AP Water Intelligence

The eight-page brief on the platform’s sources and model, rebuilt from this week’s data.

Download PDF
Andhra Pradesh · Groundwater intelligence

AP Water Intelligence

Data sources and the groundwater model

A technical note for review · figures from the weekly refresh of 19 October 2026

3D relief of Andhra Pradesh's mandals

Depth to water in September, each mandal against its own usual September, as the platform’s 3D view draws it: blue shallower than usual, slate about usual, amber deeper, red deeper than any September on record.

670mandals mapped, each compared with its own history
21public and official data sources, combined every week
0.91 maverage nowcast error; 13% better than the last reading
Monautomated refresh; publishes only if about 430 tests pass
Inside
01What it isthe question, three rules, the pipeline
02Data sources21 sources, what each gives and does
03The modelmethod, tests, release rule
04This weekfour maps of 670 mandals
05Agriculturethe crop, the season, the check
06Open questionslimits, and where review helps
07Closing the gapsix inputs, and what each moves
prucodes.github.io/ap-groundwater-fusionPrototype · not an official measurement
01 · What it is

Every mandal's water, checked weekly

A working prototype for the Chief Minister’s office and district officers. Every Monday it reports where groundwater, rain, soil moisture and crops stand in each of Andhra Pradesh’s 670 mandals, and what is likely next.

The question is local: is this mandal’s water table where it usually is for this month, is it heading somewhere it has not been, and do rain, soil and crops agree? Three rules run through the whole platform.

Measured and modelled stay apart

A piezometer reading is never shown as an estimate, nor an estimate as a reading. Every figure carries its source and date.

Each place against its own past

Depth swings with the season and the aquifer, so a mandal is compared with its own readings for the same month, never a statewide threshold.

Open inputs, tested weekly

Every input is open or public. A scheduled job re-fetches, rebuilds, tests and republishes the site each week.

How it fits together
Groundwater, measuredRain and climateLand, crops and peopleAPWRIMS piezometers, monthlyCGWB via INGRES · GRACE-DACHIRPS v3 · NASA POWERNOAA ENSO · ECMWF · TerraClimateNOAA VHP · Sentinel-2WorldCereal · SoilGrids · WorldPopWeekly pipeline · every Monday, automatedFetch every source, join it to 670 mandal outlines, compare each mandal with its own pastGate: about 430 data tests and a data-contract check; nothing publishes if one failsGroundwater modelRule-based analysesBoosted trees on the monthly change · 19 inputsMedian + conformal 80% band · now and 3 months aheadDrought manual · recharge watch · summer outlookCrop water check · each mandal against its own pastPublished: the site, maps, 3D views, district and constituency briefs, a weekly PDF

Measured readings pass straight through to the pages. Only the model’s estimates and the rule-based calls are derived, and each says so. The site’s screens: a weekly overview, Monsoon Watch, Drought Watch, a mandal map, the 3D depth view, Summer and Rabi outlooks, a crop water check, and briefs by district and assembly constituency; a one-page digest is printed each week.

The site itself
Monsoon Watch
Drought Watch
Mandal Map
Summer Outlook
AP Water Intelligence · data sources and model · prucodes.github.io/ap-groundwater-fusion2 / 8
02 · Data sources

One measured series, twenty supporting ones

Only the first row is what the model predicts; the rest are inputs, cross-checks or context, each joined to the same 670 mandal outlines before use.

Water

the measured target and official context
SourceWhat it givesResolution · cadenceUsed for
APWRIMSAP Water Resources Information & Management SystemDepth to water below ground per mandal, from state piezometers, 2014 to datemandal · monthlyThe measured series the model learns from and every page reports
APWRIMS context feedsAP DES gauges · NRSC soil moisture · reservoirsMandal rain gauges, modelled soil moisture, reservoir storage and canal releasesmandal · daily to weeklyDrought triggers, the crop check, reservoir context
CGWB assessment via INGREShosted by IIT Hyderabad2024 Dynamic Ground Water Resource Assessment: stage of extraction and categoryunit · annualThe official safe → over-exploited label
CGWB station levelsvia India Data PortalCentral network well readings, 2013 to 2023station · seasonalChecks the forecast on wells it never saw
Jal Jeevan Mission IMISDept. of Drinking Water and SanitationDrinking-water schemes by source; sources testedblock · weeklyWho drinks from wells past their record
NASA GRACE-DAGRACE gravimetry assimilated in a land modelGroundwater storage percentilecoarse grid · weeklyRegional storage context only
AP Data LakeAI Living Labs, access-controlledThe State's official groundwater snapshot and mandal outlinesmandal · on requestOfficial boundaries; well-count check

Rain and climate

model inputs and climate context
SourceWhat it givesResolution · cadenceUsed for
CHIRPS v3.0UC Santa Barbara Climate Hazards CenterSatellite-and-gauge rainfall, 1981 to date0.05° · monthlyThe model’s rainfall input, anomalies, SPI
IMD gridded rainfallIndia Meteorological DepartmentObserved rainfall (Pai et al. 2014)0.25° · yearly releaseCross-check of CHIRPS; not the input
NASA POWERMERRA-2 reanalysisRainfall≈55 km · monthlyFallback where CHIRPS is missing
TerraClimateUniversity of IdahoActual evapotranspiration, water balance≈4 km · monthlyRain-minus-ET context (not recharge)
NOAA CPCOceanic Niño Index, ENSO outlookEl Niño state and official outlookindex · monthlyEl Niño context and composites
NOAA ERSST v5 · GHCN-CAMSwith NOAAGlobalTempSea-surface and air temperature2° / 0.5° · monthlyPacific panel, temperature record
ECMWF open forecastvia Open-MeteoReference ET and rain, 7 to 10 days ahead0.25° · dailyThe weekly crop water check

Land, crops and people

crop stress, cropland and exposure
SourceWhat it givesResolution · cadenceUsed for
NOAA STAR VHPVegetation Health ProductVegetation Condition Index4 km · weeklyCrop stress; the crop check's track record
Sentinel-2 L2AESA Copernicus via Element 84, AWSReflectance and scene classification10 m (read at 80–160 m) · 5 daysCrop check's field-scale score; water in tanks
JRC Global Surface WaterEC Joint Research Centre, 1984–2021Where water has come and gone30 m · staticTank beds, apart from lakes and aquaculture
ESA WorldCereal 2021Irrigated versus rainfed cropland10 m · one seasonIsolating rainfed fields
ESA WorldCover 2021Land cover and cropland10 m · one yearCropland mask
ISRIC SoilGridsSoil texture and water capacity250 m · staticSoil water holding in the crop check
WorldPop 2020UN-adjustedPopulation1 km · one yearPeople living in mandals at risk

Rules rather than data: the national Manual for Drought Management 2020 (drought triggers), FAO-56 (crop water use), and CGWB’s convention of comparing pre-monsoon (May) depths year to year.

AP Water Intelligence · data sources and model · prucodes.github.io/ap-groundwater-fusion3 / 8
03 · The model

This month's depth, with an 80% band

Gradient-boosted trees estimate this month’s depth and the depth three months ahead. Retrained before every month, it errs by 0.91 m on average, 13% better than carrying the last reading forward, and its 80% band held 81.1% of readings.

d̂m,t = dm,t−1 + wb · Δd̂m,tIt predicts the month-on-month change and adds it to the last reading: trees cannot extrapolate depth, but its change is stable. wb pulls toward the last reading per depth band (0.30 to 0.80).
How it is built
0119 inputsDepth lags of 1 to 12 months, CHIRPS rain over 1, 3 and 12 months, season, location, aquifer type and specific yield (CGWB)
02Learnerscikit-learn HistGradientBoosting: the median plus the 10th and 90th percentiles under quantile loss
03BlendPulled toward the last reading by a weight set per depth band on a held-out year (2023)
04Band10th to 90th percentile range widened per aquifer and season by split-conformal calibration
05No leakageEach mandal’s latest reading is held out before its own estimate; inputs use only what was known then
How it is tested
TestMonthsModelBaselineGain
Nowcast, held out (trained to Dec 2023), Jan 2024 to Sep 202622,4481.02 m1.15 m last11%
Nowcast, retrained every month16,3280.91 m1.05 m last13%
Forecast 3 months ahead, rolling origins20,9581.90 m2.22 m no change14%
80% band coverage22,44881.1%80% nominal±1.6 m
Rise or fall on CGWB’s wells, never trained on (2018–23)1,19975% right77% State wells89 / 38%
IMD rain in place of CHIRPS, retrained every month (2024–25)16,3511.04 m1.06 m CHIRPS2%, a year late
Ungauged mandal (whole mandal held out)82,3324.9 mstate averagenot released

Error grows with depth; the model wins in every band

Average absolute error on held-out months

ModelLast reading carried forward0-5 m8,926 months0.670.58 m5-10 m7,281 months0.960.82 m10-20 m4,496 months1.581.42 m20-30 m1,029 months2.752.58 m30 m+716 months4.033.91 m
Release rule. A horizon is published only if it beats both no-change and same-month-last-year by at least 5% under rolling-origin validation, with no leakage and consistent results by terrain. Three months passes; one, six and twelve do not yet, so they are not shown.
Tried and rejected, on evidence. The El Niño index made it worse. Rain over official outlines: no gain (1.797 vs 1.800 m). NASA near-real-time rain read twice as wet as CHIRPS and raised error 0.62 → 0.96 m. A month whose satellite rain is unpublished is estimated without it and says so (0.89 → 1.04 m). The State’s seven carried-forward months of 2021 are treated as missing.
AP Water Intelligence · data sources and model · prucodes.github.io/ap-groundwater-fusion4 / 8
04 · What it shows this week

Six readings of the same 670 mandals

Each map and figure is drawn from this week's published data. Each compares a mandal with its own history, and each method carries a backtest or a rule from an official manual.

Water Depth 3D

September against its usual September

309mandals deeper than usual; 128 deepest on record

Against the median of the same mandal’s earlier Septembers; within 1 m is usual.

Shallower than usual 128Within 1 m 171Deeper than usual 181Deepest on record 128
Monsoon Watch

Did this monsoon recharge the ground?

54.3%of 580 mandal series lower than in May

May-to-now against the same mandal’s median over ten seasons; flagged at 1 m short and twice its own spread. Rain 21.1% below normal.

Normal 403Short 23Severe shortfall 107
Drought Watch

What the national drought manual says

30 · 314severe · moderate, of 615 assessed

Rain deficit and dry spells under the Manual for Drought Management 2020, then impact on vegetation, soil, groundwater and reservoirs. It reads the manual; it does not declare. 479 meet Trigger 1 on department gauges, 546 on CHIRPS SPI.

Severe 30Moderate to severe 26Moderate 314Normal 90No trigger 136
Summer Outlook

Where May 2027 may break the record

126mandals past their deepest May in a typical winter

Latest reading plus the mandal’s own past winter drawdowns, against its deepest May. Backtest: past “beyond” calls came true 1 in 3, against 1 in 8 overall.

Beyond record, 10 m+ 76Beyond, under 10 m 50Only in a dry winter 217Within its record 275
Drinking water · Jal Jeevan Mission
96%of rural drinking-water schemes draw on groundwater

1,24,219 sources sit in the 126 mandals heading past their deepest May; 11,337 tested above a chemical limit.

Tanks · Sentinel-2 over JRC tank beds
76%of their usual water, 15 September to 15 October

143 of 318 tank mandals emptier than usual; the typical tank bed is 26% wet, the lowest of 8 years.

AP Water Intelligence · data sources and model · prucodes.github.io/ap-groundwater-fusion5 / 8
05 · Agriculture and water

What the season has done to the crop

Groundwater matters here because of what grows above it. 437 of 670 mandals are mostly rainfed, and across the State under a quarter of mapped cropland is irrigated — so for most of Andhra Pradesh the monsoon is the irrigation.

Crop condition · NOAA VHP, weighted to cropland

How the crop has read these four weeks

244mandals severe; 281 moderate

Vegetation Condition Index against each cell’s own record, weighted by cropland share, averaged over the four weeks to 14 October 2026. Table 3.4 of the drought manual: 60–100 normal, 40–60 moderate, under 40 severe.

Normal 145Moderate 281Severe 244
Official assessment · CGWB and the State, via INGRES

What the annual balance says

577mandals safe; 11 over-exploited

The 2025–26 Dynamic Ground Water Resources assessment, GEC-2015: a year’s recharge against a year’s draft. It is not a reading of this week, and the two maps are not in competition.

Safe 577Semi-critical 30Critical 6Over-exploited 11Salinity 40
The season the crop is standing in
-77.7%north-east monsoon so far: 27 mm against 120.7 mm normal, 1 October to 17 October 2026
603of 688 mandals deficient or scanty on 3,532 department gauges since June; -39.6% overall
76%of their usual water in 4,012 tank beds, read by Sentinel-2; 143 mandals emptier than usual, 8 fuller
23.9%of mapped cropland detected as irrigated statewide in one rabi season; the median mandal 26%
The crop water check, and how it is being scored

Each week the check compares a mandal’s rain and reference evapotranspiration, from the ECMWF open forecast, with what its crop would need. Every call is frozen the day it is made and scored three weeks later against the vegetation that followed — Sentinel-2 NDVI over rainfed cropland at 160 m, and the vegetation index. 5 weeks are on the record, none scored yet; the first verdict falls due on 31 October 2026.

A negative result, kept on the record. Pooled across mandals the check does not separate vegetation at all: the places called short most often are the chronically dry ones. It is therefore scored within a mandal and season, against that same mandal when it was called comfortable.
What is missing, and it is the heart of it. Sown area, crop and stage, and what the canals actually delivered to a field are all held by the State and not released. Until they are, the check runs on FAO-56 reference coefficients and a stated assumption about the crop — not on the mandal’s own sown record. That single release would turn a water platform into an agricultural one.
AP Water Intelligence · data sources and model · prucodes.github.io/ap-groundwater-fusion6 / 8
06 · Limits and open questions

Where a hydrogeologist's view would help most

The model knows depth well and storage, aquifers and pumping poorly. These are the questions we would most value a review on.

May outlook in 3D
May 2027 in a dry winter, from the 3D view: glowing rims mark each mandal’s deepest May on record where the projected water falls below it.
Known limits
Aquifer properties are coarse. Aquifer type is a district proxy; for 259 of 688 series the measured specific yield disagrees with the label.
Depth is not storage. A metre lost in hard rock holds far less water than in the delta; volumes are under review.
Pumping is unobserved. Extraction enters only through the official stage-of-extraction figure.
670 outlines, 688 mandals. The State’s record lists 688; the public sub-district map predates the latest splits, so 14 newer town mandals share their former town’s outline and 5 have none. All are still measured and estimated — only the map is short.
Mandal averages. APWRIMS reports a mandal’s mean across piezometers; a village’s wells can differ.
Ungauged mandals are hard. Estimating from neighbours alone errs by 4.9 m, so it is not released.
Data status. Research sample pending authorization; some series await an official location crosswalk.
Questions for review
Is there a defensible mandal-level aquifer and specific-yield layer (for example CGWB’s NAQUIM mapping) to replace the district proxy?
Would a physically based recharge and abstraction component (water-table fluctuation, irrigated area from Sentinel-2) beat the statistical model at six to twelve months, where it does not yet pass?
Which public proxy for pumping is most credible in Andhra Pradesh: agricultural power use, irrigated area, or well census counts?
Is a 5% gain over persistence and seasonal baselines, under rolling-origin validation, the right bar for releasing a forecast to administrators?
Can GRACE-scale storage be usefully downscaled to mandals, or should it stay regional context?
Sources
AP Water Intelligence · data sources and model · prucodes.github.io/ap-groundwater-fusion7 / 8
07 · Closing the gap

Six inputs, and the number each one moves

In an ordinary season the forecast is already at the practical ceiling for any method. Almost all of the headroom that remains sits in seasons that break the pattern — when the water table is set by what is pumped rather than by what fell, and pumping is the one driver the model cannot see.

Ranked by the error each would reduce
Station-level DWLR history, with coordinates. An ungauged mandal is interpolated between mandal means today and errs by 4.9 m, five times the nowcast. Well locations would let it run between wells inside aquifer structure, and let an estimate be made below mandal scale.
The NAQUIM aquifer framework — boundaries, thickness, specific yield, transmissivity. Aquifer type is a district proxy, contradicted by the measured specific yield in 259 of 688 series. Hard rock is the worst terrain in both tests: 1.40 m against 0.78 m on the coast for the nowcast, 5.7 m against 3.8 m when a mandal is ungauged. Real aquifer properties would also make depth-to-storage defensible.
Agricultural feeder supply hours, and the pump-set census. Extraction reaches the model only through the official stage-of-extraction figure, which is annual. Feeder supply hours against connected load would give a weekly, mandal-scale proxy for what is actually drawn — the one input aimed squarely at the regime where the forecast fails.
DWLR telemetry, not only the history. Readings are monthly, so even a perfect estimate is a month behind. Telemetry would make instrumented mandals measured and current rather than modelled, and turn a backtest into continuous validation.
An authorized APWRIMS export, full history. Training begins June 2015; a longer authorized record is accuracy at no modelling cost. It is also the gate on any official use, since the present series is a research sample pending authorization.
Field verification on twenty to thirty mandals. Every figure in this brief is a backtest. Measuring a small sample after an estimate has been published, then comparing, is the one step that turns validation into verification. There is no field ground truth today.
Which of these reaches the ceiling. In an ordinary season the forecast calls direction right 89%, ahead of the 87% that the State’s own wells and CGWB’s agree with each other — the practical ceiling for any method. All the headroom left is in the pattern-breaking season, where it falls to 38% against the measured wells’ 49%. The first and third — well locations and a pumping proxy — are aimed at that regime.
What this project can give back
A systematic gap between the two well networks. Over 16,097 readings paired within 20 km and the same month, CGWB’s wells read 4.3 m shallower than the State’s, correlating 0.41.
259 series whose aquifer label its own specific yield contradicts, already listed mandal by mandal and ready to hand over.
Quality control on the State’s own record. The same model that estimates a level also flags a reading inconsistent with that well’s history and its neighbours — which is how a drifting recorder or a transcription error is found across a network of this size.
What that would build. With the first three in place, this same weekly machine — 21 sources, 670 mandals, every Monday — would estimate a level where no well stands, inside real aquifers rather than a district proxy. The pipeline already runs; what it lacks is the department’s measurement, not the method.
AP Water Intelligence · data sources and model · prucodes.github.io/ap-groundwater-fusion8 / 8