Articles | Volume 18, issue 8
https://doi.org/10.5194/hess-18-3015-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-18-3015-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
14 Aug 2014
Research article |
| 14 Aug 2014
Simulation of rainfall time series from different climatic regions using the direct sampling technique
F. Oriani
Centre for Hydrogeology and Geothermics, University of Neuchâtel, Neuchâtel, Switzerland
J. Straubhaar
Centre for Hydrogeology and Geothermics, University of Neuchâtel, Neuchâtel, Switzerland
P. Renard
Centre for Hydrogeology and Geothermics, University of Neuchâtel, Neuchâtel, Switzerland
G. Mariethoz
School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales, Australia
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Nibedita Samal, Meenakshi KV, Akshay Singhal, Sanjeev Kumar Jha, and Fabio Oriani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-155, https://doi.org/10.5194/hess-2024-155, 2024
Revised manuscript not accepted
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A statistical approach is developed for the first time to generate hourly rainfall from daily records at multiple avalanche sites of mountainous terrain. The approach reproduces complex temporal patterns of past rainfall information in the future by using auxiliary information from nearby sites. The high temporal resolution data produced is reliable and also produces extreme rainfall patterns well. The hourly precipitation data can be used for better prediction of avalanches and landslides.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data, 16, 731–742, https://doi.org/10.5194/essd-16-731-2024, https://doi.org/10.5194/essd-16-731-2024, 2024
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Modern and fossil pollen data contain precious information for reconstructing the climate and environment of the past. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
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Preprint under review for ESSD
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Karst phenomena arise through dissolution and the resulting landscapes are characterised by caves which focus the transport of water underground. To better understand geometry of these conduits at various scales, we mapped caves with laser scanners and built models of the walls to constitute the KarstConduitCatalogue. These mapping techniques allow us to measure cave geometries accurately. This paper describes how we acquired and curated the dataset, and explores possible geoscientific uses.
Nibedita Samal, Meenakshi KV, Akshay Singhal, Sanjeev Kumar Jha, and Fabio Oriani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-155, https://doi.org/10.5194/hess-2024-155, 2024
Revised manuscript not accepted
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A statistical approach is developed for the first time to generate hourly rainfall from daily records at multiple avalanche sites of mountainous terrain. The approach reproduces complex temporal patterns of past rainfall information in the future by using auxiliary information from nearby sites. The high temporal resolution data produced is reliable and also produces extreme rainfall patterns well. The hourly precipitation data can be used for better prediction of avalanches and landslides.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data, 16, 731–742, https://doi.org/10.5194/essd-16-731-2024, https://doi.org/10.5194/essd-16-731-2024, 2024
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Modern and fossil pollen data contain precious information for reconstructing the climate and environment of the past. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
Chloé Fandel, Ty Ferré, François Miville, Philippe Renard, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 27, 4205–4215, https://doi.org/10.5194/hess-27-4205-2023, https://doi.org/10.5194/hess-27-4205-2023, 2023
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From the surface, it is hard to tell where underground cave systems are located. We developed a computer model to create maps of the probable cave network in an area, based on the geologic setting. We then applied our approach in reverse: in a region where an old cave network was mapped, we used modeling to test what the geologic setting might have been like when the caves formed. This is useful because understanding past cave formation can help us predict where unmapped caves are located today.
Alexis Neven, Valentin Dall'Alba, Przemysław Juda, Julien Straubhaar, and Philippe Renard
The Cryosphere, 15, 5169–5186, https://doi.org/10.5194/tc-15-5169-2021, https://doi.org/10.5194/tc-15-5169-2021, 2021
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We present and compare different geostatistical methods for underglacial bedrock interpolation. Variogram-based interpolations are compared with a multipoint statistics approach on both test cases and real glaciers. Using the modeled bedrock, the ice volume for the Scex Rouge and Tsanfleuron glaciers (Swiss Alps) was estimated to be 113.9 ± 1.6 million cubic meters. Complex karstic geomorphological features are reproduced and can be used to improve the precision of underglacial flow estimation.
Alexis Neven, Pradip Kumar Maurya, Anders Vest Christiansen, and Philippe Renard
Earth Syst. Sci. Data, 13, 2743–2752, https://doi.org/10.5194/essd-13-2743-2021, https://doi.org/10.5194/essd-13-2743-2021, 2021
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The shallow underground is constituted of sediments that present high spatial variability. This upper layer is the most extensively used for resource exploitation (groundwater, geothermal heat, construction materials, etc.). Understanding and modeling the spatial variability of these deposits is crucial. We present a high-resolution electrical resistivity dataset that covers the upper Aare Valley in Switzerland. These data can help develop methods to characterize these geological formations.
Valentin Dall'Alba, Philippe Renard, Julien Straubhaar, Benoit Issautier, Cédric Duvail, and Yvan Caballero
Hydrol. Earth Syst. Sci., 24, 4997–5013, https://doi.org/10.5194/hess-24-4997-2020, https://doi.org/10.5194/hess-24-4997-2020, 2020
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Due to climate and population evolution, increased pressure is put on the groundwater resource, which calls for better understanding and models. In this paper, we describe a novel workflow to model the geological heterogeneity of coastal aquifers and apply it to the Roussillon plain (southern France). The main strength of the workflow is its capability to model aquifer heterogeneity when only sparse data are available while honoring the local geological trends and quantifying uncertainty.
Pierre-Olivier Bruna, Julien Straubhaar, Rahul Prabhakaran, Giovanni Bertotti, Kevin Bisdom, Grégoire Mariethoz, and Marco Meda
Solid Earth, 10, 537–559, https://doi.org/10.5194/se-10-537-2019, https://doi.org/10.5194/se-10-537-2019, 2019
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Natural fractures influence fluid flow in subsurface reservoirs. Our research presents a new methodology to predict the arrangement of these fractures in rocks. Contrary to the commonly used statistical models, our approach integrates more geology into the simulation process. The method is simply based on the drawing of images, can be applied to any type of rocks in various geological contexts, and is suited for fracture network prediction in water, geothermal, or hydrocarbon reservoirs.
Guillaume Pirot, Tipaluck Krityakierne, David Ginsbourger, and Philippe Renard
Hydrol. Earth Syst. Sci., 23, 351–369, https://doi.org/10.5194/hess-23-351-2019, https://doi.org/10.5194/hess-23-351-2019, 2019
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To localize the source of a contaminant in the subsurface, based on concentration observations at some wells, we propose to test different possible locations and minimize the misfit between observed and simulated concentrations. We use a global optimization technique that relies on an expected improvement criterion, which allows a good exploration of the parameter space, avoids the trapping of local minima and quickly localizes the source of the contaminant on the presented synthetic cases.
Adrian A. S. Barfod, Troels N. Vilhelmsen, Flemming Jørgensen, Anders V. Christiansen, Anne-Sophie Høyer, Julien Straubhaar, and Ingelise Møller
Hydrol. Earth Syst. Sci., 22, 5485–5508, https://doi.org/10.5194/hess-22-5485-2018, https://doi.org/10.5194/hess-22-5485-2018, 2018
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The focus of this study is on the uncertainty related to using multiple-point statistics (MPS) for stochastic modeling of the upper 200 m of the subsurface. The main research goal is to showcase how MPS methods can be used on real-world hydrogeophysical data and show how the uncertainty related to changing the underlying MPS setup propagates into the finalized 3-D subsurface models.
Adrian A. S. Barfod, Ingelise Møller, Anders V. Christiansen, Anne-Sophie Høyer, Júlio Hoffimann, Julien Straubhaar, and Jef Caers
Hydrol. Earth Syst. Sci., 22, 3351–3373, https://doi.org/10.5194/hess-22-3351-2018, https://doi.org/10.5194/hess-22-3351-2018, 2018
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Three-dimensional geological models are important to securing and managing groundwater. Such models describe the geological architecture, which is used for modeling the flow of groundwater. Common geological modeling approaches result in one model, which does not quantify the architectural uncertainty of the geology.
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Francesco Serinaldi
Hydrol. Earth Syst. Sci., 28, 3191–3218, https://doi.org/10.5194/hess-28-3191-2024, https://doi.org/10.5194/hess-28-3191-2024, 2024
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Johannes Meuer, Laurens M. Bouwer, Frank Kaspar, Roman Lehmann, Wolfgang Karl, Thomas Ludwig, and Christopher Kadow
EGUsphere, https://doi.org/10.5194/egusphere-2024-1392, https://doi.org/10.5194/egusphere-2024-1392, 2024
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Our study focuses on filling in missing precipitation data using an advanced neural network model. Traditional methods for estimating missing climate information often struggle in large regions where data is scarce. Our solution, which incorporates recent advances in machine learning, captures the intricate patterns of precipitation over time, especially during extreme weather events. Our model shows good performance in reconstructing large regions of missing rainfall radar data.
Golbarg Goshtasbpour and Uwe Haberlandt
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Revised manuscript accepted for HESS
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We provide a method how to estimate extreme rainfall from radar observations. Extreme value statistic is applied for observed radar rainfall covering different areas from point size up to about 1000 km2. The rainfall extremes are supposed to be as higher as smaller the area is. This behaviour could not be confirmed by the radar observations. The reason is the limited single point sampling approach of the radar data. New multiple point sampling strategies are proposed to mitigate this problem.
David A. Jimenez, Andrea Menapace, Ariele Zanfei, Eber José de Andrade Pinto, and Bruno Brentan
Hydrol. Earth Syst. Sci., 28, 1981–1997, https://doi.org/10.5194/hess-28-1981-2024, https://doi.org/10.5194/hess-28-1981-2024, 2024
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Most studies that aim to identify the impacts of climate change employ general circulation models. However, due to their low spatial resolution, it is necessary to apply downscaling techniques. This work assesses the performance of three methodologies in developing frequency analyses and estimating the number of rainy days and total precipitation per year. Quantile mapping and regression trees excelled in frequency analysis, and the delta method best estimated multiyear total precipitation.
Ivan Vorobevskii, Jeongha Park, Dongkyun Kim, Klemens Barfus, and Rico Kronenberg
Hydrol. Earth Syst. Sci., 28, 391–416, https://doi.org/10.5194/hess-28-391-2024, https://doi.org/10.5194/hess-28-391-2024, 2024
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High-resolution precipitation data are often a “must” as input for hydrological and hydraulic models (i.e. urban drainage modelling). However, station or climate projection data usually do not provide the required (e.g. sub-hourly) resolution. In the work, we present two new statistical models of different types to disaggregate precipitation from a daily to a 10 min scale. Both models were validated using radar data and then applied to climate models for 10 stations in Germany and South Korea.
Marc Sanuy, Juan C. Peña, Sotiris Assimenidis, and José A. Jiménez
Hydrol. Earth Syst. Sci., 28, 283–302, https://doi.org/10.5194/hess-28-283-2024, https://doi.org/10.5194/hess-28-283-2024, 2024
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The work presents the first classification of weather types associated to compound events of extreme rainfall and coastal storms. These are found to be characterized by upper-level lows and troughs in conjunction with Mediterranean cyclones, resulting in severe to extreme coastal storms combined with convective systems. We used objective classification methods coupled with a Bayesian Network, testing different variables, domains and number of weather types.
Carmelo Cammalleri, Carlo De Michele, and Andrea Toreti
Hydrol. Earth Syst. Sci., 28, 103–115, https://doi.org/10.5194/hess-28-103-2024, https://doi.org/10.5194/hess-28-103-2024, 2024
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Precipitation and soil moisture have the potential to be jointly used for the modeling of drought conditions. In this research, we analysed how their statistical inter-relationship varies across Europe. We found some clear spatial patterns, especially in the so-called tail dependence (which measures the strength of the relationship for the extreme values). The results suggest that the tail dependence needs to be accounted for to correctly assess the value of joint modeling for drought.
Mijael Rodrigo Vargas Godoy, Yannis Markonis, Oldrich Rakovec, Michal Jenicek, Riya Dutta, Rajani Kumar Pradhan, Zuzana Bešťáková, Jan Kyselý, Roman Juras, Simon Michael Papalexiou, and Martin Hanel
Hydrol. Earth Syst. Sci., 28, 1–19, https://doi.org/10.5194/hess-28-1-2024, https://doi.org/10.5194/hess-28-1-2024, 2024
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The study introduces a novel benchmarking method based on the water cycle budget for hydroclimate data fusion. Using this method and multiple state-of-the-art datasets to assess the spatiotemporal patterns of water cycle changes in Czechia, we found that differences in water availability distribution are dominated by evapotranspiration. Furthermore, while the most significant temporal changes in Czechia occur during spring, the median spatial patterns stem from summer changes in the water cycle.
Yuan Li, Kangning Xü, Zhiyong Wu, Zhiwei Zhu, and Quan J. Wang
Hydrol. Earth Syst. Sci., 27, 4187–4203, https://doi.org/10.5194/hess-27-4187-2023, https://doi.org/10.5194/hess-27-4187-2023, 2023
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Hetal P. Dabhi, Mathias W. Rotach, and Michael Oberguggenberger
Hydrol. Earth Syst. Sci., 27, 2123–2147, https://doi.org/10.5194/hess-27-2123-2023, https://doi.org/10.5194/hess-27-2123-2023, 2023
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Spatiotemporally consistent high-resolution precipitation data on climate are needed for climate change impact assessments, but obtaining these data is challenging for areas with complex topography. We present a model that generates synthetic gridded daily precipitation data at a 1 km spatial resolution using observed meteorological station data as input, thereby providing data where historical observations are unavailable. We evaluate this model for a mountainous region in the European Alps.
Yuxin Li, Sisi Chen, Jun Yin, and Xing Yuan
Hydrol. Earth Syst. Sci., 27, 1077–1087, https://doi.org/10.5194/hess-27-1077-2023, https://doi.org/10.5194/hess-27-1077-2023, 2023
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Flash drought is referred to the rapid development of drought events with a fast decline of soil moisture, which has serious impacts on agriculture, the ecosystem, human health, and society. While flash droughts have received much research attention, there is no consensus on its definition. Here we used a stochastic water balance framework to quantify the timing of soil moisture crossing different thresholds, providing an efficient tool for diagnosing and monitoring flash droughts.
Arun Ramanathan, Pierre-Antoine Versini, Daniel Schertzer, Remi Perrin, Lionel Sindt, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 26, 6477–6491, https://doi.org/10.5194/hess-26-6477-2022, https://doi.org/10.5194/hess-26-6477-2022, 2022
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Reference rainfall scenarios are indispensable for hydrological applications such as designing storm-water management infrastructure, including green roofs. Therefore, a new method is suggested for simulating rainfall scenarios of specified intensity, duration, and frequency, with realistic intermittency. Furthermore, novel comparison metrics are proposed to quantify the effectiveness of the presented simulation procedure.
Judith Meyer, Malte Neuper, Luca Mathias, Erwin Zehe, and Laurent Pfister
Hydrol. Earth Syst. Sci., 26, 6163–6183, https://doi.org/10.5194/hess-26-6163-2022, https://doi.org/10.5194/hess-26-6163-2022, 2022
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We identified and analysed the major atmospheric components of rain-intense thunderstorms that can eventually lead to flash floods: high atmospheric moisture, sufficient latent instability, and weak thunderstorm cell motion. Between 1981 and 2020, atmospheric conditions became likelier to support strong thunderstorms. However, the occurrence of extreme rainfall events as well as their rainfall intensity remained mostly unchanged.
Yuan Liu and Daniel B. Wright
Hydrol. Earth Syst. Sci., 26, 5241–5267, https://doi.org/10.5194/hess-26-5241-2022, https://doi.org/10.5194/hess-26-5241-2022, 2022
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We present a new approach to estimate extreme rainfall probability and severity using the atmospheric water balance, where precipitation is the sum of water vapor components moving in and out of a storm. We apply our method to the Mississippi Basin and its five major subbasins. Our approach achieves a good fit to reference precipitation, indicating that the rainfall probability estimation can benefit from additional information from physical processes that control rainfall.
Yuan Li, Zhiyong Wu, Hai He, and Hao Yin
Hydrol. Earth Syst. Sci., 26, 4975–4994, https://doi.org/10.5194/hess-26-4975-2022, https://doi.org/10.5194/hess-26-4975-2022, 2022
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The relationship between atmospheric intraseasonal signals and precipitation is highly uncertain and depends on the region and lead time. In this study, we develop a spatiotemporal projection, based on a Bayesian hierarchical model (STP-BHM), to address the above challenge. The results suggest that the STP-BHM model is skillful and reliable for probabilistic subseasonal precipitation forecasts over China during the boreal summer monsoon season.
Lionel Benoit, Lydie Sichoix, Alison D. Nugent, Matthew P. Lucas, and Thomas W. Giambelluca
Hydrol. Earth Syst. Sci., 26, 2113–2129, https://doi.org/10.5194/hess-26-2113-2022, https://doi.org/10.5194/hess-26-2113-2022, 2022
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This study presents a probabilistic model able to reproduce the spatial patterns of rainfall on tropical islands with complex topography. It sheds new light on rainfall variability at the island scale, and explores the links between rainfall patterns and atmospheric circulation. The proposed model has been tested on two islands of the tropical Pacific, and demonstrates good skills in simulating both site-specific and island-scale rain behavior.
Jana Ulrich, Felix S. Fauer, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6133–6149, https://doi.org/10.5194/hess-25-6133-2021, https://doi.org/10.5194/hess-25-6133-2021, 2021
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The characteristics of extreme precipitation on different timescales as well as in different seasons are relevant information, e.g., for designing hydrological structures or managing water supplies. Therefore, our aim is to describe these characteristics simultaneously within one model. We find similar characteristics for short extreme precipitation at all considered stations in Germany but pronounced regional differences with respect to the seasonality of long-lasting extreme events.
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.
Jieru Yan, Fei Li, András Bárdossy, and Tao Tao
Hydrol. Earth Syst. Sci., 25, 3819–3835, https://doi.org/10.5194/hess-25-3819-2021, https://doi.org/10.5194/hess-25-3819-2021, 2021
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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.
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.
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