AquaScope

Open-source Python toolkit for water data, hydrology, and agricultural water management — with an AI engine that recommends and auto-executes research methodologies.

Install · Examples · CLI · Features · Docs · Roadmap · Discussions

if AquaScope helps your research.

AquaScope unifies 15 global water-data APIs behind one Python schema, then layers a full scientific computing stack on top — from Bulletin 17C flood frequency to FAO-56 crop water requirements — wrapped in an AI engine that scores 26 research methodologies against your dataset and auto-executes 7 analysis pipelines. Validated against the CAMELS benchmark with 525 tests.

• 🌊 Pull water data from USGS, FAO AQUASTAT, FAO WaPOR, GEMStat, EU WFD, Copernicus ERA5, Taiwan MOENV/WRA/Civil IoT/DataGov, Japan MLIT, Korea WAMIS, OpenMeteo, UN SDG 6, US Water Quality Portal — one unified Python API.
• 📈 Run hydrological analyses — Bulletin 17C flood frequency (GEV / LP3 / Gumbel / non-stationary GEV / EMA), baseflow separation, rating curves, 22 hydrological signatures.
• 🌾 Plan agricultural water — FAO-56 Penman-Monteith ET₀, crop water requirements for 20 crops, irrigation scheduling, soil water balance with auto-irrigation.
• 🤖 Ask the AI engine — describe your goal in plain English and get a recommended methodology, scored against your dataset profile and auto-executed. LLM enhancement via OpenAI, Groq (free), HuggingFace (free), or local Ollama.
• 📊 Visualise + report — 16 plot types, Q-Q / P-P diagnostics, Markdown / HTML reports with embedded figures, threshold alerts (WHO / EPA / EU WFD).
• 🗺️ Spatial hydrology — DEM processing, D8 flow direction, watershed delineation, Strahler ordering.

For the full capability list see docs/features.md.

pip install aquascope              # core — collectors + hydrology
pip install "aquascope[all]"       # everything — ML, viz, spatial, dashboard

Feature-group extras:

pip install "aquascope[ml]"           # sklearn, xgboost, statsmodels
pip install "aquascope[viz]"          # matplotlib, seaborn, folium
pip install "aquascope[scientific]"   # xarray, netcdf4, h5py
pip install "aquascope[spatial]"      # rasterio, geopandas, shapely
pip install "aquascope[dashboard]"    # streamlit
pip install "aquascope[forecast]"     # prophet, torch (for LSTM)

For development:

git clone https://github.com/Rekin226/aquascope.git
cd aquascope
pip install -e ".[all,dev]"

from aquascope.api import flood_analysis

result = flood_analysis(daily_discharge, method="gev", return_periods=[10, 50, 100])
print(result.return_levels)
#   return_period  return_level  lower_ci  upper_ci
# 0           10        1840.2     1690.4    2010.6
# 1           50        2530.7     2280.1    2820.9
# 2          100        2870.4     2540.6    3260.5

Switch method to "lp3", "gumbel", "gpd", or "ns_gev" for non-stationary analysis. Pass censored=True for EMA on records with peak-over-threshold gaps.

from aquascope.api import baseflow_analysis, compute_all_signatures

bf  = baseflow_analysis(daily_discharge, method="eckhardt")   # or "lyne_hollick"
sig = compute_all_signatures(daily_discharge)

print(bf.bfi)                  # baseflow index, e.g. 0.42
print(sig["Q5"], sig["Q95"])   # high-flow / low-flow exceedances
print(sig["flashiness"])       # Richards-Baker flashiness index

22 signatures across magnitude, variability, timing, recession, and flashiness — see docs/features.md.

from aquascope.collectors import USGSCollector, AquastatCollector, WaporCollector

usgs = USGSCollector()
flow = usgs.collect(station_id="01646500", parameter="00060", days=365)

aquastat = AquastatCollector()
egy_water = aquastat.collect(country="EGY", variables=[4263, 4253, 4312])

wapor = WaporCollector()
et = wapor.collect(
    bbox=(30.5, 29.8, 31.1, 30.2),
    variable="RET",
    start_date="2026-04-01",
    end_date="2026-07-31",
)

Every collector returns records in the same Pydantic schema, so downstream analyses don't care where the data came from. See docs/data_sources.md for the full list.

from datetime import date
from aquascope.agri import (
    penman_monteith_daily,
    crop_water_requirement,
    SoilWaterBalance,
)
from aquascope.agri.water_balance import SoilProperties

# Reference ET (FAO-56 Penman-Monteith) — Cairo, July
eto = penman_monteith_daily(
    t_min=18.0, t_max=32.0, rh_min=40, rh_max=80,
    u2=2.0, rs=22.0, latitude=30.0, elevation=70, doy=180,
)

# Crop water requirement for maize from planting through harvest
cwr = crop_water_requirement(eto_series, crop="maize", planting_date=date(2026, 4, 1))

# Soil water balance with auto-irrigation triggers
soil    = SoilProperties(field_capacity=0.30, wilting_point=0.15, root_depth=1.0)
balance = SoilWaterBalance(soil).auto_irrigate(
    etc=cwr.etc, precip=precip_series, efficiency=0.7,
)
print(balance.total_irrigation_mm, balance.deficit_days)

from aquascope.ai_engine import recommend

# Describe your dataset and goal — get ranked, scored methodologies
recs = recommend(
    parameters=["DO", "BOD5", "COD"],
    n_records=4_500,
    temporal=True,
    spatial=False,
    goal="detect long-term pollution trends with seasonality",
)

for r in recs[:3]:
    print(f"{r.score:.2f}  {r.method_id:<20}  {r.rationale}")
# 0.92  mann_kendall          Strong fit: temporal data, >30 records, trend goal
# 0.87  stl_decomposition     Seasonal patterns + multi-year data
# 0.81  prophet               Forecasting-capable, handles seasonality natively

Then auto-execute the top result with run_pipeline(recs[0].method_id, df).

from aquascope.api import detect_changepoints, fit_copula

cps  = detect_changepoints(annual_runoff, method="pettitt")
cop  = fit_copula(rainfall, runoff, family="auto")    # AIC-selects Gaussian/Clayton/Gumbel/Frank
print(cps.change_year, cps.p_value)
print(cop.family, cop.theta, cop.aic)

from aquascope.api import bayesian_regression

# Annual rainfall → runoff with full posterior + convergence diagnostics
posterior = bayesian_regression(X=annual_precip, y=annual_runoff)

print(posterior.posterior_mean)
# {'beta_0': 12.4, 'beta_1': 0.82, 'sigma2': 41.6}

print(posterior.credible_intervals["beta_1"])
# (0.78, 0.86)        ← 95% credible interval on slope

print(posterior.r_hat)
# {'beta_0': 1.00, 'beta_1': 1.00, 'sigma2': 1.00}    ← Gelman–Rubin, converged

print(posterior.dic, posterior.effective_sample_size["beta_1"])
# 124.7  9842.0       ← model fit + effective sample size

Switch to MCMC with degree>1 for polynomial models, or pass prior_precision for informative priors. Conjugate linear, polynomial, and Metropolis-Hastings backends are all available.

AquaScope ships a 14-command CLI for the most common workflows:

# Collect data
aquascope collect --source usgs --station 01646500 --days 365
aquascope collect --source wapor --bbox 30.5,29.8,31.1,30.2 --variable RET --start-date 2026-04-01

# Hydrological analysis
aquascope hydro --method flood_frequency --file discharge.csv
aquascope hydro --method baseflow --file discharge.csv

# Agriculture planning
aquascope agri plan --crop maize --planting-date 2026-04-01 --lat 30.0 --lon 31.25

# AI recommendation + natural-language problem solving
aquascope recommend --parameters DO,BOD5,COD --goal "pollution trend detection"
aquascope solve --problem "Assess flood risk for a 100-year return period"

# Interactive Streamlit dashboard
aquascope dashboard

Run aquascope --help for the full command list.

12 unified data sources spanning four regions:

• 🌎 Americas — USGS (streamflow + WQ), Water Quality Portal (400+ agencies)
• 🌍 Europe — EU Water Framework Directive, Copernicus ERA5
• 🌏 Asia-Pacific — Taiwan MOENV / WRA / Civil IoT / DataGov, Japan MLIT, Korea WAMIS
• 🌐 Global — GEMStat (170 countries), UN SDG 6, OpenMeteo, FAO AQUASTAT, FAO WaPOR

Full details, endpoints, and API-key requirements: docs/data_sources.md. Want to add your country's water service? See adding a data source.

• 534 tests — covering every collector, hydrology method, and pipeline (525 passing in the core suite; spatial tests require rasterio)
• CAMELS benchmark — a 10-catchment validation subset of the CAMELS dataset ships with the repo at data/camels_benchmark/ and runs as part of CI
• Every method cited — equations, decision trees, and DOI references for all 26 methodologies live in the theory guide
• JOSS paper in submission — see paper.md and paper.bib

We welcome contributions from the global water and agriculture research community. Highest-impact contributions right now:

• New data source collectors — your country / region
• New research methodologies — expand the AI recommender
• New crop coefficients — extend the FAO Kc table
• Jupyter tutorials and validation studies — compare against HEC-SSP, R packages, etc.

See CONTRIBUTING.md, the adding a data source guide, and the adding a methodology guide.

If you use AquaScope in your research, please cite:

@software{aquascope2026,
  title   = {AquaScope: Open-Source Water Data Aggregation, Hydrological Analysis, and Agricultural Water Management Toolkit},
  author  = {AquaScope Contributors},
  year    = {2026},
  url     = {https://github.com/Rekin226/aquascope},
  version = {0.5.0},
  license = {MIT}
}

MIT — see LICENSE.