An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting.

Microalgae have received increasing attention as a potential feedstock for biofuel or biobased products. Forecasting the microalgae growth is beneficial for managers in planning pond operations and harvesting decisions. This study proposed a biomass forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2), ensemble data assimilation (DA), and numerical weather prediction Global Ensemble Forecast System (GEFS) ensemble meteorological forecasts. The novelty of this study is to seek the use of ensemble DA to improve both BGM and MASS2 model initial conditions with the assimilation of biomass and water temperature measurements and consequently improve short-term biomass forecasting skills. This study introduces the theory behind the proposed integrated biomass forecasting system, with an application undertaken in pseudo-real-time in three outdoor ponds cultured with Chlorella sorokiniana in Delhi, California, United States. Results from all three case studies demonstrate that the biomass forecasting system improved the short-term (i.e., 7-day) biomass forecasting skills by about 60% on average, comparing to forecasts without using the ensemble DA method. Given the satisfactory performances achieved in this study, it is probable that the integrated BGM-MASS2-DA forecasting system can be used operationally to inform managers in making pond operation and harvesting planning decisions.

Real-time ensemble microalgae growth forecasting with data assimilation.

Accurate short-range (e.g., 7 days) microalgae growth forecasts will be beneficial for both the production and harvesting of microalgae. This study developed an operational microalgae growth forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2) hydrodynamic model, and ensemble data assimilation (DA). The novelty of this study is the use of ensemble DA to sequentially update the BGM model's initial condition (IC) with the assimilation of measured biomass optical density to improve short-range biomass forecasting skills. The forecasting system was run in pseudo-real-time and validated against observed Monoraphidium minutum 26B-AM growth in two outdoor pond cultures located in Mesa, Arizona, United States. We found the DA forecasting system could improve the 7-day microalgae forecasting skill by about 85% on average compared to model forecasts without DA. These results suggest the potential accuracy of biomass growth forecasts may be sufficient to inform real-time operational decisions, such as pond operation and harvest planning, for commercial-scale microalgae production.

Balancing Water Sustainability and Productivity Objectives in Microalgae Cultivation: Siting Open Ponds by Considering Seasonal Water-Stress Impact Using AWARE-US.

Microalgae have great potential as an energy and feed resource. Here we evaluate the water use associated with freshwater algae cultivation and find it is possible to scale U.S. algae biofuel production to 20.8 billion liters of renewable diesel annually without significant water-stress impact. Among potential sites, water-stress is significantly more variable than algae productivity across location and season. Thus, it is possible to reduce water-stress impact, quantified as water scarcity footprint, through the choice of algae site location. We test three site-selection criteria based on (1) biomass productivity, (2) water-use efficiency, and (3) water-stress impact and find that adding water-stress constraints to productivity-based ranking of suitable sites reduces water-stress impact by 97% and water consumption by half, compared with biomass-productivity ranking alone, with little productivity impact (<1.7% per-site on average). With 20.8 billion liters, algae could meet 19.7% of U.S. jet fuel demand with a freshwater demand of less than 1.4% of U.S. irrigation consumption. Evaluating water-stress impact is important because the impact of unit water consumption on water stress varies significantly across regions and seasons. Considering seasonal water balances allows producers to understand the combined seasonal effects of hydrologic flows and productivity, thereby avoiding potential short-term water stress.

Siting algae cultivation facilities for biofuel production in the United States: trade-offs between growth rate, site constructability, water availability, and infrastructure.

Locating sites for new algae cultivation facilities is a complex task. The climate must support high growth rates, and cultivation ponds require appropriate land and water resources, as well as transportation and utility infrastructure. We employ our spatiotemporal Biomass Assessment Tool (BAT) to select promising locations based on the open-pond cultivation of Arthrospira sp. and strains of the order Sphaeropleales. A total of 64,000 sites across the southern United States were evaluated. We progressively applied screening criteria and tracked their impact on the number of potential sites, geographic location, and biomass productivity. Both strains demonstrated maximum productivity along the Gulf of Mexico coast, with the highest values on the Florida peninsula. In contrast, sites meeting all selection criteria for Arthrospira were located along the southern coast of Texas and for Sphaeropleales were located in Louisiana and southern Arkansas. Results were driven mainly by the lack of oil pipeline access in Florida and elevated groundwater salinity in southern Texas. The requirement for low-salinity freshwater (<400 mg L(-1)) constrained Sphaeropleales locations; siting flexibility is greater for salt-tolerant species like Arthrospira. Combined siting factors can result in significant departures from regions of maximum productivity but are within the expected range of site-specific process improvements.

Integrated evaluation of cost, emissions, and resource potential for algal biofuels at the national scale.

Costs, emissions, and resource availability were modeled for the production of 5 billion gallons yr(-1) (5 BGY) of renewable diesel in the United States from Chlorella biomass by hydrothermal liquefaction (HTL). The HTL model utilized data from a continuous 1-L reactor including catalytic hydrothermal gasification of the aqueous phase, and catalytic hydrotreatment of the HTL oil. A biophysical algae growth model coupled with weather and pond simulations predicted biomass productivity from experimental growth parameters, allowing site-by-site and temporal prediction of biomass production. The 5 BGY scale required geographically and climatically distributed sites. Even though screening down to 5 BGY significantly reduced spatial and temporal variability, site-to-site, season-to-season, and interannual variations in productivity affected economic and environmental performance. Performance metrics based on annual average or peak productivity were inadequate; temporally and spatially explicit computations allowed more rigorous analysis of these dynamic systems. For example, 3-season operation with a winter shutdown was favored to avoid high greenhouse gas emissions, but economic performance was harmed by underutilized equipment during slow-growth periods. Thus, analysis of algal biofuel pathways must combine spatiotemporal resource assessment, economic analysis, and environmental analysis integrated over many sites when assessing national scale performance.

A GIS cost model to assess the availability of freshwater, seawater, and saline groundwater for algal biofuel production in the United States.

A key advantage of using microalgae for biofuel production is the ability of some algal strains to thrive in waters unsuitable for conventional crop irrigation such as saline groundwater or seawater. Nonetheless, the availability of sustainable water supplies will provide significant challenges for scale-up and development of algal biofuels. We conduct a partial techno-economic assessment based on the availability of freshwater, saline groundwater, and seawater for use in open pond algae cultivation systems. We explore water issues through GIS-based models of algae biofuel production, freshwater supply (constrained to less than 5% of mean annual flow per watershed) and costs, and cost-distance models for supplying seawater and saline groundwater. We estimate that, combined, these resources can support 9.46 × 10(7) m(3) yr(-1) (25 billion gallons yr(-1)) of renewable biodiesel production in the coterminous United States. Achievement of larger targets requires the utilization of less water efficient sites and relatively expensive saline waters. Despite the addition of freshwater supply constraints and saline water resources, the geographic conclusions are similar to our previous results. Freshwater availability and saline water delivery costs are most favorable for the coast of the Gulf of Mexico and Florida peninsula, where evaporation relative to precipitation is moderate. As a whole, the barren and scrub lands of the southwestern U.S. have limited freshwater supplies, and large net evaporation rates greatly increase the cost of saline alternatives due to the added makeup water required to maintain pond salinity. However, this and similar analyses are particularly sensitive to knowledge gaps in algae growth/lipid production performance and the proportion of freshwater resources available, key topics for future investigation.

Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States.

The current methods for designing hydrological infrastructure rely on precipitation-based intensity-duration-frequency curves. However, they cannot accurately predict flooding caused by snowmelt or rain-on-snow events, potentially leading to underdesigned infrastructure and property damage. To address these issues, next-generation intensity-duration-frequency (NG-IDF) curves have been developed for the open condition, characterizing water available for runoff from rainfall, snowmelt, and rain-on-snow. However, they lack consideration of land use land cover (LULC) factors, which can significantly affect runoff processes. We address this limitation by expanding open area NG-IDF dataset to include eight vegetated LULCs over the continental United States, including forest (deciduous, evergreen, mixed), shrub, grass, pasture, crop, and wetland. This NG-IDF 2.0 dataset offers a comprehensive analysis of hydrological extreme events and their associated drivers under different LULCs at a continental scale. It will serve as a useful resource for improving standard design practices and aiding in the assessment of infrastructure design risks. Additionally, it provides useful insights into how changes in LULC impact flooding magnitude, mechanisms, timing, and snow water supply.

Datasets for characterizing extreme events relevant to hydrologic design over the conterminous United States.

Despite the close linkage between extreme floods and snowmelt, particularly through rain-on-snow (ROS), hydrologic infrastructure is mostly designed based on standard precipitation Intensity-Duration-Frequency curves (PREC-IDF) that neglect snow processes in runoff generation. For snow-dominated regions, such simplification could result in substantial errors in estimating extreme events and infrastructure design risk. To address this long-standing problem, we applied the Next Generation IDF (NG-IDF) technique to estimate design basis extreme events for different durations and return periods in the conterminous United States (CONUS) to distinctly represent the contribution of rain, snowmelt, and ROS events to the amount of water reaching the land surface. A suite of datasets were developed to characterize the magnitude, trend, seasonality, and dominant mechanism of extreme events for over 200,000 locations. Infrastructure design risk associated with the use of PREC-IDF was estimated. Accuracy of the model simulations used in the analyses was confirmed by long-term snow data at over 200 Snowpack Telemetry stations. The presented spatially continuous datasets are readily usable and instrumental for supporting site-specific infrastructure design.