Stefania Grimaldi

Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges

Accurate, precise and timely forecasts of flood wave arrival time, depth and velocity at each point of the floodplain are essential to reduce damage and save lives. Current computational capabilities support hydraulic models of increasing complexity over extended catchments. Yet a number of sources of uncertainty (e.g., input and boundary conditions, implementation data) may hinder the delivery of accurate predictions. Field gauging data of water levels and discharge have traditionally been used for hydraulic model calibration, validation and real-time constraint. However, the discrete spatial distribution of field data impedes the testing of the model skill at the two-dimensional scale. The increasing availability of spatially distributed remote sensing (RS) observations of flood extent and water level offers the opportunity for a comprehensive analysis of the predictive capability of hydraulic models. The adequate use of the large amount of information offered by RS observations triggers a series of challenging questions on the resolution, accuracy and frequency of acquisition of RS observations; on RS data processing algorithms; and on calibration, validation and data assimilation protocols. This paper presents a review of the availability of RS observations of flood extent and levels, and their use for calibration, validation and real-time constraint of hydraulic flood forecasting models. A number of conclusions and recommendations for future research are drawn with the aim of harmonising the pace of technological developments and their applications.

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Fluvial flooding is one of the most catastrophic natural disasters threatening people’s lives and possessions. Flood forecasting systems, which simulate runoff generation and propagation processes, provide information to support flood warning delivery and emergency response. The forecasting models need to be driven by input data and further constrained by historical and real-time observations using batch calibration and/or data assimilation techniques so as to produce relatively accurate and reliable flow forecasts. Traditionally, flood forecasting models are forced, calibrated and updated using in-situ measurements, e.g., gauged precipitation and discharge. The rapid development of hydrologic remote sensing offers a potential to provide additional/alternative forcing and constraint to facilitate timely and reliable forecasts. This has brought increasing interest to exploring the use of remote sensing data for flood forecasting. This paper reviews the recent advances on integration of remotely sensed precipitation and soil moisture with rainfall-runoff models for rainfall-driven flood forecasting. Scientific and operational challenges on the effective and optimal integration of remote sensing data into forecasting models are discussed.

Effective Representation of River Geometry in Hydraulic Flood Forecast Models

Bathymetric data are a critical input to hydraulic models. However, river depth and shape cannot be systematically observed remotely, and field data are both scarce and expensive to collect. In flood modeling, river roughness and geometry compensate for each other, with different parameter sets often being able to map model predictions equally well to the observed data, commonly known as equifinality. This study presents a numerical experiment to investigate an effective yet parsimonious representation of channel geometry that can be used for operational flood forecasting. The LISFLOOD-FP hydraulic model was used to simulate a hypothetical flood event in the Clarence catchment (Australia). A high-resolution model simulation based on accurate bathymetric field data was used to benchmark coarser model simulations based on simplified river geometries. These simplified river geometries were derived from a combination of globally available empirical formulations, remote sensing data, and a limited number of measurements. Model predictive discrepancy between simulations with field data and simplified geometries allowed an assessment of the geometry impact on inundation dynamics. In this study site, the channel geometrical representation for a reliable inundation forecast could be achieved using remote sensing-derived river width values combined with a few measurements of river depth sampled at strategic locations. Furthermore, this study showed that spatially distributed remote sensing-derived inundation levels at the very early stages of a flood event have the potential to support the effective diagnosis of errors in model implementations. Plain Language Summary Floods are among the most frequent and destructive natural disasters worldwide. An accurate and reliable flood forecast can provide vital information for land management and emergency response. Flood forecasts are achieved using numerical models that are able to predict the depth, velocity, and arrival time of the flood wave at each point of the valley. The accuracy of these predictions is strongly related to the quality of the three-dimensional representation of the valley. In particular, information on river geometry (that is cross-section shape, depth, and width) is critical to the application of these numerical models. However, it is impossible to measure river geometry along the entire river length, especially in large basins. This study developed a method to represent river geometry using a limited amount of time and money. Specifically, this objective can be achieved using available satellite imagery complemented with a few measurements. Moreover, this study showed that flood forecast skill can be improved by combining information from satellite and numerical models. Albeit simple, the river geometry representation proposed in this study can support the accurate prediction of floodplain inundation. This method was described and tested using, as example, a hypothetical flood event in an Australian catchment.

Optimized glcm-based texture features for improved SAR-based flood mapping

Flood maps are indispensable to regional prioritization and effective resource distribution, and are required by policy makers, insurance firms, and disaster-relief agencies. SAR (Synthetic Aperture Radar) image classification is widely used for flood mapping, although the utilization of image texture has not been well explored. This study proposes a novel SAR-based flood mapping technique that uses optimized Gray Level Co-occurrence Matrix (GLCM)-based texture features, for more accurate flood-extent extraction from COSMO-SkyMed data. The approach involves the extraction of omnidirectional texture features through the use of an optimal window size, followed by independent component transform, which captures most of the information in the first three components and reduces data dimensionality. Flood maps that are derived using a support vector machine classifier were verified against aerial photographs. The presented approach increased the overall classification accuracy by nearly 1.5%.

A Semi-quantitative Model to Assess the Costeffectiveness of Soil and Water Conservation Measures

Soil and Water Conservation (SWC) measures have been regularly employed in the Sahelian area to reduce soil erosion and reservoir siltation. However, a proper cost-effectiveness analysis of the impact of SWC interventions on the catchment sediment budget is rarely carried out. In this paper, a semi-quantitative model is proposed to evaluate the cost-effectiveness of SWC measures at a catchment scale. Focusing on a case study located in Burkina Faso, the catchment sediment budget was estimated for an hypothetical SWC intervention by means of morphological and pedologic parameters and dam sedimentation rates. The proposed methodology showed interesting potentials for land and water management in the Sahelian region. In particular, where data for model calibration and validation are scarce, and when financial resources are limited, the proposed methodology can provide a diagnostic on sediment transport processes and reservoir capacity loss in order to design and implement suitable SWC actions.

Designing and Building Gabion Check Dams in Burkina Faso

Gabion check dams (GCDs) are among the most diffused soil and water conservation practices in Burkina Faso, used to cope with soil loss and reservoir siltation. Specifically, CGDs are flexible, permeable structures built in gullies to create a sedimentation bench that decreases the average upstream slope. The consequent slowing-down of the flowing water limits flood-wave sediment transport capacity reducing soil loss upstream, reduces the amount of trapped sediment in reservoirs and promotes water infiltration into the soil. The present work provides concise guidelines for the design and implementation of GCDs in Burkina Faso. This was achieved gathering the experience developed in the frame of several cooperation and development projects led by the Italian non-governmental organization. The theoretical elements and procedural steps to perform an appropriate design and implementation of GCDs are described and discussed, including hydrological and hydraulic methods to assess stream flow characteristics, spillway functioning, siltation rate, dimensions and stability of the structure.

Experimental and numerical modeling of waves routing on permeable soils

A study on waves routing on permeable, rough boundaries may be of great interest for several purposes, e.g. the optimal use of water in agriculture, the analysis of the interaction between surface and underground water resources, the optimization of innovative solutions for urban runoff mitigation and treatment. The analysis was organized in two phases; a first experimental study on infiltration was completed by the complete numerical modeling of waves routing on a permeable soil. The experimental setup was located in the Laboratory Giorgio Bidone of Polytechnic University of Turin (DIATI). A horizontal, prismatic (width 0.30m, height 0.20 m), 9m long channel was filled with a dry sandy layer of constant thickness (0.10m). A mattress made of polyurethane foam laid on the sandy layer in order to model vegetation or surface roughness. Lifting a gate, we allowed the water stored in the upstream reservoir to flow into the channel. Water flowed through the mattress while infiltrating into the sand (soil). Several combinations of three sand particle size distributions, three mattress types, and three inflow hydrographs allowed a study on the main parameters leading the propagation of waves on permeable soils. Each test was video-recorded, the detection of the profiles of the surface waves propagating through the mattress allowed to focus only on the infiltration process. Laboratory tests pointed out the hydraulic parameters of the three sands used. The implementation of appropriate numerical codes (one original and one commercial - Hydrus 2D, Simunek et al, 2002) led to semi-quantitative, propaedeutic, preliminary conclusions. The complete analysis of the propagation of waves on permeable soils was then achieved through the numerical modeling of a specific case study, i.e. the biofilter installed at Monash University in Melbourne. A biofilter is a vegetated, permeable retention basin useful for urban runoff treatment. Onsite recorded data of flow rates and soil moisture values allowed the calibration and verification of a numerical model resulting from the coupling of original numerical codes with one open source software (WinSRFR, Strelkoff et al, 1990) and a revised version of a commercial software (Hydrus 2D; Simunek et al, 2002). The resulting numerical model is useful for both diagnostic and planning purposes; it allows the definition of the parameters (geometry, soil type, vegetation type) that may enhance hydrologic and pollutant removal performances of the facility according to local climate conditions (frequency, intensity, volume of precipitation, temperature)