Thursday, August 15, 2019
M108, Kentucky International Convention Center
ABSTRACT WITHDRAWN
Xoco Shinbrot, Department of Natural Resources, Cornell University, Ithaca, NY, Lyssette E. Muñoz-Villers, Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Mexico City, Mexico, Kelly W. Jones, Human Dimensions of Natural Resources, Colorado State University, Fort Collins, CO,
Robert H. Manson, Red de Ecología Funcional, Instituto de Ecología, A.C., Xalapa, Veracruz, Mexico, Alex S. Mayer, Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI, Sergio López-Ramírez, Graduate Program in Civil and Environmental Engineering, Michigan Technological University, Houghton, MI, Melissa López-Portillo, Centro de Ciencias de la Atmósfera, Universidad Nacional Autonoma de México, Mexico City, Mexico and Miriam Ramos-Escobedo, Global Water Watch - Mexico, Coatepec, Veracruz, Mexico
Xoco Shinbrot, Cornell University;
Lyssette E. Muñoz-Villers, Universidad Nacional Autónoma de México;
Kelly W. Jones, Colorado State University;
Robert H. Manson, Instituto de Ecología, A.C.;
Alex S. Mayer, Michigan Technological University;
Sergio López-Ramírez, Michigan Technological University;
Melissa López-Portillo, Universidad Nacional Autonoma de México;
Miriam Ramos-Escobedo, Global Water Watch - Mexico
While community-based water monitoring appears critical for evaluating programs making payments for hydrological services in Mexico little is known about the factors affecting monitor involvement data reliability. Monitors for rainfall (12) and stream flow (35) in two watersheds in central Veracruz, Mexico, were recruited and trained using manual rain gauges and a float-velocity method, respectively. Monitor attrition was low over one year with data reliability being generally high across sites (versus automatic rain gauges and leveloggers) and helpful in improving hydrological models. Surveys suggested unemployed individuals motivated by their interest in learning, and with disposable time and income, are best.
