Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration

• Main Authors: Mehmet Cüneyd Demirel, Alparslan Özen, Selen Orta, Emir Toker, Hatice Kübra Demir, Ömer Ekmekcioğlu, Hüsamettin Tayşi, Sinan Eruçar, Ahmet Bilal Sağ, Ömer Sarı, Ecem Tuncer, Hayrettin Hancı, Türkan İrem Özcan, Hilal Erdem, Mehmet Melih Koşucu, Eyyup Ensar Başakın, Kamal Ahmed, Awat Anwar, Muhammet Bahattin Avcuoğlu, Ömer Vanlı, Simon Stisen, Martijn J. Booij
• Format: Article
• Language: English
• Published: MDPI AG 2019-10-01
• Series: Water
• Subjects: hbv; grace; smap; esa cci sm v04.4; amsr-e; moselle river
• Online Access: https://www.mdpi.com/2073-4441/11/10/2083

Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information. Firstly, the most important parameters are selected using sensitivity analysis, and then these parameters are included in a subsequent model calibration. The results of our multi-objective calibration reveal a substantial contribution of remote sensing products to the lumped model calibration, even if their spatially-distributed information is lost during the spatial aggregation. Inclusion of new observations, such as groundwater levels from wells and remotely sensed soil moisture to the calibration improves the model’s physical behavior, while it keeps a reasonable water balance that is the key objective of every hydrologic model.