  Recent advances in data-generating techniques led to an explosive growth of
geo-spatiotemporal data. In domains such as hydrology, ecology, and
transportation, interpreting the complex underlying patterns of spatiotemporal
interactions with the help of deep learning techniques hence becomes the need
of the hour. However, applying deep learning techniques without domain-specific
knowledge tends to provide sub-optimal prediction performance. Secondly,
training such models on large-scale data requires extensive computational
resources. To eliminate these challenges, we present a novel distributed
domain-aware spatiotemporal network that utilizes domain-specific knowledge
with improved model performance. Our network consists of a pixel-contribution
block, a distributed multiheaded multichannel convolutional (CNN) spatial
block, and a recurrent temporal block. We choose flood prediction in hydrology
as a use case to test our proposed method. From our analysis, the network
effectively predicts high peaks in discharge measurements at watershed outlets
with up to 4.1x speedup and increased prediction performance of up to 93\%. Our
approach achieved a 12.6x overall speedup and increased the mean prediction
performance by 16\%. We perform extensive experiments on a dataset of 23
watersheds in a northern state of the U.S. and present our findings.
