Assessing Landscape Connectivity and River Water Quality Changes Using an 8-Day, 30-Meter Land Cover Dataset

Wednesday, 17 December 2014
Ioannis Kamarinas1, Jason Julian1, Braden Owsley2, Kirsten de Beurs2 and Andrew Hughes3, (1)Texas State University, Geography, San Marcos, TX, United States, (2)University of Oklahoma, Geography and Environmental Sustainability, Norman, OK, United States, (3)National Institute of Water and Atmospheric Research - NIWA, Hamilton, New Zealand
Water quality is dictated by interactions among geomorphic processes, vegetation characteristics, weather patterns, and anthropogenic land uses over multiple spatio-temporal scales. In order to understand how changes in climate and land use impact river water quality, a suite of data with high temporal resolution over a long period is needed. Further, all of this data must be analyzed with respect to connectivity to the river, thus requiring high spatial resolution data. Here, we present how changes in climate and land use over the past 25 years have affected water quality in the 268 sq. km Hoteo River catchment in New Zealand. Hydro-climatic data included daily solar radiation, temperature, soil moisture, rainfall, drought indices, and runoff at 5-km resolution. Land cover changes were measured every 8 days at 30-m resolution by fusing Landsat and MODIS satellite imagery. Water quality was assessed using 15-min turbidity (2011-2014) and monthly data for a suite of variables (1990-2014). Watershed connectivity was modeled using a corrected 15-m DEM and a high-resolution drainage network. Our analyses revealed that this catchment experiences cyclical droughts which, when combined with intense land uses such as livestock grazing and plantation forest harvesting, leaves many areas in the catchment disturbed (i.e. exposed soil) that are connected to the river through surface runoff. As a result, flow-normalized turbidity was elevated during droughts and remained relatively low during wet periods. For example, disturbed land area decreased from 9% to 4% over 2009-2013, which was a relatively wet period. During the extreme drought of 2013, disturbed area increased to 6% in less than a year due mainly to slow pasture recovery after heavy stocking rates. The relationships found in this study demonstrate that high spatiotemporal resolution land cover datasets are very important to understanding the interactions between landscape and climate, and how these interactions affect water quality.