Emerging Contaminants Occurrence and Streamflow Responses to Extreme Climate Conditions in an Agricultural Watershed

• Autor: Jaimes Correa, Juan Carlos
• Formato: tesis doctoral
• Fecha de Publicación: 2017
• Institución: Superintendencia Nacional de Educación Superior Universitaria
• Repositorio: Registro Nacional de Trabajos conducentes a Grados y Títulos - RENATI
• Lenguaje: inglés
• OAI Identifier: oai:renati.sunedu.gob.pe:renati/6908
• Enlace del recurso: https://renati.sunedu.gob.pe/handle/sunedu/3448667
• Nivel de acceso: acceso abierto
• Materia: Antibióticos veterinarios; Condiciones climáticas extremas; Modelamiento hidrológico; Calidad del agua; Recursos hídricos; Muestreadores integrativos de químicos orgánicos polares; https://purl.org/pe-repo/ocde/ford#2.01.01

Runoff from agricultural fields is a significant non-point source of pollution to water bodies, as it transports sediments, nutrients, pesticides, and veterinary pharmaceuticals (VA). Climate change intensifies the hydrological cycle, generating more extreme hydrometeorological and climate conditions (EHCC) that lead to floods and droughts. However, there is limited information regarding those impacts on water quality in agricultural areas. The aim of this dissertation is to evaluate the occurrence of VA and streamflow response to EHCC in the Shell Creek (SC) watershed, Nebraska. Streamflow and water quality are simulated using the Soil and Water Assessment Tool (SWAT). VA are detected using Polar Organic Chemical Integrative Samplers (POCIS) and simulated using the pesticide subroutine in SWAT. Model performance is measured by the Nash-Sutcliffe Efficiency (NSE) coefficient. Streamflow response is assessed through statistical analysis of flows and loading pollutants in periods of EHCC. VA concentrations range from 0.0003 to 68 ng/L and some display significant temporal variation. The hydrologic model reproduces monthly flows with NSE of 0.40-0.92. It overestimates lower flows by 1-2 m3/s in years with very wet and dry summer. A rain gage-forced model simulates medium-flow conditions (10-90th percentiles) closer to observations, although, it overestimates lower flows (≤ 10th) and underestimates higher flows (≥ 90th) up to 0.1 and 1 m3/s, respectively. The water quality model adequately reproduces monthly flows, sediments, and nutrients (NSE = 0.61-0.82). However, it poorly reproduces atrazine and VA (NSE ≤ 0.01). This study demonstrates the utility of POCIS for monitoring ambient levels of pharmaceuticals, and their occurrence confirms that agricultural activities influence surface water quality. Rain gages located outside SC incorporate the spatiotemporal variation of precipitation. Their integration with radar precipitation data can support hydrologic models and bring quantitative information to watershed managers. Continuous water quality monitoring is needed for an adequate implementation of hydrological models in small agricultural watersheds.