Add English model card

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- OneScience
+- Earth Science
+- Hydrology
+- Differentiable Physics
+- Ungauged Basins
+- Climate Trends
+frameworks: PyTorch
+---
+<p align="center"><strong><span style="font-size: 30px;">Delta-HBV</span></strong></p>
+# Model Introduction
+Delta-HBV is a differentiable, mass-conserving, physics-informed hydrological model with dynamic parameter learning.
+
+Paper: The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment  
+https://doi.org/10.5194/hess-27-2357-2023
+# Model Description
+The model was proposed by Pennsylvania State University and KAUST. It was trained with CAMELS daily forcing, 35 basin attributes, and streamflow. An LSTM drives 16 parallel HBV components and dynamic beta and gamma parameters for ungauged-basin and historical-trend assessment.
+# Use Cases
+| Use Case | Description |
+|---|---|
+| Ungauged hydrology | Evaluate spatially held-out streamflow. |
+| Physical variables | Predict discharge and evapotranspiration. |
+| Ungauged regions | Test extrapolation to contiguous held-out regions. |
+| Historical trends | Assess annual and high/low-flow trend preservation. |
+| ModelScope/OneCode execution | Validate data, training, inference, metrics, and visualization. |
+| Multi-GPU training | Start multi-process training through `torchrun`. |
+# Usage Instructions
+Use a GPU or DCU when available; CPU supports the default smoke configuration.
+```bash
+hf download OneScience-Group/Delta-HBV --local-dir ./Delta-HBV
+cd Delta-HBV
+python scripts/fake_data.py
+```
+For single-process training, use:
+```bash
+python scripts/train.py
+```
+For multi-process training, use:
+```bash
+torchrun --standalone --nproc_per_node=2 scripts/train.py
+```
+Run inference and evaluation with:
+```bash
+python scripts/inference.py
+python scripts/result.py
+```
+Training optimizes streamflow after a 365-day warm-up. Inference produces finite 730-day discharge and ET sequences; evaluation reports NSE, ET RMSE, and trend error.
+## Trained Weights
+No weights are bundled under `weight/`. The paper does not provide a directly loadable official pretrained checkpoint, so no weight link is listed; the authors' code archive is not presented as model weights.
+# Citation and License
+This repository is an independent engineering reproduction of the public Delta-HBV specifications.
+
+The original paper is licensed under CC BY 4.0; the original paper, official code, CAMELS, and MODIS data remain subject to their respective licenses and terms.