scholarly journals Improved input to distributed hydrologic model in areas with sparse subdaily rainfall data using multivariate daily rainfall disaggregation

2018 ◽  
Vol 20 (4) ◽  
pp. 784-797 ◽  
Author(s):  
Marija Ivković ◽  
Andrijana Todorović ◽  
Jasna Plavšić
Keyword(s):  
Daily Rainfall ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Rain Gauges ◽  
Observation Network ◽  
Hourly Rainfall ◽  
Extreme Flood ◽  
Flood Simulations

Abstract Flood forecasting relies on good quality of observed and forecasted rainfall. In Serbia, the recording rain gauge network is sparse and rainfall data mainly come from dense non-recording rain gauges. This is not beneficial for flood forecasting in smaller catchments and short-duration events, when hydrologic models operating on subdaily scale are applied. Moreover, differences in rainfall amounts from two types of gauges can be considerable, which is common in operational hydrological practice. This paper examines the possibility of including daily rainfall data from dense observation networks in flood forecasting based on subdaily data, using the extreme flood event in the Kolubara catchment in May 2014 as a case study. Daily rainfall from a dense observation network is disaggregated to hourly scale using the MuDRain multivariate disaggregation software. The disaggregation procedure results in well-reproduced rainfall dynamics and adjusts rainfall volume to the values from the non-recording gauges. The fully distributed wflow_hbv model, which is under development as a forecasting tool for the Kolubara catchment, is used for flood simulations with two alternative hourly rainfall data. The results show an improvement when the disaggregated rainfall from denser network is used, thus indicating the significance of better representation of rainfall temporal and spatial variability for flood forecasting.

scholarly journals Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 49-56
Author(s):  
S.JOSEPHINE VANAJA ◽  
B.V. MUDGAL ◽  
S.B. THAMPI
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Runoff ◽  
Cyclonic Storm ◽  
Rain Gauges ◽  
Runoff Modeling

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

scholarly journals Relationships between lightning and rainfall intensities during rainy events in Cyprus

2010 ◽  
Vol 23 ◽  
pp. 87-92 ◽  
Author(s):  
S. Michaelides ◽  
K. Savvidou ◽  
K. Nicolaides
Keyword(s):  
Rain Gauge ◽  
Rainfall Data ◽  
Time Lags ◽  
Spatial Relationships ◽  
Time And Space ◽  
Data Set ◽  
Rain Gauges ◽  
Hourly Rainfall ◽  
Temporal And Spatial ◽  
The Relationship

Abstract. The objective of this work is to study the relationship between the number of lightning recorded by a network of lightning detectors and the amount of rainfall recorded by the network of automatic rain gauges, during rainy events in Cyprus. This study aims at revealing possible temporal and spatial "relationships" between rainfall and lightning intensities. The data used are based on the available records of hourly rainfall data and the "associated" lightning data, with respect to both time and space. The search for temporal and spatial relationships between lightning and rainfall is made by considering various time-lags between lightning and rainfall, and by varying the area around the rain gauge which the associated lightning data set refers to. The methodology adopted in this paper is a statistical one and rainy events registered under the European Project "FLASH" are examined herein.

scholarly journals Subdaily Rainfall Estimation through Daily Rainfall Downscaling Using Random Forests in Spain

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 125 ◽  
Author(s):  
Javier Diez-Sierra ◽  
Manuel del Jesus
Keyword(s):  
Daily Rainfall ◽  
Rainfall Data ◽  
Machine Learning Techniques ◽  
Explanatory Variables ◽  
Rain Gauges ◽  
Daily Rainfall Data ◽  
Learning Techniques ◽  
Hourly Rainfall ◽  
Rainfall Statistics ◽  
Poisson Cluster

Subdaily rainfall data, though essential for applications in many fields, is not as readily available as daily rainfall data. In this work, regression approaches that use atmospheric data and daily rainfall statistics as predictors are evaluated to downscale daily-to-subdaily rainfall statistics on more than 700 hourly rain gauges in Spain. We propose a new approach based on machine learning techniques that improves the downscaling skill of previous methodologies. Results are grouped by climate types (following the Köppen–Geiger classification) to investigate possible missing explanatory variables in the analysis. The methodology is then used to improve the ability of Poisson cluster models to simulate hourly rainfall series that mimic the statistical behavior of the observed ones. This approach can be applied for the study of extreme events and for daily-to-subdaily precipitation disaggregation in any location of Spain where daily rainfall data are available.

scholarly journals Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)

2018 ◽  
Vol 10 (12) ◽  
pp. 1879 ◽  
Author(s):  
Véronique Michot ◽  
Daniel Vila ◽  
Damien Arvor ◽  
Thomas Corpetti ◽  
Josyane Ronchail ◽  
...  
Keyword(s):  
Amazon Basin ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Spatial Coverage ◽  
Rainfall Regimes ◽  
Trmm 3B42 ◽  
Good Agreement

Knowledge and studies on precipitation in the Amazon Basin (AB) are determinant for environmental aspects such as hydrology, ecology, as well as for social aspects like agriculture, food security, or health issues. Availability of rainfall data at high spatio-temporal resolution is thus crucial for these purposes. Remote sensing techniques provide extensive spatial coverage compared to ground-based rainfall data but it is imperative to assess the quality of the estimates. Previous studies underline at regional scale in the AB, and for some years, the efficiency of the Tropical Rainfall Measurement Mission (TRMM) 3B42 Version 7 (V7) (hereafter 3B42) daily product data, to provide a good view of the rainfall time variability which is important to understand the impacts of El Nino Southern Oscilation. Then our study aims to enhance the knowledge about the quality of this product on the entire AB and provide a useful understanding about his capacity to reproduce the annual rainfall regimes. For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013, with the aim to know whether 3B42 is reliable for climate studies. Analysis of quantitative (Bias, Relative RMSE) and categorical statistics (POD, FAR) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions. In the latter, the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation. In general, a good agreement is observed between rain gauge derived regimes and those from 3B42; however, performance is better in the rainy period. Finally, an original way to validate the estimations is by taking into account the interannual variability of rainfall regimes (i.e., the presence of sub-regimes): four sub-regimes in the northeast AB defined from rain gauges and 3B42 were found to be in good agreement. Furthermore, this work examined whether TRMM 3B42 V7 rainfall estimates for all the grid points in the AB, outgoing longwave radiation (OLR) and water vapor flux patterns are consistent in the northeast of AB.

scholarly journals TEMPORAL RAINFALL DISAGGREGATION BY A SIMPLE RANDOM CASCADE MODEL

2018 ◽  
Vol 80 (6) ◽  
Author(s):  
Siti Mariam Saad ◽  
Abdul Aziz Jemain ◽  
Noriszura Ismail
Keyword(s):  
Rainfall Variability ◽  
Daily Rainfall ◽  
Poisson Model ◽  
Rain Gauge ◽  
Peninsular Malaysia ◽  
Cascade Model ◽  
Statistical Measures ◽  
Hourly Rainfall ◽  
Cascade Models ◽  
Log Normal

This study evaluates the utility and suitability of a simple discrete multiplicative random cascade model for temporal rainfall disaggregation. Two of a simple random cascade model, namely log-Poisson and log-Normal  models are applied to simulate hourly rainfall from daily rainfall at seven rain gauge stations in Peninsular Malaysia. The cascade models are evaluated based on the capability to simulate data that preserve three important properties of observed rainfall: rainfall variability, intermittency and extreme events. The results show that both cascade models are able to simulate reasonably well the commonly used statistical measures for rainfall variability (e.g. mean and standard deviation) of hourly rainfall. With respect to rainfall intermittency, even though both models are underestimated, the observed dry proportion, log-Normal  model is likely to simulate number of dry spells better than log-Poisson model. In terms of rainfall extremes, it is demonstrated that log-Poisson and log-Normal  models gave a satisfactory performance for most of the studied stations herein, except for Dungun and Kuala Krai stations, which both located in the east part of Peninsula.

scholarly journals A Comparison Between Global Satellite Mapping of Precipitation Data and High-Resolution Radar Data – A Case Study of Localized Torrential Rainfall over Japan

2021 ◽  
Vol 16 (4) ◽  
pp. 786-793
Author(s):  
Yoshiaki Hayashi ◽  
Taichi Tebakari ◽  
Akihiro Hashimoto ◽  
◽  
Keyword(s):  
High Resolution ◽  
Heavy Rainfall ◽  
Daily Rainfall ◽  
Radar Data ◽  
Rain Gauge ◽  
Hourly Rainfall ◽  
The Difference ◽  
Microwave Imager ◽  
Satellite Mapping

This paper presents a case study comparing the latest algorithm version of Global Satellite Mapping of Precipitation (GSMaP) data with C-band and X-band Multi-Parameter (MP) radar as high-resolution rainfall data in terms of localized heavy rainfall events. The study also obliged us to clarify the spatial and temporal resolution of GSMaP data using high-accuracy ground-based radar, and evaluate the performance and reporting frequency of GSMaP satellites. The GSMaP_Gauge_RNL data with less than 70 mm/day of daily rainfall was similar to the data of both radars, but the GSMaP_Gauge_RNL data with over 70 mm/day of daily rainfall was not, and the calibration by rain-gauge data was poor. Furthermore, both direct/indirect observations by the Global Precipitation Measurement/Microwave Imager (GPM/GMI) and the frequency thereof (once or twice) significantly affected the difference between GPM/GMI data and C-band radar data when the daily rainfall was less than 70 mm/day and the hourly rainfall was less than 20 mm/h. Therefore, it is difficult for GSMaP_Gauge to accurately estimate localized heavy rainfall with high-density particle precipitation.

scholarly journals Investigation of Discrepancies in Satellite Rainfall Estimates over Ethiopia

2014 ◽  
Vol 15 (6) ◽  
pp. 2347-2369 ◽  
Author(s):  
Matthew P. Young ◽  
Charles J. R. Williams ◽  
J. Christine Chiu ◽  
Ross I. Maidment ◽  
Shu-Hua Chen
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Shallow Convection ◽  
Rain Gauges ◽  
The Mean ◽  
Satellite Rainfall ◽  
Convective Cells ◽  
Rainfall Estimates

Abstract Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.

scholarly journals Analysis of the Behavior of Daily Maximum Rainfall within the Department of Atlántico, Colombia

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2453
Author(s):  
Orlando M. Viloria-Marimón ◽  
Álvaro González-Álvarez ◽  
Javier A. Mouthón-Bello
Keyword(s):  
Time Series ◽  
Daily Rainfall ◽  
Information Criterion ◽  
Rain Gauge ◽  
Caribbean Region ◽  
Daily Maximum ◽  
Maximum Rainfall ◽  
Rain Gauges ◽  
Stationary Conditions ◽  
Areal Rainfall

In the Colombian Caribbean region, there are few studies that evaluated the behavior of one of the most commonly used variables in hydrological analyses: the maximum daily rainfall (Pmax-24h). In this study, multiannual Pmax-24h time series from 19 rain gauges, located within the department of Atlántico, were analyzed to (a) determine possible increasing/decreasing trends over time, (b) identify regions with homogeneous behavior of Pmax-24h, (c) assess whether the time series are better suited under either a stationary or non-stationary frequency analysis, (d) generate isohyetal maps under stationary, non-stationary, and mixed conditions, and (e) evaluate the isohyetal maps by means of the calculation of areal rainfall (Pareal) in nine watersheds. In spite of the presence of both increasing and decreasing trends, only the Puerto Giraldo rain gauge showed a significant decreasing trend. Also, three regions (east, central, and west) with similar Pmax-24h behavior were identified. According to the Akaike information criterion test, 79% of the rain gauges showed better fit under stationary conditions. Finally, statistical analysis revealed that, under stationary conditions, the errors in the calculation of Pareal were more frequent, while the magnitude of the errors was larger under non-stationary conditions, especially in the central–south region.

Influence of systematic errors from tipping bucket rain gauges on recorded rainfall data

1998 ◽  
Vol 37 (11) ◽  
pp. 121-129 ◽  
Author(s):  
Rolf Fankhauser
Keyword(s):  
Depth Resolution ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Urban Hydrology ◽  
Data Series ◽  
Data Recording ◽  
Time Step ◽  
Recording Time ◽  
Rain Gauges ◽  
Evaporation Losses

Tipping bucket rain gauges (TBR) are widely used in urban hydrology. The present study investigated the uncertainties in recorded rainfall intensity induced by the following properties of the TBR: depth resolution i.e. the bucket volume, calibration parameters, wetting and evaporation losses and the method of data recording (time between tips or tips per minute). The errors were analysed by means of a TBR simulator i.e. a simulation program that models the behaviour of a TBR. Rainfall data disaggregated to 6 seconds from measured 1-min data and randomly varied were taken as input to the simulator. Different TBR data series were produced by changing the properties of the simulated rain gauge. These data series together with the original rainfall events were used as input to a rainfall-runoff model. Computed overflow volume and peak discharge from a combined sewer overflow (CSO) weir were compared. Errors due to depth resolution (i.e. the bucket size) proved to be small. Therefore TBRs with a depth resolution up to 0.254 mm can be used in urban hydrology without inducing significant errors. Wetting and evaporation losses caused small errors. The method of data recording had also little influence. For larger bucket volumes variable time step recording induced smaller errors than tips per minute recording.

scholarly journals An objective cross-validation framework for mapping rainfall hazard based on rain gauge data

2018 ◽  
Author(s):  
Juliette Blanchet ◽  
Emmanuel Paquet ◽  
Pradeebane Vaittinada Ayar ◽  
David Penot
Keyword(s):  
Goodness Of Fit ◽  
Cumulative Distribution Function ◽  
Cross Validation ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Rain Gauge Data ◽  
Weather Patterns ◽  
Rain Gauges ◽  
Validation Framework

Abstract. We propose an objective framework for estimating rainfall cumulative distribution function within a region when data are only available at rain gauges. Our methodology is based on the evaluation of several goodness-of-fit scores in a cross-validation framework, allowing to assess goodness-of-fit of the full distribution but with a particular focus on its tail. Cross-validation is applied both to select the most appropriate statistical distribution at station locations and to validate the mapping of these distributions. Our methodology is applied to daily rainfall in the Ardèche catchment in South of France, a 2260 km2 catchment with strong disparities in rainfall distribution. Results show preference for a mixture of Gamma distribution over seasons and weather patterns, with parameters interpolated with thin plate spline across this region. However the framework presented in this paper is general and could be likewise applied in any region, with possibly different conclusion depending on the subsequent rainfall processes.

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