Temporal variability of the optimal monitoring setup assessed using information theory. Issue 9 (26th September 2015)

• Record Type: Journal Article
• Authors: Fahle, Marcus; Hohenbrink, Tobias L.; Dietrich, Ottfried; Lischeid, Gunnar
• Is Part Of: Water resources research. Volume 51:Issue 9(2015:Sep.)
• Page Start: 7723
• Page End: 7743
• Publication Date: 2015-09-26
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2015WR017137
• Languages: English
• ISSNs: 0043-1397

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Hydrology is rich in methods that use information theory to evaluate monitoring networks. Yet in most existing studies, only the available data set as a whole is used, which neglects the intraannual variability of the hydrological system. In this paper, we demonstrate how this variability can be considered by extending monitoring evaluation to subsets of the available data. Therefore, we separately evaluated time windows of fixed length, which were shifted through the data set, and successively extended time windows. We used basic information theory measures and a greedy ranking algorithm based on the criterion of maximum information/minimum redundancy. The network investigated monitored surface and groundwater levels at quarter‐hourly intervals and was located at an artificially drained lowland site in the Spreewald region in north‐east Germany. The results revealed that some of the monitoring stations were of value permanently while others were needed only temporally. The prevailing meteorological conditions, particularly the amount of precipitation, affected the degree of similarity between the water levels measured. The hydrological system tended to act more individually during periods of no or little rainfall. The optimal monitoring setup, its stability, and the monitoring effort necessary were influenced by the meteorological forcing. Altogether, the methodology presented can help achieve a monitoring network design that has a more even performance or covers the conditions of interest (e.g., floods or droughts) best.</p> </abstract>