DarthReca/hydro-chronos

HydroChronos

HydroChronos is designed to forecast water dynamics using multispectral satellite images, climate variables, and DEM. It covers lakes and rivers of the US, Europe, and Brazil.

• Repository: GitHub
• Paper: Arxiv

Dataset Details

The dataset comprises Landsat-5 (L) TOA and Sentinel-2 (S) TOA images. There are 6 coherently aligned bands for both satellites:

Landsat	Sentinel	Description	Central Wavelength (L/S)
B1	B2	Blue	485/492 nm
B2	B3	Green	560/560 nm
B3	B4	Red	660/665 nm
B4	B8	NIR	830/833 nm
B5	B11	SWIR	1650/1610 nm
B7	B12	SWIR	2220/2190 nm

They are coupled with climate variables from TERRACLIMATE and Copernicus GLO30-DEM. There is no ground truth. We directly work with Modified Normalized Difference Water Index (MNDWI)

• Curated by: Daniele Rege Cambrin
• License: Creative Commons Attribution Non Commercial 4.0

Dataset Structure

To load the dataset with TorchGeo, please refer to the repository.

All climate data are contained in climate.h5. Each identifier contains the following data:

Key	Shape	Data Type	Description
climate	(14, T)	int16	Contains the 14 TerraClimate variables.
time	(T)	uint32	The timestamp for each time step.

The data for the two satellites and DEM are contained in the respective folders. For portability, the whole dataset is divided into parts. You can easily iterate over the whole dataset using the _main.h5 files, since they contain the external links to the correct file

Key	Shape	Data Type	Description
bands	(6, T, 256, 256)	uint8/16	Contains the 6 multispectral image bands.
dem	(1, 256, 256)	int16	Digital Elevation Model for the location.
qa_mask	(T, 256, 256)	bool	Cloud mask for each time step.
months	(T,)	uint8	The month (1-12) for each time step.
years	(T,)	int64	The year for each time step.
x	(256,)	float32	The x-coordinate of the location in WGS84.
y	(256,)	float32	The y-coordinate of the location in WGS84.

Citation

@inproceedings{10.1145/3748636.3762732,
  author = {Rege Cambrin, Daniele and Poeta, Eleonora and Pastor, Eliana and Corley, Isaac and Cerquitelli, Tania and Baralis, Elena and Garza, Paolo},
  title = {HydroChronos: Forecasting Decades of Surface Water Change},
  year = {2025},
  isbn = {9798400720864},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3748636.3762732},
  doi = {10.1145/3748636.3762732},
  abstract = {Forecasting surface water dynamics is crucial for water resource management and climate change adaptation. However, the field lacks comprehensive datasets and standardized benchmarks. In this paper, we introduce HydroChronos, a large-scale, multi-modal spatiotemporal dataset for surface water dynamics forecasting designed to address this gap. We couple the dataset with three forecasting tasks. The dataset includes over three decades of aligned Landsat 5 and Sentinel-2 imagery, climate data, and Digital Elevation Models for diverse lakes and rivers across Europe, North America, and South America. We also propose AquaClimaTempo UNet, a novel spatiotemporal architecture with a dedicated climate data branch, as a strong benchmark baseline. Our model significantly outperforms a Persistence baseline for forecasting future water dynamics by +14\% and +11\% F1 across change detection and direction of change classification tasks, and by +0.1 MAE on the magnitude of change regression. Finally, we conduct an Explainable AI analysis to identify the key climate variables and input channels that influence surface water change, providing insights to inform and guide future modeling efforts.},
  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},
  pages = {265–276},
  numpages = {12},
  keywords = {spatiotemporal forecasting, surface water dynamics, remote sensing, explainable AI, multi-modal data fusion},
  location = {The Graduate Hotel Minneapolis, Minneapolis, MN, USA},
  series = {SIGSPATIAL '25}
}