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., 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.

Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling

“…Other studies combined soil moisture and groundwater observations at the point (Carrer et al, 2019) or large (i.e. remote sensing) (Demirel et al, 2019;Nijzink et al, 2018) scale to characterise catchment-scale storage. Overall, our approach is complementary to these others as some sort of SWC data transformation specific to the study site remains a requirement for catchment storage characterisation.…”

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.…”

Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

“…In fact, radar penetration does not exceed a 5 cm soil top layer [22]. SM's sensitivity to maximum storage capacity has already been reported, although not fully assessed for many soil types [62]. Soil water storage, S, is assumed to correspond to a constant moisture value throughout the soil profile.…”

“…Despite these constraints, satellite products have a large potential for assessing terrestrial water balance dynamics and for understanding their spatial and temporal variability at multiple scales [18]. Most studies referenced above used hydrological modelling based on water balance approaches applied at large spatial scales and a common conclusion was that the joint assimilation of soil moisture and total water storage remote data improves model performance [60][61][62]. Furthermore, the validation of radar products is commonly performed with meteorological data matrices that are obtained from satellite data, such as NOOA or Meteosat (e.g., [18,[60][61][62][63][64][65]).…”

“…From this, it is evident that research on the relationship between soil water storage, computed with ground meteorological data, and the ESA CCI SM combined product is still lacking. In fact, water balance computations require the definition of a maximum soil water storage, or soil water storage capacity, whose effect on the relationship with SM is hardly reported in the literature [62,67]. This is an important and not yet fully explored issue that affects the use of satellite products measuring SM at smaller spatial scales, as the soil water storage capacity may present a wide range of spatial variation, depending on soil and crop characteristics [68].…”