters. We find widespread sensitivity
-              across all parameters without clearly defined values that they
-              should take, thus, recommending an uncertainty driven approach to
-              synthetic drainage network modelling. This study showcases
-              significant potential of SWMManywhere for research and industry
-              applications to provide drainage network models in urban areas
-              where traditional approaches are impractical.",
-  journal  = "EarthArXiv",
-  month    =  oct,
-  year     =  2024,
-  doi = "10.31223/X5GT5X"
+@article{Dobson2025,
+author = {Dobson, Barnaby and Jovanovic, Tijana and Alonso-Álvarez, Diego and Chegini, Taher},
+doi = {10.1016/j.envsoft.2025.106358},
+journal = {Environmental Modelling \& Software},
+title = {{SWMManywhere: A Workflow for Generation and Sensitivity Analysis of Synthetic Urban Drainage Models, Anywhere}},
+url = {https://doi.org/10.1016/j.envsoft.2025.106358},
+year = {2025}
 }
 
 @ARTICLE{Khurelbaatar2021-sp,
@@ -342,4 +317,26 @@ @misc{OpenStreetMap-overture
    title = {{Overture Maps Foundation}},
    howpublished = "\url{ https://overturemaps.org/ }",
    year = {2024},
- }
+ }
+
+ @article{sanne2024pysewer,
+  title={Pysewer: A Python Library for Sewer Network Generation in Data Scarce Regions},
+  author={Sanne, Moritz and Khurelbaatar, Ganbaatar and Despot, Daneish and van Afferden, Manfred and Friesen, Jan},
+  journal={Journal of Open Source Software},
+  volume={9},
+  number={104},
+  pages={6430},
+  year={2024},
+  doi={10.21105/joss.06430},
+}
+
+@article{mcdonnell2020pyswmm,
+  title={PySWMM: the python interface to stormwater management model (SWMM)},
+  author={McDonnell, Bryant E and Ratliff, Katherine and Tryby, Michael E and Wu, Jennifer Jia Xin and Mullapudi, Abhiram},
+  journal={Journal of open source software},
+  volume={5},
+  number={52},
+  pages={1},
+  year={2020},
+  doi={10.21105/joss.02292}
+}

docs/paper/paper.md

@@ -28,17 +28,17 @@ bibliography: paper.bib
 
 ## Summary
 
-Urban drainage network models (UDMs) are useful for a variety of purposes, most notably simulating and alleviating pluvial flooding. Despite the clear usefulness of UDMs, they are often not used owing to the difficulty and expense of creating them. This creates a significant gap for users attempting to generate UDMs if they are not able to perform an expensive underground survey.
+Urban drainage network models (UDMs) contain pipe and manhole information for drainage networks in urban areas. When driven by precipitation timeseries data, they can be used to simulate the flow of water through the network, which is useful for a variety of purposes, most notably simulating and alleviating pluvial flooding. Despite the clear usefulness of UDMs, they are often not used owing to the difficulty and expense of creating them. This creates a significant gap for users attempting to generate UDMs if they are not able to perform an expensive underground survey.
 
 ## Statement of need
 
-A variety of literature exists to derive UDMs from GIS data, producing hydraulically feasible models that closely approximate real-world systems [@Blumensaat2012-hd;@Chahinian2019-lg;@Reyes-Silva2022-pr;@Chegini2022-oo]. We identify some key limitations of these approaches, most notably the lack of automatic data acquisition and preprocessing, that all approaches are closed-source to date, and that a key feature of such an approach should be to facilitate extension and customisation.
+A variety of literature exists to derive UDMs from GIS data, producing hydraulically feasible models that closely approximate real-world systems [@Blumensaat2012-hd;@Chahinian2019-lg;@Reyes-Silva2022-pr;@Chegini2022-oo]. We identify some key limitations of these approaches, most notably the lack of automatic data acquisition and preprocessing, that all approaches are closed-source to date, and that a key feature of such an approach should be to facilitate extension and customisation. An open-source approach exists for sanitary sewer systems, however it does not provide automatic data acquisition [@sanne2024pysewer].
 
-SWMManywhere is an open-source Python package designed for the global synthesis of urban drainage networks. SWMManywhere integrates publicly available geospatial data and automates data acquisition and preprocessing, reducing the technical burden on users. Designed for both researchers and practitioners in urban water management, SWMManywhere responds to the limitations of existing methods by providing an end-to-end, open-source, and customisable solution.
+SWMManywhere is an open-source Python package designed for the global synthesis of urban drainage networks. SWMManywhere integrates publicly available geospatial data and automates data acquisition and preprocessing, reducing the technical burden on users. Designed for both researchers and practitioners in urban water management, SWMManywhere responds to the limitations of existing methods by providing an end-to-end, open-source, and customisable solution. Although SWMManywhere has been used in research applications [@Dobson2025], currently missing is a description focussed on the software implementation and key features, which we provide below.
 
 ## Features
 
-SWMManywhere includes a variety of key features aimed to improve useability and usefulness. A command line interface (CLI) offers a flexible workflow, providing an accessible entry point to using and customising synthesis. Its parameterized design enables detailed sensitivity analyses, allowing users to understand and manage uncertainties inherent in urban drainage modelling [@Dobson2024-dv]. By emphasizing user control, SWMManywhere allows tuning of outputs with parameters to meet local requirements, making it adaptable to a wide range of scenarios. We provide further details on the data and general approach below.
+SWMManywhere includes a variety of key features aimed to improve useability and usefulness. A command line interface (CLI) offers a flexible workflow, providing an accessible entry point to using and customising synthesis. Its parameterized design enables detailed sensitivity analyses, allowing users to understand and manage uncertainties inherent in urban drainage modelling [@Dobson2025]. By emphasizing user control, SWMManywhere allows tuning of outputs with parameters to meet local requirements, making it adaptable to a wide range of scenarios. We provide further details on the data and general approach below.
 
 ### Data
 
@@ -49,14 +49,14 @@ A variety of datasets were selected to enable SWMManywhere to be applied globall
 | Data Source | Description | Reference |
 |-------------|-------------| --------- |
 | **OpenStreetMap (OSM)** | Provides global street and river data, used to define potential pipe locations and outfall points for drainage networks. | [@Boeing2017;@OpenStreetMap] |
-| **Google-Microsoft Open Buildings** | A dataset of global building footprints, used for estimating impervious surfaces essential for runoff calculations. | [@OpenStreetMap-overture] |
+| **Google-Microsoft Open Buildings** | A dataset of global building footprints, used for estimating impervious surfaces essential for runoff calculations. | [@OpenStreetMap-overture;@VIDA2023] |
 | **NASADEM** | Provides 30m resolution global digital elevation model (DEM) data to support sub-catchment delineation and slope calculation. | [@Crippen2016] |
 
-These datasets are global in their coverage, and we consider them of sufficient quality in locations that we have tested [@Dobson2024-dv], however, we urge users to check data in their specific case study.
+These datasets are global in their coverage, and we consider them of sufficient quality in locations that we have tested [@Dobson2025], however, we urge users to check data in their specific case study.
 
 ### Approach and customisation
 
-The core task in SWMManywhere is to begin with a 'starting graph' (e.g., an OSM street graph), refine this graph first into manhole locations and potential pipe locations, eliminate pipes from unnecessary locations, and then dimension the resulting pipe network so that it can be simulated in a software such as [SWMM](https://www.epa.gov/sites/default/files/2019-02/documents/epaswmm5_1_manual_master_8-2-15.pdf). These operations take place in an iterative approach, where each function takes a graph, and returns the transformed graph, thus each operation is referred to as a 'graph function'. The use of graph functions in SWMManywhere enables modular packaging of functions, easy customisation of the approach (e.g., by adding/removing/reordering graph functions), and explicit definition of parameters for each graph function. Explanations for making these customisations are available in the [documentation](https://imperialcollegelondon.github.io/SWMManywhere/). Ultimately, this customisability facilitates exploring uncertainty in urban drainage modelling in a way that reflects not just the model itself but the model creation process, as is demonstrated in [@Dobson2024-dv].
+The core task in SWMManywhere is to begin with a 'starting graph' (e.g., an OSM street graph), refine this graph first into manhole locations and potential pipe locations, eliminate pipes from unnecessary locations, and then dimension the resulting pipe network which is then simulated in the software [SWMM](https://www.epa.gov/sites/default/files/2019-02/documents/epaswmm5_1_manual_master_8-2-15.pdf) using the `pyswmm` package [@mcdonnell2020pyswmm]. These operations take place in an iterative approach, where each function takes a graph, and returns the transformed graph, thus each operation is referred to as a 'graph function'. The use of graph functions in SWMManywhere enables modular packaging of functions, easy customisation of the approach (e.g., by adding/removing/reordering graph functions), and explicit definition of parameters for each graph function. Explanations for making these customisations are available in the [documentation](https://imperialcollegelondon.github.io/SWMManywhere/). Ultimately, this customisability facilitates exploring uncertainty in urban drainage modelling in a way that reflects not just the model itself but the model creation process, as is demonstrated in [@Dobson2025].
 
 We visualise the example from the [extended demonstration](https://imperialcollegelondon.github.io/SWMManywhere/notebooks/extended_demo/) in the documentation to illustrate how changing relatively few parameter values in a strategic way can dramatically change the nature of the synthesised network \autoref{fig:fig1}.
 
@@ -70,8 +70,8 @@ Because manhole and pipe locations rarely coincide between a synthetic and surve
 
 While we believe that SWMManywhere is a useful tool it has a variety of current limitations that present an exciting outlook for future research. Key improvements to the overall realism of the approach may be made in the future, in particular,
 
-- Based on the findings of a sensitivity analysis [@Dobson2024-dv], better identification of manhole locations and outfalls will be critical to narrowing uncertainty in simulation outputs and improving realism.
-- Capturing the gradual evolution of a network over time is known to be important in UDM synthesis [@Rauch2017-jz], and further illustrated by SWMManywhere results [@Dobson2024-dv]. We do not know of a global database that provides the information that would be necessary to capture this, but it may exist in the future or for local applications.
+- Based on the findings of a sensitivity analysis [@Dobson2025], better identification of manhole locations and outfalls will be critical to narrowing uncertainty in simulation outputs and improving realism.
+- Capturing the gradual evolution of a network over time is known to be important in UDM synthesis [@Rauch2017-jz], and further illustrated by SWMManywhere results [@Dobson2025]. We do not know of a global database that provides the information that would be necessary to capture this, but it may exist in the future or for local applications.
 
 # Acknowledgements