Hydroclimate extremes simulation & prediction
Simulation and prediction of extreme hydro-meteorological events using wavelet-based spectral transformation and machine learning.
Understanding · Modelling · Forecasting
Hydroclimate eXtremes From flood to drought — across scales
We advance the prediction of extreme hydrometeorological events — floods, droughts and compound hazards — by developing wavelet-based spectral transformation methods, multi-model ensemble frameworks, and physics-informed machine learning.
01 / About
Hydroclimate Extremes Modelling & Forecasting.
HydroclimateX Lab develops advanced methods for predicting extreme hydrometeorological events — floods, droughts, and compound hazards — across timescales from seasonal to decadal. Our work integrates wavelet-based spectral transformation, multi-model ensemble frameworks, and physics-informed machine learning to bridge cutting-edge research and operational water management.
We maintain the open-source WASP (WAvelet System Prediction) toolkit, available in R, Python and MATLAB, along with companion tools for wavelet-based quantile mapping (WQM), predictor identification (NPRED), and synthetic data generation (synthesis).
Explore our open-source tools ↗02 / Research interests
Five connected research directions.
Human–water systems & multi-agent modelling
Modelling interactions among climate, water infrastructure, agriculture, and human decision-making to support adaptive water management.
Hydroclimate multi-hazard prediction and risk assessment
Prediction and risk assessment of floods, landslides, and compound hydroclimate hazards using satellite observations, terrain information, and advanced modelling.
Hydrological–hydrodynamic modelling
Hydrological-hydrodynamic, hydroecological, and water quality process modelling for integrated watershed management.
Climate change impact assessment
Post-processing and bias correction of climate and weather forecast models using frequency-domain quantile mapping (WQM).
Research approach
From spectral transformation to operational decisions.
- 01 Observe Multi-source hydroclimate observations · reanalysis · GCM outputs
- 02 Transform Wavelet spectral transformation to enhance predictive frequency bands
- 03 Predict Models built on spectrally refined predictors using WASP
- 04 Decide Actionable forecasts for water management, agriculture, and energy
03 / Publications
Peer-reviewed research.
Selected publications from the lab. The full list is synced weekly from Google Scholar. Papers are grouped into research directions using title keywords. Showing current records.
04 / Showcase
Open tools in production.
Research method · Open software · Live application
WASP · WAvelet System Prediction.
WASP refines predictor representation using wavelet theory for improved hydrologic prediction. Explore the published methodology and open-source R, Python and MATLAB packages, then launch the dedicated interactive application.
Bias correction · Ensemble precipitation · Live application
WQM · Wavelet-based Quantile Mapping.
WQM corrects precipitation forecast bias across temporal scales and reconstructs an ensemble that represents timing uncertainty. Explore the method, then compare the official two-station demonstration.
Synthetic data · Reproducible experiments · Live application
synthesis · Statistical data generator.
Generate representative stochastic and chaotic systems directly in the browser. Adjust model parameters, inspect time-series and state-space plots, and download reproducible data.
Research method · Open software · Live flood simulation
LISFLOOD · Community flood risk assessment.
Run LISFLOOD-FP rainfall scenarios over Qixia District, Nanjing and inspect flood hazard, risk, depth and velocity layers. The methodology page documents the design storms, the DEFRA hazard rating, the risk matrix and data source.
05 / Contact
Join the HydroclimateX Lab.
We welcome motivated MSc and PhD students and postdoctoral researchers interested in hydroclimate extremes, AI/ML for environmental prediction, and open-source scientific software development.
- Hydroclimate extremes modelling and forecasting
- Machine learning methods and AI for water resources
- Multi-agent systems and human–water feedbacks
- Open-source toolkit development (R / Python / Web)