An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

Abstract

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture-data assimilation, SM-DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM-DA in recent years (e.g. the Advanced SCATterometer -AS-CAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM-DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014-February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM-DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM-DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM-DA framework for flash flood risk mitigation.

Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8

“…The potential of surface moisture retrieval using Sentinel-1 C-band data have already been tested during the pre-launch phase [40,41]. Compared to other surface moisture approaches from different Earth observation data sources [42,43] and hydrological approaches [44,45], Sentinel-1 data has, so far, only been used in a few studies for surface moisture mapping. Gao et al [46] have been analyzing the synergistic usage of Sentinel-1 and Sentinel-2 for surface moisture mapping at coarse spatial scale (100 m).…”

Defining a Trade-off Between Spatial and Temporal Resolution of a Geosynchronous SAR Mission for Soil Moisture Monitoring

“…400 mm) [56]. The basin was selected because it has optimal conditions for SM retrieval from SAR acquisitions, i.e., predominant flat topography, low percentage of areas covered by urban settlements and forests [20] (Figure 2). starting from the SM maps of the 'reference' model simulation) as they were produced by a POLAR SAR system, like the S1 constellation.…”

“…Different approaches can be exploited to quantify G, however, normally it is a constant value in both space and time. In this exercise, different G values, ranging from 0 (OL) to 1 (DI) with step of 0.2, were used during the SM-DA experiment to carry out a sensitivity analysis for the G parameter [20,32]. Due to its formulation, the Nudging is an efficient (i.e., computationally inexpensive) assimilation algorithm, useful for operational applications.…”

“…Due to its formulation, the Nudging is an efficient (i.e., computationally inexpensive) assimilation algorithm, useful for operational applications. This is the reason why it was selected for the SM-DA experiments, as well as the fact that several research studies have proven its effectiveness for SM-DA applications (e.g., References [5,20,32,33,38,61] amongst others). It is also important to say that, during the SM-DA experiment, the assimilation was not carried out in river channels, urban areas, and when the modelled land surface temperature (LST) was lower than 0 • C. This temperature can be associated with frozen soil conditions, which are likely to produce inaccurate SM estimates from the radar [62].…”

Using a multi-hypothesis framework to improve the understanding of flow dynamics during flash floods

“…The current availability of high-resolution telemetry measurements with high spatial coverage (for example, Sentinel-1-based satellite Earth Observation data (Enenkel et al, 2016;Cenci et al, 2017)) offers the opportunity to conduct a qualitative assessment of soil moisture patterns. The temporal resolution (up to six days) is not adapted to flash-flood time scales and prevents their use for real-time evaluation of hydrological simulations.…”