Analysis of high-resolution rainfall data

1995 ◽  
pp. 114-120 ◽  
Author(s):  
K. P. Georgakakos ◽  
M. B. Sharifi ◽  
P. L. Sturdevant
Keyword(s):  
High Resolution ◽  
Rainfall Data

scholarly journals Sensitivity of point scale surface runoff predictions to rainfall resolution

2007 ◽  
Vol 11 (2) ◽  
pp. 965-982 ◽  
Author(s):  
A. J. Hearman ◽  
C. Hinz
Keyword(s):  
High Resolution ◽  
Western Australia ◽  
Surface Runoff ◽  
Rainfall Data ◽  
Soil Water Storage ◽  
Point Scale ◽  
Deep Drainage ◽  
Model Predictions ◽  
Infiltration Excess ◽  
Saturation Excess

Abstract. This 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 at the point scale. The bounded random cascade model, parameterized to three locations in Western Australia, 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 partitioned water into infiltration excess, infiltration, storage, deep drainage, saturation excess and surface runoff, where the fluxes into and out of the soil store were controlled by thresholds. The results of the numerical modelling were scaled by relating soil infiltration properties to soil draining properties, and in turn, relating these to average storm intensities. 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 all three rainfall locations tested. For soils susceptible to both infiltration excess and saturation excess, total runoff sensitivity was scaled by relating drainage coefficients 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 were determined (ln g*<2). Infiltration excess predicted from high resolution rainfall was short and intense, whereas saturation excess produced from low resolution rainfall was 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 larger scale phenomena such as hillslope runoff and runon. It offers insight into how rainfall resolution can affect predicted amounts of water entering the soil and thus soil water storage and drainage, possibly changing our understanding of the ecological functioning of the system or predictions of agri-chemical leaching. The application of this sensitivity analysis to different rainfall regions in Western Australia showed that locations in the tropics with higher intensity rainfalls are more likely to have differences in infiltration excess predictions with different rainfall resolutions and that a general understanding of the prevailing rainfall conditions and the soil's infiltration capacity can help in deciding whether high rainfall resolutions (below 1 h) are required for accurate surface runoff predictions.

Validation of Climate Research Unit High Resolution Time-Series Rainfall Data over Three Source Region: Results of 52 Years

2013 ◽  
Vol 726-731 ◽  
pp. 3542-3546 ◽  
Author(s):  
Jonathan Arthur Quaye-Ballard ◽  
Ru An ◽  
Richard Ruan ◽  
Kwaku Amaning Adjei ◽  
Samuel Akorful-Andam
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Source Region ◽  
Research Unit ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Climate Research Unit ◽  
Resolution Time ◽  
Map Algebra ◽  
Climate Research

The purpose of this paper was to validate the rainfall data of Climate Research Unit high resolution Time-Series version 3.1 (CRU TS 3.1) with meteorological ground-based Rain Gauge (RG) measurements and determine the possibility of its integration with ground-measured rainfall. The research primarily advocates on the need for complementing ground-based datasets with CRU TS 3.1global datasets for sustainable studies in protecting the environment. The Source Region of the Yellow, Yangtse and Lancang Rivers (SRYYLR), China was taken as the study area. The data was validated by using the data from seventeen meteorological RG stations at SRYYLR. Statistical technique based on Linear Regression (LR), Cumulative Residual Series Analysis (CRSA) and Geo-Spatial techniques based on batch processing, cell statistics, map algebra, re-sampling, extraction by mask, geo-statistical interpolation and profiling along transects by interpolation of a line were used. The study revealed that although CRU TS 3.1 datasets are underestimated compared to the RG datasets, they can be efficiently and effectively be used for rainfall trend analysis with 90% level of confidence because of the analyses by different techniques revealed similar profile trends.

Urban Drainage Systems modelling for Early Warning Service Using Data-Driven Modelling

2020 ◽  
Author(s):  
Solomon Seyoum ◽  
Boud Verbeiren ◽  
Patrick Willems
Keyword(s):  
High Resolution ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Data ◽  
Data Driven ◽  
Peak Flows ◽  
Previous Time ◽  
Pluvial Flooding ◽  
Drainage Channels

&lt;p&gt;Urban catchments are characterized by a high degree of imperviousness, as well as a highly modified landscape and interconnectedness. The hydrological response of such catchments is usually complex and fast and sensitive to precipitation variability at small scales. To properly model and understand urban hydrological responses, high-resolution precipitation measurements to capture spatiotemporal variability is crucial input.&lt;/p&gt;&lt;p&gt;In urban areas floods are among the most recurrent and costly disasters, as these areas are often densely populated and contain vital infrastructure. Runoff from impervious surfaces as a result of extreme rainfall leads to pluvial flooding if the system&amp;#8217;s drainage capacity is exceeded. Due to the fast onset and localised nature of pluvial flooding, high-resolution models are needed to produce fast simulations of flood forecasts for early warning system development. Data-driven models for predictive modelling have been gaining popularity, due to the fact they require minimal inputs and have shorter processing time compared to other types of models.&lt;/p&gt;&lt;p&gt;Data-driven models to forecast peak flows in drainage channels of Brussels, Belgium are being developed at sub-catchment scale, as a proxy for pluvial flooding within the FloodCitiSense project. FloodCitiSense aims to develop an urban pluvial flood early warning service. The effectiveness of these models relies on the input data resolution among others. High-temporal resolution rainfall and runoff data from 13 rainfall and 13 flow gauging stations in Brussels for several years is collected (Open data from Flowbru.be) and the data-driven models for forecasting peak flows in drainage channels are build using the Random Forest classification model.&lt;/p&gt;&lt;p&gt;Optimal model inputs are determined to increase model performance, including rainfall and runoff information from the current time step, as well as additional information derived from previous time steps.&lt;/p&gt;&lt;p&gt;The additional inputs are determined by progressively including rainfall data from neighboring stations and runoff from previous time steps equivalent to the lag time equal to the forecasting horizon, in our case two hours. The data-driven model we develop has the form as shown in the following equation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Q&lt;sub&gt;t&lt;/sub&gt; = f(Q&lt;sub&gt;t-lag&lt;/sub&gt;, &amp;#8721;RF&lt;sub&gt;i,j&lt;/sub&gt;)&amp;#160; &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;for &lt;strong&gt;i&lt;/strong&gt; is the number of rainfall stations considered and &lt;strong&gt;j&lt;/strong&gt; is the time&amp;#160; from &lt;strong&gt;t-lag&lt;/strong&gt; to &lt;strong&gt;t&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;Where &lt;strong&gt;&lt;em&gt;Q&lt;sub&gt;t&lt;/sub&gt; &amp;#160;&lt;/em&gt;&lt;/strong&gt;is the flow at a flow station at time &lt;strong&gt;&lt;em&gt;t&lt;/em&gt;&lt;/strong&gt;, &lt;strong&gt;&lt;em&gt;Q&lt;sub&gt;t-lag &lt;/sub&gt;&lt;/em&gt;&lt;/strong&gt;is the lagged flow at the station and &lt;strong&gt;&lt;em&gt;RF&lt;sub&gt;i,j &lt;/sub&gt;&lt;/em&gt;&lt;/strong&gt;is the rainfall values for station &lt;strong&gt;&lt;em&gt;i&lt;/em&gt;&lt;/strong&gt; and time &lt;strong&gt;&lt;em&gt;j&lt;/em&gt;&lt;/strong&gt;.&lt;/p&gt;&lt;p&gt;For Brussels nine relevant sub-catchments were identified based on historical flood frequency for which we are building data-driven flood forecasting models. For each sub-catchment, RF models are being trained and tested. More than 200,000 data point were available for training and testing the models. For most of the flow stations the data-driven models perform well with R-squared values up to 0.84 for training and 0.6 for testing for a 2-hour forecast horizon.&amp;#160;&lt;/p&gt;&lt;p&gt;To improve the reliability of the data-driven models, as next step, we are including radar rainfall data input, which has the ability to capture temporal and spatial variability of rainfall from localized convective storms to large scale moving storms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;KEYWORDS&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Data driven models, FloodCitiSense, Flood Early Warning System, Urban pluvial flooding&lt;/p&gt;

scholarly journals Observation of Short Duration Rainfall in Yogyakarta

2019 ◽  
Vol 8 (11) ◽  
pp. 2741-2745
Keyword(s):  
High Resolution ◽  
Short Duration ◽  
Distribution Patterns ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Observation Data ◽  
Rainfall Distribution ◽  
Rainfall Events ◽  
Hourly Rainfall ◽  
Mitigation Effort

On the observation of hourly rainfall data in Java Island, for the modelling watershed purpose, it can be seen that short duration rainfall events are the most dominant. The percentage of short duration rainfall event is almost 70% of the observation data. By using the high resolution of hourly rainfall data with 5 minutes’ intervals, it can be easily to describe the rainfall distribution patterns that occur. This research observed high resolution of hourly rainfall data in hilly and mountainous at Mount Merapi area in Yogyakarta. It purposed to mitigation effort due to the rainfall events that often falls with short duration and high intensity.

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