Fusing Earth observation embeddings and spaceborne terrain with meteorological forcing for flood peak depth prediction from high-water marks

Wen Zhou; Shaowen Wang; Upmanu Lall; Venkatesh Merwade; Dingqi Ye

cs.LGNov 3, 2026

Fusing Earth observation embeddings and spaceborne terrain with meteorological forcing for flood peak depth prediction from high-water marks

Authors: Wen Zhou, Shaowen Wang, Upmanu Lall, Venkatesh Merwade, Dingqi Ye

Organizations: Department of Geography and Geographic Information Science · CyberGIS Center for Advanced Digital and Spatial Studies · Siebel School of Computing and Data Science · School of Complex Adaptive Systems, College of Global Futures, Arizona State University, Tempe, AZ, USA ·

Abstract

Flood peak depth on normally dry land governs damage and risk to life, but is observed only sparsely, by post-event high-water mark (HWM) surveys, and depth derived from satellite flood extent is produced after the event and inherits the vertical error of the digital elevation model (DEM) it is computed from. Deep-learning flood models have in turn been trained on hydrodynamic simulations rather than on observed depths. We ask whether flood peak depth can be learned from field observations and predicted before the peak from spaceborne terrain, geospatial foundation-model embeddings and antecedent meteorological forcing. We assembled 5689 USGS HWM surveys from 39 events (2011-2025) in 24 U.S. states and Puerto Rico, assigned each a peak time from nearby sensors, paired each with a rain-free window at the same site, and linked them to Copernicus GLO-30 terrain, AlphaEarth embeddings and 144 h of hourly forcing masked to the lead time. A cross-modal fusion network trained on these observations predicts depth at the surveyed marks with a mean absolute error of 0.39 m 6 h ahead of the peak. Supervision by surveyed depth corrects the DEM error implicitly: differencing the measured peak water level with the same DEM gives 0.93 m at the same marks, 2.4 times larger, although it uses the measured water level and the model does not. Forcing accounts for 77% of the attributed reduction in depth error; terrain and the embeddings are statistically equivalent to each other and contribute the remaining 23%. On eleven held-out events the model still separates flooded from rain-free conditions but no longer resolves depth at the marks (R2 = 0.03), so the skill reported here is confined to the events and regions sampled. Field-surveyed depths can thus supervise flood depth prediction and correct terrain error that extent-based estimation cannot, while transfer to unseen storms remains the open problem.

Figures & tables

Flood degree	Level 1	Level 2	Level 3
Depth $d$	$d\leq 0.3$ m	$0.3~\text{m}<d\leq 1.0$ m	$d>1.0$ m

Table 1: Flood severity levels used by the auxiliary classification task

Input	Product and source	Spatial resolution and footprint	Temporal coverage	Role
Labels and reference data
High-water marks	USGS Short-Term Network (STN) Flood Event Viewer surveys	Point	39 events, 2011–2025	Labels: peak elevation and depth; sample locations
Peak timing	USGS peak summaries, water-level sensors and rapid-deployment gauges within 10 km of the HWM	Point	Per event	Assigns the flood peak time ( $t=0$ )
Ground elevation at the HWM	USGS 3DEP lidar-based elevation	Point	Static	Anchor of the residual elevation target (Section 3.3)
Rain-free windows	NOAA Analysis of Record for Calibration (AORC) hourly precipitation	Gridded, nearest cell	$\pm$ 8–60 days around the peak	Selects the rain-free windows
Model inputs

Table 2: Inputs of the benchmark, their sources and their role in TCM-Former.

Extractor	Lead time	Depth $H$ (m)		Residual $E_{r}$ (m)		Severity level
Extractor	Lead time	MAE	RMSE	MAE	RMSE	MIF1	MAF1
Three-tier descriptor	6 h	0.257	0.468	0.530	0.818	0.727	0.642
	12 h	0.280	0.486	0.528	0.805	0.718	0.643
	18 h	0.273	0.487	0.536	0.820	0.709	0.624
	24 h	0.271	0.489	0.543	0.816	0.717	0.639
TabPFN-TS	6 h	0.258	0.457	0.541	0.812	0.728	0.647

Table 3: Flood depth (primary task) and auxiliary-task skill of TCM-Former at different lead times with the three-tier hydrometeorological descriptor, and with the two learned temporal encoders at the 6-h lead time (mean over three seeds; s.d.

\leq

0.04 m for errors and

\leq

0.018 for F1 scores). H: flood depth (height above ground); Er: residual flood peak elevation; MIF1 and MAF1: micro- and macro-F1 of flood degree classification.

Modalities	Depth $H$ (m)		Residual $E_{r}$ (m)		Severity level
Modalities	MAE	RMSE	MAE	RMSE	MIF1	MAF1
DEM, Meteo, AE	0.257	0.468	0.530	0.818	0.727	0.642
DEM	0.431	0.643	0.828	1.136	0.429	0.378
AE	0.410	0.625	0.801	1.174	0.474	0.426
Meteo	0.310	0.538	0.727	1.148	0.629	0.559
DEM, Meteo	0.269	0.485	0.550	0.848	0.724	0.644

Table 4: Flood depth (primary task) and auxiliary-task skill for different modality combinations. MAE-H and RMSE-H: MAE and RMSE of flood depth (height above ground); MAE-Er and RMSE-Er: MAE and RMSE of the residual flood peak elevation; MIF1 and MAF1: micro- and macro-F1 of flood degree classification. 6-h lead time; mean over three seeds; errors in m.

Dataset	Depth $H$ (m)		Residual $E_{r}$ (m)		Severity level
Dataset	MAE	RMSE	MAE	RMSE	MIF1	MAF1
Random split	0.257	0.468	0.530	0.818	0.727	0.642
Event split	0.386	0.648	0.997	1.489	0.614	0.537

Table 5: Generalization of TCM-Former to unseen flood events (6-h lead time; mean over three seeds; errors in m). The random split is the test set of Table 3 (885 samples); the event split holds out 11 whole events (897 samples).

Sampling	Depth $H$ (m)		Residual $E_{r}$ (m)		Severity level
Sampling	MAE	RMSE	MAE	RMSE	MIF1	MAF1
4-intra	0.374	0.649	0.982	1.423	0.655	0.550
8-intra	0.359	0.582	0.775	1.090	0.650	0.577
16-intra	0.361	0.628	0.903	1.273	0.646	0.557
32-intra	0.356	0.620	0.787	1.128	0.623	0.562

Table 6: Generalization of TCM-Former to spatially disjoint sub-regions within HUC-4 watersheds (6-h lead time; mean over three seeds; errors in m).

Methods	Lead time	Depth $H$ (m)		Residual $E_{r}$ (m)		Severity level
	Lead time	MAE	RMSE	MAE	RMSE	MIF1	MAF1
Ours (TCM-Former)	6 h	0.257	0.468	0.530	0.818	0.727	0.642
TFT	6 h	0.261	0.456	0.759	1.152	0.694	0.632
LSTM+CNN-ViT	6 h	0.267	0.461	0.545	0.814	0.722	0.653
Majority class + constant depth (training median)	—	0.416	0.744	—	—	0.599	0.250
Class-prior random classifier + training mean depth	—	0.487	0.617	—	—	0.440	0.332

Table 7: Comparison of TCM-Former and baseline models at the 6-h lead time (random split; mean over three seeds; TFT depth predictions clipped at zero, as in its loss).

Subset of test HWMs	$n$	Median	Depth MAE (m)
(random split)		$\|$ GLO-30 $-$ 3DEP $\|$ (m)	WSE $-$ GLO-30	WSE $-$ 3DEP	TCM-Former
All HWMs	567	1.66	0.93	0.51	0.39
$\|$ GLO-30 $-$ 3DEP $\|$ $<$ 1 m	183	0.55	0.65	0.39	0.37
$\|$ GLO-30 $-$ 3DEP $\|$ 1–3 m	207	1.68	1.10	0.54	0.37
$\|$ GLO-30 $-$ 3DEP $\|$ $>$ 3 m	177	6.47	1.03	0.61	0.44
Tree canopy	99	5.30	1.08	0.69	0.45

Table 8: Depth error at the 567 surveyed HWMs of the random test set, by local GLO-30 error and land cover. Differenced depths subtract GLO-30 (converted to NAVD88, the datum of the marks) or the 3DEP lidar ground at the mark from the surveyed water-surface elevation (WSE), with negative depths set to zero, i.e. the depth an extent-based method would return given a perfect water surface; TCM-Former predicts depth at the 6-h lead time (mean over three seeds). Land cover from NLCD 2021 (tree canopy: forest and woody wetland; built-up: developed, low to high intensity).

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Variable	Description
time	Hourly flood peak timestamp string
temperature_2m	Air temperature at 2 meters above ground
relative_humidity_2m	Relative humidity at 2 meters above ground
dewpoint_2m	Dew point temperature at 2 meters above ground
apparent_temperature	“Feels like” temperature (derived from temperature, wind, humidity, radiation, etc.)
precipitation	Total precipitation (commonly rain + snow water equivalent)

Appendix

Table A1: The used time-series factors obtained from Open-Meteo API

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