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Tufa Dinku, Emmanouil N. Anagnostou, and Marco Borga

Abstract

This paper investigates a multicomponent radar-based rainfall estimation algorithm that includes optimum parameter estimation and error correction schemes associated with radar operation over mountainous terrain. Algorithm preprocessing steps include correction for terrain blocking, adjustment for rain attenuation, and interpolation of reflectivity data from polar radar coordinates to a three-level (1, 2, and 3 km) vertically integrated Cartesian grid. The error correction schemes investigated herein include a simple but efficient approach to correct for the vertical variation of reflectivity and a stochastic filtering approach for mean-field radar-rainfall bias adjustment. The primary algorithm parameters are estimated through a global optimization scheme. Eight major flood-inducing storm events observed coincidentally by a C-band weather radar and 39 rain gauge stations over an alpine region of northeast Italy are used. We describe sensitivity analysis of the parameter values obtained from global optimization, the improvements in accuracy owing to the implementation of the different preprocessing and error correction schemes, and the overall improvement achieved as compared with previous algorithm studies in the area. The advantages of performing vertical integration versus using the lowest-available-elevation radar field, applying stochastic filtering versus the deterministic approach for mean field bias, and estimating the algorithm parameters through optimization are demonstrated.

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Marco Borga, Paolo Boscolo, Francesco Zanon, and Marco Sangati

Abstract

The 29 August 2003 storm on the upper Tagliamento River basin in the eastern Italian Alps is examined as a prototype for organized convective systems that dominate the upper tail of the precipitation frequency distribution and are likely responsible for the majority of flash flood peaks in this area. The availability of high-resolution rainfall estimates from radar observations and rain gauge networks, together with flood response observations derived from stream gauge data and post-event surveys, provides the opportunity to study the hydrometeorological and hydrological mechanisms associated with this extreme storm and the associated flood. The flood occurred at the end of a climatic anomaly of prolonged drought and warm conditions over Europe and the Mediterranean region. A characteristic of the event is its organization in well-defined banded structures, some of which persisted in the same locations for the duration of the event. The steadiness of these rainbands led to highly variable precipitation accumulations and, associated with orographic enhancement, played a central role in the space–time organization of the storm. Two dominant controls on extreme flood response are recognized and analyzed: steadiness of convective bands and dry antecedent soil moisture conditions.

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Yiwen Mei, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, and Marco Borga

Abstract

This study investigates the error characteristics of six quasi-global satellite precipitation products and their error propagation in flow simulations for a range of mountainous basin scales (255–6967 km2) and two different periods (May–August and September–November) in northeast Italy. Statistics describing the systematic and random error, the temporal similarity, and error ratios between precipitation and runoff are presented. Overall, strong over-/underestimation associated with the near-real-time 3B42/Climate Prediction Center morphing technique (CMORPH) products is shown. Results suggest positive correlation between the systematic error and basin elevation. Performance evaluation of flow simulations yields a higher degree of consistency for the moderate to large basin scales and the May–August period. Gauge adjustment for the different satellite products is shown to moderate their error magnitude and increase their correlation with reference precipitation and streamflow simulations. Moreover, ratios of precipitation to streamflow simulation error metrics show dependencies in terms of magnitude and variability. Random error and temporal dissimilarity are shown to reduce from basin-average rainfall to the streamflow simulations, while the systematic error exhibits no clear pattern in the rainfall–runoff transformation.

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Marco Borga, Emmanouil N. Anagnostou, and Witold F. Krajewski

Abstract

Brightband effects are one of the more important causes of vertical variability of reflectivity and severely affect the accuracy of rainfall estimates from ground-based radar. Monte Carlo simulation experiments are performed to investigate the efficiency of a procedure for the correction of errors related to the vertical variability of reflectivity. The simulation model generates three-dimensional radar reflectivity fields. Brightband effects are simulated through a physically based model of melting-layer reflectivity observations. Sensitivity of the correction procedure for a number of different precipitation scenarios and radar systems is analyzed. Overall, the identification method is found to be a robust procedure for correction of brightband effects. Results indicate a dependence of the effectiveness of the correction procedure on mean altitude and spatial variability of the melting layer.

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Yiwen Mei, Emmanouil N. Anagnostou, Efthymios I. Nikolopoulos, and Marco Borga
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Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, and Marco Borga

Abstract

Effective flash flood warning procedures are usually hampered by observational limitations of precipitation over mountainous basins where flash floods occur. Satellite rainfall estimates are available over complex terrain regions, offering a potentially viable solution to the observational coverage problem. However, satellite estimates of heavy rainfall rates are associated with significant biases and random errors that nonlinearly propagate in hydrologic modeling, imposing severe limitations on the use of these products in flood forecasting. In this study, the use of three quasi-global and near-real-time high-resolution satellite rainfall products for simulating flash floods over complex terrain basins are investigated. The study uses a major flash flood event that occurred during 29 August 2003 on a medium size mountainous basin (623 km2) in the eastern Italian Alps. Comparison of satellite rainfall with rainfall derived from gauge-calibrated weather radar estimates showed that although satellite products suffer from large biases they could represent the temporal variability of basin-averaged precipitation. Propagation of satellite rainfall through a distributed hydrologic model revealed that systematic error in rainfall was severely magnified when transformed to error in runoff under dry initial soil conditions. Simulation hydrographs became meaningful only after recalibrating the model for each satellite rainfall input separately. However, the unrealistic values of model parameters after recalibration show that this approach is erroneous and that model recalibration using satellite rainfall data should be treated with care. Overall, this study highlights the need for improvement of satellite rainfall retrieval algorithms in order to allow a more appropriate use of satellite rainfall products for flash flood applications.

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Yiwen Mei, Emmanouil N. Anagnostou, Efthymios I. Nikolopoulos, and Marco Borga

Abstract

Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near–real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003–10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May–August) and cold (September–December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

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William Amponsah, Lorenzo Marchi, Davide Zoccatelli, Giorgio Boni, Marco Cavalli, Francesco Comiti, Stefano Crema, Ana Lucía, Francesco Marra, and Marco Borga

Abstract

Postflood indirect peak flow estimates provide key information to advance understanding of flash flood hydrometeorological processes, particularly when peak observations are combined with flood simulations from a hydrological model. However, indirect peak flow estimates are affected by significant uncertainties, which are magnified when floods are associated with important geomorphic processes. The main objective of this work is to advance the integrated use of indirect peak flood estimates and hydrological model simulations by developing and testing a procedure for the assessment of the geomorphic impacts–related uncertainties. The methodology is applied to the analysis of an extreme flash flood that occurred on the Magra River system in Italy on 25 October 2011. The event produced major geomorphic effects and peak discharges close to the maxima observed for high-magnitude rainstorm events in Europe at basin scales ranging from 30 to 1000 km2. Results show that the intensity of geomorphic impacts has a significant effect on the accuracy of postflood peak discharge estimation and model-based flood response analysis. It is shown that the comparison between rainfall–runoff model simulations and indirect peak flow estimates, accounting for uncertainties, may be used to identify erroneous field-derived estimates and isolate consistent hydrological simulations. Comparison with peak discharges obtained for other Mediterranean flash floods allows the scale-dependent flood response of the Magra River system to be placed within a broader hydroclimatological context. Model analyses of the hydrologic response illustrate the role of storm structure and evolution for scale-dependent flood response.

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Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Faisal Hossain, Mekonnen Gebremichael, and Marco Borga

Abstract

The study presents a data-based numerical experiment performed to understand the scale relationships of the error propagation of satellite rainfall for flood evaluation applications in complex terrain basins. A satellite rainfall error model is devised to generate rainfall ensembles based on two satellite products with different retrieval accuracies and space–time resolutions. The generated ensembles are propagated through a distributed physics-based hydrologic model to simulate the rainfall–runoff processes at different basin scales. The resulted hydrographs are compared against the hydrograph obtained by using high-resolution radar rainfall as the “reference” rainfall input. The error propagation of rainfall to stream runoff is evaluated for a number of basin scales ranging between 100 and 1200 km2. The results from this study show that (i) use of satellite rainfall for flood simulation depends strongly on the scale of application (catchment area) and the satellite product resolution, (ii) different satellite products perform differently in terms of hydrologic error propagation, and (iii) the propagation of error depends on the basin size; for example, this study shows that small watersheds (<400 km2) exhibit a higher ability in dampening the error from rainfall to runoff than larger-sized watersheds, although the actual error increases as drainage area decreases.

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Yagmur Derin, Emmanouil Anagnostou, Alexis Berne, Marco Borga, Brice Boudevillain, Wouter Buytaert, Che-Hao Chang, Guy Delrieu, Yang Hong, Yung Chia Hsu, Waldo Lavado-Casimiro, Bastian Manz, Semu Moges, Efthymios I. Nikolopoulos, Dejene Sahlu, Franco Salerno, Juan-Pablo Rodríguez-Sánchez, Humberto J. Vergara, and Koray K. Yilmaz

Abstract

An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000–13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cévennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBR products are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network.

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