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

Abstract

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. HBV. This has been rarely done for conceptual models as satellite data are often used in spatial calibration of the distributed models. Three different soil moisture products from ESA CCI SM v04.4, AMSR-E and SMAP, and total water storage anomalies from 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 analyse 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 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 behaviour while it keeps a reasonable water balance that is the key objective of every hydrologic model.

The Added Value of Different Data Types for Calibrating and Testing a Hydrologic Model in a Small Catchment

“…Previous studies used ground‐based or alternatively remote sensing products or their combination as such additional information on hydrologic processes. Soil moisture (Kundu et al, 2017; Kunnath‐Poovakka et al, 2016; Parajka et al, 2006; Rajib et al, 2016; Shahrban et al, 2018), evapotranspiration (Gui et al, 2019; Herman et al, 2018; Immerzeel & Droogers, 2008; Kunnath‐Poovakka et al, 2016), and groundwater level data (Demirel et al, 2019; Seibert, 2000) were often used for model calibration to improve the model's internal consistency. These studies showed the added value of different observations besides runoff, for example, for soil moisture, evapotranspiration, and groundwater levels.…”

On constraining a lumped hydrological model with both piezometry and streamflow: results of a large sample evaluation

“…For instance, the isoWATFLOOD model (Stadnyk et al, 2013;Stadnyk and Holmes, 2020) is calibrated using both streamflow and isotopic -δ 18 O -data, but a visual -and thus rather subjective -evaluation of calibration by the modeller is necessary; Jian et al (2017) used, in a catchment where only a few streamflow measurements were available, river level data, and added three new parameters to a hydrological model to simulate the rating curve. Whereas infield measurements are not always common, satellite data are broadly available around the world, and numerous studies have used them in hydrological models: Immerzeel and Droogers (2008) used satellite evaporation to calibrate the SWAT distributed model through a composite criterion and got a better representation of the actual evaporation and less equifinality in parameter determination; Mostafaie et al (2018) performed a multi-objective calibration using NSE for streamflow and total water storage from GRACE satellite data; Milzow et al (2011) combined several satellite datasets -surface soil moisture, radar altimetry and total water storage -to calibrate a semi-distributed model in a catchment with few streamflow measurements through a composite of nine criteria; Demirel et al (2019) explored the use of dif-ferent combinations of objective functions, computed based on several satellite products measuring soil moisture and water storage, to calibrate a conceptual model, and achieved little gain in streamflow simulation performance; and Dembélé et al (2020) performed a composite calibration of a distributed model with four datasets -measured streamflow and satellite evaporation, soil moisture and water storage -and improved the model's representation of processes at the expense of a small degradation of the streamflow simulation performance.…”