|Title||Modelling Discharge and Sediment Yield at Catchment Scale Using Connectivity Components|
|Author(s)||Masselink, Rens J.H.; Keesstra, Saskia D.; Temme, Arnaud J.A.M.; Seeger, Manuel; Giménez, Rafael; Casalí, Javier|
|Source||Land Degradation and Development 27 (2016)4. - ISSN 1085-3278 - p. 933 - 945.|
Soil Physics and Land Management
Soil Geography and Landscape
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Connectivity - Discharge - Model - N-Spain - Sediment yield|
Knowledge about connectivity and what affects it, through space and time, is needed for taking appropriate action at the right place and/or time by stakeholders. Various conceptual frameworks for hydrological and sediment connectivity have been developed in recent years. For most of these frameworks, the objective was to conceptualise connectivity, not necessarily to infer it from measurements. Studies focussing on measurements of connectivity have so far not been done often. Because of lack of data on connectivity, few real-world data have been used in recent connectivity modelling studies. The aim of this study was to demonstrate that existing data can be used to assess governing factors of connectivity, and how these change over time. Data from three catchments in Navarre, Northern Spain, were used to assess factors that influence hydrologic and sediment connectivity. These factors were used as components in a linear model for discharge and suspended-sediment yield. Three components of connectivity were distinguished: topographical, biological and soil. Changes in the topographical component for the studied periods were considered relatively small, and, therefore, kept constant. Changes in the biological component were determined using the Normalised Difference Vegetation Index. Changes in the soil component were assessed using an Antecedent Precipitation Index. Nash-Sutcliffe model efficiency coefficients were between 0·49 through 0·62 for the discharge models and between 0·23 through 0·3 for the sediment-yield models. We recommend applying the model at smaller spatial scales than catchment scale to minimise the lumping of spatial variability in the components.