Variability Of Rainfall In Space Research Articles

Critical scales to explain urban hydrological response: an application in Cranbrook, London

Abstract. Rainfall variability in space and time, in relation to catchment characteristics and model complexity, plays an important role in explaining the sensitivity of hydrological response in urban areas. In this work we present a new approach to classify rainfall variability in space and time and we use this classification to investigate rainfall aggregation effects on urban hydrological response. Nine rainfall events, measured with a dual polarimetric X-Band radar instrument at the CAESAR site (Cabauw Experimental Site for Atmospheric Research, NL), were aggregated in time and space in order to obtain different resolution combinations. The aim of this work was to investigate the influence that rainfall and catchment scales have on hydrological response in urban areas. Three dimensionless scaling factors were introduced to investigate the interactions between rainfall and catchment scale and rainfall input resolution in relation to the performance of the model. Results showed that (1) rainfall classification based on cluster identification well represents the storm core, (2) aggregation effects are stronger for rainfall than flow, (3) model complexity does not have a strong influence compared to catchment and rainfall scales for this case study, and (4) scaling factors allow the adequate rainfall resolution to be selected to obtain a given level of accuracy in the calculation of hydrological response.

Effects of rainfall spatial variability and intermittency on shallow landslide triggering patterns at a catchment scale

AbstractThe occurrence of shallow landslides is often associated with intense and prolonged rainfall events, where infiltrating water reduces soil strength and may lead to abrupt mass release. Despite general understanding of the role of rainfall water in slope stability, the prediction of rainfall‐induced landslides remains a challenge due to natural heterogeneity that affect hydrologic loading patterns and the largely unobservable internal progressive failures. An often overlooked and potentially important factor is the role of rainfall variability in space and time on landslide triggering that is often obscured by coarse information (e.g., hourly radar data at spatial resolution of a few kilometers). To quantify potential effects of rainfall variability on failure dynamics, spatial patterns, landslide numbers and volumes, we employed a physically based “Catchment‐scale Hydromechanical Landslide Triggering” (CHLT) model for a study area where a summer storm in 2002 triggered 51 shallow landslides. In numerical experiments based on the CHLT model, we applied the measured rainfall amount of 53 mm in different artificial spatiotemporal rainfall patterns, resulting in between 30 and 100 landslides and total released soil volumes between 3000 and 60,000 m3 for the various scenarios. Results indicate that low intensity rainfall below soil's infiltration capacity resulted in the largest mechanical perturbation. This study illustrates how small‐scale rainfall variability that is often overlooked by present operational rainfall data may play a key role in shaping landslide patterns.

Spatial variability of rainfall on a sub‐kilometre scale

AbstractThe variability of rainfall in space and time is an essential driver of many processes in nature but little is known about its extent on the sub‐kilometre scale, despite many agricultural and environmental experiments on this scale. A network of 13 tipping‐bucket rain gauges was operated on a 1·4 km2 test site in southern Germany for four years to quantify spatial trends in rainfall depth, intensity, erosivity, and predicted runoff. The random measuring error ranged from 10% to 0·1% in case of 1 mm and 100 mm rainfall, respectively. The wind effects could be well described by the mean slope of the horizon at the stations. Except for one station, which was excluded from further analysis, the relative differences due to wind were in maximum ±5%. Gradients in rainfall depth representing the 1‐km2 scale derived by linear regressions were much larger and ranged from 1·0 to 15·7 mm km−1 with a mean of 4·2 mm km−1 (median 3·3 mm km−1). They mainly developed during short bursts of rain and thus gradients were even larger for rain intensities and caused a variation in rain erosivity of up to 255% for an individual event. The trends did not have a single primary direction and thus level out on the long term, but for short‐time periods or for single events the assumption of spatially uniform rainfall is invalid on the sub‐kilometre scale. The strength of the spatial trend increased with rain intensity. This has important implications for any hydrological or geomorphologic process sensitive to maximum rain intensities, especially when focusing on large, rare events. These sub‐kilometre scale differences are hence highly relevant for environmental processes acting on short‐time scales like flooding or erosion. They should be considered during establishing, validating and application of any event‐based runoff or erosion model. Copyright © 2009 John Wiley & Sons, Ltd.

Combining radar and rain gauge rainfall estimates using conditional merging

AbstractThe Hydrologist's traditional tool for measuring rainfall is the rain gauge. Rain gauges are relatively cheap, easy to maintain and provide a direct and suitably accurate estimate of rainfall at a point. What rain gauges fail to capture well is the spatial variability of rainfall with time, an important aspect for the credible modelling of a catchment's response to rainfall. This spatial variability is particularly evident at short timescales of up to several days. As the period of accumulation increases, the expected spatial variability is reduced and rain gauges provide improved spatial rainfall estimates. Because of the fractal variability of rainfall in space, simple interpolation between rain gauges does not provide an accurate estimate of the true spatial rainfall field, at short time scales.Weather radar provides (with a single instrument) a highly detailed representation of the spatial structure and temporal evolution of rainfall over a large area. Estimated rainfall rates are derived indirectly from measurements of reflectivity and are therefore subject to a combination of systematic and random errors.This article describes a recently proposed merging technique and presents an application to simulated rainfall fields. The technique employed is Conditional Merging (Ehret, 2002), which makes use of Kriging to extract the optimal information content from the observed data. A mean field based on the Kriged rain gauge data is adopted, while the spatial detail from the radar is retained, reducing bias, but keeping the spatial variability observed by the radar. The variance of the estimate is reduced in the vicinity of the gauges where they are able to provide good information on the true rainfall field. Copyright © 2005 Royal Meteorological Society

Flood forecasting and infiltration modeling/Prévision de crue et modélisation de l’infiltration

The role of accuracy in the representation of infiltration on the effectiveness of real-time flood forecasting models was investigated. A simple semi-distributed model of conceptual type with adaptive estimate of hydraulic characteristics included in the infiltration component was selected. Infiltration was described by a very accurate approach recently formulated for complex rainfall patterns, or alternatively through a simpler formulation known as an extension of the classical time compression approximation. The results indicated that, for situations involving a significant rainfall variability in space, the inaccuracy in the representation of infiltration cannot be corrected by the adaptive component of the rainfall–runoff model. A preliminary analysis of the role of an approximation of saturated hydraulic conductivity to be used in each homogeneous area of the semi-distributed model used both in non-adaptive version and in real-time is also presented.

The collaborative historical African rainfall model: description and evaluation

AbstractIn Africa the variability of rainfall in space and time is high, and the general availability of historical gauge data is low. This makes many food security and hydrologic preparedness activities difficult. In order to help overcome this limitation, we have created the Collaborative Historical African Rainfall Model (CHARM). CHARM combines three sources of information: climatologically aided interpolated (CAI) rainfall grids (monthly/0.5° ), National Centers for Environmental Prediction reanalysis precipitation fields (daily/1.875° ) and orographic enhancement estimates (daily/0.1° ). The first set of weights scales the daily reanalysis precipitation fields to match the gridded CAI monthly rainfall time series. This produces data with a daily/0.5° resolution. A diagnostic model of orographic precipitation, VDELB—based on the dot‐product of the surface wind V and terrain gradient (DEL) and atmospheric buoyancy B—is then used to estimate the precipitation enhancement produced by complex terrain. Although the data are produced on 0.1° grids to facilitate integration with satellite‐based rainfall estimates, the ‘true’ resolution of the data will be less than this value, and varies with station density, topography, and precipitation dynamics. The CHARM is best suited, therefore, to applications that integrate rainfall or rainfall‐driven model results over large regions.The CHARM time series is compared with three independent datasets: dekadal satellite‐based rainfall estimates across the continent, dekadal interpolated gauge data in Mali, and daily interpolated gauge data in western Kenya. These comparisons suggest reasonable accuracies (standard errors of about half a standard deviation) when data are aggregated to regional scales, even at daily time steps. Thus constrained, numerical weather prediction precipitation fields do a reasonable job of representing large‐scale diurnal variations. Published in 2003 by John Wiley & Sons, Ltd.

An application of artificial neural networks for rainfall forecasting

Rainfall forecasting is important for many catchment management applications, in particular for flood warning systems. The variability of rainfall in space and time, however, renders quantitative forecasting of rainfall extremely difficult. The depth of rainfall and its distribution in the temporal and spatial dimensions depends on many variables, such as pressure, temperature, and wind speed and direction. Due to the complexity of the atmospheric processes by which rainfall is generated and the lack of available data on the necessary temporal and spatial scales, it is not feasible generally to forecast rainfall using a physically based process model. Recent developments in artificial intelligence and, in particular, those techniques aimed at pattern recognition, however, provide an alternative approach for developing of a rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are one such technique. Presented in this paper are the results of a study investigating the application of ANNs to forecast the spatial distribution of rainfall for an urban catchment. Three alternative types of ANNs, namely multilayer feedforward neural networks, partial recurrent neural networks, and time delay neural networks, were identified, developed and, as presented in this paper, found to provide reasonable predictions of the rainfall depth one time-step in advance. The data requirements for and the accuracy obtainable from these three alternative types of ANNs are discussed.

A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

Artificial neural networks (ANNs), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems, such as function approximation and pattern recognition. Due to their powerful capability and functionality, ANNs provide an alternative approach for many engineering problems that are difficult to solve by conventional approaches. Rainfall forecasting has been a difficult subject in hydrology due to the complexity of the physical processes involved and the variability of rainfall in space and time. In this study, ANNs were adopted to forecast short-term rainfall for an urban catchment. The ANNs were trained to recognise historical rainfall patterns as recorded from a number of gauges in the study catchment for reproduction of relevant patterns for new rainstorm events. The primary objective of this paper is to investigate the effect of temporal and spatial information on short-term rainfall forecasting. To achieve this aim, a comparison test on the forecast accuracy was made among the ANNs configured with different orders of lag and different numbers of spatial inputs. In developing the ANNs with alternative configurations, the ANNs were trained to an optimal level to achieve good generalisation of data. It was found in this study that the ANNs provided the most accurate predictions when an optimum number of spatial inputs was included into the network, and that the network with lower lag consistently produced better performance.

サンプリング頻度に対する三次元レーダーエコーの月平均特性の感度

Estimates of any precipitation characteristics based on temporally sparse observations entail uncertainty because of the natural variability of rainfall in space and time. This study measures the sampling-related uncertainties of monthly mean reflectivity profile and surface rainfall distribution. Radar and rain gauge data collected during the 1993/94 monsoon season at Darwin, Australia, are used to show the sensitivity of monthly three-dimensional radar-echo and precipitation characteristics to the frequency of observation. The data are partitioned into convective, stratiform, and anvil components according to the horizontal and vertical structure of the echoes. The analyses of this Study reveal the expected trend that the uncertainties of estimated precipitation characteristics using infrequent, observations scale with rainfall amount. The results have implications for climatological studies using spaceborne observation platforms revisiting a given area intermittently. The Tropical Rainfall Measuring Mission (TRMM) satellite radar, which will revisit a given 500 km by 500 km region approximately twice daily, will likely encounter significant problems in estimating the vertical profile of radar reflectivity in the tropics. Monthly mean reflectivity statistics (based on observations within 150 km of the Darwin radar) exhibit a sampling-related uncertainty of about 20 % in both rain and snow. In addition, the radar signal of the TRMM satellite will be highly attenuated below the 0°C level, and the precipitation radar will be insensitive to reflectivity less than about 20 dBZ. Therefore, the spaceborne radar will have an obscured view of the vertical precipitation structure. Reliable reflectivity statistics based on TRMM satellite radar data may be obtained primarily within an altitude range of about 5-7.5 km-an altitude range though that is important for cloud electrification because of the mixed-phase Precipitation processes taking place there. The sampling uncertainty, signal attenuation, and radar sensitivity vary with precipitation type. Moreover, estimation of the convective rain fraction will be compromised by uncertainties in the echo classification as well as a choice of Z - R relation. These results imply the importance of information collected by ground validation site radars to improve upon TRMMf satellite estimates of precipitation characteristitcs and the derived vertical profile of lattent heating.