Fix float32 dtype leak in flood_depth/curve_number_runoff dask backends#3509
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…ds (#3499) numpy and cupy cast the input to float64, but _flood_depth_dask and _cn_runoff_dask operated on the input array without a cast, so a float32 input returned float32 on the dask and dask+cupy backends. Both functions document a float64 output. Cast hand/p to float64 at the top of the dask helpers so all four backends agree. Adds float32-input dtype tests across numpy, dask, cupy, and dask+cupy. Updates the sweep-accuracy state CSV. Closes #3499
…lood-2026-06-25 # Conflicts: # xrspatial/flood.py # xrspatial/tests/test_flood.py
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Closes #3499
flood_depthandcurve_number_runoffdocument a float64 output. The numpy and cupy helpers cast the input to float64, but_flood_depth_daskand_cn_runoff_daskoperated on the input array with no cast, so a float32 input returned float32 on the dask and dask+cupy backends.Change
handto float64 at the top of_flood_depth_dask.pto float64 at the top of_cn_runoff_dask.The dask+cupy wrappers reuse the dask helpers, so all four backends now return float64. The other flood functions already returned float64 on every backend (the float64
_TAN_MINclamp, the1.0/0.0literals ininundation, andnp.interpinvegetation_roughness).Backend coverage
numpy, cupy, dask+numpy, dask+cupy. Added 8 dtype tests (one per function per backend) that feed a float32 input and assert a float64 result. Verified locally on a CUDA host (96 flood tests pass).
Test plan
pytest xrspatial/tests/test_flood.py(96 passed, includes cupy and dask+cupy)