Assessing flash flood erosion following storm Daniel in Libya

Domain: earth_science
Published: 2024-08-20
Editorial outcome: major_revision

Abstract
The eastern Mediterranean basin is witnessing increased storm activity impacting populous urban coastal areas that historically were not prone to catastrophic flooding. In the fall of 2023, Storm Daniel struck the eastern coast of Libya, causing unprecedented flash floods with a tragic death toll and large-scale infrastructure damages. We use Sentinel-1A C-band SAR images to characterize the resulting flash flood erosion and sediment load dynamics across the watersheds and to map damages within coastal cities at their outlets. Our results suggest that sediment loading, resulting from surface erosion, increased the density of turbid streams. The above exacerbated the catastrophic impact of the flash floods in the coastal cities of Derna and Susah, where 66% and 48% of their respective urban surface have experienced moderate-to-high damages. Our findings highlight the increased vulnerability of coastal watersheds in arid areas within the eastern Mediterranean basin due to the forecasted increase in hydroclimatic extremes and call for a transformative coastal management approach to urgently implement nature-based solutions and land-use changes to mitigate these rising risks.

Introduction
Severe cyclone-type storms, also known as medicanes, are generated from the combination of a cold cut-off low in the middle to upper troposphere and warm seawater, naturally occurring during the autumn season1. They form in the central Mediterranean basin but typically dissipate at sea2. However, these medicanes have increased in duration and magnitude over the last decades and moved toward the low-lying eastern Mediterranean coasts of Egypt and Libya, leading to catastrophic landfall2. Over the last decades, the impacts of these hydroclimatic extremes were puzzling due to their sparse occurrence3 and the lack of in-situ observations as a result of the low population density in these coastal areas. Furthermore, the impacts of these intensifying storms remain poorly quantified as most post-assessment efforts naturally focus on identifying the damages in localized urban zones, overlooking the regional physical drivers that modulate the magnitude of these flash floods. Notably, the vulnerabilities for these low-lying arid sandy coasts were low until the recent sharp increase in local population due to the civil war causing inhabitants displacements and immigration from other African countries4, as well as the rise in medicanes intensity. The impacts of these medicanes have recently been subject to both public and scientific attention2,5,6.

In September 2023, the medicane Storm Daniel7 developed in Greece over the Ionian Sea and strengthened while moving southward across the eastern Mediterranean Sea. The storm was amplified by the exceptionally warm waters resulting from the extreme summer heat in southern Europe that year8. Storm Daniel reached the northeastern coast of Libya on September 10th, 2023, with high winds of 120 km h−1 and 25 hours of cumulative precipitation reaching 240 mm (up to 414 mm in the city of Al-Bayda). The above-observed precipitation during the storm landfall is close to the annual average of 270 mm yr−1 for this area9. Although rare, the area is known to experience intermittent extreme rainstorms and flash floods during the fall season. Notably, over the past 80 years, five flash floods have been recorded in Wadi-Derna, a dry riverbed, occurring in October 1942, October 1959, October 1968, November 1986 and September 201110.

The extreme hydroclimatic conditions generated by Storm Daniel caused deluges and mudflows in the storm landfall area (see Fig. 1) primarily constituted of rangelands (grasses and shrubs) and bare soils11. Despite the limited urban footprint in the storm landfall area10, substantial infrastructure damage has been observed, including 5% of the road network being destroyed, 50% becoming inaccessible10, and the collapse of two flood-control dams upstream of the city of Derna on September 11th12 (see Fig. 1). The latter triggered a deadly flash flood flowing through the city, located on the river’s deltaic outlet (see Fig. 2), destroying a substantial portion of the city buildings, urban infrastructure, and bridges13, which resulted in the accumulation of 8.8 million tons of debris14. For instance, 10% of the houses were destroyed, and 18.5% were damaged10. The storm notably affected the city of Derna but also other coastal cities. For example, 28% of the houses in Susah were destroyed10. Albayda, Al-Marj, Shahat, Taknis, Battah, Tolmeita, Bersis, Tokra, and Al-Abyar have also witnessed severe damages, causing a total of 5898 casualties, 8000 missing, 44800 people displaced, and 18838 houses damaged or destroyed in the whole coastal area encompassing Benghazi, Jabal Al Akhdar, Al Marj and Derna14 (see Figs. 3 and 4). These figures mark Storm Daniel as the deadliest storm in all the African continent since 190010.

This eastern coast of Libya is one of the main hubs for energy export to Europe. This catastrophe will cost US$1.8 billion in repairing the coastal damages10. This figure includes the devastation of crops and harvests across 16209 ha, along with the destruction of vital topsoil for 4.6% of the agricultural area10. Additionally, 74363 animals perished, accounting for 3.2% of the region’s livestock strategic reserve10.

Furthermore, this catastrophe is worsened by pre-existing humanitarian needs and restricted road access due to political instability and intermittent civil wars since 201115. These factors impede the reconstruction efforts, potentially leading to regional negative economic growth over the coming years and delaying the recovery of the national economy10.

As such, characterizing the vulnerability of watersheds and their outlets is essential for effective flood management, avoiding further humanitarian disasters in this instable area. However, only limited mapping of the impacts of Storm Daniel was explored, focusing mainly on the coastal urban areas and neglecting the inland parts constituting the watersheds. For instance, a few days following this catastrophic landfall, Copernicus Emergency Management Service (CEMS) conducted ‘rapid flood’ mapping from photo-interpretation for the cities of Derna, Benghazi, Al Marj, Al Bayda, Shahat, Tobruk and Susah. They delineated the flood extent and delivered an assessment emergency mapping16. Then, the European Union, United Nations, and World Bank Group collaborated to publish, in January 2024, a comprehensive report on the catastrophe10, including ‘flood intensity mapping’ and the flooding extent. In their report, they emphasize the limited availability of ground-truthing data, leading to the use of social media analytics, cell phone data, and night light satellite observations. However, these efforts did not characterize the flood-related soil erosion within the watershed, generating dense turbid flows, which modulate the damages of the catastrophic floods.

To better understand the role of flash flood erosion and further leverage the above-mentioned preliminary investigations, we perform a comprehensive flood surface change mapping at watershed scale levels rather than focusing solely on the small damaged urban areas at the watershed outlets. We achieve this objective by utilizing Synthetic Aperture Radar (SAR) satellite scenes to produce interferometric (InSAR) coherence images and applying the well-established Coherent Change Detection (CCD) method, which measures the temporal variations between InSAR coherence images17. The method used herein offer the advantage of assessing structural damages and soil erosion at a level of detail and resolution beyond what is achievable with the optical images used in the preliminary efforts described above.

This CCD approach enables the identification of changes in spatial patterns, such as monitoring flood erosion18–20, displacement of surface scatterers, and sediment deposition in run-off channels21, but also to assess different levels of infrastructure damages after a rainstorm22,23. These changes can be attributed to both erosion and sediment load processes. Given the inherent interconnection between the two processes, we frequently use the term “flood surface erosion mapping” in our investigation to designate both processes. However, we can delineate the sediment loading areas by conducting a comprehensive classification. This approach relies on erosion hotspots identified through CCD analysis, hydrological networks, precipitation and topography. Moreover, our investigation uses SAR images acquired under dry conditions to map the combined accumulation of (i) flooding surface erosion, (ii) sediment loading in run-off channels, and (iii) infrastructure damages in urban areas.

Using a series of eight SAR Sentinel-1 SLCs, we generate five pre-storm and two co-storm (i.e., overlapping the storm event) coherence pairs. Subsequently, we perform a principal component analysis (PCA) to reduce the dimensionality of this coherence dataset and extract temporal differences. These differences serve as a proxy for mapping flood erosion and assessing infrastructure damages. The complete description is provided in the Methods section at the end of the text, and additional information can be found in the Supplementary Information.

From this CCD–PCA analysis, we map the surface changes over an area of 115 km × 40 km and showcase the cities of Derna, Susah and a cultivated floodplain. Our results and discussion pertain to the following:
• Mapping of flood erosion at watershed scale;
• Mapping of sediment loading in wadis;
• Mapping urban damages in Derna and Susah and classifying their magnitudes;
• Mapping of flash flood sediment imbalance on watershed outlets;
• Discussing the above’s implications for other arid, low-lying urban coastal areas.

Results
Our findings pertain to using radar InSAR coherence to identify and map the surface and structural changes that are often unmeasurable from optical satellite observations, as detailed in the Methods section. In particular, we measure these changes at different scales, including watersheds and hillslopes (Fig. 4), watershed outlets (Fig. 3), coastal urban areas such as the cities of Derna (Fig. 5) and Susah (Fig. 6), and inland agricultural developments (Fig. 7), as detailed below. Notably, we show the extents and magnitudes of structural damages in the above urban areas, flood surface erosion within the watersheds, and sediment deposits in the wadis caused by the flash flood resulting from Storm Daniel. Finally, we discuss the implications of the above catastrophic processes for other arid areas.

Flood erosion at the watershed scale
Rainstorms in coastal arid watersheds, such as the one of Wadi-Derna (outlined in blue in Fig. 4), generate run-off water that converges into a network of wadis, discharging toward a common outlet at the coast. These flows are loaded with sediments resulting from upstream soil erosion, which increases the destructive nature of these flash floods. Thus, outlet areas are particularly exposed to the above hydraulic hazards that are often mitigated with a sequence of upstream dams. However, the two dams upstream of Derna have collapsed due to the anomalous hydraulic nature (i.e., flow rate and turbidity) of the floods resulting from Storm Daniel, and lack of maintenance. The extent of the damages triggered by these energetic floods and the associated dam failures are illustrated in Fig. 5. Therefore, assessing the soil erosion and sediment loading in the watersheds is essential to understand the severity of the damages.

Consequently, we apply the CCD–PCA method within the region of interest (ROI) to identify and map the soil erosion areas (see Fig. 4). Notably, the identified erosion patterns show higher occurrences where rainfall accumulation is the highest, i.e., 101–250 mm for the three days from 9–11 September 2023 (see Fig. 1). Moreover, we observe that ~22–26% of the rangelands (21.6% for croplands and 25.8% for shrublands), ~11% of the bare soils and ~11% of urban areas within the ROI are affected by flood erosion, deposits or damages derived from CCD–PCA (see section “Comparison of the flood erosion/deposits with the land cover” in the Supplementary Material for more details). Additionally, these erosion patterns are principally concentrated in areas with elevations ranging from 500 m to 800 m (see Fig. 1) characterized by a slope under 15°.

We find that erosional processes vary notably depending on each of the investigated watersheds (see erosion distribution in Fig. 4 and watershed boundaries in Fig. 8). For instance, in the Wadi-Derna watershed, surface changes mainly occur within the wadis, on moderate-slope (gradient of 4.9° on average) and bare areas, whereas in watersheds located in the hilly northern part of the ROI, surface changes are widespread across the shrublands. Further below, we explore the distribution of erosion hotspots, where the loading of eroded material occurs upstream in wadis.

Furthermore, in arid regions, farmers cultivate areas situated in wadi floodplains, where the topsoil presents favorable conditions for farming. Floodplains are characterized by their rich soil quality, notably marked by a substantial organic matter content, especially within the dammed wadi floodplains24, as commonly observed in this region of Libya. Moreover, floodplains also present higher soil moisture and finer soil particles24, and they often provide access to subsurface water resources for irrigation. This groundwater arises from focus recharge during rainstorms, replenishing subsurface freshwater lenses beneath wadis25.

Flash floods, however, outwash wadis and their floodplains, eroding these fertile soils24. Notably, Fig. 7 showcases an inland cultivated floodplain before and after the flood caused by Storm Daniel, revealing the changes from cloud-free optical satellite images and CCD–PCA analysis. Although visual examination of the optical images delineates the extent of flood surface erosion affecting a substantial area spanning 7 km × 2 km, our CCD–PCA observations offer “automated” surface change detection with enhanced sensitivity, providing finer details. From this detection, we can assess the impact of the water flow over vulnerable farms.

These flood erosions restrict the long-term sustainability of farming developments, thereby impacting food security for the local population. Thus, it is imperative to implement comprehensive flood management strategies to mitigate flood hazards posed to crops facing changing environmental conditions.

Sediment loading in wadis
In semi-arid regions, precipitation, in the form of scarce and heavy rainfalls, leads to rapid surface run-off within slopes, hillslope sediment fluxes, erosion and sediment loading in the wadis, valleys, or low-lying areas26–28. In this context, lithology, erodibility, slope gradient29,30, and land use are the main factors characterizing surface run-off, erosion and sediment load31.

We perform a first-order classification of this run-off erosion and sediment balance dynamics on steep gradients above 3° and hillslopes, where loose sediments are washed away and subsequently deposited below in wadis, increasing the likelihood of future mudflow events during rainstorms. Notably, Fig. 4 indicates erosion hotspots and sediment loading after Storm Daniel, in the North and West parts of the ROI, characterized by hillslopes and shrublands (see Supplementary Fig. 1). Moreover, similar observations are made within Wadi-Derna. The watershed of Wadi-Derna features an average terrain gradient of 4.9° (±5.5), characterized by exposed bare soil that facilitates overland flow throughout the area towards the wadi. This dense turbid water flow within the wadi further contributed to the collapses of two dams upstream of Derna city.

Moreover, we identify a substantial sediment loading zone within a wadi flowing towards the vicinity of Al-Haniyah, situated outside the ROI. While this outlet was not initially the focus of our study, we have chosen to illustrate it in Fig. 3. It illustrates the conditions before and after the storm, showing destroyed and flooded segments of the sole coastal road near the wadi’s mouth.

Urban damages in Derna and Susah
Overall, our CCD–PCA mapping reveals that 18% of Susah’s surface area exhibits damage and flood surface changes. This percentage is determined by calculating the ratio of the CCD–PCA flood surface change to the total surface area within the city limits. Similarly, in Derna, two watershed outlets present notable damages, erosion and deposits, particularly within the wadis and deltas. Figure 8 shows the extent of damages within the city and its surroundings, derived from CCD–PCA, covering 9% of its surface area and 27% of Derna’s city center. These observations are validated with two distinct sources, showing a true-detection of 61–66% and 42–65%, a false-detection of 7–29% and 18–34%, and an omission error of 6–32% and 2–40%, for Derna’s city center and Susah, respectively (see “Validation of CCD–PCA” in the Methods section for more details). These figures exhibit comparable ranges of value for both cities, reflecting a constant degree of validation of the CCD–PCA method.

On the other hand, these figures underscore CCD–PCA’s complementarity nature when compared to the CEMS dataset presenting subjective damage categories, i.e., CEMS classified damages as “destroyed” and “damaged” without providing the metrics used for such visual interpretation. Given the implication of the latter statement, we find it necessary to classify the damages severity for the two cities by intersecting the CEMS damage assessment with our CCD–PCA surface changes observation. Our comprehensive classification now effectively distinguishes between areas of high, moderate, and low damage. The corresponding damage severity maps offer the large-scale snapshot capability of SAR remote-sensing from which the damage magnitude is unclassifiable, with the qualitative capability of CEMS’ photo-interpretation and ground-truthing. The assumptions and criteria to perform this classification are detailed in the “Classifying damages in Derna and Susah” section in Method.

We perform this classification within the flooded extent of Derna’s city center and observe that the damages severity for high, moderate, and low categories accounts for 36.5%, 29%, and 27% of the surface area, respectively, with 7.5% remaining undamaged (see Fig. 5). Moreover, the same classification within the urban area of Susah, as outlined in Fig. 6, reveal that 14% of the surface area exhibits high damages, 34% displays moderate damages, 39% shows low damages, and 13% remains undamaged.

This enhanced classification is an essential tool that could facilitate on-site damage assessment, coordination of humanitarian assistance and reconstruction efforts and aid decision-making for flood management.

Flash flood sediment imbalance at the watershed outlets
Extremes rainstorms, such as Storm Daniel, contribute to an intermittent and limited sediment supply from wadis to the eastern Mediterranean basin’s sandy shorelines. This limited sediment input, coupled with urbanization, waterways, bridges, and upstream dams, disrupts the wadi flow28,32, which results in shoreline retreat, beach losses and coastal flooding during storms and surges33,34. Moreover, marine currents, tide force, and wave energy35 are additional factors driving this coastline dynamic.

Other drivers contribute to the shoreline retreat, such as unregulated and excessive sand mining33, sea-level rise36, relative sea-level rise arising from coastal ground subsidence37–42, and weakening of the protective coastal barrier. The latter comprises sandbars (i.e., submerged sand ridges), extensive beach width, and dunes, constituting the primary defense against storm surges43,44. However, the combined forces of wind, waves, and rip currents can considerably modify the beach’s geomorphology, locally compromising this protective barrier.

Furthermore, the increasing aridity of the region presents an ambivalent impact on sediment supply in wadis: it not only diminishes water discharge and sediment load in wadis but also contributes to desertification, reducing vegetation that retains sediments, thereby amplifying sediment erosion and loading wadis45.

Globally, the northeastern coast of Libya indicates no substantial coastal erosion for 1984–2016, with some exceptions, including a 3.2 m yr−1 (±0.6) regression in Al Haniyah, 1.2 m yr−1 (±0.3) accretion at the mouth of Wadi-Derna, and 1 m yr−1 (±0.3) regression in an isolated beach in Susah34. The latter regression in Susah is consistent with previous findings in Westley and Andreou33 and is discussed further below in this section.

Prior to Storm Daniel, Susah suffered locally from coastal erosion, damaging the Hellenistic and Roman archaeological sites of Apollonia, its historic name33,46. For instance, Fig. 9 shows that the shoreline had retreated by ~200 m since 197433, or ~50 m according to Luijendijk, et al.34, precisely at the mouths of two wadis in Susah, revealing a disappearance of the protective barrier and/or a lack of accretion. This regression is likely associated with a decrease in the frequency and/or magnitude of rainstorms, changes in land use, and, therefore, a decrease in sediment discharge from these wadis into the sea. However, further investigations are needed to validate this hypothesis of decreased sediment supply impacting the coastline. Moreover, a better understanding of the complex interplay between local environmental factors characterizing arid regions, the influence of regional weather patterns, and the eastward shift of the medicanes, typically located in the western and central Mediterranean Sea1,2, is essential.

Our CCD–PCA analysis in Susah reveals infrastructure damages within the city and flood erosion at the wadis’ mouths, as illustrated in Fig. 9. The resulting flooded or swept-away roads led to Susah’s isolation on September 13th, 202347. In particular, the largest damage area is located at the confluence of two wadis where sediment discharge, at their mouths, potentially accreted and replenished the beach above-mentioned and protective barriers. Notably, we observe the apparent reactivation of a wadi within Susah’s delta, which had been obstructed since before 1974 (see Fig. 9). This reactivation partially confirms the hypothesis of localized shoreline retreat that started after a flow obstruction resulting from infrequent rainstorms and slow sediment loading within the wadis.

Episodic weather patterns intermittently obstruct wadi’s flow for extended periods, spanning several decades, for example. Such prolonged intervals affect public perception, leading the population to underestimate the risk48 and construct infrastructures and buildings in these vulnerable areas.

Implications for other arid coastal areas
Desert floods are widely observed in other arid areas, such as the desert of the Arabian Peninsula, Sinai (Egypt), Oman, Jordan, Israel, and Qatar, among others21,49–54. However, quantitative damage assessments of flash flood aftermath are uncommon. When such investigations are conducted, they are primarily conducted through optical photo-interpretation. This technique is typically applied in localized areas for humanitarian purposes, aiming to assess damages in urban communities to support rescue or rehabilitation efforts. However, they often fail to assess the drivers accentuating some of these events. As demonstrated in our CCD–PCA analysis, mapping damages and flood erosion at a larger scale offers various advantages, such as a more comprehensive understanding of sediment loading in wadis, identifying erosion and accretion processes, and identifying damaged crops in floodplains. As such, mapping the extent of the post-storm erosion is crucial to understanding floods and sediment transport dynamics to update existing urban plans and coastal management strategies to be more resilient to future hydroclimatic extremes.

Gravity dams, like those that collapsed in Derna, play an essential role in flood management in arid regions. These structures are designed to withstand rapid changes in hydrostatic pressure caused by changes in environmental load conditions, such as successive droughts and flash floods that can result in deformation and cracking55. Their maintenance is critical to ensure their resilience and prevent potential failures. Consequently, there is a need for fragility assessments, particularly for the dams situated in catchments showing extended erosion hotspots and sediment loading that can exacerbate the changes in hydraulic pressure caused by dense turbid water, as outlined in our investigation.

Furthermore, the primary importance of accurately mapping the flood erosion on the watershed scale is assessing the local drivers amplifying the devastating nature of flash floods under increased hydroclimatic fluctuations in the eastern Mediterranean basin and the Arabian Peninsula56. This, in turn, helps mitigate adverse effects on human lives, infrastructure, water management, agriculture, and other vital socio-economic activities in these harsh desert environments. For instance, the urbanized watershed outlets of other coastal areas such as Medjerda in Tunisia, Tripoli in Libya, Alexandria in Egypt and southwestern of the Arabian Peninsula face increased flood events with several recorded damages32. Using the CCD method in these areas following the next rainstorms will be crucial for understanding the extent and magnitude of the watersheds’ physical attributes, particularly in the context of increased droughts and floods. Evaluating the linkage between these physical characteristics and the resulting flood damages solely through optical photo-interpretation poses multiple challenges. Therefore, it is imperative to expand SAR coverage and CCD implementation across vulnerable coastal arid areas for the upcoming decades to comprehend the convoluted relation between the occurrence of these hydroclimatic extremes and their impacts in these poorly characterized yet densely populated areas. It is also essential to understand how these extremes affect coastal morphodynamics.

Finally, by implementing proper infrastructure and understanding sediment transport processes within watersheds, these hydraulic extremes can be leveraged to harvest stormwater, relieving local water scarcity. Using this stormwater can facilitate the creation of green spaces57, minimizing soil erosion and improving the drainage of future stormwater, among other benefits such as urban agriculture58 and reduction of heat islands in cities59. Our findings suggest that redesigning a substantial part of the coastal cities in arid areas is necessary to improve their resilience to extreme events. This conclusion is evidenced by the severity of observed damages across Derna city center and Susah, typical expanding deltaic cities in the eastern Mediterranean basin, revealing that 66% and 48% of their respective surface area have experienced moderate-to-high damages. These figures underscore the need for urban planners and policymakers to rethink the urban fabric of these coastal cities, where hydroclimatic extremes, though rare and sparse, are often underestimated48,60. As such, we call for the urgent need to undertake strategic land-use transformations in several of these areas, notably by creating green spaces to control soil erosion61. Implementing such policies under the current socio-economic instabilities and the lack of public awareness of hydroclimatic risks48 remains a challenge.

Methods
In this section, we first expose the gained value in using SAR radar rather than optical images to identify surface changes after rainstorms in arid and semi-arid areas. Then, we introduce the CCD–PCA method for producing the flood erosion map. Additionally, we leverage the resulting flood erosion map to evaluate the erosion hotspots, an important step to estimate sediment loading spatially in wadis. This latter assessment is essential as sediment load intensifies the turbidity of flash floods and influence the likelihood of mudflow occurrences in watershed outlets during future rainstorms. Moreover, we classify the damages severity in the cities of Derna and Susah using the CEMS damage assessment map. Finally, we elaborate on the validation and limitations of the CCD–PCA method.

Gained value in using radar observations
Figure 2 shows the before/after map of the flooding in Derna, captured by Sentinel-2 multispectral and Sentinel-1A SAR satellites for the summer and fall of 2023. Storm Daniel’s extensive cloud coverage obstructs satellite optical observations, yet some parts of the city reveal traces of mudflow. In contrast, SAR’s ability to penetrate the clouds enables the assessment of the entire region. For instance, on the SAR amplitude image, we identify the river breach in Derna’s delta resulting from the characteristic specular reflection of the radar wave on the water. However, it is challenging to visually assess the building damages, flood surface erosion, sediment deposits, and flooded extent from SAR amplitude images only due to the relatively lower spatial resolution compared to the optical ones.

Moreover, saturated soils exhibit higher dielectric properties, producing high radar amplitudes. Thus, wet flooded extents can be identified using the Non-Coherence Change Detection (NCCD) technique23,62. NCCD is performed by comparing SAR backscatter amplitudes between acquisitions to identify specular reflections on flooded areas and increases in dielectric on wet soils. To this end, we compare the backscattering amplitudes between the “dry” acquisition of 2023–06–21 and the “wet” acquisition of 2023–09–13 (see Fig. 2); however, we do not observe significant dielectric changes within the city. Notably, we calculate a negligible amplitude increase of 0.5 dB after the flash flood, with a median value of −7.1 dB for 2023-06-21 and −7.6 dB for 2023-09-13, over the flooded city. Therefore, using NCCD is not relevant in our investigations.

Furthermore, unlike SAR amplitude images, Single Look Complex (SLC) radar data products comprise both amplitude and phase information. The phase is crucial when assessing changes in surface scattering between acquisitions. Notably, the surface damages and erosional processes caused by a flash flood alter the phase spatial distribution of the scattered radar returns between pre and post-storm acquisitions over the impacted area. Moreover, the temporal variability of the radar phase is described in (1), (2), and (3); these equations encompass the contribution of the phase difference in the interferometric SAR (InSAR) coherence that uses SLC SAR images.

Therefore, the NCCD above-mentioned example over Derna and radar scattering principle underscore the rationale for using SLC SAR images rather than SAR amplitude images to observe flood aftermaths in arid regions.

CCD–PCA method
The initial step of the CCD–PCA approach involves producing a series of InSAR coherence images. These images are derived from SLC SAR scenes that contain amplitude and phase information for each pixel, which varies with soil dielectric and surface scattering. Therefore, by comparing the amplitude and phase of two distinct co-registered SLC SAR scenes representing the same footprint at different dates, we can assess the surface changes. The spatiotemporal variability of the amplitude and phase is estimated by calculating the cross-correlation, or InSAR coherence ɣ, between two SLC scenes, as expressed in (1) and (3)63.

ɣ = |E[c1c2*]| / (E[|c1|2]1/2 E[|c2|2]1/2), 0≤ɣ≤1 (1)

with c = Aeiϕ (2)

ɣ = |E[A1A2ei(ϕ1−ϕ2)]| / (E[A12]1/2 E[A22]1/2), 0≤ɣ≤1 (3)

Where ɣ is in the InSAR coherence or complex correlation coefficient, c is an SLC SAR image, c* is the complex conjugation of c, c1 and c2 are the co-registered complex SAR reference and secondary images, respectively, A1 and A2 are the amplitudes, and ϕ1 and ϕ2 are the phases of these complex SAR images. Moreover, the angle brackets represent the kernel window where coherence is estimated through spatial averaging using an adaptive nonlocal–InSAR (ANL–InSAR) filter64.

When computing InSAR coherence between pre-storm and post-storm SAR images acquired under dry conditions, we capture the changes in surface scattering relative to flood surface erosion (see acquisitions of the SAR images in Supplementary Table 2). However, it is essential to acknowledge that soil dielectric is assumed to remain constant between the two dry acquisitions; thus the dielectric changes contribution is negligible in the coherence. Moreover, coherence pairs should be cautiously selected, considering temporal and perpendicular baselines, to minimize spatial decorrelation arising from topography and multi-pass satellite acquisition geometry. Although some residuals could remain, the CCD approach mitigates them.

Figure 10 presents a diagram illustrating the CCD method. The selection of InSAR pairs used in this CCD method is an essential part of the approach; they are listed in Supplementary Table 4. First, it helps to reduce spatial decorrelation by selecting pairs showing small perpendicular baselines65 (e.g., <100 m). Second, our pair selection excludes potential soil moisture signals in the InSAR coherence by selecting only acquisitions under dry soil conditions. Particularly, we produce seven coherence pairs from dry acquisitions with varying perpendicular and temporal baselines ranging from 23 m to 68 m and 12–72 days, respectively. Five of these pairs correspond to pre-storm coherence with temporal baselines of 12, 24, 36, and 48 days. These five pairs serve as ‘reference coherences’ or ‘background coherences’ for isolating the magnitude of coherence attributed solely to vegetation and surface roughness. The remaining two pairs overlap the rainstorm event and contain the “change” information that we aim to identify in this study.

The CCD process could be performed by calculating the coherence difference between two pairs of similar perpendicular and temporal baselines, subtracting ɣpost-storm from ɣpre-storm23. Alternatively, an averaged version option would be subtracting the average of several ɣpre-storm pairs from the average of several ɣpost-storm pairs. Instead, we opt for a different approach, using the PCA to synthesize the information from these seven pairs. PCA reduces a dataset’s redundancy and linear dimensionality66. From this statistical analysis, we extract two significant principal components (PCs) representing linear combinations of the original dataset. However, due to the non-linear nature of coherence decay over an extended period, we limit our analysis to coherence pairs with a temporal baseline of less than 84 days. The choice of this maximum temporal baseline would differ from one climatic region to another. Hence, to reproduce the PCA data reduction process in regions other than eastern Libya and identify a suitable maximum temporal baseline, we propose an approach detailed in the section “Requirement for effective PCA in the CCD”, in Supplementary Information.

In our investigation, the first PC represents 92% of the common information within the dataset, and the second PC accounts for 4.5% of the variability that is not captured by the first PC67,68. The first PC can be interpreted as an averaged and standardized pre-storm coherence, while the second PC can be interpreted as the flood erosion signature (see Figs. 1, 8 and 9). On this second PC, we apply a threshold defined as [mean + δ; maximum coherence value] to facilitate the visual interpretation of our CCD–PCA results and create a shapefile illustrating the post-storm changes.

Furthermore, to test the PCA’s sensitivity, its intrinsic error, in identifying any potential erosion preceding the rainstorm and other residual errors, we conduct a similar PCA by excluding the two ɣpost-storm acquisitions above-mentioned (see coherence pairs in Supplementary Table 4). In this scenario, the common information within the dataset constitutes 96% of the information, while the second PC accounts for 1.5% only, characterizing pre-storm erosion. Then, by intersecting this CCD–PCA pre-storm erosion with the CCD–PCA post-storm erosion, we can isolate the false-positive identifications (commission errors) in the CCD–PCA post-storm erosion. This intrinsic error in the CCD–PCA method accounts for a 13% occurrence probability (refer to “Identification of the intrinsic CCD–PCA error” in Supplementary Information for details). This bias identification improves the reliability of our flood erosion mapping process.

Additionally, we use ground-truthing for validation over urban areas, as developed in the “Validation of CCD–PCA” section further below.

Classifying damages in Derna and Susah
This damage classification is based on the CCD–PCA validation with the CEMS map, detailed in “Validation of CCD–PCA/Validation using the CEMS damage assessment map” from the Methods section, where both datasets are intersected within a grid, where each grid cell measures 50 m × 50 m. The CEMS dataset encompasses the two following damage scenari:
• Case 1: With damaged or destroyed buildings, destroyed bridges, damaged and destroyed roads
• Case 2: With destroyed buildings, destroyed bridges, damaged and destroyed roads

To conduct this classification, we first assume perfect exactitude in damage detection for both datasets while acknowledging the possibility of damage omission. Moreover, we consider that the CCD–PCA exclusively detects substantial damages or destruction but cannot identify low-magnitude damages. Based on these assumptions, we classify the damages as high, moderate and low according to the following criteria (see Supplementary Table 8):
• High damages: when the damages observed from the CCD–PCA identification are also identified in the CEMS map (Case 1 and Case 2); also when Case 2 damages are not identified (omission) by CCD–PCA:
(Case 1 (True detection of occurrence) ∩ Case 2 (True detection of occurrence)) ∪ Case 2 (omission)
• Moderate damages: when only CCD–PCA identifies a surface change (false-detection):
Case 1 (False detection) ∪ Case 2 (False detection)
• Low damages: when Case 1 damages are omitted by CCD–PCA; however, we remove from this selection the “High damages” instances of Case 2 damages omitted by CCD–PCA:
Case 1 (omission) − Case 2 (omission)

Classifying sediment loading
Our assessment of sediment loading in wadis relies on the identification of areas showing high erosion risk or erosion hotspots69. To identify these hotspots within the watersheds, we implement a simplified version of the Revised Universal Soil Loss Equation (RUSLE) theoretical method30. The RUSLE method involves rainwater erosivity, soil erodibility, land surface cover, slope gradient and length, and soil erosion management index. Conversely, our CCD–PCA flood surface change detection already integrates the identification of erosion patterns. Thus, we subsequently intersect these CCD–PCA results with 3-day rainfall accumulation and slope gradient variables in order to derive the erosion hotspots (see Supplementary Table 7).

The first variable considered here is the 3-day rainfall accumulation during Storm Daniel; we chose a conservative estimation threshold of 50 mm, which enables the covering of the whole ROI (see Fig. 1). The second variable corresponds to the post-storm flood erosion, which is a metric derived from our CCD–PCA method. We define erosion as significant when more than 5% of the surface area within any 2 km × 2 km bin shows a CCD–PCA signal (i.e., 0.2 km2). The third variable under consideration is the average topographic slope gradient within a 2 km × 2 km bin; we use the threshold angle of 3°, a common gradient threshold employed in arid and semi-arid regions to characterize erosion70,71. Assigning weights to these above-mentioned variables is challenging; therefore, multivariate statistical techniques such as factor analysis (e.g., PCA) can be performed to determine weighting factors72. Instead, in our investigation, we apply a first-order binary classification based on thresholding to identify erosion hotspots arising from Storm Daniel. The corresponding classification criteria are addressed in the Supplementary Information’s “Sediment loading assessment” section.

Lastly, after characterizing the erosion hotspots, we assess the areas of sediment loading by intersecting the identified hotspots with the locations of wadis.

The intermediary mapping is displayed in Supplementary Information’s “Sediment loading assessment” section. Moreover, the methodology used to derive the wadi networks from the NASADEM is presented in the Supplementary Information’s “Identification of wadis” section.

Furthermore, to leverage this erosion hotspots assessment, we recommend applying the same CCD–PCA method but in the ascending direction (opposite to the descending direction used in this study). This second iteration would allow erosion mapping in the radar-shadowed areas and contribute to a more robust assessment of the erosion dynamics within the slopes.

Validation of CCD–PCA
We validate our CCD–PCA observations over the cities of Derna and Susah through verification with two distinct sources. The first source was published in the European Union, et al.10 report and represents the flooding intensity map generated from flow modeling derived from a 12-meter resolution Digital Surface Model (DSM). Although this dataset does not consider the density of habitations and the stormwater drain infrastructures, the authors used this model as a direct proxy for assessing the magnitude of damage. Consequently, they determined that 10% and 28% of the housing in Derna and Susah, respectively, were destroyed. These findings align in magnitude with our assessment of surface changes in Derna and Susah, revealing that 9% and 18% of their respective surface areas were impacted (see “Urban damages in Derna and Susah” section for details).

The second source was produced by Copernicus Emergency Management Service (CEMS) and represents the damage assessment evaluated through photo-interpretation, as depicted in Fig. 8. This photo-interpretation reveals distinct categories, including ‘flood traces’ extents, ‘buildings destroyed’, ‘buildings damaged’, ‘bridges and elevated highways destroyed’ and ‘roads destroyed or damaged’—the term ‘photo-interpretation’ is mentioned in the product metadata. The two categories, ‘buildings destroyed’ and ‘buildings damaged’, are subjective and, therefore, influence our validation assessment.

Our CCD–PCA product, in its current state, identifies surface changes without providing precise information about the specific nature of these changes. However, we assume that the CCD detections within urban areas are primarily attributed to infrastructure damages rather than surface erosion and sediment deposits.

Validation using the flooding intensity map. In Derna, the high-intensity flow model corresponds closely to the areas of damage observed on the CEMS damage assessment map and CCD–PCA map (see Fig. 8). In particular, both the CCD–PCA and the high-intensity flooding model accurately delineate the flood extent within the watershed outlets of Derna.

In Susah, neither the damage assessment map nor the CCD–PCA map aligns well with the flood intensity map (see Fig. 9). This shows the limit in comparing datasets of different natures.

Validation using the CEMS damage assessment map. In contrast to the previous validation source, the nature and spatial resolution of the CEMS damage assessment map facilitate quantitative validation of the CCD–PCA results. To achieve this for Derna city center and Susah, we intersect the CCD–PCA results with the CEMS dataset within a grid, where each bin measures 50 m × 50 m.

Given the inherent differences between the two maps (i.e., the CCD–PCA is derived from radar scattering while the CEMS map is generated from photo-interpretation), we divide the CEMS dataset into two distinct maps: Case 1 map includes damaged and destroyed buildings, as well as damaged/destroyed bridges and roads, while Case 2 map comprises solely destroyed buildings, with damaged/destroyed bridges and roads.

Within the flooding extent16 of the city center of Derna, we determine that the CCD–PCA map corresponds to the CEMS-Case 1 map for 61% of the area (true-detection), with a false-detection of 7% and an omission error of 32% (see Supplementary Fig. 2, presented in the Supplementary Material under the “Validation of CCD–PCA in Derna and Susah” section). With the CEMS-Case 2 map, we obtain a true-detection rate of 65.5%, a false-detection rate of 29% and an omission error of 5.5%. The high false-detection rate of 29% in Case 2 and low false-detection of 7% in Case 1 confirm that the CCD–PCA method identifies the CEMS’s “destroyed buildings” but is also somehow sensitive to CEMS’s “damaged buildings”. However, the high omission error of 32% in Case 1 suggests that identifying CEMS’s “damaged buildings” remains challenging and is subject to the subjective interpretation of the term “damaged” within the CEMS dataset. Moreover, we suggest that the omission and false-detection errors arise from three possible causes: inaccuracies in the CEMS map, extensive sediment deposits in the streets, or intrinsic error in the CCD–PCA analysis (see “CCD–PCA method” section above).

We perform the same validation over the flood extent of Susah and obtain for Case 1 a true-detection rate of 42%, a false-detection rate of 18%, and an omission error of 40%. For Case 2, we determine a true-detection rate of 64.5%, a false-detection rate of 34%, and an omission error of 1.5% (see Supplementary Fig. 3). These percentages observed in Susah are comparable to those for Derna, suggesting similar sensitivity of radar scattering to infrastructure damage magnitude observations in both cities.

We attribute this CCD–PCA sensitivity to damage magnitude to the difference in phase spatial variability. Specifically, “destroyed buildings” are associated with substantial phase spatial variability, while “damaged buildings” exhibit a more subtle phase spatial variability.

We find that our CCD–PCA damage map aligns well with the CEMS dataset for both cities, achieving a true-detection rate of up to ~65% of the surface area. Furthermore, we suggest that the distinct multi-temporal InSAR coherence sensitivity to ‘damaged’ and ‘destroyed’ infrastructures enables the derivation of enhanced damage severity maps from CCD–PCA and CEMS datasets. This is achieved in the “Urban damages in Derna and Susah” section (see Figs. 5 and 6).

Limitations
We identify two sets of limitations restricting our flood surface change mapping. The first set affects the background coherence in the CCD–PCA Methods section. These limitations arise from the influence of the topography in the CCD, introducing spatial decorrelation related to the multi-pass satellite acquisition geometry, and from the potential soil moisture content during the dry acquisitions, i.e., characterized without precipitation in the days preceding the acquisitions. However, these first limitations are negligible when the radar acquisitions are cautiously selected (see details in the CCD–PCA Method).

The second set of limitations is associated with pre-storm erosion, referring to potential sediment deposition through wind, residual noise from surface roughness, and changes in vegetation coverage. We present a comprehensive discussion and assessment of these pre-storm erosion ambiguities in the Methods and Supplementary Information (refer to “Identification of the intrinsic CCD–PCA error”). These above limitations result in a 13% false-positive error across the whole ROI.

Furthermore, we underline that PCA requires a linearity within the InSAR coherence dataset. Although InSAR coherence decays exponentially over an extended period, our study, constrained to a short period of 72 days, maintains a linear decay. This linear characteristic allows the applicability of PCA within this timeframe, thereby ensuring the prerequisite for its reliable use in our CCD–PCA analysis (see “Requirement for effective PCA in the CCD” section in Supplementary Information for details).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Comments and Suggestions for Authors\nMany thanks for the review invitation to the manuscript titled “Storm Daniel's Catastrophic Flood\nAftermath in Libya”. It is read with great interest and is a well-written manuscript. This study\nutilized Coherent Change Detection in conjunction with Principal Component Analysis to detect\nrunoff erosion using seven coherence pairs. The collected data included SAR images pre- and post-\nDanial's storm for Derna and Susah cities in Libya, in addition to precipitation, topographic data\nand watershed extent. Flood erosion maps were presented, which are useful for policymaking to\naddress vulnerabilities and avoid probable future damages. However, it is well known in hydrology\nand hydraulics that areas around ephemeral streams or watershed drainage networks are erosion-\nprone regions. This paper is interesting from my point of view and my recommendation is to make\na major revision before publication in Nature Communications.\nSome comments are as follows:\nIn the introduction section, the knowledge gap should be clearly defined.\nRegarding line 94, is it possible to differentiate between sediment deposition and flood erosion\nusing CCD?\nIn line 373, it is recommended to provide quantitative validation such as the ROC curve as\nimplemented by Wahba et al. (2023), “A Novel Estimation of the Composite Hazard of Landslides\nand Flash Floods Utilizing an Artificial Intelligence Approach.”\nFor results, comparison with the literature is lacking.\nInvestigation of associated morphological characteristics to facilitate the mapping of similar regions\nbefore disaster occurrences.\nFor Figure. 5, many words and lines are not clear.\nIn line 725, it seems that Train and Teros are typos.", "reviewer_2": "The paper discusses the flooding event originating from Medicane Daniel. In particular, the authors\nfocus on assessing the consequences in terms of soil and shoreline erosion following this extremely\ndamaging event.\nThe authors used a fairly traditional method based on SAR satellite imagery and the accuracy of\nthe resulting maps seems rather weak: it is calculated by comparing the result of the pre-event\nanalysis with the post-event analysis, without validation from other sources or from other imagery\n(there are optical images that could provide information on flooded areas. The accuracy in my\nopinion is therefore unreliable.\nThe paper is shown to be original because there have not been many studies to date on Medicane\nDaniel and the flooding it caused. However, I think the article needs considerations at least in the\nintroduction about the consequences of the hurricane in terms of deaths, evacuees and damage\ncaused. There are already a couple of studies in the literature that I found by searching quickly\nonline that I think it is not difficult for the authors to add these numbers to frame the problem.\nThe authors estimate 9 % and 18 % erosion in the surface area of the cities of Derna and Susah,\nand this seems to be the only tangible result of this research. The authors repeat this no less than\n4 times within the manuscript although it is not clear how this percentage was calculated. I would\nclarify what numbers these assessments come from.\nThe results shown by the authors cannot be a meaningful reference until it is clarified how the\nmaps were obtained. It is not clear which pairs of images were considered to draw the maps of\nflooded areas or how they were combined. The method and supplementary materials are so\nincomplete that it seems like a maze to get to the bottom of what was done.\nThe methods and analyses performed need much clarification because under these conditions it is\nnot possible to reproduce the work\n1) Which images were selected? Since only a few images are mentioned, it is requested to include\na table with Sentinel-1 (reading the dates from Figure 5 is very difficult) and Sentinel-2 images.\n2) Explain the method following a step-by-step scheme, starting with SAR image pre-processing\nand continuing with CCD and PCA techniques in a single discussion. The division of the method\nbetween the main text and supplementary materials is really difficult to follow. Certain concepts\nare repeated, others are left out.\n3) It would be desirable to show flooded areas resulting from single SAR images. Indeed, the\nresulting erosion maps seems very large and I would like to see the comparison with the single\nimages.\n4) Since the 'rapid flood' maps were also produced by Copernicus Emergency Management Service\nit would be good to see the flooded areas produced by them and make a comparison to indicate\nand evaluate the real value added by this analysis.\nI think there needs to be a substantial revision of the work.", "reviewer_3": "The paper presents an analysis of erosion caused by flooding from Storm Daniel in Libya in\nSeptember 2023. This event was very extreme and had a high impact on the area, with several\nthousand deaths recorded. As such, it will form an important case study, particularly when\nconsidering the potential of climate change to increase the frequency of such events. In my view, a\ncomprehensive analysis of the event (i.e. its causal factors, the multifaceted impacts, emergency\nresponse, risk management, and changing likelihood under climate change), would be good paper\nfor Nature Communications. However, although the analysis presented is generally sound, the\npaper as submitted falls short of the level needed.\nThe primary focus and contribution of the paper is on the mapping of areas of erosion using C-\nband SAR images, using a Coherent Change Detection method. Much of the paper is devoted to the use\nof this method, and an explanation of its applicability to other areas, and it would be good\nto see this analysis published. However, beyond quantifying the area impacted (as a percentage of\nselected areas of interest, so not directly meaningful), no further analysis of impact is made. Are\nthese areas populated? What is the land use of the areas eroded? How many people were\naffected? What is the estimated economic impact? Further, no analysis of deposition of eroded\nsediments was performed, although the impacts of this is likely significant. In addition, while the\ndam collapses upstream of Derna are mentioned, no analysis specific to this is included (e.g.,\ncause, and erosion/ other damage directly resulting from the collapses). As a consequence of the\nnarrow focus of the analysis, the title of the paper is not appropriate and requires revision.\nIn its current form, I cannot recommend publication of the manuscript in Nature Communications.\nI consider that the work is worthy of being published, but it is not at an appropriate level for this\njournal. Either a substantial reworking is required or, more likely, it should be submitted to a\ndiscipline specific journal, such as one in the area of geomorphology or hazards."}, "author_response": "Response to Reviewer #1: Reviewer’s comments are in black and author’s replies are in blue.\nWe thank Reviewer #1 for his thorough and constructive reviews, comments, and suggestions, which we\nhave fully incorporated in this revised version of the manuscript. We believe this new improved version\nadds more clarity and addresses all the uncertainties highlighted by Reviewer#1.\nComments and Suggestions for Authors\nMany thanks for the review invitation to the manuscript titled “Storm Daniel's Catastrophic Flood\nAftermath in Libya”. It is read with great interest and is a well-written manuscript. This study utilized\nCoherent Change Detection in conjunction with Principal Component Analysis to detect runoff erosion\nusing seven coherence pairs. The collected data included SAR images pre- and post-Daniel's storm for\nDerna and Susah cities in Libya, in addition to precipitation, topographic data and watershed extent. Flood\nerosion maps were presented, which are useful for policymaking to address vulnerabilities and avoid\nprobable future damages. However, it is well known in hydrology and hydraulics that areas around\nephemeral streams or watershed drainage networks are erosion-prone regions. This paper is interesting from\nmy point of view and my recommendation is to make a major revision before publication in Nature\nCommunications.\nSome comments are as follows:\nComment 1: In the introduction section, the knowledge gap should be clearly defined.\nAccepted: We agree with the Reviewer #1 that the knowledge gap was unclear. The studies conducted on\nStorm Daniel in Libya have been predominantly centered on coastal cities. However, there has been a\nnotable lack of research on watershed-scale erosion assessment and hydrological dynamics. In our revised\nmanuscript, we can now read lines 83-97 p3:\n“As such, characterizing the vulnerability of watersheds and their outlets is essential\nfor effective flood management, avoiding further humanitarian disasters in this\ninstable area. However, only limited mapping of the impacts of Storm Daniel was\nexplored, focusing mainly on the coastal urban areas and neglecting the inland parts\nconstituting the watersheds. For instance, a few days following this catastrophic\nlandfall, Copernicus Emergency Management Service (CEMS) conducted ‘rapid\nflood’ mapping from photo-interpretation for the cities of Derna, Benghazi, Al Marj,\nAl Bayda, Shahat, Tobruk and Susah. They delineated the flood extent and delivered\nan assessment emergency mapping 16. Then, the European Union, United Nations, and\nWorld Bank Group collaborated to publish, in January 2024, a comprehensive report\non the catastrophe 10, including ‘flood intensity mapping’ and the flooding extent. In\ntheir report, they emphasize the limited availability of ground truthing data, leading to\nthe use of social media analytics, cell phone data, and night light satellite observations.\nHowever, these efforts did not characterize the runoff soil erosion within the\nwatershed, generating dense turbid flows, which modulate the damages of the\ncatastrophic floods”\nComment 2: Regarding line 94, is it possible to differentiate between sediment deposition and flood\nerosion using CCD?\nAccepted: We added a clarification in the introduction that explains that CCD identifies surface changes\nand without discriminating the deposition from erosion. However, we performed in the revised manuscript\na classification to identify sediment loading from CCD and other physical factors.\nLines 107 to 112 p3 in the introduction now reads:\n“This CCD approach enables the identification of changes in spatial patterns, such as\nmonitoring flood erosion 18-20, displacement of surface scatterers, and sediment\ndeposition in run-off channels 21, but also to assess different levels of infrastructure\ndamages after a rainstorm 22,23. These changes can be attributed to both erosion and\nsediment load processes. Given the inherent interconnection between the two processes,\nwe frequently use the term “flood surface erosion mapping” in our investigation to\ndesignate both processes.”\nAnd from line 112 to 115 p3 we can read:\n“However, we can delineate the sediment loading areas by conducting a comprehensive\nclassification. This approach relies on erosion hotspots identified through CCD analysis,\nhydrological networks, precipitation and topography.”\nComment 3: In line 373, it is recommended to provide quantitative validation such as the ROC curve as\nimplemented by Wahba et al. (2023), “A Novel Estimation of the Composite Hazard of Landslides and\nFlash Floods Utilizing an Artificial Intelligence Approach.”\nAccepted: We acknowledge the importance of quantitative validation in our investigation using ground\ntruth data. Therefore, in our revised version, we integrate a quantitative validation within the “Validation\nusing the CEMS damage assessment map” section in p15-16.\nWe determine in Lines 608 to 609 p 16 that:\n“We find that our CCD–PCA damage map aligns well with the CEMS dataset for both\ncities, achieving a true-detection rate of up to ~65 % of the surface area.”\nComment 4: For results, comparison with the literature is lacking. Investigation of associated\nmorphological characteristics to facilitate the mapping of similar regions before disaster occurrences.\nAccepted: We have included new contextual information in introduction, on the flood damage itself in\nLines 56 to 72 p 1:\n“The extreme hydroclimatic conditions generated by Storm Daniel caused deluges and\nmudflows in the storm landfall area (…) For instance, 10 % of the houses were\ndestroyed, and 18.5 % were damaged 10. (…)These figures mark Storm Daniel as the\ndeadliest storm in all the African continent since 1900 10.”\nWe added a whole section on the “Gained value in using radar observations” to study the flood erosion, in\nlines 364 to 393 p10:\n“Figure 2 shows the before/after map of the flooding in Derna, captured by Sentinel-2\nmultispectral and Sentinel-1A SAR satellites for the summer and fall of 2023. (…) wet\nflooded extents could be identified using the Non-Coherence Change Detection (NCCD)\ntechnique 23,59. NCCD is performed by comparing SAR backscatter amplitudes between\nacquisitions to identify specular reflections on flooded areas and increases in dielectric\non wet soils.(…) Therefore, the NCCD above-mentioned example over Derna and radar\nscattering principle underscore the rationale for using SLC SAR images rather than SAR\namplitude images to observe flood aftermaths in arid regions.”\nLine 355 to 359 p9 now read:\n“Additionally, we leverage the resulting flood erosion map to evaluate the erosion\nhotspots, an important step to estimate sediment loading spatially in wadis. This latter\nassessment is essential as sediment load intensifies the turbidity of flash floods and\ninfluences the likelihood of mudflow occurrences in watershed outlets during future\nrainstorms.”\nWe added a comprehensive classification to identify the sediment loading in wadis, which can modulate\nfuture flash floods. Lines 200 to 203 p6, read:\n“We perform a first-order classification of this run-off erosion and sediment balance\ndynamics on steep gradients above 3 º and hillslopes, where loose sediments are washed\naway and subsequently deposited below in wadis, increasing the likelihood of future\nmudflow events during rainstorms.”\nComment 5: For Figure. 5, many words and lines are not clear.\nAccepted: Figure 5 became Figure 10 in the revised version. We simplified the diagram and improved the\nresolution. The list of coherence pairs that was previously listed in the figure is now enumerated in Table\nI.\nFigure 10. Coherent change detection (CCD) is associated with the principal component analysis (PCA)\nmethod to identify the flood erosion occurring during and after Storm Daniel. The timeline displays the\ndates of the eight SAR acquisitions used to produce the coherence pairs. The coherence pairs are listed in\nTable I.\nComment 6: In line 725, it seems that Train and Teros are typos.\nAccepted: T and T are now remplaced by H and H in line 1163 p44\nrain eros rain eros\nResponse to Reviewer #2: Reviewer’s comments are in black and author’s replies are in blue.\nWe thank Reviewer #2 for his thorough and constructive reviews, comments, and suggestions, which we\nhave fully incorporated in this revised version of the manuscript. We believe this revised improved version\nadds more clarity and addresses all the uncertainties highlighted by Reviewer#2.\nFurthermore, we have made publicly available all the datasets used in this investigation to allow our results\nreproducibility, including the SAR raw images, CCD–PCA products, and ArcGIS package maps in the\nfollowing repository: https://doi.org/10.17605/OSF.IO/M9C7R\nThe paper discusses the flooding event originating from Medicane Daniel. In particular, the authors\nfocus on assessing the consequences in terms of soil and shoreline erosion following this extremely\ndamaging event.\nComment 1: The authors used a fairly traditional method based on SAR satellite imagery and the accuracy\nof the resulting maps seems rather weak: it is calculated by comparing the result of the pre-event analysis\nwith the post-event analysis, without validation from other sources or from other imagery (there are optical\nimages that could provide information on flooded areas. The accuracy in my opinion is therefore unreliable.\nAccepted: We agree that the method used is fairly traditional, which minimize technical errors, however\nits application to this unique case in Libya is the novelty of this paper. As now mentioned in Line 101 to\n104 in p3:\n“We achieve this objective by utilizing Synthetic Aperture Radar (SAR) satellite scenes\nto produce interferometric (InSAR) coherence images and applying the well-\nestablished Coherent Change Detection (CCD) method, which measures the temporal\nvariations between InSAR coherence images 17. “\nMoreover, we agree that the term “accuracy” is too general and leads to misunderstandings depending the\ncontext. Therefore, in our revised version, we removed the term “accuracy” and replaced it with precise\nwording depending the context, such as “omission error”, “commission error” (=“false-detection”), and\n“intrinsic error”.\nFor instance, the “CCD-PCA Method” Section now reads in lines 460 to 463 p12, now reads:\n“This intrinsic error in the CCD–PCA method accounts for a 13 % occurrence\nprobability (refer to “Identification of the intrinsic CCD–PCA error” in\nSupplementary Materials for details). This bias identification improves the reliability\nof our flood erosion mapping process.”\nWe also agree that the exactitude of the measure needs validation from other sources. Therefore, in our\nrevised manuscript we provide a qualitative and quantitative validation of our CCD method with two\ndistinct datasets. Line 545 to 557 p15 of “Validation of CCD–PCA” section reads:\n“We validate our CCD–PCA observations over the cities of Derna and Susah through\nverification with two distinct sources. The first source was published in the European\nUnion, et al. report and represents the flooding intensity map generated from flow\nmodeling, derived from a 12–meter resolution Digital Surface Model (DSM). (…).The\nsecond source was produced by Copernicus Emergency Management Service 16 and\nrepresents the damage assessment evaluated through photo-interpretation, as depicted\nin Figure 7.(…)”\nFurthermore, the “Urban damages in Derna and Susah” Results and discussion section now reads in lines\n222-227 p6:\n“These observations are validated with two distinct sources, showing a true-detection\nof 61 – 66 % and 42 – 65 %, a false-detection of 7 – 29 % and 18 – 34 %, and an\nomission error of 6 – 32 % and 2 – 40 %, for Derna’s city center and Susah,\nrespectively (see “Validation of CCD–PCA” in the Method section for more details).\nThese figures exhibit comparable ranges of value for both cities, reflecting a constant\ndegree of validation of the CCD–PCA method.”\nComment 2: The paper is shown to be original because there have not been many studies to date on\nMedicane Daniel and the flooding it caused. However, I think the article needs considerations at least in the\nintroduction about the consequences of the hurricane in terms of deaths, evacuees and damage caused.\nThere are already a couple of studies in the literature that I found by searching quickly online that I think it\nis not difficult for the authors to add these numbers to frame the problem.\nAccepted: In our first version we noted “catastrophic death toll” and “deadly flash flood” without giving\nnumbers because the figures were only estimations. At the time of writing, the death toll was estimated to\nbe between 5000 and 20000, then at the time of the submission the figure stood at 11000. In our revised\nmanuscript, we provide the updated numbers as well as the socio-economic context of the region.\nIn the introduction, we can read now from line 56 to 82 p1-2:\n“The extreme hydroclimatic conditions generated by Storm Daniel caused\ndeluges and mudflows in the storm landfall area (see Figure 1) primarily constituted\nof rangelands (grasses and shrubs) and bare soils 11. Despite the limited urban\nfootprint in the storm landfall area 10, significant infrastructure damage has been\nobserved, including 5 % of the road network being destroyed, 50 % becoming\ninaccessible 10, and the collapse of two flood-control dams upstream of the city of\nDerna on September 11th 12 (see Figure 1). The latter triggered a deadly flash flood\nflowing through the city, located on the river’s deltaic outlet (see Figure 2), destroying\na substantial portion of the city buildings, urban infrastructure, and bridges 13, which\nresulted in the accumulation of 8.8 million tons of debris14. For instance, 10 % of the\nhouses were destroyed, and 18.5 % were damaged 10. The storm notably affected the\ncity of Derna but also other coastal cities. For example, 28 % of the houses in Susah\nwere destroyed 10. Albayda, Al-Marj, Shahat, Taknis, Battah, Tolmeita, Bersis, Tokra,\nand Al-Abyar have also witnessed severe damages, causing a total of 5898 casualties,\n8000 missing, 44800 people displaced, and 18838 houses damaged or destroyed in the\nwhole coastal area encompassing Benghazi, Jabal Al Akhdar, Al Marj and Derna 14\n(see Figure 3 and Figure 4). These figures mark Storm Daniel as the deadliest storm\nin all the African continent since 1900 10.\nThis eastern coast of Libya is one of the main hubs for energy export to Europe.\nThis catastrophe will cost US$ 1.8 billion in repairing the coastal damages 10. This\nfigure includes the devastation of crops and harvests across 16209 ha, along with the\ndestruction of vital topsoil for 4.6 % of the agricultural area 10. Additionally, 74363\nanimals perished, accounting for 3.2 % of the region's livestock strategic reserve 10.\nFurthermore, this catastrophe is worsened by pre-existing humanitarian needs\nand restricted road access due to political instability and intermittent civil wars since\n2011 15. These factors impede the reconstruction efforts, potentially leading to regional\nnegative economic growth over the coming years and delaying the recovery of the\nnational economy 10.”\nComment 3: The authors estimate 9 % and 18 % erosion in the surface area of the cities of Derna and\nSusah, and this seems to be the only tangible result of this research. The authors repeat this no less than 4\ntimes within the manuscript although it is not clear how this percentage was calculated. I would clarify\nwhat numbers these assessments come from.\nAccepted: These percentages are determined by calculating the ratio of the CCD flood erosion area to the\ntotal surface area within the city limits. No elaborate calculation was performed to retrieve these numbers.\nIn the “Urban damages in Derna and Susah” section of Results and Discussion, we can now read in the\nrevised version from line 216 to 222 p6:\n“Overall, our CCD–PCA mapping reveals that 18 % of Susah’s surface area exhibits\ndamage and flood surface changes. This percentage is determined by calculating the\nratio of the CCD–PCA flood surface change to the total surface area within the city\nlimits. Similarly, in Derna, two watershed outlets present notable damages, erosion\nand deposits, particularly within the wadis and deltas. Figure 7 shows the extent of\ndamages within the city and its surroundings, derived from CCD–PCA, covering 9 %\nof its surface area and 27 % of Derna’s city center.”\nMoreover, we now provide a damage severity classification by combining our CCD-PCA results with the\nobservations from Copernicus Emergency Management Service. Lines 233 to 245 p6-7 now read:\n“Our comprehensive classification now effectively distinguishes between areas of\nhigh, moderate, and low damage. The corresponding damage severity maps offer the\nlarge-scale snapshot capability of SAR remote-sensing from which the damage\nmagnitude is unclassifiable, with the qualitative capability of CEMS’ photo-\ninterpretation and ground-truthing. The assumptions and criteria to perform this\nclassification are detailed in the “Classifying damages in Derna and Susah” section\nin Method.\nWe perform this classification within the flooded extent of Derna’s city center and\nobserve that the damage severity for high, moderate, and low categories accounts for\n36.5 %, 29 %, and 27 % of the surface area, respectively, with 7.5 % remaining\nundamaged (see Figure 5). Moreover, the same classification within the urban area of\nSusah, as outlined in Figure 6, reveals that 14 % of the surface area exhibits high\ndamages, 34 % displays moderate damages, 39 % shows low damages, and 13 %\nremains undamaged.”\nThe classification method used to combine the datasets and calculate these values, is detailed in the\n“Classifying damages in Derna and Susah” section from Method and in Table II, from line 470 to 508 p13-\n14.\nComment 4: The results shown by the authors cannot be a meaningful reference until it is clarified how\nthe maps were obtained. It is not clear which pairs of images were considered to draw the maps of flooded\nareas or how they were combined.\nAccepted: The pairs of images considered are indeed crucial and were provided in Figure 10. For better\nclarity, we improved the revised manuscript by simplifying the Figure 10 and placing the list of the\ncoherence pairs (Table I) in the CCD-PCA Method section. Moreover, we now list the SAR images\nemployed for identification of flooded erosion (Table III) in the “Supplementary materials” section (see\ntable in Comment 6).\nThe Method section in lines 467 p12 now reads:\nTable I. Coherence pairs used in the CCD–PCA analysis.\nCCD– CCD–PCA\nPCA with without Interval Perpendicular\nInSAR coherence pair\nstorm storm (days) baseline (m)\noccurrence occurrence\n2023–06–21 2023–08–08 × × 48 68\n2023–07–03 2023–07–27 × × 24 45\n2023–07–03 2023–07–15 × × 12 50\n2023–07–27 2023–08–20 × × 24 53\n2023–07–15 2023–08–20 × × 36 41\n2023–08–20 2023–09–25 × 36 41\n2023–07–27 2023–10–07 × 72 23\n“Figure 10. Coherent change detection (CCD) is associated with the principal component analysis (PCA) method\nto identify the flood erosion occurring during and after Storm Daniel. The timeline displays the dates of the eight\nSAR acquisitions used to produce the coherence pairs. The coherence pairs are listed in Table I.”\nComment 5: The method and supplementary materials are so incomplete that it seems like a maze to get\nto the bottom of what was done. The methods and analyses performed need much clarification because\nunder these conditions it is not possible to reproduce the work.\nAccepted: Ensuring the reproducibility of our investigation was a priority. We addressed this concern in\nresponse to Comment 4 and Comment 5.\nMoreover, we reorganized the Method section, which now enclose a sequential description to explain the\nCCD-PCA Method, two classifications (damage severity classification and sediment loading classification),\nand CCD-PCA validation. Furthermore, we redesigned the CCD-PCA Method diagram (Figure 10) and\nplaced the information that are non-essential in the supplementary material section.\nAdditionally, we uploaded the SAR raw images, CCD–PCA products, and ArcGIS package maps in the\nfollowing repository: https://doi.org/10.17605/OSF.IO/M9C7R\nComment 6: 1) Which images were selected? Since only a few images are mentioned, it is requested to\ninclude a table with Sentinel-1 (reading the dates from Figure 5 is very difficult) and Sentinel-2 images.\nAccepted: We simplified this figure and now listed the InSAR pairs in Table I (see answer to comment 4).\nMoreover, the SAR images are listed in Table IV, and the Multispectral images are listed in Table V in the\n“Supplementary materials” in page 34:\nList of satellite images used for CCD–PCA processing and visualization\nTable IV and Table V, presented below, enumerate the SAR and Multispectral images, respectively,\nutilized in this study to identify and visualize flood erosion.\nTable IV. C-band SAR images used in the CCD–PCA\nDirection, Relative\nDate Satellite\nPolarization and orbit Scene ID Wavelength\n(yyyy–mm–jj) name\nLevel number\n2023–06–21 Sentinel–1A Desc., VV, L1 7 386783 5.6 cm\n2023–07–03 Sentinel–1A Desc., VV, L1 7 388143 5.6 cm\n2023–07–15 Sentinel–1A Desc., VV, L1 7 389532 5.6 cm\n2023-07–27 Sentinel–1A Desc., VV, L1 7 390906 5.6 cm\n2023–08–08 Sentinel–1A Desc., VV, L1 7 392297 5.6 cm\n2023–08–20 Sentinel–1A Desc., VV, L1 7 393833 5.6 cm\n2023–09–25 Sentinel–1A Desc., VV, L1 7 398421 5.6 cm\n2023–10–07 Sentinel–1A Desc., VV, L1 7 399956 5.6 cm\nRelative orbit\nDate Satellite\nType and Level Number & Wavelength\n(yyyy–mm–jj) name\nScene ID\n2023–09–13 Sentinel–1A SAR, L1 7 & 396898 5.6 cm\n50 & 34SFB Band 4 (665 nm),\n50 & 34SEA Band 3 (560 nm),\n2023–07–09 Sentinel–2B Multispectral, L2A\n50 & 34SFA Band 2 (490 nm)\n50 & 34SEB\nBand 4 (665 nm),\n2023–09–07 Sentinel–2B Multispectral, L2A 50 & 34SEB Band 3 (560 nm),\nBand 2 (490 nm)\n50 & 34SFB Band 4 (665 nm),\n50 & 34SEA Band 3 (560 nm),\n2023–09–12 Sentinel–2B Multispectral, L2A\n50 & 34SFA Band 2 (490 nm)\n50 & 34SEB\nBand 4 (665 nm),\n2023–09–15 Sentinel–2A Multispectral, L2A 50 & 34SEB Band 3 (560 nm),\nBand 2 (490 nm)\nprocessing and continuing with CCD and PCA techniques in a single discussion. The division of the method\nbetween the main text and supplementary materials is really difficult to follow. Certain concepts are\nrepeated, others are left out.\nAccepted: While the letter format is not conducive to providing an exhaustive step-by-step method that is\nalready well-documented, we agree with Reviewer#2 that our method section was difficult to follow. In our\nrevised manuscript, we provide now more details in the Method section to offer a step-by-step approach\nthat describe the CCD-PCA methodology (see also answer of Comment 5). Moreover, we only placed the\nnon-essential information in the Supplementary material section. Additionally, we updated the Method\ndiagram to provide more clarity (Figure 10).\nComment 8: 3) It would be desirable to show flooded areas resulting from single SAR images. Indeed, the\nresulting erosion maps seems very large and I would like to see the comparison with the single images.\nAccepted: We created a new Figure 2, representing the areas affected by the flash flood in the city center\nof Derna, before and after flooding, with single SAR and optical images. This figure illustrates the\ncapability of the SAR in sensing through clouds, particularly after a large rainstorm. Moreover, the figure\n2 shows the limitations of the SAR amplitude in mapping the surface changes. In contrast, the CCD\napproach (i.e., comparison between two SLC SAR images) effectively uses the amplitude and phase\ninformation of the SAR to identify these changes.\nIn particular, we added the section “gained value in using radar observations” in Methods in page 10, to\nunderscore the specificity of the SAR and of the CCD.\nWe can now read in the revised manuscript from line 370 to 393 p10:\n“However, it is challenging to visually assess the building damages, flood surface\nerosion, sediment deposits, and flooded extent from SAR amplitude images only due\nto the relatively lower spatial resolution compared to the optical ones. (…)\nTherefore, the NCCD above-mentioned example over Derna and radar scattering\nprinciple underscore the rationale for using SLC SAR images rather than SAR\namplitude images to observe flood aftermaths in arid regions.”\nComment 9: 4) Since the 'rapid flood' maps were also produced by Copernicus Emergency Management\nService it would be good to see the flooded areas produced by them and make a comparison to indicate and\nevaluate the real value added by this analysis.\nI think there needs to be a substantial revision of the work.\nAccepted: We added in this revised manuscript a comprehensive quantitative validation using the\nCopernicus Emergency Management Service (CEMS) photo-interpretation map, along with a qualitative\nvalidation utilizing a Flood Intensity Model published in January 2024 in an official report jointly delivered\nby the European Union, United Nations, and World Bank. In the revised manuscript the Figure 7 compares\nover Derna our CCD-PCA surface change map with the CEMS and Euro-UN-WB maps. Figure 8 makes\nthe same comparisons for the city of Susah.\nTo quantitatively compare the maps we extracted true-detection, false-detection, and omission error\nbetween the CCD-PCA and CEMS datasets. For instance, the new manuscript reads at lines 584 to 586 p16\nin “Validation of CCD-PCA” from the Method section:\n“(…) we determine that the CCD–PCA map corresponds to the CEMS-Case1 map for\n61% of the area (true-detection), with a false-detection of 7 % and an omission error\nof 32 %.”\nMoreover, from these above-mentioned numbers, and recognizing the distinct capabilities of CCD-PCA\nand photo-interpretation in delineating the extent and degree of damages, we derived a classification to\nassess the severity of damages. Notably, Figure 5 and Figure 6 show the flood damages for the city center\nof Derna and Susah with three levels of severity (high, moderate and low).\nOur revised manuscript now shows a new section in Method from line 509 to 543 p14-15, entitled\n“Classifying damages in Derna and Susah”, presenting a comprehensive classification to assess the\ndamage severity in these urban areas. The section “urban damages in Derna and Susah” (from line 215 to\n248 p6-7) now encompasses results and discussions on this classification.\nFor instance, we now can read from line 240 to 245 p7:\n“We perform this classification within the flooded extent of Derna’s city center and\nobserve that the damage severity for high, moderate, and low categories accounts for\n36.5 %, 29 %, and 27 % of the surface area, respectively, with 7.5 % remaining\nundamaged (see Figure 5). Moreover, the same classification within the urban area of\nSusah, as outlined in Figure 6, reveals that 14 % of the surface area exhibits high\ndamages, 34 % displays moderate damages, 39 % shows low damages, and 13 %\nremains undamaged.”\nResponse to Reviewer #3: Reviewer’s comments are in black and author’s replies are in blue.\nWe thank Reviewer #3 for his thorough and constructive reviews, comments, and suggestions, which we\nhave fully incorporated in this revised version of the manuscript. We believe this revised improved version\nadds more clarity and addresses all the uncertainties highlighted by Reviewer#3.\nThe paper presents an analysis of erosion caused by flooding from Storm Daniel in Libya in\nSeptember 2023. This event was very extreme and had a high impact on the area, with several thousand\ndeaths recorded. As such, it will form an important case study, particularly when considering the potential\nof climate change to increase the frequency of such events. In my view, a comprehensive analysis of the\nevent (i.e. its causal factors, the multifaceted impacts, emergency response, risk management, and changing\nlikelihood under climate change), would be good paper for Nature Communications. However, although\nthe analysis presented is generally sound, the paper as submitted falls short of the level needed.\nComment 1: The primary focus and contribution of the paper is on the mapping of areas of erosion using\nC-band SAR images, using a Coherent Change Detection method. Much of the paper is devoted to the use\nof this method, and an explanation of its applicability to other areas, and it would be good to see this analysis\npublished.\nHowever, beyond quantifying the area impacted (as a percentage of selected areas of interest, so not directly\nmeaningful), no further analysis of impact is made. Are these areas populated? What is the land use of the\nareas eroded? How many people were affected? What is the estimated economic impact?\nAccepted: In our revised manuscript, we now provide a land cover map in Figure 11 that also shows the\nfew populated areas.\nLines 56 to 61 p2 now reads:\n“The extreme hydroclimatic conditions generated by Storm Daniel caused deluges\nand mudflows in the storm landfall area (see Figure 1) primarily constituted of\nrangelands (grasses and shrubs) and bare soils 11. Despite the limited urban\nfootprint in the storm landfall area 10, significant infrastructure damage has been\nobserved, including 5 % of the road network being destroyed, 50 % becoming\ninaccessible 10, and the collapse of two flood-control dams upstream of the city of\nDerna on September 11th 12 (see Figure 1).”\nThe exact land cover proportions are now given in lines 958 to 960 p31:\n“Notably, shrublands (37.7 %), grasslands–croplands (10.2 %), and bare areas\n(50.3 %) account for 98.2 % of the ROI's surface area, while urban areas\ncontribute only to 1.4%.”\nMoreover, we now give the proportion of flood erosion per type of land cover. Lines 159 to 163 p5 now\nreads:\n“Moreover, we observe that ~22–26 % of the rangelands (21.6 % for croplands and\n25.8 % for shrublands), ~11 % of the bare soils and ~11% of urban areas within\nthe ROI are affected by flood erosion, deposits or damages derived from CCD–PCA\n(see section “Comparison of the flood erosion/deposits with the land cover” in the\nSupplementary Material for more details).\nFurthermore, in the introduction we give the number of people being affected by the catastrophe and the\nestimated economic impact. Lines 68 to 77 p1 read:\n“(…) causing a total of 5898 casualties, 8000 missing, 44800 people displaced, and\n18838 houses damaged or destroyed in the whole coastal area encompassing\nBenghazi, Jabal Al Akhdar, Al Marj and Derna 14 (see Figure 3 and Figure 4).\nThese figures mark Storm Daniel as the deadliest storm in all the African continent\nsince 1900 10.\nThis eastern coast of Libya is one of the main hubs for energy export to Europe.\nThis catastrophe will cost US$ 1.8 billion in repairing the coastal damages 10. This\nfigure includes the devastation of crops and harvests across 16209 ha, along with\nthe destruction of vital topsoil for 4.6 % of the agricultural area 10. Additionally,\n74363 animals perished, accounting for 3.2 % of the region's livestock strategic\nreserve 10.”\nComment 2: Further, no analysis of deposition of eroded sediments was performed, although the impacts\nof this is likely significant.\nAccepted: We added in the revised version, a clarification in the introduction that explains that CCD\nidentifies surface changes and cannot discriminate erosion and deposition. The introduction now reads from\nline 107 to 115 p3:\n“This CCD approach enables the identification of changes in spatial patterns, such\nas monitoring flood erosion 18-20, displacement of surface scatterers, and sediment\ndeposition in run-off channels 21, but also to assess different levels of infrastructure\ndamages after a rainstorm 22,23. These changes can be attributed to both erosion\nand sediment load processes. Given the inherent interconnection between the two\nprocesses, we frequently use the term “flood surface erosion mapping” in our\ninvestigation to designate both processes. However, we can delineate the sediment\nloading areas by conducting a comprehensive classification. This approach relies\non erosion hotspots identified through CCD analysis, hydrological networks,\nprecipitation and topography.”\nWe present the methodology used to assess the sediment loading from line 509 to 535 p14:\n“(…)The first variable considered herein is the 3-day rainfall accumulation during\nStorm Daniel; we chose a conservative estimation threshold of 50 mm, which\nenables the covering of the whole ROI (see Figure 1). The second variable\ncorresponds to the post-storm flood erosion, which is a metric derived from our\nCCD–PCA method. We define erosion as significant when more than 5% of the\nsurface area within any 2 km × 2 km bin shows a CCD–PCA signal (i.e., 0.2 km2).\nThe third variable under consideration is the average topographic slope gradient\nwithin a 2 km × 2 km bin; we use the threshold angle of 3°, a common gradient\nthreshold employed in arid and semi-arid regions to characterize erosion 68,69. (…)\nLastly, after characterizing the erosion hotspots, we assess the areas of sediment\nloading by intersecting the identified hotspots with the locations of wadis.”\nMoreover, we present the results of the sediment loading in wadis from line 203 to 214 p6:\n“ (…)Notably, Figure 4 indicates erosion hotspots and sediment loading after\nStorm Daniel, in the North and West parts of the ROI, characterized by hillslopes\nand shrublands (see Figure 11). Moreover, similar observations are made within\nWadi Derna. The watershed of Wadi Derna features an average terrain gradient of\n4.9 º (± 5.5), characterized by exposed bare soil that facilitates overland flow\nthroughout the area towards the wadi. This dense turbid water flow within the wadi\nfurther contributed to the collapses of two dams upstream of Derna city. (…) ”\nComment 3: In addition, while the dam collapses upstream of Derna are mentioned, no analysis specific\nto this is included (e.g., cause, and erosion/ other damage directly resulting from the collapses).\nAccepted: We added a discussion of the causes of the collapses in our revised manuscript. However,\nrigorous analysis on the causes is still under review by other authors from University of South Carolina and\nJackson State University. They submitted a manuscript in Nature Portfolio focusing on the topic. The\nmanuscript is already available in preprint. The manuscript from Nemmem et al., entitles “Rising Waters,\nFalling Dams: Deciphering the Derna Flood Disaster” and is available through this link:\nhttps://assets.researchsquare.com/files/rs-3809203/v1_covered_e2714159-e53f-4045-9211-\n2efaac034afa.pdf\nFrom line 148 to 153 p4, we can now read:\n“Thus, outlet areas are particularly exposed to the above hydraulic hazards that\nare often mitigated with a sequence of upstream dams. However, the two dams\nupstream of Derna have collapsed due to the anomalous hydraulic nature (i.e., flow\nrate and turbidity) of the floods resulting from Storm Daniel, and lack of\nmaintenance. The extent of the damages triggered by these energetic floods and the\nassociated dam failures are illustrated in Figure 5.”\nIn Line 200 to 209 p6, we can now read:\n“We perform a first-order classification of this run-off erosion and sediment\nbalance dynamics on steep gradients above 3 º and hillslopes, where loose\nsediments are washed away and subsequently deposited below in wadis, increasing\nthe likelihood of future mudflow events during rainstorms. Notably, Figure 4\nindicates erosion hotspots and sediment loading after Storm Daniel, in the North\nand West parts of the ROI, characterized by hillslopes and shrublands (see Figure\n11). Moreover, similar observations are made within Wadi Derna. The watershed\nof Wadi Derna features an average terrain gradient of 4.9 º (± 5.5), characterized\nby exposed bare soil that facilitates overland flow throughout the area towards the\nwadi. This dense turbid water flow within the wadi further contributed to the\ncollapses of two dams upstream of Derna city.”\nFrom line 311 to 314 p8, we can now read:\n“Gravity dams, like those that collapsed in Derna, play an essential role in flood\nmanagement in arid regions. These structures are designed to withstand rapid\nchanges in hydrostatic pressure caused by changes in environmental load\nconditions, such as successive droughts and flash floods that can result in\ndeformation and cracking 55. (…)”\nComment 4: As a consequence of the narrow focus of the analysis, the title of the paper is not appropriate\nand requires revision.\nAccepted: In our revised version, the new title now reads:\n“Assessing Flash Flood Erosion Following Storm Daniel in Libya”\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nI am genuinely appreciative of the authors' diligent efforts in addressing the comments provided.\nIt is evident that they have thoroughly considered and clarified the points raised, resulting in a\nmanuscript that is now suitable for publication in Nature Communications.\nReviewer #2 (Remarks to the Author):\nI thank the authors for the thorough and complete revision of the paper. They edited the document\na lot also based on my comments and now the study is complete and I think it deserves to be\npublished. I really liked the new form and the descriptions are now very clear.\nReviewer #3 (Remarks to the Author):\nThe paper has been substantially revised from the earlier version, and I consider that all concerns\nraised have been addressed adequately. I suggest some improvements are needed to the figures\nto bring them up to a good standard:\nFigure 1:\n- No need to show the red box (just crop the figure to the region of interest\n- The DEM colours are not colour blind friendly, and although continuous colour bar is used, the\nshading is discrete. I suggest using a simpler shade (e.g. greyscale), with hill shading included.\nFigures 5-8, 12-13:\n- These would be clearer if a map basemap were used, rather than an image.\nOtherwise, I consider that the paper is worth of publication and congratulate the authors for their\ngood work.\nANSWERS (in blue) TO REVIEWER#3 COMMENTS (in black)\nReviewer #3 (Remarks to the Author):\nThe paper has been substantially revised from the earlier version, and I consider that all concerns raised\nhave been addressed adequately. I suggest some improvements are needed to the figures to bring them up\nto a good standard:\nFigure 1:\n- No need to show the red box (just crop the figure to the region of interest\nAccepted: We removed the red box in the revised manuscript.\n- The DEM colours are not colour blind friendly, and although continuous colour bar is used, the shading\nis discrete. I suggest using a simpler shade (e.g. greyscale), with hill shading included.\nAccepted: We changed the DEM colors in the revised manuscript. However, we did not apply hillshading,\nas it amplifies the DEM imperfections in the gently sloping areas.\nFigures 5-8, 12-13:\n- These would be clearer if a map basemap were used, rather than an image.:\nDeclined: Our study focuses on the dynamics of soil physical properties. By overlaying the identification\nof damages and erosion with a true-color optical map, we can better understand the geomorphological\ncontext of the area.\nOtherwise, I consider that the paper is worth of publication and congratulate the authors for their good\nwork."}]

Decision letter