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Features

Complete capability reference for AquaScope. For installation and a quick example, see the Getting started guide. For data-source details, see Data sources.

Data Collection (12 sources)

  • Taiwan — MOENV water quality, WRA levels/reservoirs, Civil IoT sensors
  • USA — USGS streamflow, Water Quality Portal (400+ agencies)
  • Global — GEMStat (170 countries), UN SDG 6, OpenMeteo weather, Copernicus climate
  • FAO — AQUASTAT country-level water use, WaPOR satellite evapotranspiration

See docs/data_sources.md for the full list with endpoints and API-key requirements.

Hydrological Analysis

  • Flood frequency — GEV, LP3 (Bulletin 17C compliant), Gumbel, GPD/POT, L-moments, non-stationary GEV, regional frequency analysis, EMA for censored data
  • Baseflow separation — Lyne-Hollick & Eckhardt digital filters
  • Flow duration curves — Weibull plotting, FDC slope
  • 22 hydrological signatures — magnitude, variability, timing, recession, flashiness
  • Rating curves — power-law fitting, segmented curves, shift detection, HEC-RAS export
  • Q-Q/P-P diagnostics — distribution fit validation with 4-panel diagnostic plots
  • Cross-validation — leave-one-out CV and coverage probability for flood frequency

Agricultural Water Management

  • FAO-56 Penman-Monteith ET₀ — reference evapotranspiration with all intermediate steps
  • Hargreaves ET₀ — temperature-only alternative
  • Crop water requirements — 20 crops with FAO-56 Kc coefficients and growth stages
  • Irrigation scheduling — effective rainfall, net/gross demand, efficiency
  • Soil water balance — daily tracking, depletion, auto-irrigation triggers
  • WaPOR productivity workflows — biomass water productivity and AETI-to-RET performance metrics

Statistical & ML Methods

  • Copula analysis — Gaussian, Clayton, Gumbel, Frank with AIC selection
  • Change-point detection — PELT, CUSUM, Pettitt test, binary segmentation
  • Bayesian UQ — conjugate linear regression, Metropolis-Hastings MCMC, Gelman-Rubin R̂
  • Model ensembles — weighted, stacking, adaptive strategies
  • Transfer learning — donor selection via signature similarity for ungauged basins
  • Predictive models — Prophet, ARIMA, SPI, Random Forest, XGBoost, Isolation Forest, LSTM

Spatial & I/O

  • Spatial hydrology — DEM processing, D8 flow direction, watershed delineation, Strahler ordering
  • Scientific I/O — WaterML 2.0, HEC-DSS/RAS, EPA SWMM, NetCDF, HDF5, GeoJSON

AI Engine & Workflows

  • 26 research methodologies — scored and ranked against dataset profiles
  • 7 auto-executable pipelines — trend analysis, WQI, PCA, RF, XGBoost, ARIMA, correlation
  • Challenge workflows — flood risk (GEV), drought severity (SPI), water quality (WHO)
  • Natural-language agent — describe your goal, get recommendations + execution

Built-in Research Methodologies (26)

Category Methodologies Pipelines
Statistical Mann-Kendall Trend, WQI/RPI, PCA + Clustering, Correlation, Bayesian Inference, Copula Dependence 4
Machine Learning LSTM, Random Forest, XGBoost, Transformer, Autoencoder Anomaly Detection 2
Time-Series ARIMA/SARIMA Forecasting 1
Process Engineering MBBR Pilot, MBR Fouling, A2O Nutrient Removal, SWMM, QUAL2K —
Spatial Analysis Satellite Eutrophication, GIS Watershed, Kriging Interpolation —
Hydrological SWAT Modelling, Isotope Hydrology, Paired Watershed Design —
Policy SDG 6 Benchmarking, IWRM Assessment —

For when-to-use-which guidance, see the methodology matrix.

Visualization & Reporting

  • 16 plot functions — time-series, box plots, heatmaps, spatial maps (Folium), FDC, hydrographs
  • Diagnostic plots — Q-Q, P-P, return level, 4-panel diagnostic panel
  • Automated reports — Markdown & HTML with embedded plots, metrics, TOC
  • Alerts — WHO, US EPA, EU WFD threshold checking

Infrastructure

  • 534 tests with CAMELS benchmark validation
  • Interactive dashboard — 7-page Streamlit app
  • 14 CLI commands — collect, recommend, eda, quality, run, solve, forecast, plot, hydro, alerts, dashboard, agri, list-methods, list-sources
  • Theory guide — mathematical equations, DOI citations, decision trees