arXiv:2606.02791v1 Announce Type: new Abstract: Watershed networks exhibit convergent topologies in which multiple tributaries merge into downstream channels,integrating diverse upstream hydrological processes. In ungauged basins, the absence of direct observations increases uncertainty and limits the ability to anticipate extreme events. This study evaluates whether an encoder-only Transformer provides an advantage over an LSTM for upstream streamflow inference under limited hydrologic information, using retrospective simulations from the NOAA National Water Model (NWM). Across both upstream-
The increasing availability of advanced AI models like Transformers and LSTMs, coupled with growing climate data and hydrological stress, makes their application to water management particularly timely.
Improving streamflow prediction in ungauged basins is critical for anticipating extreme hydrological events, which directly impacts disaster preparedness, resource allocation, and water security.
The explicit evaluation of Transformer models against LSTMs suggests a potential shift towards more sophisticated AI architectures for critical environmental forecasting, offering improved accuracy in data-scarce regions.
- · Water management agencies
- · AI model developers
- · Regions prone to water-related disasters
- · Hydrology researchers
- · Traditional hydrological modeling methods
More accurate streamflow predictions enhance early warning systems for floods and droughts.
Improved water resource planning enables better agricultural yield management and urban water supply resilience.
Reduced economic losses from extreme weather events and increased food security in vulnerable regions could result from better predictions.
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Read at arXiv cs.AI