Dataset and Scripts for: Deep Learning and Stochastic Mapping Framework for Continuous Reservoir Capacity Monitoring using Multi-Mission Satellite Data: The Case of Poechos, Peru

Dataset and Scripts for: Deep Learning and Stochastic Mapping Framework for Continuous Reservoir Capacity Monitoring using Multi-Mission Satellite Data: The Case of Poechos, Peru Authors: Juan Carlos Breña Aliaga (1,*), Joel Cruz Machacuay (2), Jorge Luis Breña Ore (3), Diego Alonzo Tacusi Cuadros (4), and Daniel Marcelino Rosales Hurtado (2) Affiliations: (1) Department of Water Resources, Universidad Nacional Agraria La Molina (UNALM), Lima 15012, Peru (2) National Water Authority (ANA), Lima 15036, Peru (3) Faculty of Chemical and Textile Engineering, National University of Engineering (UNI), Lima 15333, Peru (4) National Service of Meteorology and Hydrology of Peru (SENAMHI), Lima 15072, Peru *Correspondence: [e-mail] Related Publication: This dataset is associated with the manuscript "Deep Learning and Stochastic Mapping Framework for Continuous Reservoir Capacity Monitoring using Multi-Mission Satellite Data: The Case of Poechos, Peru" (Currently under peer review). 1. Overview This repository contains the complete dataset, high-resolution reference masks, deep learning models, and Python scripts required to reproduce the methodology and results presented in the associated research article. The primary framework reconstructs the Elevation-Area-Volume (EAV) curve of the Poechos Reservoir dynamically, resolving the orbital asynchrony between Sentinel-1 SAR imagery and SWOT altimetry using an FPN-InceptionV4 segmentation ensemble and Monte Carlo stochastic quantile mapping. Update: A supplementary, self-contained module (Folder 7) has been added to provide the independent zero-shot regional transferability dataset. This module validates the framework's scalability across three additional Andean-coastal reservoirs (San Lorenzo, Tinajones, and Gallito Ciego) without any local algorithmic fine-tuning. 2. Repository Structure The repository is organized into 7 compressed folders (.zip): 1_Scripts_and_Code.zip Contains the core Jupyter Notebooks for the Poechos case study. 01_PlanetScope_GroundTruth.ipynb 02_SAR_Preprocessing.ipynb 03_DeepLearning_Segmentation.ipynb 04_Stochastic_Quantile_Mapping.ipynb 2_DeepLearning_Dataset.zip Contains the training/validation data for the primary study area (Poechos). Sentinel_1_Tensors/ PlanetScope_Masks/ 3_Trained_Models.zip Contains the PyTorch state dictionaries (.pth). All_CrossValidation_Models/ Best_Ensemble_FPN_InceptionV4/ 4_SWOT_Altimetry.zip SWOT_Validated_Observations.csv 5_InSitu_Validation_Data.zip ANA_Operational_Volumes.csv (Historical storage records for Poechos). 6_Study_Results_and_Metrics.zip Contains the tabular data supporting the primary article's figures. 7_Supplementary_Regional_Validation.zip (NEW) A fully self-contained module for the multi-reservoir zero-shot validation. Scripts/ 05_ZeroShot_San_Lorenzo.ipynb 06_ZeroShot_Tinajones.ipynb 07_ZeroShot_Gallito_Ciego.ipynb Datasets/ (Model Inputs) Contains subfolders for San_Lorenzo/, Tinajones/, and Gallito_Ciego/. Each includes: Sentinel_1_Tensors/: Normalized SAR patches (VV, VH, VV-VH) for inference. PlanetScope_GroundTruth/: High-resolution 3m binary water masks. SWOT_Altimetry_Filtered.csv: Datum-corrected WSE observations. ANA_Operational_Records.csv: Historical in-situ storage data. Transferability_Metrics/ (Generated Outputs) Contains subfolders for San_Lorenzo/, Tinajones/, and Gallito_Ciego/. Each includes: Sentinel1_Inference_Areas.csv: Auto-generated water areas computed by the FPN-InceptionV4 ensemble. Spatial_Segmentation_Metrics.csv: Pixel-level validation metrics (mIoU, F1-Score). Stochastic_EAV_Curve.csv: Final derived capacity curve via Quantile Mapping. Validation_Plots/: Multi-panel EAV curves and 1:1 scatter plots (with embedded NSE, RMSE, and BIAS metrics). 3. System Requirements and Dependencies To execute the scripts locally or on cloud platforms (e.g., Google Colab), the following core libraries are required: Python >= 3.9 PyTorch >= 2.0 Segmentation Models PyTorch (segmentation_models.pytorch) GeoPandas, Rasterio, NumPy, Pandas, SciPy, scikit-learn 4. Usage Instructions Environment Setup: Install the required dependencies. Primary Study (Poechos): Unzip folders 1 to 6. Update the relative file paths in notebooks 01 to 04 and run them sequentially to replicate the core methodology. Regional Validation: Unzip 7_Supplementary_Regional_Validation.zip. The notebooks 05, 06, and 07 inside the Scripts/ directory can be executed independently. They will automatically read the corresponding inputs from Datasets/ and populate the Transferability_Metrics/ folder with the final tabular data and validation figures. 5. License and Citation This dataset and code are provided under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. If you use these materials in your research, please cite the corresponding peer-reviewed article. (Citation details will be updated upon final publication).