HydroCore - Watershed Models

Our Watershed Models

Chemicals, Agricultural Management and Erosion Losses

Fully Distributed Watershed Model

Applicable to Small Watershed Scales

Spatio-Temporal River-basin Eco-hydrology Analysis Model

Hybrid Watershed Model

Applicable to Medium-to-Large Watershed Scales

Subsurface NItrogen Pollution Evaluation

Soil Nitrogen Budget Model

Applicable to Large-scale / National Level

Regional Estimation of Diffuse POLlution Loads

Conceptual Watershed Model

Applicable to Large-scale / National Level

Artificial Neural Network for E-coli.

Artificial Neural Network (ANN) Model

Prediction of Stream E-coli Concentrations

Deep-learning and Ensemble Watershed Modeling Of Stream Turbidity

Integration of Ensemble Watershed Models and DNN (Deep Neural Network) Models

Watershed Water Quality Model Coupled with the WRF-Hydro Model

Summary of Our Watershed Models

Features of Our Watershed Models

1. Spatial Consistency via Square Grid Framework

Uniform Grid Discretization: Simulates the watershed by dividing it into uniform square grids of user-defined sizes.
Minimizing Errors: Minimizes analytical errors caused by intrinsic heterogeneity in model structures.
Scientific Reliability: Ensures high scientific reliability in result interpretation by securing spatial homogeneity across the entire watershed.

2. Hybrid Structure Overcoming Conventional Model Limitations

Optimal Integration: Combines the detailed spatial representation of distributed models with the high computational efficiency of semi-distributed models.
High-Precision Simulation: Enables high-precision simulations while reducing computational load, even in large-scale watersheds.
Broad Application: Applied to key models including STREAM, SNIPE, REDPOLL, and WRF-Hydro Quality.

3. Explicit Representation of East Asian Agricultural Environments (Paddy Fields)

Precise Modeling of Paddy Fields: Provides detailed simulation of hydrological and biogeochemical processes specific to paddy fields, the dominant agricultural form in East Asia.
Technical Advancement: Overcomes the limitations of Western-based models (such as SWAT, HSPF) when applied to domestic agricultural contexts.
Realistic Implementation: Explicitly incorporates actual agricultural practices, such as paddy levee/drainage height control, irrigation management, and fertilization based on crop growth stages.

4. Optimized Climate Change Impact Assessment & Forecasting

Integrated Variable Analysis: Integrates a wide range of climate-related variables, including meteorological data, land-use changes, soil $CO_2$ emissions, flood/drought risks, and water quality.
Scenario-based Evaluation: Performs comprehensive impact assessments for various scenarios in conjunction with RCP (Representative Concentration Pathways) and other climate scenarios.
Proven Performance: Demonstrates stable and accurate long-term forecasting performance for river discharge, flood prediction, and aquatic environment changes in major domestic and international watersheds.

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