✓ Verified · computed, not asserted

Verified answers

Most of the atlas helps you find data. This page answers questions — and shows its work. Two methods so far: a Before-After-Control-Impact deforestation analysis, and regional climate trends from ERA5 — each with significance, uncertainty, full provenance, nulls reported as nulls, and ill-posed questions refused rather than guessed.

Computed by verified-earth-agent on Google Earth Engine. Observational evidence, not prophecy.

Ask your own question, live →

① Deforestation · Before-After-Control-Impact

Before-After-Control-Impact on Hansen forest loss × impact variable; controls remove the regional trend. Observational evidence, not a causal prediction. · deforestation analog (BACI). Answers “Is deforestation happening here, and what are its effects?”

Rondônia, Brazil

Amazon 'arc of deforestation' — rainforest → cattle pasture forest lost 2005–2008 before 2001–2004 → after 2015–2020

●Measured signal — the trustworthy variables changed in the expected direction.

The trustworthy local signals agree with the deforestation signature: land surface temperature warmer; greenness ndvi less green. Regional/coarse variables (rainfall, wind, humidity) are directional hints at best; soil moisture is model-based and reported without a consistency verdict. Weight the strong tier; treat the rest as context.

Observational analog (a natural experiment over comparable places) — evidence, not a prediction for any one spot. Each bar shows the change versus the natural jitter between undisturbed control pixels; it must clear the ┊ tick to count as a real signal.

STRONG (local, trustworthy)Seen up close, at the cut itself — trust it.

land surface temperature· 1 km pixels

0.956 degC warmer SNR 2.59 10,500 def / 63,223 ctrl px

greenness ndvi· 1 km pixels

-0.046 NDVI less green SNR 2.01 10,500 def / 63,223 ctrl px

WEAK (regional process, coarse grid — directional hint)A regional process on a coarse grid — a hint, not proof.

rainfall· 5.6 km pixels

-7.8 mm/year no clear change SNR 0.04 512 def / 1,893 ctrl px

WEAKEST (coarse + regional — read the control-pixel count)Very coarse and regional — check the pixel count before trusting.

wind· 27.8 km pixels ⚠ NOT in the expected direction (investigate)

-0.01 m/s no clear change SNR 0.44 25 def / 62 ctrl px

humidity· 27.8 km pixels ⚠ NOT in the expected direction (investigate)

0.1 % RH no clear change SNR 0.06 25 def / 62 ctrl px

AMBIGUOUS (model-based, no expected direction — reported, not judged)From a model that can't even see the cut — reported, never judged.

soil moisture· 11.1 km pixels reported, not judged

-0.001 m3/m3 no clear change SNR 0.2 165 def / 423 ctrl px

clears the tick → trust it (for its tier) below the tick → treat as no clear change noise floor estimated, not measured ┊ the bar a real signal must clear (1× noise floor)

Provenance & full trace — reproducible

Every number came from this exact query on Google Earth Engine. Same query → same number.

DEFORESTATION ANALOG — combined report
  region [-63.5, -11.0, -60.0, -8.5]  |  forest lost 2005-2008  |  before 2001-2004 -> after 2015-2020
  Before-After-Control-Impact (BACI): change at deforested sites MINUS the same
  change at kept-forest controls. Observational evidence, NOT a causal prediction.

[STRONG  (local, trustworthy)]
  land_surface_temperature   BACI +0.956 degC    (warmer)  [def 10500px / ctrl 63223px]
  greenness_ndvi             BACI -0.046 NDVI    (less green)  [def 10500px / ctrl 63223px]

[WEAK    (regional process, coarse grid — directional hint)]
  rainfall                   BACI -7.8 mm/year (no clear change)  [def 512px / ctrl 1893px]

[WEAKEST (coarse + regional — read the control-pixel count)]
  wind                       BACI -0.01 m/s     (no clear change)  [def 25px / ctrl 62px]  <- NOT in the expected direction (investigate)
  humidity                   BACI +0.1 % RH    (no clear change)  [def 25px / ctrl 62px]  <- NOT in the expected direction (investigate)

[AMBIGUOUS (model-based, no expected direction — reported, not judged)]
  soil_moisture              BACI -0.001 m3/m3   (no clear change)  [def 165px / ctrl 423px]

VERDICT
  The trustworthy local signals agree with the deforestation signature: land surface temperature warmer; greenness ndvi less green. Regional/coarse variables (rainfall, wind, humidity) are directional hints at best; soil moisture is model-based and reported without a consistency verdict. Weight the strong tier; treat the rest as context.

Riau, Sumatra

Indonesia palm-oil + peatland clearing — rainforest → plantation forest lost 2005–2008 before 2001–2004 → after 2015–2020

●Measured signal — the trustworthy variables changed in the expected direction.

STRONG (local, trustworthy)Seen up close, at the cut itself — trust it.

land surface temperature· 1 km pixels

0.764 degC warmer SNR 1.03 8,135 def / 11,790 ctrl px

greenness ndvi· 1 km pixels

-0.021 NDVI less green SNR 0.52 8,135 def / 11,790 ctrl px

WEAK (regional process, coarse grid — directional hint)A regional process on a coarse grid — a hint, not proof.

rainfall· 5.6 km pixels ⚠ NOT in the expected direction (investigate)

15.7 mm/year no clear change SNR 0.16 321 def / 267 ctrl px

WEAKEST (coarse + regional — read the control-pixel count)Very coarse and regional — check the pixel count before trusting.

wind· 27.8 km pixels floor estimated — not measured here

0.02 m/s windier SNR 0.57 19 def / 5 ctrl px

humidity· 27.8 km pixels floor estimated — not measured here

-0.2 % RH drier SNR 0.97 19 def / 5 ctrl px

AMBIGUOUS (model-based, no expected direction — reported, not judged)From a model that can't even see the cut — reported, never judged.

soil moisture· 11.1 km pixels reported, not judged

-0.002 m3/m3 drier soil SNR 1.03 89 def / 50 ctrl px

clears the tick → trust it (for its tier) below the tick → treat as no clear change noise floor estimated, not measured ┊ the bar a real signal must clear (1× noise floor)

Provenance & full trace — reproducible

Every number came from this exact query on Google Earth Engine. Same query → same number.

DEFORESTATION ANALOG — combined report
  region [101.0, 0.0, 103.5, 2.0]  |  forest lost 2005-2008  |  before 2001-2004 -> after 2015-2020
  Before-After-Control-Impact (BACI): change at deforested sites MINUS the same
  change at kept-forest controls. Observational evidence, NOT a causal prediction.

[STRONG  (local, trustworthy)]
  land_surface_temperature   BACI +0.764 degC    (warmer)  [def 8135px / ctrl 11790px]
  greenness_ndvi             BACI -0.021 NDVI    (less green)  [def 8135px / ctrl 11790px]

[WEAK    (regional process, coarse grid — directional hint)]
  rainfall                   BACI +15.7 mm/year (no clear change)  [def 321px / ctrl 267px]  <- NOT in the expected direction (investigate)

[WEAKEST (coarse + regional — read the control-pixel count)]
  wind                       BACI +0.02 m/s     (windier)  [def 19px / ctrl 5px]
  humidity                   BACI -0.2 % RH    (drier)  [def 19px / ctrl 5px]

[AMBIGUOUS (model-based, no expected direction — reported, not judged)]
  soil_moisture              BACI -0.002 m3/m3   (drier soil)  [def 89px / ctrl 50px]

VERDICT
  The trustworthy local signals agree with the deforestation signature: land surface temperature warmer; greenness ndvi less green. Regional/coarse variables (rainfall, wind, humidity) are directional hints at best; soil moisture is model-based and reported without a consistency verdict. Weight the strong tier; treat the rest as context.

Central India (Maharashtra)

Control case — little contiguous loss; expect a null local signal forest lost 2005–2008 before 2001–2004 → after 2015–2020

○Measured flat — we checked the trustworthy variables and found no clear local change here. This is a real result, not a missing one.

No clear local change: every strong-tier variable came back below the noise floor (LST/NDVI ~ flat between deforested and control sites here). The deforestation signal in this box is weak or absent — possibly little real loss, slow recovery, or the window is too short. Do not over-read the coarse/regional hints; the trustworthy signal is null.

STRONG (local, trustworthy)Seen up close, at the cut itself — trust it.

land surface temperature· 1 km pixels

0.046 degC no clear change SNR 0.15 1,843 def / 21,713 ctrl px

greenness ndvi· 1 km pixels

-0.003 NDVI no clear change SNR 0.17 1,843 def / 21,713 ctrl px

WEAK (regional process, coarse grid — directional hint)A regional process on a coarse grid — a hint, not proof.

rainfall· 5.6 km pixels

-11.2 mm/year no clear change SNR 0.13 93 def / 666 ctrl px

WEAKEST (coarse + regional — read the control-pixel count)Very coarse and regional — check the pixel count before trusting.

wind· 27.8 km pixels ⚠ too few pixels; do not trust floor estimated — not measured here

0.05 m/s windier SNR 1.53 2 def / 19 ctrl px

humidity· 27.8 km pixels ⚠ too few pixels; do not trust floor estimated — not measured here

0 % RH no clear change SNR 0 2 def / 19 ctrl px

AMBIGUOUS (model-based, no expected direction — reported, not judged)From a model that can't even see the cut — reported, never judged.

soil moisture· 11.1 km pixels reported, not judged

0.001 m3/m3 no clear change SNR 0.24 26 def / 160 ctrl px

clears the tick → trust it (for its tier) below the tick → treat as no clear change noise floor estimated, not measured ┊ the bar a real signal must clear (1× noise floor)

Provenance & full trace — reproducible

Every number came from this exact query on Google Earth Engine. Same query → same number.

DEFORESTATION ANALOG — combined report
  region [79.5, 19.0, 83.0, 22.0]  |  forest lost 2005-2008  |  before 2001-2004 -> after 2015-2020
  Before-After-Control-Impact (BACI): change at deforested sites MINUS the same
  change at kept-forest controls. Observational evidence, NOT a causal prediction.

[STRONG  (local, trustworthy)]
  land_surface_temperature   BACI +0.046 degC    (no clear change)  [def 1843px / ctrl 21713px]
  greenness_ndvi             BACI -0.003 NDVI    (no clear change)  [def 1843px / ctrl 21713px]

[WEAK    (regional process, coarse grid — directional hint)]
  rainfall                   BACI -11.2 mm/year (no clear change)  [def 93px / ctrl 666px]

[WEAKEST (coarse + regional — read the control-pixel count)]
  wind                       BACI +0.05 m/s     (windier)  [def 2px / ctrl 19px]  <- too few pixels; do not trust
  humidity                   BACI -0 % RH    (no clear change)  [def 2px / ctrl 19px]  <- too few pixels; do not trust

[AMBIGUOUS (model-based, no expected direction — reported, not judged)]
  soil_moisture              BACI +0.001 m3/m3   (no clear change)  [def 26px / ctrl 160px]

VERDICT
  No clear local change: every strong-tier variable came back below the noise floor (LST/NDVI ~ flat between deforested and control sites here). The deforestation signal in this box is weak or absent — possibly little real loss, slow recovery, or the window is too short. Do not over-read the coarse/regional hints; the trustworthy signal is null.

Western Ghats (Karnataka)

India biodiversity hotspot — evergreen forest under conversion pressure forest lost 2005–2008 before 2001–2004 → after 2015–2020

○Measured flat — we checked the trustworthy variables and found no clear local change here. This is a real result, not a missing one.

STRONG (local, trustworthy)Seen up close, at the cut itself — trust it.

land surface temperature· 1 km pixels

0.031 degC no clear change SNR 0.08 1,149 def / 12,668 ctrl px

greenness ndvi· 1 km pixels ⚠ NOT in the expected direction (investigate)

0.001 NDVI no clear change SNR 0.06 1,149 def / 12,668 ctrl px

WEAK (regional process, coarse grid — directional hint)A regional process on a coarse grid — a hint, not proof.

rainfall· 5.6 km pixels ⚠ NOT in the expected direction (investigate)

9.3 mm/year no clear change SNR 0.05 48 def / 431 ctrl px

WEAKEST (coarse + regional — read the control-pixel count)Very coarse and regional — check the pixel count before trusting.

wind· 27.8 km pixels ⚠ NOT in the expected direction (investigate) floor estimated — not measured here

-0.01 m/s no clear change SNR 0.24 2 def / 15 ctrl px

humidity· 27.8 km pixels ⚠ too few pixels; do not trust floor estimated — not measured here

-0.1 % RH no clear change SNR 0.31 2 def / 15 ctrl px

AMBIGUOUS (model-based, no expected direction — reported, not judged)From a model that can't even see the cut — reported, never judged.

soil moisture· 11.1 km pixels reported, not judged

0 m3/m3 no clear change SNR 0.17 14 def / 109 ctrl px

clears the tick → trust it (for its tier) below the tick → treat as no clear change noise floor estimated, not measured ┊ the bar a real signal must clear (1× noise floor)

Provenance & full trace — reproducible

Every number came from this exact query on Google Earth Engine. Same query → same number.

DEFORESTATION ANALOG — combined report
  region [74.3, 13.0, 75.6, 15.2]  |  forest lost 2005-2008  |  before 2001-2004 -> after 2015-2020
  Before-After-Control-Impact (BACI): change at deforested sites MINUS the same
  change at kept-forest controls. Observational evidence, NOT a causal prediction.

[STRONG  (local, trustworthy)]
  land_surface_temperature   BACI +0.031 degC    (no clear change)  [def 1149px / ctrl 12668px]
  greenness_ndvi             BACI +0.001 NDVI    (no clear change)  [def 1149px / ctrl 12668px]  <- NOT in the expected direction (investigate)

[WEAK    (regional process, coarse grid — directional hint)]
  rainfall                   BACI +9.3 mm/year (no clear change)  [def 48px / ctrl 431px]  <- NOT in the expected direction (investigate)

[WEAKEST (coarse + regional — read the control-pixel count)]
  wind                       BACI -0.01 m/s     (no clear change)  [def 2px / ctrl 15px]  <- NOT in the expected direction (investigate)
  humidity                   BACI -0.1 % RH    (no clear change)  [def 2px / ctrl 15px]  <- too few pixels; do not trust

[AMBIGUOUS (model-based, no expected direction — reported, not judged)]
  soil_moisture              BACI +0 m3/m3   (no clear change)  [def 14px / ctrl 109px]

VERDICT
  No clear local change: every strong-tier variable came back below the noise floor (LST/NDVI ~ flat between deforested and control sites here). The deforestation signal in this box is weak or absent — possibly little real loss, slow recovery, or the window is too short. Do not over-read the coarse/regional hints; the trustworthy signal is null.

Northeast India (Assam–Meghalaya)

Shifting cultivation + clearing in the wettest Indian forests forest lost 2005–2008 before 2001–2004 → after 2015–2020

○Measured flat — we checked the trustworthy variables and found no clear local change here. This is a real result, not a missing one.

STRONG (local, trustworthy)Seen up close, at the cut itself — trust it.

land surface temperature· 1 km pixels

0.099 degC no clear change SNR 0.31 3,445 def / 24,430 ctrl px

greenness ndvi· 1 km pixels

-0.005 NDVI no clear change SNR 0.21 3,445 def / 24,430 ctrl px

WEAK (regional process, coarse grid — directional hint)A regional process on a coarse grid — a hint, not proof.

rainfall· 5.6 km pixels ⚠ NOT in the expected direction (investigate)

12.3 mm/year no clear change SNR 0.05 100 def / 690 ctrl px

WEAKEST (coarse + regional — read the control-pixel count)Very coarse and regional — check the pixel count before trusting.

wind· 27.8 km pixels ⚠ too few pixels; do not trust floor estimated — not measured here

0 m/s no clear change SNR 0.03 2 def / 20 ctrl px

humidity· 27.8 km pixels ⚠ NOT in the expected direction (investigate) floor estimated — not measured here

0.1 % RH no clear change SNR 0.4 2 def / 20 ctrl px

AMBIGUOUS (model-based, no expected direction — reported, not judged)From a model that can't even see the cut — reported, never judged.

soil moisture· 11.1 km pixels reported, not judged

0 m3/m3 no clear change SNR 0.02 29 def / 161 ctrl px

clears the tick → trust it (for its tier) below the tick → treat as no clear change noise floor estimated, not measured ┊ the bar a real signal must clear (1× noise floor)

Provenance & full trace — reproducible

Every number came from this exact query on Google Earth Engine. Same query → same number.

DEFORESTATION ANALOG — combined report
  region [91.0, 25.0, 94.0, 26.6]  |  forest lost 2005-2008  |  before 2001-2004 -> after 2015-2020
  Before-After-Control-Impact (BACI): change at deforested sites MINUS the same
  change at kept-forest controls. Observational evidence, NOT a causal prediction.

[STRONG  (local, trustworthy)]
  land_surface_temperature   BACI +0.099 degC    (no clear change)  [def 3445px / ctrl 24430px]
  greenness_ndvi             BACI -0.005 NDVI    (no clear change)  [def 3445px / ctrl 24430px]

[WEAK    (regional process, coarse grid — directional hint)]
  rainfall                   BACI +12.3 mm/year (no clear change)  [def 100px / ctrl 690px]  <- NOT in the expected direction (investigate)

[WEAKEST (coarse + regional — read the control-pixel count)]
  wind                       BACI +0 m/s     (no clear change)  [def 2px / ctrl 20px]  <- too few pixels; do not trust
  humidity                   BACI +0.1 % RH    (no clear change)  [def 2px / ctrl 20px]  <- NOT in the expected direction (investigate)

[AMBIGUOUS (model-based, no expected direction — reported, not judged)]
  soil_moisture              BACI -0 m3/m3   (no clear change)  [def 29px / ctrl 161px]

VERDICT
  No clear local change: every strong-tier variable came back below the noise floor (LST/NDVI ~ flat between deforested and control sites here). The deforestation signal in this box is weak or absent — possibly little real loss, slow recovery, or the window is too short. Do not over-read the coarse/regional hints; the trustworthy signal is null.

Want this for your own area? Open the question, draw an AOI, and download a ready-to-run notebook.

② Regional climate trends · ERA5 via Google Earth Engine

Has rainfall or temperature actually changed?

Area-weighted annual series, Theil–Sen slope + Mann–Kendall significance. ERA5 0.25° has known precipitation biases — read trends as relative change, not absolute totals.

Has annual rainfall in the Marathwada region declined from 1990 to 2020?

rainfall Marathwada 1990–2020

-1.183 mm/year (trend) no significant trend

95% CI [-8.646, 7.167] Mann–Kendall p = 0.6341

The data doesn't support a rainfall change here — an earned null, not a missing answer.

⚠ Ground-truth: gauges disagree IMD gauges ↓ · ERA5 ↑ · CHIRPS ↑ sources DISAGREE on direction — treat the trend as unresolved. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over the Indo-Gangetic Plain changed from 1980 to 2020?

temperature Indo-Gangetic Plain 1980–2020

+0.016 degC (trend) significant trend

95% CI [0.003, 0.03] Mann–Kendall p = 0.0059

Prior: IMD/IPCC consensus: India land-surface warming ~0.6-0.7C/century, accelerating.

✓ Ground-truth: gauges agree IMD gauges ↑* · ERA5 ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has rainfall over the Western Ghats crest meaningfully changed from 1991 to 2020?

rainfall Western Ghats crest 1991–2020

-0.245 mm/year (trend) no significant trend

95% CI [-16.379, 16.596] Mann–Kendall p = 1

The data doesn't support a rainfall change here — an earned null, not a missing answer.

✓ Ground-truth: gauges agree IMD gauges ↑ · ERA5 ↑ · CHIRPS ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Rainfall trend over Punjab, 1990-2020?

rainfall Punjab 1990–2020

-5.799 mm/year (trend) no significant trend

95% CI [-12.603, 1.898] Mann–Kendall p = 0.1739

The data doesn't support a rainfall change here — an earned null, not a missing answer.

⚠ Ground-truth: gauges disagree IMD gauges ↓ · ERA5 ↓ · CHIRPS ↑* sources DISAGREE on direction — treat the trend as unresolved. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Rainfall trend over Kerala, 1990-2020?

rainfall Kerala 1990–2020

+6.541 mm/year (trend) no significant trend

95% CI [-4.858, 15.146] Mann–Kendall p = 0.1438

The data doesn't support a rainfall change here — an earned null, not a missing answer.

✓ Ground-truth: gauges agree IMD gauges ↑ · ERA5 ↑ · CHIRPS ↑* all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

What was the mean annual rainfall over the Krishna basin, 2000-2020?

rainfall Krishna basin 2000–2020

1129.3mm/year

computed · ERA5/GEE · not yet scientist-verified

Has monsoon-season (JJAS) rainfall over central India changed from 1990 to 2020?

rainfall Central India 1990–2020

+1.23 mm/year (trend) no significant trend

95% CI [-9.445, 13.019] Mann–Kendall p = 0.6341

The data doesn't support a rainfall change here — an earned null, not a missing answer.

✓ Ground-truth: gauges agree IMD gauges ↑ · ERA5 ↑ · CHIRPS ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has annual rainfall over Tamil Nadu changed from 1990 to 2020?

rainfall Tamil Nadu 1990–2020

+4.088 mm/year (trend) no significant trend

95% CI [-5.939, 12.906] Mann–Kendall p = 0.3412

The data doesn't support a rainfall change here — an earned null, not a missing answer.

✓ Ground-truth: gauges agree IMD gauges ↑ · ERA5 ↑ · CHIRPS ↑* all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over Tamil Nadu changed from 1980 to 2020?

temperature Tamil Nadu 1980–2020

+0.02 degC (trend) significant trend

95% CI [0.011, 0.031] Mann–Kendall p = 0

Prior: IMD/IPCC consensus: India land-surface warming.

✓ Ground-truth: gauges agree IMD gauges ↑* · ERA5 ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has annual rainfall over Karnataka changed from 1990 to 2020?

rainfall Karnataka 1990–2020

+1.402 mm/year (trend) no significant trend

95% CI [-4.89, 9.295] Mann–Kendall p = 0.6341

The data doesn't support a rainfall change here — an earned null, not a missing answer.

✓ Ground-truth: gauges agree IMD gauges ↑ · ERA5 ↑ · CHIRPS ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over Karnataka changed from 1980 to 2020?

temperature Karnataka 1980–2020

+0.02 degC (trend) significant trend

95% CI [0.009, 0.029] Mann–Kendall p = 0

Prior: IMD/IPCC consensus: India land-surface warming.

✓ Ground-truth: gauges agree IMD gauges ↑* · ERA5 ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has annual rainfall over Gujarat changed from 1990 to 2020?

rainfall Gujarat 1990–2020

+8.447 mm/year (trend) no significant trend

95% CI [-4.367, 17.172] Mann–Kendall p = 0.1347

The data doesn't support a rainfall change here — an earned null, not a missing answer.

✓ Ground-truth: gauges agree IMD gauges ↑ · ERA5 ↑* · CHIRPS ↑* all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over Gujarat changed from 1980 to 2020?

temperature Gujarat 1980–2020

+0.018 degC (trend) significant trend

95% CI [0.009, 0.026] Mann–Kendall p = 0.0007

Prior: IMD/IPCC consensus: India land-surface warming.

✓ Ground-truth: gauges agree IMD gauges ↑* · ERA5 ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has annual rainfall over Rajasthan changed from 1990 to 2020?

rainfall Rajasthan 1990–2020

+1.414 mm/year (trend) no significant trend

95% CI [-4.819, 7.13] Mann–Kendall p = 0.5184

The data doesn't support a rainfall change here — an earned null, not a missing answer.

✓ Ground-truth: gauges agree IMD gauges ↑ · ERA5 ↑ · CHIRPS ↑* all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over Rajasthan changed from 1980 to 2020?

temperature Rajasthan 1980–2020

+0.013 degC (trend) significant trend

95% CI [0, 0.03] Mann–Kendall p = 0.0338

Prior: IMD/IPCC consensus: India land-surface warming.

✓ Ground-truth: gauges agree IMD gauges ↑* · ERA5 ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has annual rainfall over West Bengal changed from 1990 to 2020?

rainfall West Bengal 1990–2020

-6.59 mm/year (trend) no significant trend

95% CI [-16.478, 4.77] Mann–Kendall p = 0.1534

The data doesn't support a rainfall change here — an earned null, not a missing answer.

⚠ Ground-truth: gauges disagree IMD gauges ↓* · ERA5 ↓ · CHIRPS ↑ sources DISAGREE on direction — treat the trend as unresolved. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over West Bengal changed from 1980 to 2020?

temperature West Bengal 1980–2020

+0.018 degC (trend) significant trend

95% CI [0.006, 0.026] Mann–Kendall p = 0.0007

Prior: IMD/IPCC consensus: India land-surface warming.

✓ Ground-truth: gauges agree IMD gauges ↑ · ERA5 ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has annual rainfall over Madhya Pradesh changed from 1990 to 2020?

rainfall Madhya Pradesh 1990–2020

+0.524 mm/year (trend) no significant trend

95% CI [-6.436, 11.043] Mann–Kendall p = 0.8918

The data doesn't support a rainfall change here — an earned null, not a missing answer.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over Madhya Pradesh changed from 1980 to 2020?

temperature Madhya Pradesh 1980–2020

+0.016 degC (trend) significant trend

95% CI [0.006, 0.028] Mann–Kendall p = 0.0013

Prior: IMD/IPCC consensus: India land-surface warming.

✓ Ground-truth: gauges agree IMD gauges ↑* · ERA5 ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has annual rainfall over Odisha changed from 1990 to 2020?

rainfall Odisha 1990–2020

+3.624 mm/year (trend) no significant trend

95% CI [-15.228, 13.977] Mann–Kendall p = 0.5634

The data doesn't support a rainfall change here — an earned null, not a missing answer.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over Odisha changed from 1980 to 2020?

temperature Odisha 1980–2020

+0.01 degC (trend) significant trend

95% CI [0.002, 0.017] Mann–Kendall p = 0.0052

Prior: IMD/IPCC consensus: India land-surface warming.

✓ Ground-truth: gauges agree IMD gauges ↑ · ERA5 ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has annual rainfall over Assam changed from 1990 to 2020?

rainfall Assam 1990–2020

-20.4 mm/year (trend) significant trend

95% CI [-36.556, -0.498] Mann–Kendall p = 0.0249

✓ Ground-truth: gauges agree IMD gauges ↓ · ERA5 ↓ · CHIRPS ↓ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over Assam changed from 1980 to 2020?

temperature Assam 1980–2020

+0.024 degC (trend) significant trend

95% CI [0.017, 0.031] Mann–Kendall p = 0

Prior: IMD/IPCC consensus: India land-surface warming.

✓ Ground-truth: gauges agree IMD gauges ↑* · ERA5 ↑* all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has annual rainfall over the Delhi NCR changed from 1990 to 2020?

rainfall Delhi NCR 1990–2020

+0.094 mm/year (trend) no significant trend

95% CI [-7.46, 7.186] Mann–Kendall p = 1

The data doesn't support a rainfall change here — an earned null, not a missing answer.

⚠ Ground-truth: gauges disagree IMD gauges ↓* · ERA5 ↑ · CHIRPS ↑ sources DISAGREE on direction — treat the trend as unresolved. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over the Delhi NCR changed from 1980 to 2020?

temperature Delhi NCR 1980–2020

+0.009 degC (trend) no significant trend

95% CI [-0.005, 0.026] Mann–Kendall p = 0.1965

The data doesn't support a temperature change here — an earned null, not a missing answer.

Prior: IMD/IPCC consensus: India land-surface warming.

⚠ Ground-truth: gauges disagree IMD gauges ↑ · ERA5 ↓ sources DISAGREE on direction — treat the trend as unresolved. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has annual rainfall over Mumbai & the Konkan coast changed from 1990 to 2020?

rainfall Mumbai & Konkan 1990–2020

+10.047 mm/year (trend) no significant trend

95% CI [-13.514, 28.655] Mann–Kendall p = 0.3412

The data doesn't support a rainfall change here — an earned null, not a missing answer.

✓ Ground-truth: gauges agree IMD gauges ↑ · ERA5 ↑ · CHIRPS ↑ all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

How has average temperature over Mumbai & the Konkan coast changed from 1980 to 2020?

temperature Mumbai & Konkan 1980–2020

+0.022 degC (trend) significant trend

95% CI [0.015, 0.03] Mann–Kendall p = 0

Prior: IMD/IPCC consensus: India land-surface warming.

✓ Ground-truth: gauges agree IMD gauges ↑* · ERA5 ↑* all sources agree on direction. IMD = India Meteorological Dept. gauge grid (the ground-truth); * = significant.

computed · ERA5/GEE · not yet scientist-verified

Has the daytime land-surface temperature over the Delhi NCR risen since 2003?

land-surface temperature Delhi NCR 2003–2022

-0.015 degC (trend) no significant trend

95% CI [-0.054, 0.029] Mann–Kendall p = 0.3145

The data doesn't support a land-surface temperature change here — an earned null, not a missing answer.

Prior: urban heat island + regional warming.

computed · ERA5/GEE · not yet scientist-verified

Has the daytime land-surface temperature over Mumbai risen since 2003?

land-surface temperature Mumbai & Konkan 2003–2022

-0.088 degC (trend) significant trend

95% CI [-0.132, -0.033] Mann–Kendall p = 0.0008

Prior: coastal city; warming likely but sea-moderated.

computed · ERA5/GEE · not yet scientist-verified

Has the daytime land-surface temperature over Bengaluru risen since 2003?

land-surface temperature Bengaluru 2003–2022

-0.032 degC (trend) no significant trend

95% CI [-0.115, 0.034] Mann–Kendall p = 0.381

The data doesn't support a land-surface temperature change here — an earned null, not a missing answer.

Prior: rapid urban growth + heat island.

computed · ERA5/GEE · not yet scientist-verified

Has the daytime land-surface temperature over Chennai risen since 2003?

land-surface temperature Chennai 2003–2022

+0.03 degC (trend) no significant trend

95% CI [-0.027, 0.098] Mann–Kendall p = 0.4555

The data doesn't support a land-surface temperature change here — an earned null, not a missing answer.

Prior: coastal metro; warming likely but sea-moderated.

computed · ERA5/GEE · not yet scientist-verified

Has the daytime land-surface temperature over Hyderabad risen since 2003?

land-surface temperature Hyderabad 2003–2022

-0.06 degC (trend) significant trend

95% CI [-0.116, 0] Mann–Kendall p = 0.0252

Prior: rapid urban growth + heat island.

computed · ERA5/GEE · not yet scientist-verified

Has the daytime land-surface temperature over Kolkata risen since 2003?

land-surface temperature Kolkata 2003–2022

+0.049 degC (trend) significant trend

95% CI [0.021, 0.071] Mann–Kendall p = 0.001

Prior: dense delta metro; warming likely.

computed · ERA5/GEE · not yet scientist-verified

Has the daytime land-surface temperature over Ahmedabad risen since 2003?

land-surface temperature Ahmedabad 2003–2022

-0.05 degC (trend) significant trend

95% CI [-0.078, -0.007] Mann–Kendall p = 0.0252

Prior: semi-arid city + heat island.

computed · ERA5/GEE · not yet scientist-verified

Has the daytime land-surface temperature over Pune risen since 2003?

land-surface temperature Pune 2003–2022

-0.116 degC (trend) significant trend

95% CI [-0.168, -0.061] Mann–Kendall p = 0.0002

Prior: fast-growing city + heat island.

computed · ERA5/GEE · not yet scientist-verified

Has growing-season greenness (NDVI) over Punjab changed since 2003?

greenness (NDVI) Punjab 2003–2022

+0.003 NDVI (trend) significant trend

95% CI [0.002, 0.004] Mann–Kendall p = 0.0001

computed · ERA5/GEE · not yet scientist-verified

Has growing-season greenness (NDVI) over the Indo-Gangetic Plain changed since 2003?

greenness (NDVI) Indo-Gangetic Plain 2003–2022

+0.003 NDVI (trend) significant trend

95% CI [0.001, 0.003] Mann–Kendall p = 0

computed · ERA5/GEE · not yet scientist-verified

Has total water storage (the groundwater proxy) over Punjab declined? (GRACE, 2003-2016)

water storage (GRACE) Punjab 2003–2016

-2.109 (trend) significant trend

95% CI [-3.22, -1.48] Mann–Kendall p = 0.0001

Prior: well-documented NW India groundwater depletion (Rodell et al. 2009).

computed · ERA5/GEE · not yet scientist-verified

Has total water storage over the Indo-Gangetic Plain declined? (GRACE, 2003-2016)

water storage (GRACE) Indo-Gangetic Plain 2003–2016

-1.881 (trend) significant trend

95% CI [-2.59, -1.185] Mann–Kendall p = 0.0001

Prior: NW India groundwater depletion.

computed · ERA5/GEE · not yet scientist-verified

Has the eastern Arabian Sea surface warmed? (OISST, 1990-2020)

sea-surface temperature Arabian Sea (east) 1990–2020

+0.026 (trend) significant trend

95% CI [0.019, 0.032] Mann–Kendall p = 0

Prior: Indian Ocean among the fastest-warming basins.

computed · ERA5/GEE · not yet scientist-verified

Has the northern Bay of Bengal surface warmed? (OISST, 1990-2020)

sea-surface temperature Bay of Bengal (north) 1990–2020

+0.021 (trend) significant trend

95% CI [0.014, 0.029] Mann–Kendall p = 0

Prior: Indian Ocean rapid warming.

computed · ERA5/GEE · not yet scientist-verified

Have night-lights over the Delhi NCR brightened? (VIIRS, 2014-2023)

night-lights (VIIRS) Delhi NCR 2014–2023

-0.047 (trend) no significant trend

95% CI [-0.251, 0.334] Mann–Kendall p = 0.7205

The data doesn't support a night-lights (VIIRS) change here — an earned null, not a missing answer.

Prior: urban growth + electrification.

computed · ERA5/GEE · not yet scientist-verified

Have night-lights over Mumbai brightened? (VIIRS, 2014-2023)

night-lights (VIIRS) Mumbai & Konkan 2014–2023

+0.124 (trend) significant trend

95% CI [0.029, 0.203] Mann–Kendall p = 0.0024

Prior: urban growth.

computed · ERA5/GEE · not yet scientist-verified

Have night-lights over Bengaluru brightened? (VIIRS, 2014-2023)

night-lights (VIIRS) Bengaluru 2014–2023

-0.412 (trend) no significant trend

95% CI [-0.916, 0.192] Mann–Kendall p = 0.4743

The data doesn't support a night-lights (VIIRS) change here — an earned null, not a missing answer.

Prior: rapid tech-city growth.

computed · ERA5/GEE · not yet scientist-verified

Have night-lights over Hyderabad brightened? (VIIRS, 2014-2023)

night-lights (VIIRS) Hyderabad 2014–2023

-0.392 (trend) no significant trend

95% CI [-1.185, 0.415] Mann–Kendall p = 0.2105

The data doesn't support a night-lights (VIIRS) change here — an earned null, not a missing answer.

Prior: urban growth.

computed · ERA5/GEE · not yet scientist-verified

Have night-lights over the Indo-Gangetic Plain brightened? (VIIRS, 2014-2023)

night-lights (VIIRS) Indo-Gangetic Plain 2014–2023

+0.055 (trend) significant trend

95% CI [0.027, 0.07] Mann–Kendall p = 0.0003

Prior: rural electrification across the plain.

computed · ERA5/GEE · not yet scientist-verified

Has growing-season greenness (NDVI) over Marathwada changed since 2003?

greenness (NDVI) Marathwada 2003–2022

+0.003 NDVI (trend) significant trend

95% CI [0.001, 0.005] Mann–Kendall p = 0.0032

computed · ERA5/GEE · not yet scientist-verified

Has growing-season greenness (NDVI) over Gujarat changed since 2003?

greenness (NDVI) Gujarat 2003–2022

+0.003 NDVI (trend) significant trend

95% CI [0.002, 0.004] Mann–Kendall p = 0.0001

computed · ERA5/GEE · not yet scientist-verified

Has growing-season greenness (NDVI) over Madhya Pradesh changed since 2003?

greenness (NDVI) Madhya Pradesh 2003–2022

+0.003 NDVI (trend) significant trend

95% CI [0.002, 0.005] Mann–Kendall p = 0.0001

computed · ERA5/GEE · not yet scientist-verified

Has growing-season greenness (NDVI) over Tamil Nadu changed since 2003?

greenness (NDVI) Tamil Nadu 2003–2022

+0.002 NDVI (trend) significant trend

95% CI [0, 0.004] Mann–Kendall p = 0.0252

computed · ERA5/GEE · not yet scientist-verified

…and what it refuses to answer

A trust tool must fail loudly. These ill-posed questions are refused or sent back for clarification by the validator — never quietly guessed.

What is the rainfall trend over Maharashtra from 2018 to 2021?

rainfall Central India 2018–2021

⊘ Refused

trend span < 10y — the validator refuses ill-posed questions rather than guessing. That discipline is the point.

validator-enforced refusal

How has temperature changed in 'the valley' since 1990?

temperature 1990–2020

⊘ Asks to clarify

unresolved region name — the validator refuses ill-posed questions rather than guessing. That discipline is the point.

validator-enforced refusal

Has it gotten hotter and drier over Vidarbha since 1990?

AMBIGUOUS_MULTI Vidarbha 1990–2020

⊘ Splits the question

answer temperature and precipitation separately — the validator refuses ill-posed questions rather than guessing. That discipline is the point.

validator-enforced refusal

How has tropospheric NO2 (air quality) over an Indian city changed over the last decade?

nitrogen dioxide (NO2) Delhi NCR 2019–2024

⊘ Refused

the satellite NO2 record (TROPOMI, 2018+) is only ~6 years -- too short to honestly call a decadal trend. OMI reaches 2004 but is not in this GEE pipeline. Refuse rather than mislead. — the validator refuses ill-posed questions rather than guessing. That discipline is the point.

validator-enforced refusal

verified-earth-agent · golden set + computed answers (real ERA5/GEE; status: computed, not yet scientist-verified).

③ Disaster events · Black Marble nightlights

Did the power go out?

Before-After-Control-Impact on NASA Black Marble night-time lights; control-corrected, clear nights only, QA-masked. A proxy for power loss, not a meter. Answers “Did the power go out after this storm?”

Augusta, Georgia

Hurricane Helene power outages — Sep 2024 before 2024-08-15–2024-09-20 → after 2024-09-27–2024-10-04

●Measured signal — the trustworthy variables changed in the expected direction.

Night-time lights over Augusta, Georgia changed by -44% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.

STRONG (direct, local)Seen up close, at the cut itself — trust it.

night-time lights

-9.872 nW/cm²/sr (-44%) darker (likely outage) SNR 1.3 938 def / 2,172 ctrl px

clears the tick → trust it (for its tier) below the tick → treat as no clear change noise floor estimated, not measured ┊ the bar a real signal must clear (1× noise floor)

Provenance & full trace — reproducible

Every number came from this exact query on Google Earth Engine. Same query → same number.

Night-time lights over Augusta, Georgia changed by -44% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.

San Juan, Puerto Rico

Hurricane Maria power outages — Sep 2017 before 2017-08-20–2017-09-15 → after 2017-09-21–2017-10-08

●Measured signal — the trustworthy variables changed in the expected direction.

Night-time lights over San Juan, Puerto Rico changed by -33% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.

STRONG (direct, local)Seen up close, at the cut itself — trust it.

night-time lights

-8.283 nW/cm²/sr (-33%) darker (likely outage) SNR 0.51 922 def / 432 ctrl px

clears the tick → trust it (for its tier) below the tick → treat as no clear change noise floor estimated, not measured ┊ the bar a real signal must clear (1× noise floor)

Provenance & full trace — reproducible

Every number came from this exact query on Google Earth Engine. Same query → same number.

Night-time lights over San Juan, Puerto Rico changed by -33% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.

New Orleans, Louisiana

Hurricane Ida power outages — Sep 2021 before 2021-08-05–2021-08-25 → after 2021-08-30–2021-09-08

●Measured signal — the trustworthy variables changed in the expected direction.

Night-time lights over New Orleans, Louisiana changed by -26% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.

STRONG (direct, local)Seen up close, at the cut itself — trust it.

night-time lights

-11.054 nW/cm²/sr (-26%) darker (likely outage) SNR 1.11 829 def / 2,345 ctrl px

clears the tick → trust it (for its tier) below the tick → treat as no clear change noise floor estimated, not measured ┊ the bar a real signal must clear (1× noise floor)

Provenance & full trace — reproducible

Every number came from this exact query on Google Earth Engine. Same query → same number.

Night-time lights over New Orleans, Louisiana changed by -26% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.

Fort Myers, Florida

Hurricane Ian power outages — Sep 2022 before 2022-09-05–2022-09-25 → after 2022-09-29–2022-10-08

⊘Not measurable — too few comparable pixels in this box to give a trustworthy answer.

Not measurable for Fort Myers, Florida: too few cloud-free Black Marble nights (clouds and the storm obscure the surface), so an outage can't be separated from noise.

STRONG (direct, local)Seen up close, at the cut itself — trust it.

night-time lights ⚠ control overshoot — not a trustworthy estimate reported, not judged floor estimated — not measured here

-22.502 nW/cm²/sr (-101%) not measurable SNR 0.79 1,739 def / 1,299 ctrl px

clears the tick → trust it (for its tier) below the tick → treat as no clear change noise floor estimated, not measured ┊ the bar a real signal must clear (1× noise floor)

Provenance & full trace — reproducible

Every number came from this exact query on Google Earth Engine. Same query → same number.

Not measurable for Fort Myers, Florida: too few cloud-free Black Marble nights (clouds and the storm obscure the surface), so an outage can't be separated from noise.

Houston, Texas

Hurricane Beryl power outages — Jul 2024 before 2024-06-15–2024-07-05 → after 2024-07-08–2024-07-15

⊘Not measurable — too few comparable pixels in this box to give a trustworthy answer.

Not measurable for Houston, Texas: too few cloud-free Black Marble nights (clouds and the storm obscure the surface), so an outage can't be separated from noise.

STRONG (direct, local)Seen up close, at the cut itself — trust it.

night-time lights ⚠ only 1 clear night(s) — not a trustworthy estimate reported, not judged floor estimated — not measured here

▸

-43.65 nW/cm²/sr (-56%) not measurable SNR 6.39 2,207 def / 1,535 ctrl px

clears the tick → trust it (for its tier) below the tick → treat as no clear change noise floor estimated, not measured ┊ the bar a real signal must clear (1× noise floor)

Provenance & full trace — reproducible

Every number came from this exact query on Google Earth Engine. Same query → same number.

Not measurable for Houston, Texas: too few cloud-free Black Marble nights (clouds and the storm obscure the surface), so an outage can't be separated from noise.

verified-earth-agent · Google Earth Engine (status: computed, not yet scientist-verified).