1. Introduction
Globally, flash floods are among the most devastating natural hazards regarding both mortality and economic loss [
1,
2]. In particular, the Mediterranean region is projected to become increasingly exposed to flash floods due to the projected increase in hydrologic extremes [
3] and rapid population growth. Flood early warning systems are important tools to effectively mitigate flood-induced hazards. Operation of early warning systems requires good-quality observations (precipitation, streamflow, etc.), reliable model(s)—for hydrologic and weather prediction—and adequate lead time for the warning to be issued. However, each of the above factors are problematic when it comes to predicting flash floods; for example, see [
4]. In the Mediterranean region, flash floods are triggered by heavy precipitation events with accumulated rainfall higher than 100 mm, often within a few hours. Such intense rainfall events are naturally induced by quasi-stationary mesoscale convective systems [
5]. The concurrence of such heavy rainfall events superimposed on the Mediterranean catchments, characterized by small area, steep topography, and very poor land cover, often triggers devastating flash flood events. Thus, both small scale occurrence and rapid onset of flash flood events hinder observation, prediction and warning efforts. Although coordinated efforts have been made to improve the flash flood forecast skill in the Mediterranean region (e.g., The Hydrological Cycle in the Mediterranean Experiment (HyMeX) program and EU Project HYDRATE [
6] in Europe), these efforts do not span the whole Mediterranean region, and the accuracy of the models is still insufficient. Estimates of extreme rainfall rates by weather radar at space and time scales appropriate for flash floods is seen as the cornerstone for flash flood modelling [
7]. However, complex topography associated with the Mediterranean basins complicates the radar signal with ground clutter and mountain blockage [
6]. Majority of the Mediterranean basins are poorly gauged or ungauged, hence making satellite-based precipitation (SBP) retrieval algorithms potentially attractive for basin scale hydrologic studies over these regions.
Although SBP products are freely available for most regions of the world, they have certain limitations, primarily due to the indirect nature of precipitation retrieval. Satellite-based precipitation algorithms estimate precipitation rate based on remote sensing features of clouds derived from the sensors that are sensitive to visible (reflectivity of clouds), infrared (IR; cloud-top temperature; [
8], and microwave (scattering from rain/ice particles; [
9] portions of the electromagnetic spectrum. Visible and IR sensors are accessible on geostationary orbiting satellites and hence offer data at fine temporal scales. However, precipitation estimates by these sensors are rather crude because the cloud-top temperature is indirectly—and often poorly—related to precipitation. Passive Microwave (PMW) sensors on polar-orbiting satellites provide more precise estimates of precipitation but with low temporal resolutions. Recent SBP products merge information from several sources, such as rain gauges, PMW, and IR, to get the benefit of the strengths of such sources. A few free and open access SBP products to note are The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA; [
10,
11], Climate Prediction Center (CPC) MORPHing technique (CMORPH) [
12,
13]), PERSIANN: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks [
14] and PERSIANN-CDR [
15], Global Satellite Mapping of Precipitation (GSMaP) [
16,
17], and IMERG [
18]. Some of these quasi-global SBP products incorporate rain gauge information from globally available datasets (such as GPCC) which are generally insufficient for resolving spatio-temporal characteristics of rainfall required for basin scale hydrologic applications. Hence, many researchers developed bias adjustment algorithms to correct satellite-only SBP products with locally-available, relatively dense gauge networks reporting at or near real time. The nature of the bias adjustment algorithms highly depend on the density of the available gauge networks, and include deterministic bias correction models such as additive [
19], multiplicative [
20] or a combination of the additive/multiplicative models [
21], inverse distance weighting [
22], linear regression models [
23], artificial neural networks [
24] and geostatistical models such as various types of Kriging [
25,
26]. In the literature, many studies conclude that bias correction of satellite-based precipitation products using ground-based observations leads to improvement in hydrological model performance [
27].
Satellite-based remote sensing products are increasingly becoming more applicable in hydrological studies. Therefore, techniques focusing on enhancement of the quality as well as spatial and temporal resolutions of the satellite-based rainfall measurements are crucial to understanding the hydrological processes in many regions that suffer from a lack of dense ground-based measurement network. The availability of such products at spatial and temporal scales that are relevant to hydrologic and hydraulic modeling studies can sensibly improve the reliability of the simulated variables of interest [
28]. As such, the MOXXI (Measurements and Observations in the XXI century) Working Group highlighted and proposed multi-disciplinary and innovative approaches for merging all the available rainfall data sources such as rain gauges, radar data, and satellite-based products, that could provide a better rainfall estimation to improve the quality of hydrological analysis. For example, a distributed rainfall methodology was developed from satellite-based soil moisture observations by Brocca et al. [
29].
The literature on the utility of SBP products for flood simulation has been rapidly growing over the last decade [
30,
31]. Mei et al. [
32] investigated the errors in SBP products and its propagation in streamflow simulations regarding rainfall and runoff volumes and time series shape in the Eastern Italian Alps. The study concluded that the SBP products can capture the shape parameters of the events better and that the gauge adjustments have an effect on the volumetric parameters but not on the shape parameters. Kim, et al. [
33] examined the uncertainty in the satellite-derived precipitation data and its propagation through the hydrological model. They stated that GSMaP and CMORPH products suffer from the consistent underestimation of precipitation and more significantly during the wet periods. The feasibility of using satellite rainfall estimates to simulate flash floods was investigated at complex terrain basins in Northern Italy [
34]. They found that the simulated hydrographs only become meaningful after recalibration of the model, separately for each satellite precipitation products.
Only a few studies have investigated the utility of SBP products in the Mediterranean region. In particular, Stampoulis et al. [
35] studied the error analysis of SBP products for flood producing heavy precipitation events over complex terrain basins in Italy and France and found that precipitation type has an important effect on the SBP product accuracy. Similarly, Mei et al. [
32] investigated the error in SBP-driven hydrological model simulation over complex terrain in Eastern Italian Alps and found that error characteristics revealed the dependency on the flood type (rain floods vs. flash floods). They concluded that random error dampening effect is less evident for the flash flood events. Ciabatta et al. [
36] stated that integration of observed and satellite rainfall data led to more accurate rainfall input with respect to ground observations for discharge simulation over Italy. Milewski et al. [
37] evaluated the TMPA products against 125 rain gauges in northern Morocco and found that TMPA products overestimated precipitation in arid regions and underestimated in high elevations. Tramblay et al. [
38] evaluated various SBP products for hydrological modelling in Makhazine catchment, Morocco and reported that hydrological model driven by the TMPA product (Version 7) resulted in poor performance in simulating daily discharge while being adequate at the monthly timescale. Although several studies have evaluated SBP products over Turkey [
39,
40], the performance of these products has not yet been evaluated over the Mediterranean region of Turkey in hydrologic modelling.
In general, most of the previous studies for floods simulation were conducted by using the hydrological models, but recently, hydraulic models have been increasingly used in such applications. Several studies have been performed for coupling of hydrological and hydraulic models (e.g., [
41,
42,
43]), and application for hydrodynamics models (e.g., [
44,
45,
46,
47,
48]). An investigation of the coupled models in improving the flow simulations and reducing the uncertainty in arid and semi-arid regions would provide interesting perspectives. For instance, coupling the hydrological models for the upstream catchments and hydraulic models for the downstream and floods plains could potentially improve the model performance, especially for flash floods in arid and semi-arid regions.
Mediterranean coast of Turkey is prone to frequent flash floods. In Turkey, flooding is the second most important natural hazards, after the earthquakes with 22 floods and 19 deaths per year on average [
49]. Turkey is the fourth Mediterranean country with the highest loss from flash floods after Italy, France, and Romania [
50]. Moreover, the Mediterranean region of Turkey is marked by rapid population growth and urbanization, which will likely exacerbate the impacts of flash floods. Therefore, the main goal of this paper is to investigate the utility of SBP products in modelling flash flood events over the Mediterranean catchments characterized by scarce ground-based observations and steep topography. This goal is achieved in three major steps. First, SBP estimates from the GSMaP product is compared with the rain gauge-based estimates around Karpuz River basin located in the city of Antalya, Turkey. Next, a simple bias correction scheme is devised to correct the GSMaP precipitation estimates using a relatively scarce rain gauge network. Lastly, a distributed hydrologic model suited to flash flood simulation is driven by GSMaP-based precipitation estimates before and after the bias correction scheme, and the simulation performance is assessed using observed hydrographs of flash flood events together with performance statistics. This last step is seen as an independent check on the accuracy of the GSMaP product before and after the bias correction.
5. Conclusions
The Mediterranean region is projected to become increasingly vulnerable to flash floods due to a combination of factors including the projected increase in hydrologic extremes and rapid population growth. On the contrary, the Mediterranean basins are characterized by poor observation networks and complex topography which in turn hinders the efficacy of ground-based observational networks. This situation makes satellite-based precipitation (SBP) retrieval algorithms potentially attractive for modeling flash floods. Thus, the main goal of this paper was to explore the utility of a satellite-based precipitation product, GSMaP, in modelling flash flood events over the Mediterranean catchments. This goal was achieved in three major steps. First, SBP estimates from the GSMaP product was compared and evaluated with the gauge-based precipitation estimates around Karpuz River basin located in the city of Antalya, Turkey. Next, a simple bias correction scheme was devised to correct the GSMaP precipitation estimates using the relatively scarce rain gauge network. Lastly, a distributed hydrologic model, Hydro-BEAM, suited to the simulation of flash floods was driven by GSMaP-based precipitation estimates before and after the bias correction scheme, and the simulation performance was assessed using the observed hydrographs of flash flood events together with several performance statistics.
The comparison of GSMaP data with the rain gauge dataset consisted of several scenarios including different time scales (daily, monthly, seasonal), spatial scales (areal average, grid-based and grid vs. point-based/gauge-based), elevation zones and rainfall intensity thresholds. This analysis indicated that GSMaP product generally suffers from a tendency to underestimate precipitation compared to the rain gauge network as a function of the season, elevation and rainfall intensity; however, showed reasonable linear correlations. Specifically, the underestimation by GSMaP was more significant for high elevations and for high rainfall intensities, which is alarming for flash flood monitoring efforts. Moreover, GSMaP product significantly underestimated the number of daily rainfall occurrences for high rainfall intensity events (greater than 10 mm/day). On the contrary, significant overestimation by GSMaP product for low rainfall intensity events (less than 1 mm/day) was evident. Hence we suggest to include threshold-based analysis in studies focusing on evaluation and bias correction of satellite-based rainfall products. For instance, in this study, false daily light rainfall intensities (less than 1 mm/day) reported by GSMaP product were not included in the comparison and bias correction procedure.
Next, a multiplicative bias correction scheme was employed to correct the hourly GSMaP rainfall estimates using the monthly bias factors computed as the ratio of monthly total rainfall reported by GSMaP and rain gauges for each month of the year. The effectiveness of this rather simple correction scheme was tested through the investigation of the performance of the hydrological model, Hydro-BEAM, in simulating the hourly hydrographs of flash flood events in Karpuz River Basin. The results of the model simulations indicated that the performance of the model improves upon using the bias-corrected GSMaP product as input compared to using the uncorrected GSMaP product in most cases studied but others show some limitation, especially during the evaluation periods. Investigation of alternative schemes that incorporate local hydroclimatic and physiographic variables such as elevation, season and extreme events in the bias correction procedure will shed further light on the impact of these factors on the flash flood simulation and early warning system performance.