Critical rainfall conditions for the initiation of torrential flows: results from the Rebaixader catchment (Central Pyrenees)

Torrential flows like debris flows or debris floods are fast movements formed by a mix of water and different amounts of unsorted solid material. They generally occur in steep torrents and pose high risk in mountainous areas. Rainfall is their most common triggering factor and the analysis of the critical rainfall conditions is a fundamental research task. Due to their wide use in warning systems, rainfall thresholds for the triggering of torrential flows are an important outcome of such analysis and are empirically derived using data from past events. In 2009, a monitoring system was installed in the Rebaixader catchment, Central Pyrenees (Spain). Since then, rainfall data of 25 torrential flows (“TRIG rainfalls”) were recorded, with a 5-min sampling frequency. Other 142 rainfalls that did not trigger torrential flows (“NonTRIG rainfalls”) were also collected and analyzed. The goal of this work was threefold: (i) characterize rainfall episodes in the Rebaixader catchment and compare rainfall data that triggered torrential flows and others that did not; (ii) define and test Intensity–Duration (ID) thresholds using rainfall data measured inside the catchment by with different techniques; (iii) analyze how the criterion used for defining the rainfall duration and the spatial variability of rainfall influences the value obtained for the thresholds. The statistical analysis of the rainfall characteristics showed that the parameters that discriminate better the TRIG and NonTRIG rainfalls are the rainfall intensities, the mean rainfall and the total rainfall amount. The antecedent rainfall was not significantly different between TRIG and NonTRIG rainfalls, as it can be expected when the source material is very pervious (a sandy glacial soil in the study site). Thresholds were derived from data collected at one rain gauge located inside the catchment. Two different methods were applied to calculate the duration and intensity of rainfall: (i) using total duration, Dtot, and mean intensity, Imean, of the rainfall event, and (ii) using floating durations, D, and intensities, Ifl, based on the maximum values over floating periods of different duration. The resulting thresholds are considerably different (Imean = 6.20 Dtot-0.36 and Ifl_90% = 5.49 D-0.75, respectively) showing a strong dependence on the applied methodology. On the other hand, the definition of the thresholds is affected by several types of uncertainties. Data from both rain gauges and weather radar were used to analyze the uncertainty associated with the spatial variability of the triggering rainfalls. The analysis indicates that the precipitation recorded by the nearby rain gauges can introduce major uncertainties, especially for convective summer storms. Thus, incorporating radar rainfall can significantly improve the accuracy of the measured triggering rainfall. Finally, thresholds were also derived according to three different criteria for the definition of the duration of the triggering rainfall: (i) the duration until the peak intensity, (ii) the duration until the end of the rainfall; and, (iii) the duration until the trigger of the torrential flow. An important contribution of this work is the assessment of the threshold relationships obtained using the third definition of duration. Moreover, important differences are observed in the obtained thresholds, showing that ID relationships are significantly dependent on the applied methodology.Peer ReviewedPostprint (author's final draft

Rainfall thresholds derivation for warning pluvial flooding risk in urbanised areas

Aim of this work is the development of an operational tool for pluvial flooding warning in an urban area based on off-line rainfall thresholds derived by coupling a rainfall-runoff modelling and a hydraulic routing. The critical conditions considered for issue flood warnings were not only based on the water stage, but also on the extension of the flooded area. Further, a risk assessment framework for quantifying the reliability of the rainfall thresholds has been included; rainfall thresholds used in pluvial flooding warning should be influenced by the uncertainties in the rainfall characteristics (i.e. rainfall duration, depth and storm pattern). This risk assessment framework incorporates the correlated multivariate Monte Carlo simulation method, an hydraulic model for the simulation of rainfall excess propagation over surface urban drainage structures, i.e. streets and pathways. Thresholds rainfall are defined using a number of inundation criteria, to analyze the change in the rainfall threshold due to various definitions of inundation. Starting from estimated water stages and flooded area from inundation simulation rainfall thresholds can be obtained according a specific inundation criterion, including, together, a critical water depth and a critical flooding area. Finally, the second phase concerns the imminence of a possible hydrological risk by comparing the time when cumulative rainfall and rainfall thresholds meet to each other. The developed procedure has been applied to the real case study of Mondello catchment in Palermo (Italy)

Initiation of a Stable Convective Hydroclimatic Regime in Central America Circa 9000 Years BP

Many Holocene hydroclimate records show rainfall changes that vary with local orbital insolation. However, some tropical regions display rainfall evolution that differs from gradual precessional pacing, suggesting that direct rainfall forcing effects were predominantly driven by sea-surface temperature thresholds or inter-ocean temperature gradients. Here we present a 12,000 yr continuous U/Th-dated precipitation record from a Guatemalan speleothem showing that Central American rainfall increased within a 2000 yr period from a persistently dry state to an active convective regime at 9000 yr BP and has remained strong thereafter. Our data suggest that the Holocene evolution of Central American rainfall was driven by exceeding a temperature threshold in the nearby tropical oceans. The sensitivity of this region to slow changes in radiative forcing is thus strongly mediated by internal dynamics acting on much faster time scales

Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation at small scales

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. Copyright 2005 by the American Geophysical Union

Sensitivity of point scale runoff predictions to rainfall resolution

International audienceThis paper investigates the effects of using non-linear, high resolution rainfall, compared to time averaged rainfall on the triggering of hydrologic thresholds and therefore model predictions of infiltration excess and saturation excess runoff. The bounded random cascade model, parameterized to south western Australian rainfall, was used to scale rainfall intensities at various time resolutions ranging from 1.875 min to 2 h. A one dimensional, conceptual rainfall partitioning model was used that instantaneously partitions water into infiltration excess, infiltration, storage, deep drainage, saturation excess and surface runoff, where the fluxes into and out of the soil store are controlled by thresholds. For example, saturation excess is triggered when the soil water content reaches the storage capacity threshold. The results of the numerical modelling were scaled by relating soil infiltration properties to soil draining properties, and inturn, relating these to average storm intensities. By relating maximum soil infiltration capacities to saturated drainage rates (f*), we were able to split soils into two groups; those where all runoff is a result of infiltration excess alone (f*?0.2) and those susceptible to both infiltration excess and saturation excess runoff (f*>0.2). For all soil types, we related maximum infiltration capacities to average storm intensities (k*) and were able to show where model predictions of infiltration excess were most sensitive to rainfall resolution (ln k=0.4) and where using time averaged rainfall data can lead to an under prediction of infiltration excess and an over prediction of the amount of water entering the soil (ln k*>2). For soils susceptible to both infiltration excess and saturation excess, total runoff sensitivity was scaled by relating saturated drainage rates to average storm intensities (g*) and parameter ranges where predicted runoff was dominated by infiltration excess or saturation excess depending on the resolution of rainfall data was determined (ln g*<2). Infiltration excess predicted from high resolution rainfall is short and intense, whereas saturation excess produced from low resolution rainfall is more constant and less intense. This has important implications for the accuracy of current hydrological models that use time averaged rainfall under these soil and rainfall conditions and predictions of further thresholds such as erosion. It offers insight into areas where the understanding of the dynamics of high resolution rainfall is required and a means by which we can improve our understanding of the way variations in rainfall intensities within a storm relate to hydrological thresholds and model predictions

Return period curves for extreme 5-min rainfall amounts at the Barcelona urban network

Heavy rainfall episodes are relatively common in the conurbation of Barcelona and neighbouring cities (NE Spain), usually due to storms generated by convective phenomena in summer and eastern and south-eastern advections in autumn. Prevention of local flood episodes and right design of urban drainage have to take into account the rainfall intensity spread instead of a simple evaluation of daily rainfall amounts. The database comes from 5-min rain amounts recorded by tipping buckets in the Barcelona urban network along the years 1994–2009. From these data, extreme 5-min rain amounts are selected applying the peaks-over-threshold method for thresholds derived from both 95% percentile and the mean excess plot. The return period curves are derived from their statistical distribution for every gauge, describing with detail expected extreme 5-min rain amounts across the urban network. These curves are compared with those derived from annual extreme time series. In this way, areas in Barcelona submitted to different levels of flood risk from the point of view of rainfall intensity are detected. Additionally, global time trends on extreme 5-min rain amounts are quantified for the whole network and found as not statistically significant.Peer ReviewedPostprint (author's final draft

Shallow landsliding and catchment connectivity within the Houpoto Forest, New Zealand.

Active landslides and their contribution to catchment connectivity have been investigated within the Houpoto Forest, North Island, New Zealand. The aim was to quantify the proportion of buffered versus coupled landslides and explore how specific physical conditions influenced differences in landslide connectivity. Landsliding and land use changes between 2007 and 2010 were identified and mapped from aerial photography, and the preliminary analyses and interpretations of these data are presented here. The data indicate that forest harvesting made some slopes more susceptible to failure, and consequently many landslides were triggered during subsequent heavy rainfall events. Failures were particularly widespread during two high magnitude (> 200 mm/day) rainfall events, as recorded in 2010 imagery. Connectivity was analysed by quantifying the relative areal extents of coupled and buffered landslides identified in the different images. Approximately 10 % of the landslides were identified as being coupled to the local stream network, and thus directly contributing to the sediment budget. Following liberation of landslides during high-magnitude events, low-magnitude events are thought to be capable of transferring more of this sediment to the channel. Subsequent re-planting of the slopes appears to have helped recovery by increasing the thresholds for failure, thus reducing the number of landslides during subsequent high-magnitude rainfall events. Associated with this is a reduction in slope-channel connectivity. These preliminary results highlight how site specific preconditioning, preparatory and triggering factors contribute to landslide distribution and connectivity, in addition to how efficient re-afforestation improves the rate of slope recovery

Trends in the start of the wet season over Africa

A quarter of a century of daily rainfall data from the Global Telecommunications System are used to define the temporal and spatial variability of the start of the wet season over Africa and surrounding extreme south of Europe and parts of the Middle East. From 1978 to 2002, the start of the wet season arrived later in the year for the majority of the region, as time progressed. In some parts of the continent, there was an annual increase in the start date of up to 4 days per year. On average, the start of the wet season arrived 9–21 days later from 1978 to 2002, depending on the threshold used to define the start of the rains (varying from 10–30 mm over 2 days, with no dry period in the following 10 days). It is noted that the inter-annual variability of the start of the wet season is high with the range of start dates varying on average from 116 to 142 days dependent on the threshold used to determine the start date. These results may have important implications for agriculturists on all levels (from the individual farmer to those responsible for regional food supply), as knowledge of potential future climate changes starts to play an increasingly important role in the agricultural decision-making process, such as sowing and harvesting times

Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach

Short-term high-resolution precipitation forecasting has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This article introduces a pixel-based algorithm for Short-term Quantitative Precipitation Forecasting (SQPF) using radar-based rainfall data. The proposed algorithm called Pixel- Based Nowcasting (PBN) tracks severe storms with a hierarchical mesh-tracking algorithm to capture storm advection in space and time at high resolution from radar imagers. The extracted advection field is then extended to nowcast the rainfall field in the next 3. hr based on a pixel-based Lagrangian dynamic model. The proposed algorithm is compared with two other nowcasting algorithms (WCN: Watershed-Clustering Nowcasting and PER: PERsistency) for ten thunderstorm events over the conterminous United States. Object-based verification metric and traditional statistics have been used to evaluate the performance of the proposed algorithm. It is shown that the proposed algorithm is superior over comparison algorithms and is effective in tracking and predicting severe storm events for the next few hours. © 2012 Elsevier B.V