A formula for downscaling extreme sub-daily rainfall intensities

2021 ◽  
Author(s):  
Rasmus Benestad
Keyword(s):  
Climate Model ◽  
Hydrological Cycle ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Data Availability ◽  
Time Dimension ◽  
Hourly Rainfall ◽  
Idf Curves ◽  
Rainfall Statistics

<p>Global warming is associated with an increased rate of evaporation due to higher surface temperatures which also implies a higher hydrological cycle turn-around in a steady-state atmosphere with respect to the water budget. The latter is accompanied with increased atmospheric overturning and more convective activity. In addition, there have been indications of a decreasing area of 24-hr rainfall on a global scale over the last decades, suggesting that rainfall is becoming concentrated over smaller regions. There have also been indications of higher cloud tops. In sum, a consequence of an increased greenhouse effect and modified hydrological cycle is an increased probability for heavy rainfall on local scales and a greater risk of flooding. Changes in risks connected to meteorological and hydrological challenges make it necessary to adapt to new weather statistics. For instance, there is a need to estimate the frequency of heavy downpour and their return levels, both for 24-hr amounts and sub-daily timescales. It is common to account for extreme rainfall by designing infrastructure with the help of intensity-duration-frequency (IDF) curves. One problem is that the IDF curves are based on long records of hourly rainfall measurements that are not widely available. Traditional IDF curves have also been fitted assuming stationary statistics, while climate change implies non-stationary weather statistics. We propose a formula for downscaling sub-daily rainfall intensity based on 24-hr rainfall statistics that is not as limited by data availability nor assumes stationarity. This formula provides a crude and approximate and rule-of-thumb for sites with 24-hr rain gauge data and can be used in connection with downscaling of climate model results. It also represents a way of downscaling rainfall statistics in terms of the time dimension.</p>

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 Deriving intensity–duration–frequency (IDF) curves using downscaled in situ rainfall assimilated with remote sensing data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Yabin Sun ◽  
Dadiyorto Wendi ◽  
Dong Eon Kim ◽  
Shie-Yui Liong
Keyword(s):  
Remote Sensing ◽  
Daily Rainfall ◽  
Remote Sensing Data ◽  
Rainfall Events ◽  
Hourly Rainfall ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Improved Accuracy

AbstractThe rainfall intensity–duration–frequency (IDF) curves play an important role in water resources engineering and management. The applications of IDF curves range from assessing rainfall events, classifying climatic regimes, to deriving design storms and assisting in designing urban drainage systems, etc. The deriving procedure of IDF curves, however, requires long-term historical rainfall observations, whereas lack of fine-timescale rainfall records (e.g. sub-daily) often results in less reliable IDF curves. This paper presents the utilization of remote sensing sub-daily rainfall, i.e. Global Satellite Mapping of Precipitation (GSMaP), integrated with the Bartlett-Lewis rectangular pulses (BLRP) model, to disaggregate the daily in situ rainfall, which is then further used to derive more reliable IDF curves. Application of the proposed method in Singapore indicates that the disaggregated hourly rainfall, preserving both the hourly and daily statistic characteristics, produces IDF curves with significantly improved accuracy; on average over 70% of RMSE is reduced as compared to the IDF curves derived from daily rainfall observations.

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.

Finding homogeneous regions of extreme rainfall in reanalysis data

2020 ◽  
Author(s):  
Marjanne Zander ◽  
Frederiek Sperna Weiland ◽  
Albrecht Weerts
Keyword(s):  
Climate Models ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Extreme Rainfall ◽  
Regional Climate Models ◽  
Rain Gauge ◽  
Reanalysis Data ◽  
Regional Frequency Analysis ◽  
Homogeneous Regions ◽  
Rainfall Statistics

<p>In this study a methodology is developed and tested to delineate homogeneous regions of extreme rainfall around a city of interest using meteorological indices from reanalysis data.</p><p>Scenarios of future climate change established with numerical climate models are well-established tools to help inform climate adaptation policy. The latest generation of regional climate models is now employed at a grid resolution of 2 to 3 kilometers. This enables the simulation of convection; whereby intensive convective rainfall is better represented (Kendon et al., 2017). However, the associated large computational burden limits the simulation length, which poses a challenge for estimating future rainfall statistics.</p><p>Rainfall return periods are a commonly used indicator in the planning, design and evaluation of urban water systems and urban water management. In order to estimate potential future rainfall for return periods larger than the length of the simulation length, regional frequency analysis (RFA) can be applied (Li et al., 2017).  For applying RFA, time series from nearby locations are pooled, the locations considered should fall within the same hydroclimatic climate. This is a region which can be assumed statistically homogeneous for extreme rainfall (Hosking & Wallis, 2009).</p><p>Traditionally, these homogeneous regions are defined on geographical region characteristics and rain gauge statistics (Hosking & Wallis, 2009).  To make the methodology less dependent on rain gauge record availability, Gabriele & Chiaravalloti (2013) used meteorological indices derived from reanalysis data to delineate the homogeneous regions.</p><p>Here we evaluate the methodology to delineate homogeneous regions around cities. Meteorological indices are calculated from the ERA-5 reanalysis dataset (Hersbach et al., 2018) for days with extreme rainfall. The variation herein is used as a measure of homogeneity. The derived homogeneous regions will in future work be used for data pooling of convection-permitting regional climate model simulations datasets to enable the derivation of future extreme rainfall statistics.</p><p>This study is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (https://www.eucp-project.eu/), which aims to develop a regional climate prediction and projection system based on high-resolution climate models for Europe, to support climate adaptation and mitigation decisions for the coming decades.</p><p>References:</p><p>Gabriele, S., & Chiaravalloti, F. (2013). “Searching regional rainfall homogeneity using atmospheric fields”. Advances in Water Resources, 53, 163–174. https://doi.org/https://doi.org/10.1016/j.advwatres.2012.11.002</p><p>Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C., …, Zuo, H. (2018). “Operational global reanalysis: progress, future directions and synergies with NWP”, ECMWF.</p><p>Hosking, J. R. M., & Wallis, J. R. (2009). “Regional Frequency Analysis: An Approach Based on L-Moments”. The Edinburgh Building, Cambridge CB2 2RU, UK: Cambridge University Press. ISBN: 9780511529443.</p><p>Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., … Wilkinson, J. M. (2017). “Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?” BAMS, 98(1), 79–93. https://doi.org/10.1175/BAMS-D-15-0004.1</p><p> Li, J., Evans, J., Johnson, F., & Sharma, A. (2017). “A comparison of methods for estimating climate change impact on design rainfall using a high-resolution RCM.” Journal of Hydrology, 547(Supplement C), 413–427. https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.02.019</p>

scholarly journals Relationship between Surface Temperature and Extreme Rainfalls: A Multi-Time-Scale and Event-Based Analysis*

2014 ◽  
Vol 15 (5) ◽  
pp. 1999-2011 ◽  
Author(s):  
Gérémy Panthou ◽  
Alain Mailhot ◽  
Edward Laurence ◽  
Guillaume Talbot
Keyword(s):  
Relative Humidity ◽  
Rainfall Intensity ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Limiting Factor ◽  
Coastal Regions ◽  
Dewpoint Temperature ◽  
Hourly Rainfall ◽  
Event Based ◽  
The Relationship

Abstract Recent studies have examined the relationship between the intensity of extreme rainfall and temperature. Two main reasons justify this interest. First, the moisture-holding capacity of the atmosphere is governed by the Clausius–Clapeyron (CC) equation. Second, the temperature dependence of extreme-intensity rainfalls should follow a similar relationship assuming relative humidity remains constant and extreme rainfalls are driven by the actual water content of the atmosphere. The relationship between extreme rainfall intensity and air temperature (Pextr–Ta) was assessed by analyzing maximum daily rainfall intensities for durations ranging from 5 min to 12 h for more than 100 meteorological stations across Canada. Different factors that could influence this relationship have been analyzed. It appears that the duration and the climatic region have a strong influence on this relationship. For short durations, the Pextr–Ta relationship is close to the CC scaling for coastal regions while a super-CC scaling followed by an upper limit is observed for inland regions. As the duration increases, the slope of the relationship Pextr–Ta decreases for all regions. The shape of the Pextr–Ta curve is not sensitive to the percentile or season. Complementary analyses have been carried out to understand the departures from the expected Clausius–Clapeyron scaling. The relationship between dewpoint temperature and extreme rainfall intensity shows that the relative humidity is a limiting factor for inland regions, but not for coastal regions. Using hourly rainfall series, an event-based analysis is proposed in order to understand other deviations (super-CC, sub-CC, and monotonic decrease). The analyses suggest that the observed scaling is primarily due to the rainfall event dynamic.

Linking atmospheric circulation to daily rainfall patterns across the Murrumbidgee River Basin

2003 ◽  
Vol 48 (7) ◽  
pp. 233-240 ◽  
Author(s):  
S.P. Charles ◽  
B.C. Bates ◽  
N.R. Viney
Keyword(s):  
Climate Change ◽  
Atmospheric Circulation ◽  
River Basin ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Rainfall Variability ◽  
Daily Rainfall ◽  
Climate Change Impacts ◽  
River Health ◽  
Climate Model Simulations

The hydrological cycle in Australia covers an extraordinary range of climatic and hydrologic regimes. It is now widely accepted that Australian hydrology is significantly different from all other regions and continents with the partial exception of southern Africa. Rainfall variability is very high in almost all regions with respect to amount and the lengths of wet and dry spells. These factors are keys to the behaviour and health of Australian aquatic ecosystems and water resources. Thus assessment of how rainfall may change under a potential future climate is critical. For a case study of the Murrumbidgee River Basin (MRB), a statistical downscaling model that links broad scale atmospheric circulation to multi-site, daily precipitation is assessed using observed data. This model can be driven with climate model simulations to produce rainfall scenarios at the scale required by impacts models. These can then be used in probabilistic risk assessments of climate change impacts on river health. These issues will be discussed in the context of assessing the potential impacts of precipitation changes due to projected climate change on river health.

scholarly journals Influence of South Atlantic Sea Surface Temperatures on Rainfall Variability and Extremes over Southern Africa

2008 ◽  
Vol 21 (24) ◽  
pp. 6498-6520 ◽  
Author(s):  
C. J. R. Williams ◽  
D. R. Kniveton ◽  
R. Layberry
Keyword(s):  
High Resolution ◽  
Southern Africa ◽  
Climate Model ◽  
Rainfall Variability ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
South Atlantic ◽  
Sea Surface ◽  
Rainfall Extremes ◽  
Central South

Abstract It is generally agreed that changing climate variability, and the associated change in climate extremes, may have a greater impact on environmentally vulnerable regions than a changing mean. This research investigates rainfall variability, rainfall extremes, and their associations with atmospheric and oceanic circulations over southern Africa, a region that is considered particularly vulnerable to extreme events because of numerous environmental, social, and economic pressures. Because rainfall variability is a function of scale, high-resolution data are needed to identify extreme events. Thus, this research uses remotely sensed rainfall data and climate model experiments at high spatial and temporal resolution, with the overall aim being to investigate the ways in which sea surface temperature (SST) anomalies influence rainfall extremes over southern Africa. Extreme rainfall identification is achieved by the high-resolution microwave/infrared rainfall algorithm dataset. This comprises satellite-derived daily rainfall from 1993 to 2002 and covers southern Africa at a spatial resolution of 0.1° latitude–longitude. Extremes are extracted and used with reanalysis data to study possible circulation anomalies associated with extreme rainfall. Anomalously cold SSTs in the central South Atlantic and warm SSTs off the coast of southwestern Africa seem to be statistically related to rainfall extremes. Further, through a number of idealized climate model experiments, it would appear that both decreasing SSTs in the central South Atlantic and increasing SSTs off the coast of southwestern Africa lead to a demonstrable increase in daily rainfall and rainfall extremes over southern Africa, via local effects such as increased convection and remote effects such as an adjustment of the Walker-type circulation.

Changing Frequency and Intensity of Rainfall Extremes over India from 1951 to 2003

2009 ◽  
Vol 22 (18) ◽  
pp. 4737-4746 ◽  
Author(s):  
Chandra Kiran B. Krishnamurthy ◽  
Upmanu Lall ◽  
Hyun-Han Kwon
Keyword(s):  
Hydrological Cycle ◽  
National Level ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Significance Test ◽  
Precipitation Frequency ◽  
Change Models ◽  
Increasing Trend ◽  
The Individual ◽  
Increasing Trends

Abstract Using a 1951–2003 gridded daily rainfall dataset for India, the authors assess trends in the intensity and frequency of exceedance of thresholds derived from the 90th and the 99th percentile of daily rainfall. A nonparametric method is used to test for monotonic trends at each location. A field significance test is also applied at the national level to assess whether the individual trends identified could occur by chance in an analysis of the large number of time series analyzed. Statistically significant increasing trends in extremes of rainfall are identified over many parts of India, consistent with the indications from climate change models and the hypothesis that the hydrological cycle will intensify as the planet warms. Specifically, for the exceedance of the 99th percentile of daily rainfall, all locations where a significant increasing trend in frequency of exceedance is identified also exhibit a significant trend in rainfall intensity. However, extreme precipitation frequency over many parts of India also appears to exhibit a decreasing trend, especially for the exceedance of the 90th percentile of daily rainfall. Predominantly increasing trends in the intensity of extreme rainfall are observed for both exceedance thresholds.

scholarly journals From regional climate simulations to the hydrological information needed for basin scale impact studies

2010 ◽  
Vol 26 ◽  
pp. 25-31
Author(s):  
I. Portoghese ◽  
E. Bruno ◽  
M. Vurro
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Daily Rainfall ◽  
Climate Change Impacts ◽  
Stochastic Modelling ◽  
Rain Gauge ◽  
Hydrological Processes ◽  
Rainfall Regime ◽  
Basin Scale

Abstract. The accuracy of local downscaling of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes because the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows, and groundwater recharge. In this study, the output of a regional climate model (RCM) is downscaled using a stochastic modelling of the point rainfall process able to adequately reproduce the daily rainfall intermittency which is one of the crucial aspects for the hydrological processes characterizing Mediterranean environments. The historical time-series from a dense rain-gauge network were used for the analysis of the RCM bias in terms of dry and wet daily period and then to investigate the predicted alteration in the local rainfall regime. A Poisson Rectangular Pulse (PRP) model (Rodriguez-Iturbe et al., 1987) was finally adopted for the stochastic generation of local daily rainfall as a continuous-time point process with forcing parameters resulting from the bias correction of the RCM scenario.

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