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NextGen In A Box
Expands Real-World Impact Through Cutting-Edge Hydrology Modeling
by McKenzie Knight
he Cooperative Institute for Research to
Operations in Hydrology is helping revolutionize
water modeling with NextGen In A Box,
a containerized, open-source tool that is making
powerful hydrological simulation
more accessible than ever.
Until recently, configuring localized
water models using the Next Generation
Water Resource Modeling framework
required specialized knowledge and
time-intensive setup. NGIAB changes
that by streamlining the process into
a 30-minute, ready-to-run package.
CIROH researchers Yalan Song,
Arpita Patel, Leo Lonzarich and James
Halgren outlined how NGIAB supported
the development and deployment of
HBV2.0, a high-resolution hydrologic
model evaluated across 4,000 sites in
the United States. The team described
NGIAB as key to making this advanced
modeling framework consistently usable
in application, helping guarantee
accuracy and reliability across platforms.
“By providing full control over model inputs,
configurations and execution, NGIAB enables
seamless deployment across personal laptop, cloud
services and HPC environments,” said Patel, DevOps
manager and enterprise architect for CIROH. “This
solution empowers consortium members, partners and
researchers to quickly set up and run simulations to
drive innovation and push the boundaries of the field.”
At The University of Alabama, NGIAB supports a
wide range of hydrologic research across the College
of Engineering. Engineering doctoral student Savalan
Naser Neisary is using the tool to explore droughtrelated patterns.
“The main goal of my research is to operationalize
the National Water Model and NextGen framework
by connecting them to water supply forecasting and
long-term water planning under drought conditions,”
Neisary said. “I used the NGIAB and its tools, such
as calibration and preprocessing, to investigate how
the NWM and its future version, NextGen, perform
in the western United States with extensive water
infrastructure under drought and low flow conditions,
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and how artificial intelligence can improve their
performance.”
Neisary’s research is mainly focused on the
Great Salt Lake basin because the Great Salt Lake is
suffering from drought and water regulation, resulting
in a significant decline in lake elevation.
“I used NGIAB’s preprocessing tools, developed by
Josh Cunningham, to prepare input datasets, such as
precipitation, temperature, NWM retrospective data
and watershed boundaries, as input for the PP-ML
framework,” Neisary said. “I also used a calibration
tool to calibrate NGIAB parameters and simulate the
streamflow. The next steps will be to extend the study
area to the Colorado River basin and conduct a similar
analysis in Alabama with a focus on flood prediction
and flood mapping using NGIAB predictions.”
Looking ahead, CIROH plans to further expand
NGIAB’s capabilities. Upcoming enhancements will
include new visualization tools, streamlined validation
protocols and deeper integration with existing
hydrological databases. These improvements aim to
keep NGIAB at the center of next-generation water
prediction systems.
