Estimation of Intensity-Duration-Frequency (IDF) Curves from Large Scale Atmospheric Dataset by Statistical Downscaling

Abstract The consistency between rainfall projections obtained from direct climate model output and statistical downscaling is evaluated. Results are averaged across an area large enough to overcome the difference in spatial scale between these two types of projections and thus make the comparison meaningful. Undertaking the comparison using a suite of state-of-the-art coupled climate models for two forcing scenarios presents a unique opportunity to test whether statistical linkages established between large-scale predictors and local rainfall under current climate remain valid in future climatic conditions. The study focuses on the southwest corner of Western Australia, a region that has experienced recent winter rainfall declines and for which climate models project, with great consistency, further winter rainfall reductions due to global warming. Results show that as a first approximation the magnitude of the modeled rainfall decline in this region is linearly related to the model global warming (a reduction of about 9% per degree), thus linking future rainfall declines to future emission paths. Two statistical downscaling techniques are used to investigate the influence of the choice of technique on projection consistency. In addition, one of the techniques was assessed using different large-scale forcings, to investigate the impact of large-scale predictor selection. Downscaled and direct model projections are consistent across the large number of models and two scenarios considered; that is, there is no tendency for either to be biased; and only a small hint that large rainfall declines are reduced in downscaled projections. Among the two techniques, a nonhomogeneous hidden Markov model provides greater consistency with climate models than an analog approach. Differences were due to the choice of the optimal combination of predictors. Thus statistically downscaled projections require careful choice of large-scale predictors in order to be consistent with physically based rainfall projections. In particular it was noted that a relative humidity moisture predictor, rather than specific humidity, was needed for downscaled projections to be consistent with direct model output projections.

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Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-3413-2007 ◽

2007 ◽

Vol 4 (5) ◽

pp. 3413-3440 ◽

Cited By ~ 18

Author(s):

E. P. Maurer ◽

H. G. Hidalgo

Keyword(s):

Statistical Downscaling ◽

General Circulation ◽

Large Scale ◽

Impact Analysis ◽

Climate Model ◽

Daily Precipitation ◽

Circulation Model ◽

Reanalysis Data ◽

Temperature Extremes ◽

Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

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Present and LGM permafrost from climate simulations: contribution of statistical downscaling

The Cryosphere Discussions ◽

10.5194/tcd-4-2233-2010 ◽

2010 ◽

Vol 4 (4) ◽

pp. 2233-2275 ◽

Cited By ~ 1

Author(s):

G. Levavasseur ◽

M. Vrac ◽

D. M. Roche ◽

D. Paillard ◽

A. Martin ◽

...

Keyword(s):

Statistical Downscaling ◽

Large Scale ◽

Climate Models ◽

Multinomial Logistic Regression ◽

Generalized Additive Models ◽

Regional Scale ◽

Surface Air Temperature ◽

Additive Models ◽

Simple Relationship ◽

Time Period

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variability between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of surface air temperature (SAT). Then, we define permafrost distribution over Eurasia by SAT conditions. In a first validation step on present climate (CTRL period), GAM shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a stochastic generator approach through a Multinomial Logistic Regression (MLR), which directly models the probabilities of local permafrost indices. The obtained permafrost distributions appear in a better agreement with data. In both cases, the provided local information reduces the inter-variability between climate models. Nevertheless, this also proves that a simple relationship between permafrost and the SAT only is not always sufficient to represent local permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. Our SDMs do not significantly improve permafrost distribution and do not reduce the inter-variability between climate models, at this period. We show that LGM permafrost distribution from climate models strongly depends on large-scale SAT. The differences with LGM data, larger than in the CTRL period, reduce the contribution of downscaling and depend on several factors deserving further studies.

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Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables

Journal of Climate ◽

10.1175/2009jcli2873.1 ◽

2010 ◽

Vol 23 (1) ◽

pp. 17-27 ◽

Cited By ~ 19

Author(s):

Liew Juneng ◽

Fredolin T. Tangang ◽

Hongwen Kang ◽

Woo-Jin Lee ◽

Yap Kok Seng

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Statistical Downscaling ◽

Large Scale ◽

Winter Season ◽

Peninsular Malaysia ◽

Sea Surface ◽

Asia Pacific ◽

Forecast Data ◽

Multimodel Ensembles

Abstract This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

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Interpolation of Regionalized Intensity Duration Frequency (IDF) Estimates based on the observed precipitation data of Baden Wurttemberg (BW), Germany

10.5194/egusphere-egu21-12583 ◽

2021 ◽

Author(s):

Bushra Amin ◽

András Bárdossy

Keyword(s):

Mean Square Error ◽

Hydrological Cycle ◽

Information Criteria ◽

Regional Frequency Analysis ◽

Mean Square ◽

Time Duration ◽

Ungauged Sites ◽

Marquardt Algorithm ◽

Idf Curves ◽

Intensity Duration Frequency

This study is intended to carry out the spatial mapping with ordinary Kriging (OK) of regional point Intensity Duration Frequency (IDF) estimates for the sake of approximation and visualization at ungauged location. Precipitation IDF estimates that offer us valuable information about the frequency of occurrence of extreme events corresponding to different durations and intensities are derived through the application of robust and efficient regional frequency analysis (RFA) based on L-moment algorithm. IDF curves for Baden Wrttemberg (BW) are obtained from the long historical record of daily and hourly annual maximum precipitation series (AMS) provided by German Weather Service from 1960-2020 and 1949-2020 respectively under the assumption of stationarity. One of the widely used Gumbel (type 1) &#160;distribution is applied for IDF analysis because of its suitability for modeling maxima. The uncertainty in IDF curves is determined by the bootstrap method and are revealed in the form of the prediction and confidence interval for each specific time duration on graph. Five metrics such as root mean square error (RMSE), coefficient of determination (R&#178;), mean square error (MSE), Akaike information criteria (AIC) and Bayesian information criteria (BIC) are used to assess the performance of the employed IDF equation. The coefficients of 3-parameteric non-linear IDF equation is determined for various recurrence interval by means of Levenberg&#8211;Marquardt algorithm (LMA), also referred to as damped least square (DLS) method. The estimated coefficients vary from location to location but are insensitive to duration. After successfully determining the IDF parameters for the same return period, parametric contour or isopluvial maps can be generated using OK as an interpolation tool with the intention to provide estimates at ungauged locations. These estimated regional coefficients of IDF curve are then fed to the empirical intensity frequency equation that may serve to estimate rainfall intensity for design purposes for all ungauged sites. The outcomes of this research contribute to the construction of IDF-based design criteria for water projects in ungauged sites located anywhere in the state of BW.In conclusion, we conducted IDF analysis for the entire state of BW as it is considered to be more demanding due to the increased impact of climate change on the intensification of hydrological cycle as well as the expansion of urban areas rendering watershed less penetrable to rainfall and run-off, the better understanding of spatial heterogeneity of intense rainfall patterns for the proposed domain.

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An Extended Model in Estimating Consistent Quantiles for Intensity-Duration-Frequency Curves

10.5194/egusphere-egu21-7562 ◽

2021 ◽

Author(s):

Felix Fauer ◽

Jana Ulrich ◽

Oscar E. Jurado ◽

Uwe Ulbrich ◽

Henning W. Rust

Keyword(s):

Extreme Value ◽

Data Availability ◽

Extreme Value Statistics ◽

Statistical Characteristics ◽

Exceedance Probability ◽

Extended Model ◽

Adequate Model ◽

Extreme Precipitation Events ◽

Idf Curves ◽

Intensity Duration Frequency

Intensity-Duration-Frequency (IDF) curves describe the main statistical characteristics of extreme precipitation events. Providing information on the exceedance probability or return period of certain precipitation intensities for a range of durations, IDF curves are an important tool for the design of hydrological structures.Although the Generalized-Extreme-Value (GEV) distribution is an adequate model for annual precipitation maxima of a certain duration, the core problem of extreme value statistics remains: the limited data availability. Hence, it is reasonable to use a model that can describe all durations simultaneously. This reduces the total number of parameters and a more efficient usage of data is achieved. The idea of implementing a duration dependence directly into the parameters of the extreme value distribution and therefore obtaining a single distribution for a range of durations was proposed by Koutsoyiannis et al. (1998). However, while the use of the GEV is justified by a strong theoretical basis, only empirical models exist for the dependence of the parameters on duration.In this study, we compare different models regarding the dependence of the GEV parameters on duration with the aim of finding a model for a wide duration range (1 min - 5 days). We use a combination of existing model features, especially curvature for small durations and multi-scaling for all durations, and extend them by a new feature that allows flattening of the IDF curves for long durations. Using the quantile score in a cross-validation setting, we provide detailed information on the duration and probability ranges for which specific features or a systematic combination of features lead to improved modeling skill.Our results show that allowing curvature or multi-scaling improves the model only for very short or long durations, respectively, but leads to disadvantages in modeling the other duration ranges. In contrast, allowing flattening of the IDF curves leads to an improvement for medium durations between 1 hour and 1 day without affecting other duration regimes.

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Establishment of IDF-curves for precipitation in the tropical area of Central Africa - comparison of techniques and results

Natural Hazards and Earth System Science ◽

10.5194/nhess-4-375-2004 ◽

2004 ◽

Vol 4 (3) ◽

pp. 375-387 ◽

Cited By ~ 21

Author(s):

B. Mohymont ◽

G. R. Demarée ◽

D. N. Faka

Keyword(s):

Return Period ◽

Central Africa ◽

Tropical Region ◽

Data Sets ◽

Physically Based ◽

Physically Based Models ◽

Comparison Of Techniques ◽

Idf Curves ◽

Intensity Duration Frequency ◽

Technique Comparison

Abstract. The establishment of Intensity-Duration-Frequency (IDF) curves for precipitation remains a powerful tool in the risk analysis of natural hazards. Indeed the IDF-curves allow for the estimation of the return period of an observed rainfall event or conversely of the rainfall amount corresponding to a given return period for different aggregation times. There is a high need for IDF-curves in the tropical region of Central Africa but unfortunately the adequate long-term data sets are frequently not available. The present paper assesses IDF-curves for precipitation for three stations in Central Africa. More physically based models for the IDF-curves are proposed. The methodology used here has been advanced by Koutsoyiannis et al. (1998) and an inter-station and inter-technique comparison is being carried out. The IDF-curves for tropical Central Africa are an interesting tool to be used in sewer system design to combat the frequently occurring inundations in semi-urbanized and urbanized areas of the Kinshasa megapolis.

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Gridded Statistical Downscaling Based on Interpolation of Parameters and Predictor Locations for Summer Daily Precipitation in North China

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0231.1 ◽

2019 ◽

Vol 58 (10) ◽

pp. 2295-2311

Author(s):

Yonghe Liu ◽

Jinming Feng ◽

Zongliang Yang ◽

Yonghong Hu ◽

Jianlin Li

Keyword(s):

Distance Function ◽

Statistical Downscaling ◽

Spatial Dependence ◽

Large Scale ◽

North China ◽

Dynamical Downscaling ◽

Daily Precipitation ◽

Summer Precipitation ◽

Single Station ◽

Dense Grid

AbstractFew statistical downscaling applications have provided gridded products that can provide downscaled values for a no-gauge area as is done by dynamical downscaling. In this study, a gridded statistical downscaling scheme is presented to downscale summer precipitation to a dense grid that covers North China. The main innovation of this scheme is interpolating the parameters of single-station models to this dense grid and assigning optimal predictor values according to an interpolated predictand–predictor distance function. This method can produce spatial dependence (spatial autocorrelation) and transmit the spatial heterogeneity of predictor values from the large-scale predictors to the downscaled outputs. Such gridded output at no-gauge stations shows performances comparable to that at the gauged stations. The area mean precipitation of the downscaled results is comparable to other products. The main value of the downscaling scheme is that it can obtain reasonable outputs for no-gauge stations.

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

Geoscience Letters ◽

10.1186/s40562-019-0147-x ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 3

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.

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