Articles | Volume 25, issue 7
Hydrol. Earth Syst. Sci., 25, 3819–3835, 2021
https://doi.org/10.5194/hess-25-3819-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 25, 3819–3835, 2021
https://doi.org/10.5194/hess-25-3819-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article 02 Jul 2021
Research article | 02 Jul 2021
Conditional simulation of spatial rainfall fields using random mixing: a study that implements full control over the stochastic process
Jieru Yan et al.
Related authors
Jieru Yan, András Bárdossy, Sebastian Hörning, and Tao Tao
Hydrol. Earth Syst. Sci., 24, 2287–2301, https://doi.org/10.5194/hess-24-2287-2020, https://doi.org/10.5194/hess-24-2287-2020, 2020
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For applications such as flood forecasting of urban- or town-scale distributed hydrological modeling, high-resolution quantitative precipitation estimation (QPE) with enough accuracy is the most important driving factor and thus the focus of this paper. Considering the fact that rain gauges are sparse but accurate and radar-based precipitation estimates are inaccurate but densely distributed, we are merging the two types of data intellectually to obtain accurate QPEs with high resolution.
Dhiraj Raj Gyawali and András Bárdossy
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-310, https://doi.org/10.5194/hess-2021-310, 2021
Preprint under review for HESS
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In this study, different extensions of the degree-day model were calibrated on snow-cover distribution against freely available satellite snow cover images. The calibrated models simulated the distribution very well in Baden-Wuerttemberg (Germany) and Switzerland. In addition to reliable identification of snow cover, the melt outputs from the calibrated models were able to improve the flow simulations in different catchments in the study region.
András Bárdossy, Jochen Seidel, and Abbas El Hachem
Hydrol. Earth Syst. Sci., 25, 583–601, https://doi.org/10.5194/hess-25-583-2021, https://doi.org/10.5194/hess-25-583-2021, 2021
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In this study, the applicability of data from private weather stations (PWS) for precipitation interpolation was investigated. Due to unknown errors and biases in these observations, a two-step filter was developed that uses indicator correlations and event-based spatial precipitation patterns. The procedure was tested and cross validated for the state of Baden-Württemberg (Germany). The biggest improvement is achieved for the shortest time aggregations.
Jieru Yan, András Bárdossy, Sebastian Hörning, and Tao Tao
Hydrol. Earth Syst. Sci., 24, 2287–2301, https://doi.org/10.5194/hess-24-2287-2020, https://doi.org/10.5194/hess-24-2287-2020, 2020
Short summary
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For applications such as flood forecasting of urban- or town-scale distributed hydrological modeling, high-resolution quantitative precipitation estimation (QPE) with enough accuracy is the most important driving factor and thus the focus of this paper. Considering the fact that rain gauges are sparse but accurate and radar-based precipitation estimates are inaccurate but densely distributed, we are merging the two types of data intellectually to obtain accurate QPEs with high resolution.
Elena Ridolfi, Hemendra Kumar, and András Bárdossy
Hydrol. Earth Syst. Sci., 24, 2043–2060, https://doi.org/10.5194/hess-24-2043-2020, https://doi.org/10.5194/hess-24-2043-2020, 2020
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The paper presents a new, simple and model-free methodology to estimate the streamflow at partially gauged basins, given the precipitation gauged at another basin. We show that the FDC is not a characteristic of the basin only, but of both the basin and the weather. Because of the dependence on the climate, discharge data at the target site are here retrieved using the Antecedent Precipitation Index (API) of the donor site as it represents in a streamflow-like way the precipitation of the basin.
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Proc. IAHS, 380, 3–8, https://doi.org/10.5194/piahs-380-3-2018, https://doi.org/10.5194/piahs-380-3-2018, 2018
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Hydrol. Earth Syst. Sci., 22, 5735–5739, https://doi.org/10.5194/hess-22-5735-2018, https://doi.org/10.5194/hess-22-5735-2018, 2018
Demetris Koutsoyiannis, Günter Blöschl, András Bárdossy, Christophe Cudennec, Denis Hughes, Alberto Montanari, Insa Neuweiler, and Hubert Savenije
Hydrol. Earth Syst. Sci., 20, 1081–1084, https://doi.org/10.5194/hess-20-1081-2016, https://doi.org/10.5194/hess-20-1081-2016, 2016
G. Blöschl, A. Bárdossy, D. Koutsoyiannis, Z. W. Kundzewicz, I. Littlewood, A. Montanari, and H. Savenije
Hydrol. Earth Syst. Sci., 18, 2433–2435, https://doi.org/10.5194/hess-18-2433-2014, https://doi.org/10.5194/hess-18-2433-2014, 2014
F. Beck and A. Bárdossy
Hydrol. Earth Syst. Sci., 17, 4851–4863, https://doi.org/10.5194/hess-17-4851-2013, https://doi.org/10.5194/hess-17-4851-2013, 2013
M. Liu, A. Bárdossy, and E. Zehe
Hydrol. Earth Syst. Sci., 17, 4685–4699, https://doi.org/10.5194/hess-17-4685-2013, https://doi.org/10.5194/hess-17-4685-2013, 2013
N. V. Dung, B. Merz, A. Bárdossy, and H. Apel
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-1-275-2013, https://doi.org/10.5194/nhessd-1-275-2013, 2013
Revised manuscript not accepted
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Stochastic approaches
Compound flood potential from storm surge and heavy precipitation in coastal China: dependence, drivers, and impacts
Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin
Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought
Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks
A standardized index for assessing sub-monthly compound dry and hot conditions with application in China
Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues
A new discrete multiplicative random cascade model for downscaling intermittent rainfall fields
Modelling rainfall with a Bartlett–Lewis process: new developments
Nonstationary stochastic rain type generation: accounting for climate drivers
Conditional simulation of surface rainfall fields using modified phase annealing
Climate influences on flood probabilities across Europe
Flood-related extreme precipitation in southwestern Germany: development of a two-dimensional stochastic precipitation model
A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales
Mapping rainfall hazard based on rain gauge data: an objective cross-validation framework for model selection
On the skill of raw and post-processed ensemble seasonal meteorological forecasts in Denmark
Estimating radar precipitation in cold climates: the role of air temperature within a non-parametric framework
Dealing with non-stationarity in sub-daily stochastic rainfall models
Rainfall disaggregation for hydrological modeling: is there a need for spatial consistence?
Design water demand of irrigation for a large region using a high-dimensional Gaussian copula
Modeling the changes in water balance components of the highly irrigated western part of Bangladesh
A classification algorithm for selective dynamical downscaling of precipitation extremes
Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: the effect of preprocessing and post-processing on skill and statistical consistency
Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment
A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula
Censored rainfall modelling for estimation of fine-scale extremes
An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France
Precipitation extremes on multiple timescales – Bartlett–Lewis rectangular pulse model and intensity–duration–frequency curves
Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?
A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform
Regionalizing nonparametric models of precipitation amounts on different temporal scales
A combined statistical bias correction and stochastic downscaling method for precipitation
Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin
Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme
Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies
Nonstationarity of low flows and their timing in the eastern United States
Climatological characteristics of raindrop size distributions in Busan, Republic of Korea
Correction of real-time satellite precipitation with satellite soil moisture observations
Stochastic approach to analyzing the uncertainties and possible changes in the availability of water in the future based on scenarios of climate change
Attribution of European precipitation and temperature trends to changes in synoptic circulation
Spatial and temporal variability of rainfall in the Nile Basin
Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe
Imperfect scaling in distributions of radar-derived rainfall fields
Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications
The effect of flow and orography on the spatial distribution of the very short-term predictability of rainfall from composite radar images
Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne
Simulation of rainfall time series from different climatic regions using the direct sampling technique
The role of retrospective weather forecasts in developing daily forecasts of nutrient loadings over the southeast US
A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought
Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran
Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed
Jiayi Fang, Thomas Wahl, Jian Fang, Xun Sun, Feng Kong, and Min Liu
Hydrol. Earth Syst. Sci., 25, 4403–4416, https://doi.org/10.5194/hess-25-4403-2021, https://doi.org/10.5194/hess-25-4403-2021, 2021
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A comprehensive assessment of compound flooding potential is missing for China. We investigate dependence, drivers, and impacts of storm surge and precipitation for coastal China. Strong dependence exists between driver combinations, with variations of seasons and thresholds. Sea level rise escalates compound flood potential. Meteorology patterns are pronounced for low and high compound flood potential. Joint impacts from surge and precipitation were much higher than from each individually.
Jamie Towner, Andrea Ficchí, Hannah L. Cloke, Juan Bazo, Erin Coughlan de Perez, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci., 25, 3875–3895, https://doi.org/10.5194/hess-25-3875-2021, https://doi.org/10.5194/hess-25-3875-2021, 2021
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We examine whether several climate indices alter the magnitude, timing and duration of floods in the Amazon. We find significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative SST years in the central Pacific Ocean. This response is not repeated when the negative anomaly is positioned further east. These results have important implications for both social and physical sectors working towards the improvement of flood early warning systems.
Hossein Tabari, Santiago Mendoza Paz, Daan Buekenhout, and Patrick Willems
Hydrol. Earth Syst. Sci., 25, 3493–3517, https://doi.org/10.5194/hess-25-3493-2021, https://doi.org/10.5194/hess-25-3493-2021, 2021
Sebastian Scher and Stefanie Peßenteiner
Hydrol. Earth Syst. Sci., 25, 3207–3225, https://doi.org/10.5194/hess-25-3207-2021, https://doi.org/10.5194/hess-25-3207-2021, 2021
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In hydrology, it is often necessary to infer from a daily sum of precipitation a possible distribution over the day – for example how much it rained in each hour. In principle, for a given daily sum, there are endless possibilities. However, some are more likely than others. We show that a method from artificial intelligence called generative adversarial networks (GANs) can
learnwhat a typical distribution over the day looks like.
Jun Li, Zhaoli Wang, Xushu Wu, Jakob Zscheischler, Shenglian Guo, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 1587–1601, https://doi.org/10.5194/hess-25-1587-2021, https://doi.org/10.5194/hess-25-1587-2021, 2021
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We introduce a daily-scale index, termed the standardized compound drought and heat index (SCDHI), to measure the key features of compound dry-hot conditions. SCDHI can not only monitor the long-term compound dry-hot events, but can also capture such events at sub-monthly scale and reflect the related vegetation activity impacts. The index can provide a new tool to quantify sub-monthly characteristics of compound dry-hot events, which are vital for releasing early and timely warning.
Damien Raynaud, Benoit Hingray, Guillaume Evin, Anne-Catherine Favre, and Jérémy Chardon
Hydrol. Earth Syst. Sci., 24, 4339–4352, https://doi.org/10.5194/hess-24-4339-2020, https://doi.org/10.5194/hess-24-4339-2020, 2020
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This research paper proposes a weather generator combining two sampling approaches. A first generator recombines large-scale atmospheric situations. A second generator is applied to these atmospheric trajectories in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series in Switzerland. It reproduces adequately the observed climatology and improves the reproduction of extreme precipitation values.
Marc Schleiss
Hydrol. Earth Syst. Sci., 24, 3699–3723, https://doi.org/10.5194/hess-24-3699-2020, https://doi.org/10.5194/hess-24-3699-2020, 2020
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A new way to downscale rainfall fields based on the notion of equal-volume areas (EVAs) is proposed. Experiments conducted on 100 rainfall events in the Netherlands show that the EVA method outperforms classical methods based on fixed grid cell sizes, producing fields with more realistic spatial structures. The main novelty of the method lies in its adaptive sampling strategy, which avoids many of the mathematical challenges associated with the presence of zero rainfall values.
Christian Onof and Li-Pen Wang
Hydrol. Earth Syst. Sci., 24, 2791–2815, https://doi.org/10.5194/hess-24-2791-2020, https://doi.org/10.5194/hess-24-2791-2020, 2020
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The randomised Bartlett–Lewis (RBL) model is widely used to synthesise rainfall time series with realistic statistical features. However, it tended to underestimate rainfall extremes at sub-hourly and hourly timescales. In this paper, we revisit the derivation of equations that represent rainfall properties and compare statistical estimation methods that impact model calibration. These changes effectively improved the RBL model's capacity to reproduce sub-hourly and hourly rainfall extremes.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 24, 2841–2854, https://doi.org/10.5194/hess-24-2841-2020, https://doi.org/10.5194/hess-24-2841-2020, 2020
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At subdaily resolution, rain intensity exhibits a strong variability in space and time due to the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection). In this paper we explore a new method to simulate rain type time series conditional to meteorological covariates. Afterwards, we apply stochastic rain type simulation to the downscaling of precipitation of a regional climate model.
Jieru Yan, András Bárdossy, Sebastian Hörning, and Tao Tao
Hydrol. Earth Syst. Sci., 24, 2287–2301, https://doi.org/10.5194/hess-24-2287-2020, https://doi.org/10.5194/hess-24-2287-2020, 2020
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For applications such as flood forecasting of urban- or town-scale distributed hydrological modeling, high-resolution quantitative precipitation estimation (QPE) with enough accuracy is the most important driving factor and thus the focus of this paper. Considering the fact that rain gauges are sparse but accurate and radar-based precipitation estimates are inaccurate but densely distributed, we are merging the two types of data intellectually to obtain accurate QPEs with high resolution.
Eva Steirou, Lars Gerlitz, Heiko Apel, Xun Sun, and Bruno Merz
Hydrol. Earth Syst. Sci., 23, 1305–1322, https://doi.org/10.5194/hess-23-1305-2019, https://doi.org/10.5194/hess-23-1305-2019, 2019
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We investigate whether flood probabilities in Europe vary for different large-scale atmospheric circulation conditions. Maximum seasonal river flows from 600 gauges in Europe and five synchronous atmospheric circulation indices are analyzed. We find that a high percentage of stations is influenced by at least one of the climate indices, especially during winter. These results can be useful for preparedness and damage planning by (re-)insurance companies.
Florian Ehmele and Michael Kunz
Hydrol. Earth Syst. Sci., 23, 1083–1102, https://doi.org/10.5194/hess-23-1083-2019, https://doi.org/10.5194/hess-23-1083-2019, 2019
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The risk estimation of precipitation events with high recurrence periods is difficult due to the limited timescale with meteorological observations and an inhomogeneous distribution of rain gauges, especially in mountainous terrains. In this study a spatially high resolved analytical model, designed for stochastic simulations of flood-related precipitation, is developed and applied to an investigation area in Germany but is transferable to other areas. High conformity with observations is found.
Jeongha Park, Christian Onof, and Dongkyun Kim
Hydrol. Earth Syst. Sci., 23, 989–1014, https://doi.org/10.5194/hess-23-989-2019, https://doi.org/10.5194/hess-23-989-2019, 2019
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Rainfall data are often unavailable for the analysis of water-related problems such as floods and droughts. In such cases, researchers use rainfall generators to produce synthetic rainfall data. However, data from most rainfall generators can serve only one specific purpose; i.e. one rainfall generator cannot be applied to analyse both floods and droughts. To overcome this issue, we invented a multipurpose rainfall generator that can be applied to analyse most water-related problems.
Juliette Blanchet, Emmanuel Paquet, Pradeebane Vaittinada Ayar, and David Penot
Hydrol. Earth Syst. Sci., 23, 829–849, https://doi.org/10.5194/hess-23-829-2019, https://doi.org/10.5194/hess-23-829-2019, 2019
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We propose an objective framework for estimating rainfall cumulative distribution functions in a region when data are only available at rain gauges. Our methodology allows us to assess goodness-of-fit of the full distribution, but with a particular focus on its tail. It is applied to daily rainfall in the Ardèche catchment in the south of France. Results show a preference for a mixture of Gamma distribution over seasons and weather patterns, with parameters interpolated with a thin plate spline.
Diana Lucatero, Henrik Madsen, Jens C. Refsgaard, Jacob Kidmose, and Karsten H. Jensen
Hydrol. Earth Syst. Sci., 22, 6591–6609, https://doi.org/10.5194/hess-22-6591-2018, https://doi.org/10.5194/hess-22-6591-2018, 2018
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The present study evaluates the skill of a seasonal forecasting system for hydrological relevant variables in Denmark. Linear scaling and quantile mapping were used to correct the forecasts. Uncorrected forecasts tend to be more skillful than climatology, in general, for the first month lead time only. Corrected forecasts show a reduced bias in the mean; are more consistent; and show a level of accuracy that is closer to, although no higher than, that of ensemble climatology, in general.
Kuganesan Sivasubramaniam, Ashish Sharma, and Knut Alfredsen
Hydrol. Earth Syst. Sci., 22, 6533–6546, https://doi.org/10.5194/hess-22-6533-2018, https://doi.org/10.5194/hess-22-6533-2018, 2018
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This study investigates the use of gauge precipitation and air temperature observations to ascertain radar precipitation in cold climates. The use of air temperature as an additional variable in a non-parametric model improved the estimation of radar precipitation significantly. Further, it was found that the temperature effects became insignificant when air temperature was above 10 °C. The findings from this study could be important for using radar precipitation for hydrological applications.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 22, 5919–5933, https://doi.org/10.5194/hess-22-5919-2018, https://doi.org/10.5194/hess-22-5919-2018, 2018
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We propose a method for unsupervised classification of the space–time–intensity structure of weather radar images. The resulting classes are interpreted as rain types, i.e. pools of rain fields with homogeneous statistical properties. Rain types can in turn be used to define stationary periods for further stochastic rainfall modelling. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm.
Hannes Müller-Thomy, Markus Wallner, and Kristian Förster
Hydrol. Earth Syst. Sci., 22, 5259–5280, https://doi.org/10.5194/hess-22-5259-2018, https://doi.org/10.5194/hess-22-5259-2018, 2018
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Rainfall time series are disaggregated from daily to hourly values to be used for rainfall–runoff modeling of mesoscale catchments. Spatial rainfall consistency is implemented afterwards using simulated annealing. With the calibration process applied, observed runoff statistics (e.g., summer and winter peak flows) are represented well. However, rainfall datasets with under- or over-estimation of spatial consistency lead to similar results, so the need for a good representation can be questioned.
Xinjun Tu, Yiliang Du, Vijay P. Singh, Xiaohong Chen, Kairong Lin, and Haiou Wu
Hydrol. Earth Syst. Sci., 22, 5175–5189, https://doi.org/10.5194/hess-22-5175-2018, https://doi.org/10.5194/hess-22-5175-2018, 2018
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For given frequencies of precipitation of a large region, design water demands of irrigation of the entire region among three methods, i.e., equalized frequency, typical year and most-likely weight function, slightly differed, but their alterations in sub-regions were complicated. A design procedure using the most-likely weight function in association with a high-dimensional copula, which built a linkage between regional frequency and sub-regional frequency of precipitation, is recommended.
A. T. M. Sakiur Rahman, M. Shakil Ahmed, Hasnat Mohammad Adnan, Mohammad Kamruzzaman, M. Abdul Khalek, Quamrul Hasan Mazumder, and Chowdhury Sarwar Jahan
Hydrol. Earth Syst. Sci., 22, 4213–4228, https://doi.org/10.5194/hess-22-4213-2018, https://doi.org/10.5194/hess-22-4213-2018, 2018
Edmund P. Meredith, Henning W. Rust, and Uwe Ulbrich
Hydrol. Earth Syst. Sci., 22, 4183–4200, https://doi.org/10.5194/hess-22-4183-2018, https://doi.org/10.5194/hess-22-4183-2018, 2018
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Kilometre-scale climate-model data are of great benefit to both hydrologists and end users studying extreme precipitation, though often unavailable due to the computational expense associated with such high-resolution simulations. We develop a method which identifies days with enhanced risk of extreme rainfall over a catchment, so that high-resolution simulations can be performed only when such a risk exists, reducing computational expense by over 90 % while still well capturing the extremes.
Diana Lucatero, Henrik Madsen, Jens C. Refsgaard, Jacob Kidmose, and Karsten H. Jensen
Hydrol. Earth Syst. Sci., 22, 3601–3617, https://doi.org/10.5194/hess-22-3601-2018, https://doi.org/10.5194/hess-22-3601-2018, 2018
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The skill of an experimental streamflow forecast system in the Ahlergaarde catchment, Denmark, is analyzed. Inputs to generate the forecasts are taken from the ECMWF System 4 seasonal forecasting system and an ensemble of observations (ESP). Reduction of biases is achieved by processing the meteorological and/or streamflow forecasts. In general, this is not sufficient to ensure a higher level of accuracy than the ESP, indicating a modest added value of a seasonal meteorological system.
Sanjeev K. Jha, Durga L. Shrestha, Tricia A. Stadnyk, and Paulin Coulibaly
Hydrol. Earth Syst. Sci., 22, 1957–1969, https://doi.org/10.5194/hess-22-1957-2018, https://doi.org/10.5194/hess-22-1957-2018, 2018
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The output from numerical weather prediction (NWP) models is known to have errors. River forecast centers in Canada mostly use precipitation forecasts directly obtained from American and Canadian NWP models. In this study, we evaluate the forecast performance of ensembles generated by a Bayesian post-processing approach in cold climates. We demonstrate that the post-processing approach generates bias-free forecasts and provides a better picture of uncertainty in the case of an extreme event.
Md Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Pere Quintana-Seguí, and Anaïs Barella-Ortiz
Hydrol. Earth Syst. Sci., 22, 1371–1389, https://doi.org/10.5194/hess-22-1371-2018, https://doi.org/10.5194/hess-22-1371-2018, 2018
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This study investigates the use of a nonparametric model for combining multiple global precipitation datasets and characterizing estimation uncertainty. Inputs to the model included three satellite precipitation products, an atmospheric reanalysis precipitation dataset, satellite-derived near-surface daily soil moisture data, and terrain elevation. We evaluated the technique based on high-resolution reference precipitation data and further used generated ensembles to force a hydrological model.
David Cross, Christian Onof, Hugo Winter, and Pietro Bernardara
Hydrol. Earth Syst. Sci., 22, 727–756, https://doi.org/10.5194/hess-22-727-2018, https://doi.org/10.5194/hess-22-727-2018, 2018
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Extreme rainfall is one of the most significant natural hazards. However, estimating very large events is highly uncertain. We present a new approach to construct intense rainfall using the structure of rainfall generation in clouds. The method is particularly effective at estimating short-duration extremes, which can be the most damaging. This is expected to have immediate impact for the estimation of very rare downpours, with the potential to improve climate resilience and hazard preparedness.
Jérémy Chardon, Benoit Hingray, and Anne-Catherine Favre
Hydrol. Earth Syst. Sci., 22, 265–286, https://doi.org/10.5194/hess-22-265-2018, https://doi.org/10.5194/hess-22-265-2018, 2018
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We present a two-stage statistical downscaling model for the probabilistic prediction of local precipitation, where the downscaling statistical link is estimated from atmospheric circulation analogs of the current prediction day.
The model allows for a day-to-day adaptive and tailored downscaling. It can reveal specific predictors for peculiar and non-frequent weather configurations. This approach noticeably improves the skill of the prediction for both precipitation occurrence and quantity.
Christoph Ritschel, Uwe Ulbrich, Peter Névir, and Henning W. Rust
Hydrol. Earth Syst. Sci., 21, 6501–6517, https://doi.org/10.5194/hess-21-6501-2017, https://doi.org/10.5194/hess-21-6501-2017, 2017
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A stochastic model for precipitation is used to simulate an observed precipitation series; it is compared to the original series in terms of intensity–duration frequency curves. Basis for the latter curves is a parametric model for the duration dependence of the underlying extreme value model allowing a consistent estimation of one single duration-dependent distribution using all duration series simultaneously. The stochastic model reproduces the curves except for very rare extreme events.
Poulomi Ganguli and Paulin Coulibaly
Hydrol. Earth Syst. Sci., 21, 6461–6483, https://doi.org/10.5194/hess-21-6461-2017, https://doi.org/10.5194/hess-21-6461-2017, 2017
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Using statistical models, we test whether nonstationary versus stationary models show any significant differences in terms of design storm intensity at different durations across Southern Ontario. We find that detectable nonstationarity in rainfall extremes does not necessarily lead to significant differences in design storm intensity, especially for shorter return periods. An update of 2–44 % is required in current design standards to mitigate the risk of storm-induced urban flooding.
Daniele Nerini, Nikola Besic, Ioannis Sideris, Urs Germann, and Loris Foresti
Hydrol. Earth Syst. Sci., 21, 2777–2797, https://doi.org/10.5194/hess-21-2777-2017, https://doi.org/10.5194/hess-21-2777-2017, 2017
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Stochastic generators are effective tools for the quantification of uncertainty in a number of applications with weather radar data, including quantitative precipitation estimation and very short-term forecasting. However, most of the current stochastic rainfall field generators cannot handle spatial non-stationarity. We propose an approach based on the short-space Fourier transform, which aims to reproduce the local spatial structure of the observed rainfall fields.
Tobias Mosthaf and András Bárdossy
Hydrol. Earth Syst. Sci., 21, 2463–2481, https://doi.org/10.5194/hess-21-2463-2017, https://doi.org/10.5194/hess-21-2463-2017, 2017
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Parametric distribution functions are commonly used to model precipitation amounts at gauged and ungauged locations. Nonparametric distributions offer a more flexible way to model precipitation amounts. However, the nonparametric models do not exhibit parameters that can be easily regionalized for application at ungauged locations. To overcome this deficiency, we present a new interpolation scheme for nonparametric models and evaluate the usage of daily gauges for sub-daily resolutions.
Claudia Volosciuk, Douglas Maraun, Mathieu Vrac, and Martin Widmann
Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, https://doi.org/10.5194/hess-21-1693-2017, 2017
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For impact modeling, infrastructure design, or adaptation strategy planning, high-quality climate data on the point scale are often demanded. Due to the scale gap between gridbox and point scale and biases in climate models, we combine a statistical bias correction and a stochastic downscaling model and apply it to climate model-simulated precipitation. The method performs better in summer than in winter and in winter best for mild winter climate (Mediterranean) and worst for continental winter.
Aline Murawski, Gerd Bürger, Sergiy Vorogushyn, and Bruno Merz
Hydrol. Earth Syst. Sci., 20, 4283–4306, https://doi.org/10.5194/hess-20-4283-2016, https://doi.org/10.5194/hess-20-4283-2016, 2016
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To understand past flood changes in the Rhine catchment and the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. Here the link between patterns and local climate is tested, and the skill of GCMs in reproducing these patterns is evaluated.
Kue Bum Kim, Hyun-Han Kwon, and Dawei Han
Hydrol. Earth Syst. Sci., 20, 2019–2034, https://doi.org/10.5194/hess-20-2019-2016, https://doi.org/10.5194/hess-20-2019-2016, 2016
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A primary advantage of using model ensembles for climate change impact studies is to represent the uncertainties associated with models through the ensemble spread. Currently, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. However the proposed method is able to correct the bias and conform to the ensemble spread so that the ensemble information can be better used.
E. P. Maurer, D. L. Ficklin, and W. Wang
Hydrol. Earth Syst. Sci., 20, 685–696, https://doi.org/10.5194/hess-20-685-2016, https://doi.org/10.5194/hess-20-685-2016, 2016
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To translate climate model output from its native coarse scale to a finer scale more representative of that at which societal impacts are experienced, a common method applied is statistical downscaling. A component of many statistical downscaling techniques is quantile mapping (QM). QM can be applied at different spatial scales, and here we study how skill varies with spatial scale. We find the highest skill is generally obtained when applying QM at approximately a 50 km spatial scale.
S. Sadri, J. Kam, and J. Sheffield
Hydrol. Earth Syst. Sci., 20, 633–649, https://doi.org/10.5194/hess-20-633-2016, https://doi.org/10.5194/hess-20-633-2016, 2016
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Low flows are a critical part of the river flow regime but little is known about how they are changing in response to human influences and climate. We analyzed low flow records across the eastern US and identified sites that were minimally influenced by human activities. We found a general increasing trend in low flows across the northeast and decreasing trend across the southeast that are likely driven by changes in climate. The results have implications for how we manage our water resources.
S.-H. Suh, C.-H. You, and D.-I. Lee
Hydrol. Earth Syst. Sci., 20, 193–207, https://doi.org/10.5194/hess-20-193-2016, https://doi.org/10.5194/hess-20-193-2016, 2016
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This paper was written to find the climatological characteristics of raindrop size distribution (DSD) with respect to the wind direction in Busan, Korea. The data were collected by POSS disdrometer during 4 years (2001–2004).
Busan shows the tendency of land-sea breeze. When sea wind blows during rainfall period, mean size and number concentration of raindrop are smaller and larger than that of land wind blows, respectively. It means that the features of DSD depend on the wind direction.
W. Zhan, M. Pan, N. Wanders, and E. F. Wood
Hydrol. Earth Syst. Sci., 19, 4275–4291, https://doi.org/10.5194/hess-19-4275-2015, https://doi.org/10.5194/hess-19-4275-2015, 2015
G. G. Oliveira, O. C. Pedrollo, and N. M. R. Castro
Hydrol. Earth Syst. Sci., 19, 3585–3604, https://doi.org/10.5194/hess-19-3585-2015, https://doi.org/10.5194/hess-19-3585-2015, 2015
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The objective of this study was to analyze the changes and uncertainties related to water availability in the future, in the Ijuí River basin (south of Brazil), using a stochastic approach. In general the results showed a trend to increased flows. It can be concluded that there is a tendency to increase the hydrological variability during the period between 2011 and 2040, which indicates the possibility of occurrence of time series with more marked periods of droughts and floods.
A. K. Fleig, L. M. Tallaksen, P. James, H. Hisdal, and K. Stahl
Hydrol. Earth Syst. Sci., 19, 3093–3107, https://doi.org/10.5194/hess-19-3093-2015, https://doi.org/10.5194/hess-19-3093-2015, 2015
C. Onyutha and P. Willems
Hydrol. Earth Syst. Sci., 19, 2227–2246, https://doi.org/10.5194/hess-19-2227-2015, https://doi.org/10.5194/hess-19-2227-2015, 2015
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Variability of rainfall in the Nile Basin was found linked to the large-scale atmosphere-ocean interactions. This finding is vital for a number of water management and planning aspects. To give just one example, it may help in obtaining improved quantiles for flood or drought/water scarcity risk management. This is especially important under conditions of (1) questionable data quality, and (2) data scarcity. These conditions are typical of the Nile Basin and inevitably need to be addressed.
M. A. Sunyer, Y. Hundecha, D. Lawrence, H. Madsen, P. Willems, M. Martinkova, K. Vormoor, G. Bürger, M. Hanel, J. Kriaučiūnienė, A. Loukas, M. Osuch, and I. Yücel
Hydrol. Earth Syst. Sci., 19, 1827–1847, https://doi.org/10.5194/hess-19-1827-2015, https://doi.org/10.5194/hess-19-1827-2015, 2015
M. J. van den Berg, L. Delobbe, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 18, 5331–5344, https://doi.org/10.5194/hess-18-5331-2014, https://doi.org/10.5194/hess-18-5331-2014, 2014
H. Seyyedi, E. N. Anagnostou, E. Beighley, and J. McCollum
Hydrol. Earth Syst. Sci., 18, 5077–5091, https://doi.org/10.5194/hess-18-5077-2014, https://doi.org/10.5194/hess-18-5077-2014, 2014
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The paper presents a methodology for using global precipitation products from satellite remote sensing to error-correct and downscale global atmospheric reanalysis precipitation data sets. It is shown that streamflow simulations from the satellite-adjusted precipitation reanalysis give similar statistics to the ones derived by high-resolution ground-based radar rainfall data sets. This approach can be applied globally to derive improved flood frequency maps over data-poor areas.
L. Foresti and A. Seed
Hydrol. Earth Syst. Sci., 18, 4671–4686, https://doi.org/10.5194/hess-18-4671-2014, https://doi.org/10.5194/hess-18-4671-2014, 2014
A. G. Yilmaz, I. Hossain, and B. J. C. Perera
Hydrol. Earth Syst. Sci., 18, 4065–4076, https://doi.org/10.5194/hess-18-4065-2014, https://doi.org/10.5194/hess-18-4065-2014, 2014
F. Oriani, J. Straubhaar, P. Renard, and G. Mariethoz
Hydrol. Earth Syst. Sci., 18, 3015–3031, https://doi.org/10.5194/hess-18-3015-2014, https://doi.org/10.5194/hess-18-3015-2014, 2014
J. Oh, T. Sinha, and A. Sankarasubramanian
Hydrol. Earth Syst. Sci., 18, 2885–2898, https://doi.org/10.5194/hess-18-2885-2014, https://doi.org/10.5194/hess-18-2885-2014, 2014
A. AghaKouchak
Hydrol. Earth Syst. Sci., 18, 2485–2492, https://doi.org/10.5194/hess-18-2485-2014, https://doi.org/10.5194/hess-18-2485-2014, 2014
S. K. Sigaroodi, Q. Chen, S. Ebrahimi, A. Nazari, and B. Choobin
Hydrol. Earth Syst. Sci., 18, 1995–2006, https://doi.org/10.5194/hess-18-1995-2014, https://doi.org/10.5194/hess-18-1995-2014, 2014
P. Gagnon and A. N. Rousseau
Hydrol. Earth Syst. Sci., 18, 1695–1704, https://doi.org/10.5194/hess-18-1695-2014, https://doi.org/10.5194/hess-18-1695-2014, 2014
Cited articles
Adams, T.: Chapter 10 – Flood Forecasting in the United States NOAA/National
Weather Service, in: Flood Forecasting, edited by: Adams, T. E. and Pagano,
T. C., Academic Press, Boston, 249–310, https://doi.org/10.1016/B978-0-12-801884-2.00010-4, 2016. a
Bárdossy, A. and Hörning, S.: Random Mixing: An Approach to Inverse
Modeling for Groundwater Flow and Transport Problems, Trans. Porous Media, 114, 241–259, https://doi.org/10.1007/s11242-015-0608-4, 2016. a, b
Bell, T. L.: A space-time stochastic model of rainfall for satellite
remote-sensing studies, J. Geophys. Res.-Atmos., 92, 9631–9643, https://doi.org/10.1029/JD092iD08p09631, 1987. a
Berenguer, M., Sempere-Torres, D., and Pegram, G.: SBMcast – An ensemble
nowcasting technique to assess the uncertainty in rainfall forecasts by
Lagrangian extrapolation, J. Hydrol., 404, 226–240, 2011. a
Berne, A. and Krajewski, W. F.: Radar for hydrology: Unfulfilled promise or
unrecognized potential?, Adv. Water Resour., 51, 357–366, 2013. a
Bowler, N. E., Pierce, C. E., and Seed, A. W.: STEPS: A probabilistic
precipitation forecasting scheme which merges an extrapolation nowcast with
downscaled NWP, Q. J. Roy. Meteorol. Soc., 132, 2127–2155, https://doi.org/10.1256/qj.04.100, 2006. a
Brandes, E. A.: Optimizing Rainfall Estimates with the Aid of Radar, J. Appl. Meteorol., 14, 1339–1345, 1975. a
Calder, C. and Cressie, N.: Kriging and Variogram Models, in: International
Encyclopedia of Human Geography, edited by: Kitchin, R. and Thrift, N., Elsevier, Oxford, 49–55, https://doi.org/10.1016/B978-008044910-4.00461-2, 2009. a
Chilès, J.-P. and Delfiner, P.: Geostatistics: Modeling Spatial Uncertainty, J. Am. Stat. Assoc., 95, 695, https://doi.org/10.1002/9781118136188, 2000. a
Collier, C.: The impact of wind drift on the utility of very high spatial
resolution radar data over urban areas, Phys. Chem. Earth Pt. B, 24, 889–893, https://doi.org/10.1016/S1464-1909(99)00099-4, 1999. a
Crane, R. K.: Space-time structure of rain rate fields, J. Geophys. Res.-Atmos., 95, 2011–2020, 1990. a
Cristiano, E., ten Veldhuis, M.-C., and van de Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review, Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, 2017. a
Curry, G. R.: Radar Essentials: A Concise Handbook for Radar Design and
Performance Analysis, IET Digital Library, SciTech Publishing, https://doi.org/10.1049/SBRA029E, 2012. a
Deutsch, C. V. and Journel, A.: GSLIB – Geostatistical Software Library and
User's Guide (Second edition), in: Applied Geostatistics Series, Oxford
University Press, Oxford, https://doi.org/10.2307/1270548, 1998. a
Doviak, R. J. and Zrnić, D. S.: Doppler radar and weather observations, in: 2nd Edn., Academic Press, San Diego, 523–545, 1993. a
Emmanuel, I., Andrieu, H., Leblois, E., and Flahaut, B.: Temporal and spatial
variability of rainfall at the urban hydrological scale, J. Hydrol., 430, 162–172, 2012. a
Foresti, L., Reyniers, M., Seed, A., and Delobbe, L.: Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium, Hydrol. Earth Syst. Sci., 20, 505–527, https://doi.org/10.5194/hess-20-505-2016, 2016. a
Haberlandt, U.: Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event, J. Hydrol., 332, 144–157, https://doi.org/10.1016/j.jhydrol.2006.06.028, 2007. a
Hengl, T., Heuvelink, G., and Stein, A.: Comparison of kriging with external
drift and regression-kriging, Technical Note, ITC, available at: https://webapps.itc.utwente.nl/librarywww/papers_2003/misca/hengl_comparison.pdf (last access: June 2021), 2003. a
Hörning, S. and Haese, B.: RMWSPy (v 1.1): A Python code for spatial
simulation and inversion for environmental applications, Environ. Model. Softw., 138, 104970, https://doi.org/10.1016/j.envsoft.2021.104970, 2021. a
Hu, L. Y.: Combination of Dependent Realizations Within the Gradual Deformation Method, Math. Geol., 34, 953–963, https://doi.org/10.1023/A:1021316707087,
2002. a
Hu, L. Y., Blanc, G., and Noetinger, B.: Gradual Deformation and Iterative
Calibration of Sequential Stochastic Simulations, Math. Geol., 33, 475–489, https://doi.org/10.1023/A:1011088913233, 2001. a
James, W. P., Robinson, C. G., and Bell, J. F.: Radar‐Assisted Real‐Time Flood Forecasting, J. Water Resour. Plan. Manage., 119, 32–44, 1993. a
Jiang, S., Ren, L., Hong, Y., Yong, B., Yang, X., Yuan, F., and Ma, M.:
Comprehensive evaluation of multi-satellite precipitation products with a
dense rain gauge network and optimally merging their simulated hydrological
flows using the Bayesian model averaging method, J. Hydrol., 452–453, 213–225, https://doi.org/10.1016/j.jhydrol.2012.05.055, 2012. a
Kumar, P. and Foufoula-Georgiou, E.: Characterizing multiscale variability of
zero intermittency in spatial rainfall, J. Appl. Meteorol., 33, 1516–1525, 1994. a
Lantuéjoul, C.: Geostatistical Simulation, J. Roy. Stat. Soc., 52, 699–700, 2002. a
Liguori, S. and Rico-Ramirez, M. A.: A review of current approaches to
radar-based quantitative precipitation forecasts, Int. J. River Basin Manage., 12, 391–402, 2014. a
Liu, X., Yang, T., Hsu, K., Liu, C., and Sorooshian, S.: Evaluating the
streamflow simulation capability of PERSIANN-CDR daily rainfall products in
two river basins on the Tibetan Plateau, Hydrol. Earth Syst. Sci., 21, 169–181, https://doi.org/10.5194/hess-21-169-2017, 2017. a
Mantoglou, A. and Wilson, J. L.: The Turning Bands Method for simulation of
random fields using line generation by a spectral method, Water Resour. Res., 18, 1379–1394, https://doi.org/10.1029/WR018i005p01379, 1982. a
Marshall, J. and Palmer, W.: The distribution of raindrops with size, J. Appl. Meteorol., 5, 165–166, https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2, 1948. a
Méndez Antonio, B., Magaña, V., Caetano, E., da silveira, R., and
Domínguez, R.: Analysis of daily precipitation based on weather radar
information in México City, Atmosfera, 22, 299–313, 2009. a
Michaelides, S., Tymvios, F., and Michaelidou, T.: Spatial and temporal
characteristics of the annual rainfall frequency distribution in Cyprus, Atmos. Res., 94, 606–615, https://doi.org/10.1016/j.atmosres.2009.04.008, 2009. a
Michelson, D. B. and Koistinen, J.: Gauge-radar network adjustment for the
Baltic Sea Experiment, Phys. Chem. Earth Pt. B, 25, 915–920, 2000. a
Pegram, G. G. and Clothier, A. N.: High resolution space-time modelling of rainfall: the “String of Beads” model, J. Hydrol., 241, 26–41, https://doi.org/10.1016/S0022-1694(00)00373-5, 2001. a
Pierce, C., Seed, A., Ballard, S., Simonin, D., and Li, Z.: Nowcasting, in:
Doppler Radar Observations – Weather Radar, Wind Profiler, Ionospheric
Radar, and Other Advanced Applications, edited by: Bech, J. and Chau, J. L., InTech, 97–142, https://doi.org/10.5772/39054, 2012. a
Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., and Foresti, L.: Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0), Geosci. Model Dev., 12, 4185–4219, https://doi.org/10.5194/gmd-12-4185-2019, 2019. a, b
Ravalec, M. L., Noetinger, B., and Hu, L. Y.: The FFT Moving Average (FFT-MA)
Generator: An Efficient Numerical Method for Generating and Conditioning
Gaussian Simulations, Math. Geol., 32, 701–723, https://doi.org/10.1023/A:1007542406333, 2000. a
Schuurmans, J. M., Bierkens, M. F. P., Pebesma, E. J., and Uijlenhoet, R.:
Automatic Prediction of High-Resolution Daily Rainfall Fields for Multiple
Extents: The Potential of Operational Radar, J. Hydrometeorol., 8, 1204–1224, https://doi.org/10.1175/2007JHM792.1, 2007. a
Shehu, B. and Haberlandt, U.: Relevance Of Merging Radar And Rainfall Gauge
Data For Rainfall Nowcasting In Urban Hydrology, J. Hydrol., 594, 125931, https://doi.org/10.1016/j.jhydrol.2020.125931, 2020. a, b, c
Sideris, I., Gabella, M., Erdin, R., and Germann, U.: Real-time
radar–raingauge merging using spatio-temporal co-kriging with external drift
in the Alpine terrain of Switzerland, Q. J. Roy. Meteorol. Soc., 140, 1097–1111, https://doi.org/10.1002/qj.2188, 2014. a
Sinclair, S. and Pegram, G.: Combining radar and rain gauge rainfall estimates using conditional merging, Atmos. Sci. Lett., 6, 19–22,
https://doi.org/10.1002/asl.85, 2005. a, b
Todini, E.: A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements, Hydrol. Earth Syst. Sci., 5, 187–199, https://doi.org/10.5194/hess-5-187-2001, 2001. a
Uijlenhoet, R.: Raindrop size distributions and radar reflectivity–rain rate
relationships for radar hydrology, Hydrol. Earth Syst. Sci., 5, 615–628, https://doi.org/10.5194/hess-5-615-2001, 2001. a
Velasco-Forero, C. A., Sempere-Torres, D., Cassiraga, E. F., and
Gómez-Hernández, J. J.: A non-parametric automatic blending
methodology to estimate rainfall fields from rain gauge and radar data, Adv. Water Resour., 32, 986–1002, https://doi.org/10.1016/j.advwatres.2008.10.004, 2009. a
Verworn, A. and Haberlandt, U.: Spatial interpolation of hourly rainfall – effect of additional information, variogram inference and storm properties, Hydrol. Earth Syst. Sci., 15, 569–584, https://doi.org/10.5194/hess-15-569-2011, 2011. a
Wang, L. P., Ochoa-Rodriguez, S., Simões, N., Onof, C., and Maksimovic, Č.: Radar-raingauge data combination techniques: A revision and analysis of their suitability for urban hydrology, Water Sci. Technol., 68, 737–47, 2013. a
Wilson, J. W.: Integration of Radar and Raingage Data for Improved Rainfall
Measurement, J. Appl. Meteorol., 9, 489–497,
https://doi.org/10.1175/1520-0450(1970)009<0489:IORARD>2.0.CO;2, 1970. a
Wood, A. T.: When is a truncated covariance function on the line a covariance
function on the circle?, Stat. Probab. Lett. 24, 157–164,
https://doi.org/10.1016/0167-7152(94)00162-2, 1995. a
Wood, A. T. and Chan, G.: Simulation of stationary Gaussian processes in [0, 1] d, J. Comput. Graph. Stat., 3, 409–432, 1994. a
Yan, J.: Supplementary material for the paper “Conditional simulation of spatial rainfall fields using random mixing: a study that implements full control over the stochastic process”, Figshare, https://doi.org/10.6084/m9.figshare.14864910.v1, 2021. a
Yan, J. and Bárdossy, A.: Short time precipitation estimation using weather radar and surface observations: With rainfall displacement information integrated in a stochastic manner, J. Hydrol., 574, 672–682,
https://doi.org/10.1016/j.jhydrol.2019.04.061, 2019. a
Yan, J., Bárdossy, A., Hörning, S., and Tao, T.: Conditional simulation of surface rainfall fields using modified phase annealing, Hydrol. Earth Syst. Sci., 24, 2287–2301, https://doi.org/10.5194/hess-24-2287-2020, 2020. a, b
Yilmaz, K. K., Hogue, T. S., Hsu, K.-L., Sorooshian, S., Gupta, H. V., and
Wagener, T.: Intercomparison of Rain Gauge, Radar, and Satellite-Based
Precipitation Estimates with Emphasis on Hydrologic Forecasting, J.
Hydrometeorol., 6, 497–517, https://doi.org/10.1175/JHM431.1, 2005. a
Short summary
Accurate spatial precipitation estimates are important in various fields. An approach to simulate spatial rainfall fields conditioned on radar and rain gauge data is proposed. Unlike the commonly used Kriging methods, which provide a Kriged mean field, the output of the proposed approach is an ensemble of estimates that represents the estimation uncertainty. The approach is robust to nonlinear error in radar estimates and is shown to have some advantages, especially when estimating the extremes.
Accurate spatial precipitation estimates are important in various fields. An approach to...
