263,721 research outputs found
Uncertainty in Estimation of Debris-Flow Triggering Rainfall: Evaluation and Impact on Identification of Threshold Relationships
No full textOperational debris-flow warning systems are often based on the use of empirical rainfall thresholds derived from rain gauge observations. However, rain gauges are usually located away from the actual debris-flow locations thus estimation of triggering rainfall properties from rain gauges can be associated with considerable uncertainty. This work examines the uncertainty in gauge-based estimation of debris-flow triggering rainfall and evaluates its impact on the identification of rainfall thresholds used for debris-flows prediction. These issues are assessed by using high-resolution radar data to represent "actual" space-time patterns of precipitation at and around the debris-flow initiation points. Rain-gauge network sampling is simulated by randomly sampling radar-rainfall fields. Rainfall is estimated by using three rainfall interpolation methods: Nearest neighbor (NN), inverse distance weighting (IDW), and ordinary kriging (OK). Comparison of results from these three methods shows that no particular benefit in intensity-duration threshold estimation is obtained by using approaches that are more complex than the NN method. NN provides estimates with smaller bias than IDW and OK but larger estimation variance. On average, decrease in gauge density leads to increased underestimation of debris-flow rainfall and subsequently this results in large underestimation of the intensity-duration thresholds
The relative role of hillslope and river network routing in the hydrologic response to spatially variable rainfall fields
No full textThis paper introduces a new methodology and a rainfall spatial organisation index to examine the relative role of hillslope and channel residence times in the analysis of the significance of spatial rainfall representation in catchment flood response modelling. The relationship between the flood response, the hillslope and channel residence time representation and the spatial organisation of the rainfall fields is obtained by extending the ‘spatial moments of catchment rainfall’ statistics (Zoccatelli et al., 2011) to the hillslope system. The flood prediction error generated by assuming spatially uniform rainfall is related to the spatial organisation of the rainfall fields by means of the scaled spatial moment of order one for the channel network and the hillslope system. The methodology provides a basis for a more general consideration of the relationship between the flood response dependence to spatial rainfall organisation and catchment size. The methodology is illustrated based on data from five extreme flash floods occurred in various European regions in the period 2002–2007. Discharge data are available either from streamgauges or from post-flood surveys for 27 catchments, ranging in size between 36 and 982 km2. High space–time resolution radar rainfall fields are also available for the analyses. These data are used to implement a distributed hydrological model simulating the runoff generation by infiltration excess and explicitly representing the surface flow paths across both the hillslopes and the river network. The hydrological model is alternatively forced with spatially-distributed and spatially-uniform rainfall input, to analyse the factors controlling the sensitivity of the model output to the spatial rainfall data. Our results show that the spatial variability of the rainfall can influence the flash-flood hydrographs for catchments as small as 50 km2, and that the dependence of flood hydrograph shape to spatial rainfall variability cannot be treated as scale dependent relative to the size of the catchment.
The rainfall index can be exploited as similarity index for classifying catchments and flood events according to the hillslopes/channel residence times and to provide guidance on the space and time resolution of the rainfall monitoring system required to predict the flood response
Modeling satellite precipitation errors over mountainous terrain: The influence of gauge density, seasonality, and temporal resolution
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Sensitivity of flood frequency analysis to data record, statistical model, and parameter estimation methods: An evaluation over the contiguous United States
No full textThe current statistical methods applied in flood frequency analysis require long data records to obtain reliable estimates, particularly for long return periods. Moreover, the choice of the statistical model and the parameter estimation procedure may introduce uncertainty in the estimates. In this work, we investigate the sensitivity of flood frequency analysis to various sample sizes, statistical models, and parameter estimation methods over six major hydrological regions in the contiguous United States. Results show that flood frequency estimates based on annual maximum series approach convergence to the reference values (estimates derived from 70 years record) in terms of median for 35-year or longer records. However, the uncertainty remains significant and a record of 35 years (20 years) is associated with similar to 50% (100%) larger uncertainty on the estimated 100-year flood. The generalised extreme value distribution combined with maximum likelihood estimation method is associated with the largest uncertainty, while the log-Pearson type III exhibits comparable bias and smaller uncertainty. Application of the partial duration series approach to 20-year records shows no significant advantage. Our findings suggest that the hydroclimatic characteristics of the catchments exhibit limited impact on the uncertainty
Precipitation frequency analyses based on radar estimates: An evaluation over the contiguous United States
No full textThe lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002-2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-h) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24 h). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes
Metastatistical Extreme Value analysis of hourly rainfall from short records: Estimation of high quantiles and impact of measurement errors
No full textThis study expands the Metastatistical Extreme Value (MEV) framework to sub-daily rainfall frequency analysis and compares it to extreme value theory methods in presence of short records and measurement errors. Ordinary events are identified based on the temporal autocorrelation of hourly data and modeled with a Weibull distribution. MEV is compared to extreme value theory methods in the estimation of long return period quantiles from actual data (160 rain gauges with at least 60-year record in the contiguous United States) and on synthetic data perturbed with measurement errors typical of remote sensing rainfall estimation. MEV tends to underestimate the 100-year return period quantiles of hourly rainfall when 5-20 years of actual data are used, but presents diminished uncertainty. When a good model of the ordinary events and adequate number of events per year are available, MEV is able to provide information on the 100-year return period quantiles from 10-20, or even 5 years of data with significantly reduced uncertainty (<30% uncertainty for 5-year records). MEV estimates of 100-year return period quantiles from short records are much less sensitive than extreme value theory methods to additive/multiplicative errors, presence of cap values in the estimates, and missing of extreme values. Results from this study strongly support the use of MEV for rainfall frequency analyses based on remotely sensed datasets
Space–time organization of debris flows-triggering rainfall and its effect on the identification of the rainfall threshold relationship
No full textPredicting debris flow occurrence in Eastern Italian Alps based on hydrological and geomorphological modelling
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