Paper Citation Record · LEDGER

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

As of 23 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2602.16579.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2602.16579 v2

Coverage vector

measured 42 of 42 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-02T22:32:48.032340Z

measured 42 of 42 standing notices

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Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 7f93ed05-36d3-4902-af5a-82020212707e · outbound

This paper cites Alfieri et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Alfieri et al

Reference 1

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Observation 0143a2d2-502a-42f1-8597-c95179bae08f · outbound

This paper cites an unresolved cited work.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:42.888768Z

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Observation 94a6f961-9d98-472a-ae03-5e00001abdf6 · outbound

This paper cites Prudhomme et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Prudhomme et al

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:43.056750Z

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Observation 60e3c1cc-9762-4152-a005-d250affab68a · outbound

This paper cites Grimaldi et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Grimaldi et al

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:43.190090Z

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Observation 0bc3f239-61a9-437e-ab03-0d1fa37ef2d0 · outbound

This paper cites Lang et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Lang et al

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:43.346159Z

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Observation eca124ee-d73d-4ff2-9c4f-9555ba86bd05 · outbound

This paper cites Moldovan et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Moldovan et al

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:43.551152Z

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Observation a8add708-9bbc-4310-b947-47674c2a5add · outbound

This paper cites an unresolved cited work.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:43.719435Z

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Observation 3a7640fd-f235-444f-9264-bd718817c624 · outbound

This paper cites Kratzert, M.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Kratzert, M

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:43.858180Z

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Observation ee8cbfc8-1510-44b7-8e70-70864db296bc · outbound

This paper cites Kratzert, D.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Kratzert, D

Reference 9

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Observation bf46260d-ac75-4009-9547-9ff0fdb0d7cb · outbound

This paper cites Ruparell et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Ruparell et al

Reference 10

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Observation 631c829b-6276-459b-a4c3-ea957a32dfb3 · outbound

This paper cites an unresolved cited work.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:44.301358Z

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Observation 5b541286-fa5b-4418-a682-30eecf5da600 · outbound

This paper cites Nearing et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Nearing et al

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:44.466646Z

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Observation 9934edf9-9ed7-4613-9ee9-e193c1a7924e · outbound

This paper cites Scholz, M.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Scholz, M

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:44.607690Z

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Observation d3f5aeb7-74e6-4381-b5e1-2e78acf0132a · outbound

This paper cites Moshe et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Moshe et al

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:44.723458Z

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Observation 7aafe1be-b2a4-45a0-a113-b6d5b5492953 · outbound

This paper cites an unresolved cited work.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Unresolved cited work

Reference 15

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Observation 8b344e37-2334-40e4-9e9f-893d84571c0d · outbound

This paper cites Yang et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Yang et al

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:45.010595Z

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Observation 87c85417-65fe-45f3-9064-2b2b61f8b447 · outbound

This paper cites Bindas et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Bindas et al

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:45.211459Z

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Observation cb12853c-e474-4118-b4de-e259bbceafe7 · outbound

This paper cites Kraft et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Kraft et al

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:45.335570Z

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Observation 07c5fe24-a960-4e93-a7e9-a03d712e69eb · outbound

This paper cites Kraft, M.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Kraft, M

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:45.417531Z

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Observation 2f1f88eb-61c7-4692-ac8b-ce3bdec40d2c · outbound

This paper cites Nearing et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Nearing et al

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:45.520877Z

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Observation 128a5450-62bb-4c3b-8bce-f44912f02aa7 · outbound

This paper cites an unresolved cited work.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:45.652734Z

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Observation a6813dc9-54ac-4fcc-b610-41d0b460eb36 · outbound

This paper cites Kratzert et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Kratzert et al

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:45.798585Z

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Observation a2790419-4a30-427d-b69a-ca26f26984f9 · outbound

This paper cites Hydrodiffusion: Diffusion-based probabilistic streamflow forecasting with a state space backbone.arXiv preprint arXiv:2512.12183, 2025.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Hydrodiffusion: Diffusion-based probabilistic streamflow forecasting with a state space backbone.arXiv preprint arXiv:2512.12183, 2025

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:45.929058Z

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Observation 8be8cbf5-51ba-4f4a-8b2c-532ae53b03f1 · outbound

This paper cites Bi- ascast: Learning and adjusting real time biases from meteorological forecasts to enhance runoff predictions.EGUsphere, pages 1–25, 2025.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Bi- ascast: Learning and adjusting real time biases from meteorological forecasts to enhance runoff predictions.EGUsphere, pages 1–25, 2025

Reference 24

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Observation e29cb69b-57fc-4a09-952d-05d027353639 · outbound

This paper cites Shalev and F.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Shalev and F

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:46.114739Z

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Observation 0f2c0fc5-b723-4dd1-a9b9-cb05b507fbd2 · outbound

This paper cites Ryd and G.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Ryd and G

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:46.276622Z

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Observation 4d7198b6-cbce-413e-b065-f2d1d097ac8c · outbound

This paper cites A compre- hensive, multisource database for hydrometeorological modeling of 14,425 north american watersheds.Scientific Data, 7(1):243, 2020.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS A compre- hensive, multisource database for hydrometeorological modeling of 14,425 north american watersheds.Scientific Data, 7(1):243, 2020

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:46.417530Z

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Observation 8471df72-9286-447d-a9f7-16260d60c263 · outbound

This paper cites Newman, Naoki Mizukami, and Martyn P.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Newman, Naoki Mizukami, and Martyn P

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:46.546079Z

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Observation 2e445529-ed11-48a0-8b3f-ef160df48af8 · outbound

This paper cites Färber, H.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Färber, H

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:46.662672Z

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Observation cd2aec3a-8f79-46ef-8bfb-e7d3b9acb7eb · outbound

This paper cites Gupta, Harald Kling, Koray K.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Gupta, Harald Kling, Koray K

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:46.752826Z

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Observation 886afe60-bc48-4143-ba37-278fdc469b3f · outbound

This paper cites Prates, Fréderic Vitart, Peter Bauer, and David Richardson.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Prates, Fréderic Vitart, Peter Bauer, and David Richardson

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:46.845960Z

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Observation 80cef152-5b7c-40b2-a183-60cdba4f729f · outbound

This paper cites Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution.Scientific Data, 6(1):283, 2019.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution.Scientific Data, 6(1):283, 2019

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:46.973911Z

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Observation 25d5d11b-520f-48d5-b1a1-e9802ff36392 · outbound

This paper cites Neuralhydrology — a python library for deep learning research in hydrology.Journal of Open Source Software, 7(71):4050, 2022.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Neuralhydrology — a python library for deep learning research in hydrology.Journal of Open Source Software, 7(71):4050, 2022

Reference 33

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Observation d2e0dbf5-fa18-48ae-b042-f6c6623d1cb1 · outbound

This paper cites Kratzert et al.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Kratzert et al

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:47.243065Z

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Observation f1c952c1-ac58-49b9-a4f2-011708d2444c · outbound

This paper cites Miralles, María Piles, Nemesio J.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Miralles, María Piles, Nemesio J

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:47.306841Z

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Observation aa882138-0ac0-459c-9f1d-67c2a11ae434 · outbound

This paper cites Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios.Journal of Hydrology, 424–425:264– 277, 2012.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios.Journal of Hydrology, 424–425:264– 277, 2012

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:47.444740Z

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Observation 58ee6484-6302-44ad-9787-e41514d977e4 · outbound

This paper cites Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores.Hydrology and Earth System Sciences, 23(10):4323–4331, 2019.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores.Hydrology and Earth System Sciences, 23(10):4323–4331, 2019

Reference 37

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Observation 4e350667-3725-40f6-9efe-728ff410f781 · outbound

This paper cites GloFAS v4 calibration hydrological model per- formance.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS GloFAS v4 calibration hydrological model per- formance

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:47.644991Z

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Observation 2d3af561-0c81-410b-91d8-2fdb3b4bc093 · outbound

This paper cites Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.Transactions of the ASABE, 50(3):885–900, 2007.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.Transactions of the ASABE, 50(3):885–900, 2007

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:47.764024Z

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Observation c7567bf8-2864-4135-8d71-f741cfbb81cd · outbound

This paper cites Barnes, Eve C.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Barnes, Eve C

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:47.837032Z

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Observation a001ea23-defd-4b8a-a02a-ca282158704c · outbound

This paper cites Effectiveness and efficiency of public flood preparedness and emergency response.Hydrology and Earth System Sciences, 21(4):2001–2017, 2017.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Effectiveness and efficiency of public flood preparedness and emergency response.Hydrology and Earth System Sciences, 21(4):2001–2017, 2017

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-02T22:32:47.945270Z

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Unavailable: canonical work link unavailable.

Observation 683496ae-da5e-4633-a109-02b10897ee2b · outbound

This paper cites Global prediction of extreme floods in ungauged watersheds, 2023.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Global prediction of extreme floods in ungauged watersheds, 2023

Reference 42

Resolution: verified exact; doi, observed 2026-08-02T22:34:22.754045Z

Source-reported events for the cited work

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Pith citing papers

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