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A New Era of Water Forecasting
The American West is entering a period when water can no longer be managed by looking only at yesterday’s river levels or relying on historical averages. Warmer winters, shrinking snowpack, intense atmospheric rivers, prolonged drought, and increasingly unpredictable runoff are changing the way communities must think about water.
In Washington state, that challenge is becoming a real-world test of how satellite technology, artificial intelligence, machine learning, and operational experience can work together. NASA’s Earth-observation data is now being incorporated into machine-learning river forecasts used by Tacoma Power to manage the Cowlitz River during one of the West’s most unusual water years.
The story is bigger than one river or one utility. It demonstrates how information collected hundreds of miles above Earth can influence decisions made inside control rooms, reservoirs, hydroelectric facilities, and emergency-management offices on the ground.
The Cowlitz River Faces a Year of Extremes
The 2025–26 winter brought an unusual combination of weather conditions to the western United States. Much of the region experienced warmer temperatures, meaning a greater percentage of precipitation arrived as rain rather than snow.
That distinction matters enormously.
Snowpack functions like a natural reservoir. Mountain snow accumulates during winter and gradually melts during spring and early summer, releasing water over a much longer period. Rain, by contrast, can move rapidly through watersheds, producing sudden river surges while leaving much less water stored naturally in the mountains for later use.
Across the West, January, February, and March 2026 recorded the lowest monthly Western snow cover in NASA’s MODIS satellite record dating back to 2001.
For Washington’s Cowlitz River, the consequences were particularly dramatic.
One Winter Produced Two Opposite Problems
In December 2025, a powerful atmospheric river carried a concentrated stream of Pacific moisture into the Pacific Northwest. The storm produced one of the largest one-day inflow surges ever recorded at Tacoma Power’s hydroelectric project.
For reservoir operators, however, a huge amount of water arriving quickly is not necessarily good news.
The water came largely as rain rather than being stored as snow at higher elevations. Instead of building a mountain reservoir that could slowly release water through the summer, the storm pushed substantial amounts of water downstream almost immediately.
By the end of the season, snowpack across the watershed remained only around 20% to 50% of normal levels.
The problem then changed.
As winter gave way to spring, rainfall declined and the expected snowmelt reservoir simply was not there.
Washington Declares a Drought Emergency
On April 8, Washington state placed every watershed, including the Cowlitz, under a drought emergency.
From April through June, peak daily inflows into Tacoma Power’s project ranked among the lowest on record. The utility therefore had less incoming water available to replenish its reservoirs before the summer period, when electricity demand can rise sharply.
This created a difficult balancing act.
Operators had to preserve water for future electricity generation while simultaneously maintaining appropriate river flows, supporting aquatic ecosystems, preparing for potential storms, and leaving enough flexibility to respond to rapidly changing conditions.
A wrong decision could have consequences months later.
NASA Data Moves From Space Into Operational Decisions
This is where NASA’s Earth-observation technology becomes particularly important.
NASA satellites collect enormous quantities of information about the planet, including observations of snow cover, vegetation, land conditions, and other environmental variables.
One important source is the VIIRS instrument aboard the Suomi National Polar-orbiting Partnership satellite.
The data can provide a broad view of snow and vegetation conditions across entire watersheds, including remote areas where traditional ground-based monitoring stations are sparse.
That wider perspective becomes especially valuable when conditions change faster than conventional monitoring networks can capture.
Turning Satellite Observations Into AI Forecasts
Upstream Tech’s HydroForecast system combines weather forecasts, river measurements, and NASA satellite-derived information to estimate how much water is likely to move through a watershed.
The system updates its forecasts every two hours.
That speed is critical.
Reservoir managers do not simply need to know what happened yesterday. They need to estimate what may happen over the next several hours, the next day, and eventually across an entire season.
HydroForecast is designed to support that decision-making process by using machine-learning models trained on historical information from hundreds of watersheds.
Machine Learning Learns How Landscapes Move Water
The basic concept is straightforward, although the underlying modeling is highly sophisticated.
The system collects years of satellite observations, weather forecasts, and actual river-flow measurements.
Machine-learning models then search for relationships between environmental conditions and the amount of water eventually appearing in rivers.
Over time, the models learn patterns associated with rainfall, snow accumulation, snowmelt, vegetation conditions, temperature, and watershed behavior.
When the system encounters a new set of conditions, it can use those learned relationships to estimate how the river may respond.
The advantage is not simply speed. It is the ability to combine multiple sources of environmental information into a single forecasting process.
Snow Is Only Part of the Equation
One of the most interesting elements of this approach is the use of vegetation information.
Snowpack is an obvious indicator of future water availability, but landscapes are more complicated than snow alone.
Vegetation conditions can influence how water interacts with the ground. Soil moisture, evaporation, plant growth, temperature, and land conditions all affect the eventual movement of water through a watershed.
According to Upstream Tech, testing across multiple basins showed that incorporating snow and vegetation observations improved forecast skill.
NASA’s satellite data therefore acts as more than a simple snow map. It provides another layer of environmental context that machine-learning systems can use to understand watershed behavior.
Tacoma Power Uses AI Alongside Human Expertise
Importantly, HydroForecast does not replace Tacoma Power’s existing monitoring systems or human operators.
The utility combines the forecasts with stream gauges, snow stations, operational data, and professional judgment.
That human-machine relationship is crucial.
A forecast is a decision-support tool, not an instruction that must automatically be followed.
During the December atmospheric river, the NASA-informed short-term forecasts helped Tacoma Power anticipate how much water could reach the hydroelectric project and prepare for rapidly changing river conditions.
Operators still had to interpret those predictions within the broader requirements of dam operations, public safety, environmental obligations, and power generation.
The Problem Reversed in Spring
By spring, the same forecasting technology was helping with the opposite problem.
Instead of preparing for too much water arriving too quickly, operators were increasingly concerned about too little water arriving later.
Tacoma Power used seasonal forecasting to monitor the growing risk of weak runoff.
As a result, operators kept reservoirs higher than usual to preserve water for the summer.
But that strategy introduced another risk.
A reservoir kept unusually full has less room available to absorb a sudden major storm.
This meant operators could not simply conserve every possible drop. They also had to remain ready for another atmospheric river.
Short-term forecasts therefore remained essential.
The strategy became a constant balancing act: preserve water for a dry future without becoming dangerously unprepared for a sudden wet event.
The Meaning of “Playing Defense”
Tacoma Power described its short-term forecasting strategy during this period as a way to “play defense.”
That phrase captures the new reality of water management.
The utility had to prepare simultaneously for contradictory possibilities.
There could be drought.
There could be flooding.
There could be extreme heat.
There could be another atmospheric river.
There could be increased electricity demand.
There could be environmental requirements that limited how water could be managed.
The best operational strategy was therefore not to predict one perfect future but to maintain enough awareness and flexibility to respond as conditions evolved.
Reservoirs Become Strategic Buffers
Tacoma Power entered summer 2026 with reservoir levels near average despite the difficult spring.
That achievement matters because stored water provides multiple benefits.
It supports hydroelectric generation.
It helps maintain required river flows.
It supports fish and aquatic habitats.
It enables recreational activities.
It gives the utility additional flexibility during heat waves.
And it can provide additional resilience if unexpected power-generation problems occur.
In other words, a reservoir is not merely an energy asset.
It is a strategic buffer against uncertainty.
Why Hydropower Depends on Better Forecasting
Hydroelectric power is often described as renewable energy, but its reliability depends heavily on water availability.
A hydroelectric facility cannot generate electricity from water that is not available.
Climate-driven changes in snowpack and precipitation patterns therefore create a direct challenge for hydropower operators.
Historically, snowpack provided a relatively predictable seasonal storage mechanism.
When precipitation increasingly arrives as rain, that natural storage mechanism becomes less dependable.
The result is a greater need for forecasting.
Better forecasts cannot create water, but they can help operators make smarter decisions with the water that exists.
NASA’s Broader Role in Water Intelligence
Tacoma Power is only one example of NASA Earth science being incorporated into water-management decisions.
NASA has also worked with the U.S. Department of Agriculture’s Natural Resources Conservation Service to incorporate satellite-based snow and groundwater information into machine-learning water-supply forecasts.
The National Oceanic and Atmospheric Administration’s Colorado Basin River Forecast Center uses MODIS and VIIRS observations to help adjust snowmelt rates in forecasting models.
The Bureau of Reclamation also uses NASA and NASA-derived snow information, together with other datasets, to support reservoir operations in California’s San Joaquin Basin.
These efforts reveal a broader transformation.
Satellite observations that once seemed primarily useful for scientific research are increasingly becoming operational infrastructure.
NASA and the U.S. Drought Monitor
NASA’s contribution extends beyond individual rivers and reservoirs.
NASA data and research have long helped inform the U.S. Drought Monitor, the weekly assessment used by farmers, water managers, public agencies, and communities across the country.
In 2026, NASA became a formal partner in producing the Drought Monitor, expanding its role from supplying scientific information to participating directly in the assessment process.
The agency took its first turn authoring the Drought Monitor during the week of August 17.
That development is significant because drought assessment increasingly depends on combining many different types of information.
Ground observations alone cannot provide complete coverage.
Satellite observations alone cannot explain every local condition.
Machine learning alone cannot understand every operational constraint.
The future therefore lies in combining them.
Deep Analysis: How AI-Powered River Forecasting Works
At a technical level, a simplified forecasting pipeline can be represented as:
Satellite observations
↓
Snow + vegetation products
↓
Weather forecasts
↓
River and stream-gauge measurements
↓
Historical watershed datasets
↓
Machine-learning model
↓
Short-term + seasonal forecasts
↓
Reservoir operations
↓
Human decision-making
The important concept is data fusion.
A machine-learning system can combine multiple environmental variables rather than depending on one measurement.
A simplified conceptual model might look like:
features = [ snow_cover, vegetation_index, temperature, precipitation, soil_conditions, river_flow, weather_forecast ]
forecast = model.predict(features)
if forecast.inflow > flood_threshold: prepare_for_high_inflow()
elif forecast.inflow < drought_threshold: conserve_reservoir_water()
else: maintain_normal_operations()
Real operational forecasting systems are considerably more sophisticated than this example, but the principle remains similar: environmental observations are converted into predictive signals that help humans make decisions.
Building a Forecasting Pipeline
A simplified data-processing workflow could also resemble:
Retrieve environmental datasets
download satellite_data
Validate incoming observations
validate satellite_data
Combine satellite and hydrological measurements
merge snow_data river_gauge weather_data
Prepare machine-learning features
python prepare_features.py
Generate a forecast
python run_forecast.py
Validate forecast quality
python evaluate_forecast.py
In production environments, however, reliability is just as important as model accuracy.
If satellite data stops arriving, weather feeds fail, river gauges malfunction, or an automated processing pipeline breaks, the forecasting system can become less useful at exactly the moment operators need it most.
That is why operational machine learning requires redundancy, monitoring, validation, and fallback systems.
Why Data Reliability Matters as Much as AI Accuracy
One of the most revealing details from the project is the importance placed on uninterrupted access to NASA data.
A highly accurate model is not particularly useful if its inputs arrive late.
Operational forecasting requires fresh observations at predictable intervals.
The system needs to know what conditions look like now before it can estimate what may happen next.
This creates a new cybersecurity and reliability challenge.
As environmental forecasting becomes increasingly digital, data pipelines become critical infrastructure.
A failure in a satellite-data ingestion system, cloud platform, API, weather feed, or machine-learning service could affect downstream decision-making.
AI Is Becoming Part of Physical Infrastructure
This story also demonstrates a broader shift in artificial intelligence.
AI is no longer limited to chatbots, recommendation engines, coding assistants, and office software.
Machine-learning systems are increasingly being integrated into physical infrastructure.
They can influence how electricity is generated, how reservoirs are operated, how droughts are assessed, how emergency resources are allocated, and how communities prepare for extreme weather.
That makes AI reliability a public-interest issue.
When an AI forecast influences a reservoir decision, errors are no longer merely inconvenient.
They can have environmental, economic, and safety consequences.
The Importance of Human Oversight
For that reason, human judgment remains essential.
Forecasting systems can identify patterns that humans may miss, but operators understand local conditions, regulatory requirements, infrastructure limitations, and unusual circumstances that may not be represented perfectly in historical training data.
The strongest model is therefore not necessarily the one that eliminates humans.
It is the one that gives experienced humans better information at the right moment.
This is an important lesson for AI deployment across critical infrastructure.
Climate Change Makes Historical Data Less Reliable
Another major issue is that climate conditions are changing the assumptions behind historical datasets.
Machine-learning models learn from the past.
But what happens when the future increasingly behaves differently from the past?
A watershed that historically accumulated predictable snowpack may now experience warmer winters and more rainfall.
A river that normally reaches its highest levels during a particular period may experience increasingly irregular surges.
A model trained exclusively on historical patterns could struggle when conditions move outside the range represented in its training data.
That is why satellite observations and continuously updated datasets are so important.
AI Needs Continuous Adaptation
Future forecasting systems will likely need to become increasingly adaptive.
Instead of treating the environment as a static system, models will need to account for changing climate conditions, evolving land use, infrastructure modifications, and shifting precipitation patterns.
That does not mean simply retraining a model more often.
It means continuously evaluating whether the assumptions underlying the model remain valid.
Forecast accuracy should be monitored across different weather regimes.
Models should be tested against rare events.
Operators should know when confidence is low.
And automated systems should be designed to fail safely rather than silently producing unreliable predictions.
The Cybersecurity Dimension
There is also a cybersecurity lesson hiding beneath this environmental story.
Water-management systems are critical infrastructure.
If satellite feeds, forecasting platforms, operational technology, cloud environments, or communications systems become compromised, the consequences could extend beyond lost data.
Organizations deploying AI into water and energy infrastructure should therefore treat forecasting platforms as part of their security architecture.
That includes strict access controls, strong authentication, network segmentation, encrypted communications, secure APIs, logging, anomaly detection, backup data sources, and tested recovery procedures.
A useful operational principle is:
Monitor forecast data pipelines
monitor satellite_ingestion
monitor weather_api
monitor river_gauges
monitor model_outputs
Validate unexpected changes
if anomaly_detected; then trigger_manual_review fi
Maintain fallback capabilities
if data_source_unavailable; then switch_to_backup_source fi
The exact implementation will vary, but the philosophy is universal: critical AI systems need graceful degradation.
The Future of Water Management May Be Predictive
Traditional water management often focused on measuring what had already happened.
Modern systems are increasingly focused on estimating what is about to happen.
That is a profound shift.
Instead of simply observing river levels, operators can analyze the conditions likely to produce future inflows.
Instead of waiting for drought impacts to become obvious, agencies can combine satellite observations and predictive models to identify emerging risks.
Instead of treating each storm as an isolated event, machine-learning systems can compare current conditions with patterns observed across hundreds of watersheds.
The result is a move from reactive management toward predictive resilience.
What Undercode Say:
1. Satellite Data Is Becoming Infrastructure
NASA’s Earth-observation data is no longer confined to scientific research.
It is becoming part of operational decision-making.
2. AI Cannot Create Water
Machine learning does not solve drought directly.
Its value comes from helping humans use limited water more intelligently.
3. Snowpack Is a Critical Natural Reservoir
When winter precipitation arrives as rain instead of snow, the entire seasonal water cycle changes.
4. Warmer Winters Create Forecasting Challenges
Less snow means less predictable summer runoff.
That makes accurate forecasting increasingly important.
5. Extreme Weather Creates Opposite Risks
A single winter can produce destructive flooding followed by serious drought.
Water managers must therefore prepare for both extremes.
6. Reservoir Management Is a Balancing Act
Keeping reservoirs full protects against drought.
Keeping them too full can reduce protection against major storms.
7. Forecast Timing Matters
A forecast that arrives too late can be nearly as problematic as an inaccurate forecast.
Operational systems need information continuously.
8. NASA Provides Valuable Global Coverage
Satellites can observe areas where ground sensors are limited or unavailable.
That wider geographic coverage can improve situational awareness.
9. Vegetation Data Adds Another Layer
Snow is not the only variable controlling watershed behavior.
Vegetation conditions can provide additional information about how landscapes process water.
10. Machine Learning Finds Complex Relationships
Traditional models depend heavily on predefined relationships.
Machine learning can identify patterns across large historical datasets.
11. Human Operators Still Matter
AI should support experts rather than blindly replace them.
The Cowlitz example demonstrates the value of combining automated forecasts with operational judgment.
12. Critical Infrastructure Needs Explainable Decisions
When AI influences water or power operations, users need confidence in the system’s outputs.
Knowing when a model is uncertain can be as important as knowing its prediction.
- AI Reliability Is Becoming a Public-Safety Issue
A failed recommendation system is inconvenient.
A failed infrastructure forecasting system can become dangerous.
14. Data Pipelines Are New Critical Assets
Satellite feeds, weather APIs, river gauges, databases, and cloud services all contribute to the final forecast.
Each dependency can become a potential failure point.
15. Cybersecurity Must Follow AI Into Infrastructure
As AI becomes embedded in water and energy systems, security teams must protect both the model and the data feeding it.
16. Backup Systems Are Essential
No single satellite, API, model, or sensor should become an irreplaceable dependency.
17. Climate Change Challenges Historical Assumptions
A model can only learn from the data available to it.
If the climate changes significantly, historical patterns may become less reliable.
18. Continuous Validation Is Necessary
Forecasting systems should be tested against new conditions instead of assuming yesterday’s accuracy will continue forever.
19. Rare Events Matter
Atmospheric rivers and severe droughts can create conditions that appear infrequently in historical records.
Those events deserve specific testing.
20. Forecast Confidence Should Be Communicated
Decision-makers need more than a single number.
They need to understand uncertainty and potential ranges.
21. Better Forecasts Can Improve Energy Resilience
More accurate water predictions can help hydropower operators plan generation more effectively.
22. Water and Electricity Are Closely Connected
Changes in precipitation can ultimately influence electricity availability and regional power planning.
23. Reservoirs Provide Flexibility
Stored water can act as a buffer against both supply shortages and unexpected operational problems.
- Environmental Needs Must Remain Part of the Equation
Water management is not only about electricity production.
Fish, aquatic ecosystems, recreation, agriculture, and communities also depend on predictable water supplies.
25. Government Agencies Are Increasingly Collaborating
NASA, NOAA, USDA, the Bureau of Reclamation, utilities, and technology companies are contributing different pieces of the forecasting ecosystem.
26. Public Data Can Produce Private Innovation
NASA’s freely available observations can become inputs for commercial forecasting technologies.
That creates value beyond the original scientific mission.
27.
People may never know that a machine-learning model helped determine how a reservoir was operated.
That does not make the technology less important.
- The Best AI May Be Quiet AI
Infrastructure AI does not need to produce flashy demonstrations.
It needs to work reliably, continuously, and safely.
29. Two-Hour Updates Are Operationally Significant
Frequent forecasting allows operators to respond to rapidly changing weather and river conditions.
30. Prediction Enables Better Preparation
The earlier operators understand changing conditions, the more options they have.
- More Data Does Not Automatically Mean Better Decisions
Data must be timely, accurate, compatible, and properly interpreted.
32. Model Governance Will Become More Important
Organizations need clear procedures for validating, monitoring, and overriding AI-generated forecasts.
- AI Should Have a Safe Failure Mode
When data becomes unreliable, systems should alert humans rather than confidently producing questionable outputs.
34. Climate Adaptation Will Require Digital Tools
As environmental conditions become more volatile, technology can help institutions adapt faster.
35. Satellite Networks Will Become More Valuable
The ability to continuously observe large geographic areas will become increasingly important for water planning.
36. Forecasting Could Become More Local
As models improve, increasingly detailed watershed-level predictions may become possible.
37. AI Could Improve Regional Coordination
Better forecasts could eventually help multiple utilities and agencies coordinate water and energy resources more efficiently.
- The Cowlitz Is a Preview of a Larger Transformation
What is happening in Washington could become increasingly common across drought-prone regions.
39. Water Security Is Becoming Data Security
Protecting water resources increasingly means protecting the digital systems used to understand them.
40. The Biggest Lesson Is Resilience
The real achievement is not predicting the future perfectly.
It is giving decision-makers enough information to remain flexible when the future refuses to behave predictably.
✅ NASA Earth Data Is Being Used in Water Forecasting
The article accurately describes NASA satellite-derived information being incorporated into machine-learning water forecasting systems.
The data includes observations related to snow cover and vegetation conditions.
These datasets can provide valuable environmental information across large watersheds.
✅ The 2025–26 Western Snow Season Was Exceptionally Weak
The article states that January, February, and March recorded the lowest Western snow cover for those months in the NASA MODIS record since 2001.
This supports the central argument that the region entered 2026 with unusually limited snow-based water storage.
The combination of warmth and precipitation arriving as rain increased the pressure on downstream water systems.
✅ Tacoma Power Uses Forecasting for Reservoir Operations
The Cowlitz Hydro Project relies on forecasting alongside traditional monitoring systems and operator judgment.
The technology is used to anticipate inflows and support operational planning.
The article does not claim that AI independently controls the dams, which is an important distinction.
✅ Satellite Data Does Not Replace Ground Measurements
NASA observations provide broad geographic coverage, but Tacoma Power continues to use stream gauges, snow stations, and professional expertise.
That layered approach is more resilient than relying on a single information source.
It also reduces the risk that one sensor or dataset becomes the sole basis for a critical operational decision.
❌ AI Alone Cannot Solve the Western Water Crisis
Machine learning can improve prediction and planning, but it cannot manufacture precipitation or restore depleted snowpack.
The underlying water shortage remains an environmental and climate challenge.
AI is best understood as a force multiplier for better decisions rather than a replacement for water conservation or climate adaptation.
Prediction
(+1) AI Will Become a Standard Layer of Water Management
The most likely future is not that AI replaces hydrologists, reservoir operators, or government agencies.
Instead, machine learning will increasingly become another layer of operational intelligence.
(+1) Satellite Forecasting Will Expand
As satellite observations become more frequent and models become more capable, utilities will have access to increasingly detailed information about snow, vegetation, soil, precipitation, and runoff.
This could improve forecasting across regions where conventional ground monitoring is limited.
(+1) Extreme Weather Will Accelerate Adoption
More volatile weather creates a stronger economic incentive to predict water conditions earlier.
Utilities and governments have a practical reason to invest in better forecasting: uncertainty is expensive.
(+1) AI Will Connect Water and Energy Planning
As hydropower becomes more sensitive to changing precipitation patterns, water forecasting and electricity planning will become increasingly interconnected.
Future energy-management systems may use the same environmental forecasts to anticipate both water availability and power-generation capacity.
(+1) Cybersecurity Will Become a Core Requirement
As these systems become more important, security will move from an afterthought to a fundamental requirement.
Organizations will increasingly need protected data pipelines, redundant feeds, authenticated APIs, continuous monitoring, and recovery plans for AI-driven infrastructure.
(+1) The Winning Model Will Be Human + AI
The strongest operational systems will combine machine speed with human experience.
AI can process enormous amounts of data rapidly, while experienced operators can recognize unusual circumstances and make decisions when conditions fall outside the model’s expectations.
(+1) Water Forecasting Could Become More Predictive Than Reactive
The broader trend is clear: instead of waiting for drought or flooding to become obvious, institutions will increasingly try to identify the risk earlier.
That shift could give communities more time to conserve water, adjust reservoir levels, prepare infrastructure, protect ecosystems, and manage electricity demand.
(+1) NASA’s Open Data Will Continue Creating Unexpected Value
The Cowlitz example shows how public scientific data can become part of commercial technology and critical infrastructure.
As AI systems become more data-hungry, high-quality government datasets could become increasingly valuable building blocks for practical applications.
(+1) The Future of Water Management Will Be Built Around Uncertainty
The goal will not be perfect prediction.
It will be resilience.
The most valuable systems will help decision-makers understand what is likely to happen, what could happen instead, and how much flexibility remains to respond.
And as the American West faces increasingly complicated water conditions, that ability may become just as important as the water itself.
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References:
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