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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2014)

The carbon footprint of embedded water in California's agricultural products has not been thoroughly studied before. However, related topics have been on the table to solve the sustainable development problems. This research tries to find out the energy consumption of the water embedded in the exported agricultural products from California. It also aims to figure out the nation scale distribution of the carbon footprint that was studied in this research. The results show that the water footprint of the agricultural products is around 22.3 Gm3 and the total carbon footprint is 8.9 million metric tons of CO2. The top three regions that have the highest water and carbon footprint are European Union 27, China, and Japan. We studied the carbon footprint by researching the water footprint of all the agricultural products. Based on the energy usage per unit water sources published by the California Energy Commission and the global average water footprint of agricultural products from previous work (Hoekstra, 2007), we calculated the carbon-equivalent of the agricultural products' carbon footprint. And based on the result that the carbon footprint of the embedded water of agricultural products is only 2% of the total carbon budget with the water footprint contributes to 25% of the total, we believe that California has performed well in managing the energy in agriculture-water area, but it still has improvement space in the management of the water resource.

    Cover page: Carbon Footprint of Water Embedded in Agricultural Products Exported from California
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2018)
    Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.
      Cover page: HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
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