Lynker worked with Denver Water, the University of Colorado and NCAR/UCAR staff to review a range of standard methods for characterizing future extreme precipitation and temperature events under climate change. The methods reviewed include downscaling projects like LOCA, MACA, BCSD and the CORDEX archive as well as point downscaling statistical approaches. This work involved both an extensive literature review covering the foundations of the methods as well as independent assessments of results from each method. Once the literature review was complete, Lynker performed detailed statistical analyses to help Denver Water understand increases in frequency and intensity of extreme rainfall and heat events at a large set of locations.
Solution specific articles for Water & Environmental Resources.
A climate-change impact analysis was completed for a confidential client in California. The purpose of the study was to provide climate-impacted hydrology (streamflow, irrigation water demand) for a future planning horizon. Our team provided a low-cost solution by utilizing existing Bureau of Reclamation (USBR) Coupled Model Intercomparison Project (CMIP5) climate change runs processed through the Variable Infiltration Capacity (VIC) hydrologic model. The future climate-impacted hydrology from 2020-2049 (streamflow, evapotranspiration) was compared to a baseline hydrology from 1970-1999, to determine a monthly set of “change factors” for water supply and water demand (streamflow and irrigation water demand, respectively). The change factors were applied to the historical hydrology datasets to create climate-adjusted timeseries for streamflow and water demand, which were used to evaluate future water supply conditions.
The Eastern Snake Plain region of Idaho produces approximately 21 percent of the state’s goods and services, resulting in an estimated value of $10 billion annually, and water is the critical element in the region’s productivity. The development of both groundwater and surface-water water use across the Eastern Snake Plain has led to conjunctive management of the common water resource. In an on-going contract with the Idaho Ground Water Appropriators (IGWA), Lynker provides oversight on both development of a regional groundwater model of the Eastern Snake Plain Aquifer (ESPAM) and its application in water resource management. ESPAM is a groundwater model developed by the State of Idaho as a tool to help quantify the impacts of current water use practices and/or proposed alternatives on the common water resource of the State across the Eastern Snake Plain.
Over the course of the multi-year, multi-task relationship, Lynker has provided the following services to the groundwater appropriators: water rights and expert witness support, groundwater modeling, conjunctive used analysis, consumptive use modeling, development and review of mitigation plans, and hydrologic baseline and historical water use analysis.
Under contract with Mount Werner Water & Sanitation, Lynker was tasked with hydrologic and hydrogeologic data review and compilation for model inputs and framework development of a Yampa River alluvial aquifer MODFLOW model. Analysis included detailing the geographic and climatic setting and quantifying surface water groundwater interactions based on observed surface water and groundwater trends, aquifer testing of alluvial properties, and groundwater modeling. Modeling analysis included discretizing Yampa river channel geometry, developing streamflow records for model calibration, developing an annual water budget for the system, defining aquifer geometry and properties, and quantifying streambed conductance and associated accretions and depletions to the Yampa River and other surface water features.
The groundwater modeling analysis was used to assess present and potential future production from the alluvial aquifer infiltration galleries, quantify associated impacts to surface water features, and defining a capture zone to protect against water contamination concerns.
United Utilities in the United Kingdom requested cutting-edge water resources modeling to more fully understand its water supply, especially as it relates to improving drought resilience. Lynker Technologies deployed a two-part stochastic simulation technique to model both the inter-annual flow sequences using a non-homogeneous Markov Chain (NHMC) model and the intra-annual variability using a k-nearest neighbor (KNN) nonparametric method. The flow simulation was performed simultaneously at four gauge locations so that the water system could be analyzed together as it is operated by the client.
The final output was 5,000 years of simulated streamflow at a daily timestep, which preserved spatial correlation between the sites, decadal variability of the streamflow, and seasonal trends while using empirically-based methods. The simulated streamflow data greatly expanded upon the 50-year observed record, which provides improved estimates of for instance the 100-year drought recurrence interval.
In an effort to provide the public with relevant information during the DEIS public comment period, Lynker undertook a modeling study to estimate the downstream impacts of a Tailings Storage Facility failure, if it were to occur.
Lynker used publicly available data describing the physiography and hydrology of the region, and data published by PLP describing the proposed TSF design and other mine site characteristics, to build a model of tailings release and downstream transport. Lynker developed our model using the FLO-2D software package, one of the few flood modeling packages capable of simulating the non-Newtonian flows that characterize tailings failures, and one that is commonly utilized by the mining industry for similar purposes (e.g., Knight Piesold, 2014; TetraTech, 2015).
Lynker used a comprehensive sensitivity analysis to evaluate how outcomes vary with different model parameters, and developed a set of failure scenarios to bracket the range of potential downstream impacts for different release volumes and durations. Our results provide regulators and the public with information that will be valuable during the public comment period for the DEIS.
Lynker assisted the NOAA National Weather Service (NWS) North Central River Forecast Center (NCRFC) develop an enhanced Runoff Risk Guidance capability using the NWS Distributed Hydrologic Model. This project developed and implemented a multi-agency collaborative Runoff Risk Advisory Forecast (RRAF) tool for manure producers and nutrient applicators in multiple states that provides a dynamic, real-time model-based decision support tool for farmers and land managers to help with short-term application decisions.
Farmers often apply manure and nutrients to fields at times dictated as much by factors such as farming schedules and manure storage capacity than simply by when the plants need them. This often leads to poorly timed applications. Application prior to a rainfall-runoff event might mobilize nutrients to the hillslope and channel and away from where they are needed, resulting in detrimental impacts on water quality such as algal blooms and hypoxia, and at spatial scales from the contamination of local drinking wells to fish kills in the Great Lakes and Gulf of Mexico. Getting the timing right for application of nutrients is key in this regard. Specifically, it is critical to not apply nutrients before heavy rain or anticipated snowmelt which could carry nutrients right off the fields and into nearby water bodies.
Lynker helped develop and then enhance the RRAF by integrating, testing and validating the higher resolution distributed hydrologic model, thus enabling assessments of risk to be conducted at a finer spatial scale. Lynker also worked on RRAF stakeholder outreach and training. The project has helped NWS meet its Weather-Ready Nation mission of improving and expanding decision support services to a wide array of decision-makers and leveraging its capabilities into new areas such as water quality across the entire hydrologic spectrum ranging from local streams to oceans.
Reservoir operation is a complex human-controlled activity that substantially alters surface hydrology. A reservoir operator needs to account for reservoir gains and losses, and other upstream and downstream conditions, in order to make decisions about daily releases that meet operating targets. The National Water Model (NWM) version 2.0 is anticipated to include representation of approximately 5,300 reservoirs. By utilizing a levelpool routing, the current NWM does not represent complex reservoir dynamics, which negatively affects the accuracy of the model’s simulations and forecasts.
Lynker scientists at the NWC are utilizing machine learning techniques based on historical records of release decisions to improve the NWM’s operational forecasting skill. Deep neural networks (i.e., feed-forward backpropagation with three hidden layers and a considerable number of neurons) have been utilized to train the NWM to think like a reservoir operator. K-fold cross-validation and dropout techniques were considered to train generalized models and avoid the overfitting problem, which is commonly ignored in the application of ML techniques in hydrology and water resources fields. There is no need for rule curves to train the model and the proposed technique has the potential to scale well to the national level. Application of the developed models for two study basins, Apalachicola-Chattahoochee-Flint (ACF) and Colorado Headwaters, have shown promising results by mainly utilizing observed previous day release, reservoir inflow, previous day storage and day of the year as inputs to the model.
Lynker leads the Geospatial Intelligence Division (GID) at NOAA’s Office of Water Prediction (OWP) National Water Center (NWC). GID is responsible for taking outputs from the new NOAA National Water Model (NWM) and visualizing these data to make them more useful to stakeholders, including NWS River Forecast Center (RFC) staff at regional forecast offices and Emergency Managers (EMs) on the ground. Both need to rapidly understand and use NWM outputs to save lives, so effective visualization of the NWM data is critical. The Lynker team also develops NWM data visualizations for water managers who are more interested in longer range (e.g. seasonal) water flow forecasts for water supply and planning, and drought monitoring.
GID has developed an automated real-time system to acquire, post-process, stylize, and publish NWM output data in real-time. Cycling hourly and providing streamflow and land surface conditions forecasts at over 2.7 million locations CONUS-wide, the National Water Model produces billions of data points per day. Because users are unable to efficiently make use of this massive hourly data output without the aid of data post processing and enhanced map viewing, significant development was required for not only the automatic detection of important hydrologic activity across the country, but the visualization of these phenomena across the millions of forecasting locations. Through this project, the Lynker team has developed a variety of data post-processing and optimization techniques for the purpose of visualization. Dr. Graeme Aggett leads the GID team, and Brad Bates continues to be a key technologist supporting this complex data visualization effort.
NOAA’s new National Water Model (NWM) has allowed the National Weather Service (NWS) to expand its hydrologic prediction capabilities from ~3,600 river forecast points to over 2.7 million stream reaches, reaching many previously underserved locations. However, deriving actionable intelligence from the NWM is challenging because it produces hundreds of gigabytes of data each day. NWS forecasters need to be able to quickly analyze NWM data before issuing streamflow guidance, thus methods are needed to synthesize and present NWM data in real-time, in such a way that it can easily aid in the decision-support process. Lynker scientists at the NOAA/NWS National Water Center, located in Tuscaloosa, Alabama, have played a vital role in developing a series of real-time data services that process and visualize NWM output in such a way that it can be used by the NWS’s stakeholders to make quick, informed hydrologic decisions.
Over the past year and half, Lynker has worked with the National Water Center (NWC) to set-up an Enterprise Geographic Information System (EGIS) that has the capability to host dynamic web data services, maps, and applications. Further, our team at the NWC has implemented a workflow to: intercept the latest NWM data as it becomes available, post-process this data, and ultimately to create dynamic, interactive web-based GIS data services from post-processed NWM data. These data services provide a diverse array of hydrologic information, for both current and forecasted conditions, about: high flow and floods, low flow and droughts, seasonal anomalies, soil moisture levels, the rate of change in streamflow conditions, and the timing and probability of extreme streamflow conditions.
Lynker staff have provided a number of services to the State of Wyoming resulting in a deep understanding of the hydrologic and institutional factors that govern water resources on the North Platte River. Most of these services provided technical support in the Nebraska v. Wyoming litigation and included development of a water allocation and reservoir operations model of the North Platte River, development of an accounting tool to track balances of accounts in the federal reservoirs, development of a database of historical diversions and operations, and statistical analyses of streamflows on the North Platte River and its tributaries.
Since settlement of the litigation, the extensive knowledge of the river gained by Lynker staff continues to be applied by the Wyoming Board of Control through the North Platte River Accounting Program that Lynker staff developed and maintains and upgrades upon request. For that program, Lynker staff:
1) Developed a custom GUI interface for calculating and reporting natural flow and storage deliveries on the North Platte River between Wyoming and Nebraska irrigators,
2) Designed an application to collect and record river and ditch flow gauging stations and import those readings into a database,
3) Automated flow calculations from USGS gage rating tables and gage heights, and
4) Developed an archiving system to ensure that results are reproducible long into the future.