Urban Lawn Irrigation Using Non-Potable Water

Several options are available to reduce the amount of potable water used for landscape irrigation. First, potable water used for irrigation could be eliminated completely and replaced by effluent or low quality water that does not meet standards for human consumption. Municipalities generate significant amounts of sewage effluent water or often have poor quality groundwater sources available. The opportunity exists to utilize such waters as alternative irrigation sources if grasses are identified and selected that can tolerate both the climatic conditions for the area and salt accumulation in the soil. Second, adopting an efficient method of irrigation, such as subirrigation systems, could reduce water consumption, and third, a combination of all these measures could be used. Research at New Mexico State University has investigated the use of cold and salt tolerant turfgrasses in combination with saline water and subsurface irrigation systems for turfgrass applications. Data were collected for turfgrass establishment, root zone salinity build-up, and turfgrass quality. Results indicate that turf areas can be established and maintained at an adequate quality with saline irrigation if salt tolerant grasses are used

An approach for prospective forecasting of rock slope failure time

Rock slope failures globally account for most single-event landslide disasters. Climatic changes in mountain areas boost failure activity and the demand for reliable failure time forecasts. State-of-the-art prediction models are often confused with high-frequency slope deformation data. Prospectively, they provide ambiguous forecasts as data filtering, starting point definition and forecast uncertainty remain arbitrary. Here, we develop a prospective failure time forecast model that applies multiple filtering and inverse velocity percentiles to minimize subjective decisions. We test the concept with 14 historic slope failures of 10$^{2}$-10$^{8}$ m$^{3}$ including 46 displacement datasets from different sensors. After automatic detection of the onset of acceleration, the failure time of all events is forecasted to within −1 ± 17 h for higher-frequency data and −1 ± 4 d for daily data with a final mean uncertainty of 1 ± 1 d and 7 ± 4 d that is estimated in real-time. This prospective approach overcomes previous long-standing problems by introducing a robust and uniform concept across various types of catastrophic slope failures and sensors

Obstacles to demand response: why industrial companies do not adapt their power consumption to volatile power generation

Various flexibility options in power systems, such as storage, grid expansion, and demand flexibility, gain increasing importance to balance the intermittent power supply of renewables. On the demand side, especially the industrial sector represents promising potential for Demand Response, i.e., the alignment of its power demand with the current power supply of renewables. However, there exist various obstacles that currently prevent companies from investing in new or (fully) exploiting existing flexibility potentials. In this paper, we investigate how economic, regulatory, technological, organizational, behavioral, informational, and competence obstacles pose barriers for companies to adjust their power consumption flexibly. For this purpose, we combine both a structured literature analysis and a case study. For the case study, we conduct 16 interviews with energy experts from companies from different industries. Our findings reveal that due to technical risk of disrupting the production process, lacking revenues, and too low cost savings, companies do not flexibilize their power consumption. Moreover, in particular, contradictory legislative incentives and missing IT standardization and interoperability represent key obstacles. Therefore, our results constitute a basis for targeted policy making in order to foster the exploitation of (existing) flexibility potential of industrial companies on the demand side

Digital Decarbonization: Design Principles for an Enterprise-wide Emissions Data Architecture

The need for corporate decarbonization to mitigate climate change is reflected in a growing number of political measures to transparently disclose the environmental impact of corporate activities. Due to increasing reporting obligations, companies must constantly evaluate their own as well as suppliers' products and processes with respect to emissions data. To date, guidelines on how to design a data architecture focusing on the collection, storage, transformation, distribution, and disclosure of emissions data throughout an entire company are still lacking. Working with the design science research paradigm, we develop seven design principles for an enterprise-wide emissions data architecture (EEDA). We develop and iterate these principles by performing a structured literature review and semi-structured interviews. Taking this emission-centric perspective on data architecture, we foster the active engagement for a structured enterprise-wide approach for managing emissions data and coping with the increased demand for emissions reporting

Base temperatures affect accuracy of growing degree day model to predict emergence of bermudagrasses

AbstractThe germination of bermudagrass [Cynodon dactylon (L.) Pers.] under different temperature regimes has been extensively investigated, but a discrepancy remains between laboratory studies and field results. Thermal requirements calculated in growing degree days (GDD) have been found to differ within the same species depending on the location of the study. The accumulation of GDD may vary under different thermal conditions from seeding to seedling emergence and could depend on TBASE used in the calculation. The most widely used TBASE for bermudagrass is 5 °C. However, laboratory studies have suggested that a base temperature of 15 °C would more accurately predict seedling emergence. In this field study, we investigated the effect of using TBASE 5 °C vs. TBASE 15 °C on the estimation of GDD required by bermudagrass to emerge. Ten cultivars were seeded in northeastern Italy on three dates between 10 March and the end of April in 2013 and 2014. Number of emerged seedlings was counted weekly and soil temperature at 1‐cm depth was recorded significant differences in seedling emergence between bermudagrass genotypes were found. Results demonstrated that the algorithm used to calculate GDD is strongly influenced by the TBASE used and to include a TBASE of 15 °C explains germination and emergence more accurately than a TBASE of 5 °C

How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

In course of the energy transition, the growing share of Renewable Energy Sources (RES) makes electricity generation more decentralized and intermittent. This increases the relevance of exploiting flexibility potentials that help balancing intermittent RES supply and demand and, thus, contribute to overall system resilience. Digital technologies, in the form of automated trading algorithms, may considerably contribute to flexibility exploitation, as they enable faster and more accurate market interactions. In this paper, we develop an integrated algorithmic framework that finds an optimal trading strategy for flexibility on multiple markets. Hence, our work supports the trading of flexibility in a multi-market environment that results in enhanced market integration and harmonization of economically traded and physically delivered electricity, which finally promotes resilience in highly complex electricity systems

Obstacles to demand response: Why industrial companies do not adapt their power consumption to volatile power generation

Various flexibility options in power systems, such as storage, grid expansion, and demand flexibility, gain increasing importance to balance the intermittent power supply of renewables. On the demand side, especially the industrial sector represents promising potential for Demand Response, i.e., the alignment of its power demand with the current power supply of renewables. However, there exist various obstacles that currently prevent companies from investing in new or (fully) exploiting existing flexibility potentials. In this paper, we investigate how economic, regulatory, technological, organizational, behavioral, informational, and competence obstacles pose barriers for companies to adjust their power consumption flexibly. For this purpose, we combine both a structured literature analysis and a case study. For the case study, we conduct 16 interviews with energy experts from companies from different industries. Our findings reveal that due to technical risk of disrupting the production process, lacking revenues, and too low cost savings, companies do not flexibilize their power consumption. Moreover, in particular, contradictory legislative incentives and missing IT standardization and interoperability represent key obstacles. Therefore, our results constitute a basis for targeted policy making in order to foster the exploitation of (existing) flexibility potential of industrial companies on the demand side

Turfgrasses for New Mexico

Guide containing general information on the use of turfgrass in New Mexico, and varieties suitable for growth in New Mexico climates