Articles | Volume 20, issue 5
Research article 17 May 2016
Research article | 17 May 2016
Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme
Kue Bum Kim et al.
No articles found.
Qiang Dai, Jingxuan Zhu, Shuliang Zhang, Shaonan Zhu, Dawei Han, and Guonian Lv
Hydrol. Earth Syst. Sci., 24, 5407–5422,Short summary
Rainfall is a driving force that accounts for a large proportion of soil loss around the world. Most previous studies used a fixed rainfall–energy relationship to estimate rainfall energy, ignoring the spatial and temporal changes of raindrop microphysical processes. This study proposes a novel method for large-scale and long-term rainfall energy and rainfall erosivity investigations based on rainfall microphysical parameterization schemes in the Weather Research and Forecasting (WRF) model.
Xichao Gao, Zhiyong Yang, Dawei Han, Guoru Huang, and Qian Zhu
Hydrol. Earth Syst. Sci. Discuss.,
Manuscript not accepted for further reviewShort summary
Input errors and parameter errors are two main sources of uncertainties in hydrological model calibration. We developed a new Bayesian framework for automatic calibration of the Storm Water Management Model (SWMM), simultaneously considering parameter and input uncertainties and verified the framework with a case study. The results shows that calibration considering both parameter and input uncertainties captures peak flow much better that only considering parameter uncertainty.
Lu Zhuo, Qiang Dai, Binru Zhao, and Dawei Han
Hydrol. Earth Syst. Sci., 24, 2577–2591,Short summary
Soil moisture plays an important role in hydrological modelling. However, most existing in situ observation networks rarely provide sufficient coverage to capture soil moisture variations. Clearly, there is a need to develop a systematic approach, so that with the minimal number of sensors the soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed (WRF, PCA, CA), which can provide very efficient soil moisture estimations.
Cristina Prieto, Dhruvesh Patel, and Dawei Han
Nat. Hazards Earth Syst. Sci., 20, 1045–1048,
Lu Zhuo, Qiang Dai, Dawei Han, Ningsheng Chen, and Binru Zhao
Hydrol. Earth Syst. Sci., 23, 4199–4218,Short summary
This study assesses the usability of WRF model-simulated soil moisture for landslide monitoring in northern Italy. In particular, three advanced land surface model schemes (Noah, Noah-MP, and CLM4) are used to provide multi-layer soil moisture data. The results have shown Noah-MP can provide the best landslide monitoring performance. It is also demonstrated that a single soil moisture sensor located in plain area has a high correlation with a significant proportion of the study area.
Xuehong Zhu, Qiang Dai, Dawei Han, Lu Zhuo, Shaonan Zhu, and Shuliang Zhang
Hydrol. Earth Syst. Sci., 23, 3353–3372,Short summary
Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering human mobility. Several scenarios, including diverse flooding types and various responses of residents to flooding, were considered.
Binru Zhao, Qiang Dai, Dawei Han, Huichao Dai, Jingqiao Mao, and Lu Zhuo
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript not accepted
Qi Chu, Zongxue Xu, Yiheng Chen, and Dawei Han
Hydrol. Earth Syst. Sci., 22, 3391–3407,Short summary
The effects of WRF domain configurations and spin-up time on rainfall were evaluated at high temporal and spatial scales for simulating an extreme sub-daily heavy rainfall (SDHR) event. Both objective verification metrics and subjective verification were used to identify the likely best set of the configurations. Results show that re-evaluation of these WRF settings is of great importance in improving the accuracy and reliability of the rainfall simulations in the regional SDHR applications.
Dong-Ik Kim, Hyun-Han Kwon, and Dawei Han
Hydrol. Earth Syst. Sci. Discuss.,
Manuscript not accepted for further reviewShort summary
This study introduces a new QM approach based on a composite distribution of a generalized Pareto distribution for the upper tail and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases compared with that of the conventional method for the extremes. The proposed approach can provide a useful alternative for the bias correction of a regional-scale modeled data with a limited network of rain gauges.
Binru Zhao, Huichao Dai, Dawei Han, and Guiwen Rong
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript not acceptedShort summary
This study compared the hydrological model performance of different sub-annual calibration schemes, which take into account intra-annual variations of climate. Two methods recognizing climatic patterns were applied to partition sub-periods with hydroclimatic similarities. The effect of time scales on sub-annual calibration schemes was also studied. Results indicate when using sub-annual calibration schemes, the selection of partitioning method and time scale is important to model performances.
Lu Zhuo and Dawei Han
Hydrol. Earth Syst. Sci., 21, 3267–3285,Short summary
Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from remote sensing and land surface modelling. The result shows a significant improvement of the soil moisture state accuracy; the method can be easily applied in other catchments.
Jun Zhang, Dawei Han, Yang Song, and Qiang Dai
Hydrol. Earth Syst. Sci. Discuss.,
Preprint retractedShort summary
We explore unit hydrograph (UH) affected by geomorphology that could be used in ungauged catchments. Virtual catchments approach (VCA) is used instead of gauged catchments in runoff modelling. Catchment shape is newly introduced and the agreement of the results with the hydrological principles verifies the reliability of VCA. With the robust VCA, a large amount of catchments can be created with desirable features to explore a more comprehensive equation that can be used in ungauged catchments.
Remko Nijzink, Christopher Hutton, Ilias Pechlivanidis, René Capell, Berit Arheimer, Jim Freer, Dawei Han, Thorsten Wagener, Kevin McGuire, Hubert Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 20, 4775–4799,Short summary
The core component of many hydrological systems, the moisture storage capacity available to vegetation, is typically treated as a calibration parameter in hydrological models and often considered to remain constant in time. In this paper we test the potential of a recently introduced method to robustly estimate catchment-scale root-zone storage capacities exclusively based on climate data to reproduce the temporal evolution of root-zone storage under change (deforestation).
S. Ceola, B. Arheimer, E. Baratti, G. Blöschl, R. Capell, A. Castellarin, J. Freer, D. Han, M. Hrachowitz, Y. Hundecha, C. Hutton, G. Lindström, A. Montanari, R. Nijzink, J. Parajka, E. Toth, A. Viglione, and T. Wagener
Hydrol. Earth Syst. Sci., 19, 2101–2117,Short summary
We present the outcomes of a collaborative hydrological experiment undertaken by five different international research groups in a virtual laboratory. Moving from the definition of accurate protocols, a rainfall-runoff model was independently applied by the research groups, which then engaged in a comparative discussion. The results revealed that sharing protocols and running the experiment within a controlled environment is fundamental for ensuring experiment repeatability and reproducibility.
J. Liu and D. Han
Hydrol. Earth Syst. Sci., 17, 3639–3659,
J. Liu, M. Bray, and D. Han
Hydrol. Earth Syst. Sci., 17, 3095–3110,
J. G. Barr, V. Engel, J. D. Fuentes, D. O. Fuller, and H. Kwon
Biogeosciences, 10, 2145–2158,
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Stochastic approachesAssessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analoguesA standardized index for assessing sub-monthly compound dry and hot conditionsA new discrete multiplicative random cascade model for downscaling intermittent rainfall fieldsModelling rainfall with a Bartlett–Lewis process: new developmentsNonstationary stochastic rain type generation: accounting for climate driversConditional simulation of surface rainfall fields using modified phase annealingClimate influences on flood probabilities across EuropeFlood-related extreme precipitation in southwestern Germany: development of a two-dimensional stochastic precipitation modelA hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescalesMapping rainfall hazard based on rain gauge data: an objective cross-validation framework for model selectionOn the skill of raw and post-processed ensemble seasonal meteorological forecasts in DenmarkEstimating radar precipitation in cold climates: the role of air temperature within a non-parametric frameworkDealing with non-stationarity in sub-daily stochastic rainfall modelsRainfall 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 copulaModeling the changes in water balance components of the highly irrigated western part of BangladeshA classification algorithm for selective dynamical downscaling of precipitation extremesSeasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: the effect of preprocessing and post-processing on skill and statistical consistencyEvaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchmentA nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian PeninsulaCensored rainfall modelling for estimation of fine-scale extremesAn adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in FrancePrecipitation extremes on multiple timescales – Bartlett–Lewis rectangular pulse model and intensity–duration–frequency curvesDoes 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 transformRegionalizing nonparametric models of precipitation amounts on different temporal scalesA combined statistical bias correction and stochastic downscaling method for precipitationCan local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basinTechnical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studiesNonstationarity of low flows and their timing in the eastern United StatesClimatological characteristics of raindrop size distributions in Busan, Republic of KoreaCorrection of real-time satellite precipitation with satellite soil moisture observationsStochastic approach to analyzing the uncertainties and possible changes in the availability of water in the future based on scenarios of climate changeAttribution of European precipitation and temperature trends to changes in synoptic circulationSpatial and temporal variability of rainfall in the Nile BasinInter-comparison of statistical downscaling methods for projection of extreme precipitation in EuropeImperfect scaling in distributions of radar-derived rainfall fieldsSatellite-driven downscaling of global reanalysis precipitation products for hydrological applicationsThe effect of flow and orography on the spatial distribution of the very short-term predictability of rainfall from composite radar imagesEffect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of MelbourneSimulation of rainfall time series from different climatic regions using the direct sampling techniqueThe role of retrospective weather forecasts in developing daily forecasts of nutrient loadings over the southeast USA baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States droughtLong-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in IranStochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershedA spatial bootstrap technique for parameter estimation of rainfall annual maxima distributionBias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble meanInverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land ModelInteraction of valleys and circulation patterns (CPs) on spatial precipitation patterns in southern GermanyDevelopment and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation
Damien Raynaud, Benoit Hingray, Guillaume Evin, Anne-Catherine Favre, and Jérémy Chardon
Hydrol. Earth Syst. Sci., 24, 4339–4352,Short summary
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.
Jun Li, Zhaoli Wang, Xushu Wu, Jakob Zscheischler, Shenglian Guo, and Xiaohong Chen
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript accepted for HESSShort summary
We introduce a daily-scale index, termed as the standardized compound drought and heat index (SCDHI), to measure the intensity of compound dry and hot conditions. SCDHI can not only monitor the long-term compound dry and hot events, but 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 and hot events, which are vital to release early and timely warnings.
Hydrol. Earth Syst. Sci., 24, 3699–3723,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,
Edmund P. Meredith, Henning W. Rust, and Uwe Ulbrich
Hydrol. Earth Syst. Sci., 22, 4183–4200,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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.
E. P. Maurer, D. L. Ficklin, and W. Wang
Hydrol. Earth Syst. Sci., 20, 685–696,Short summary
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,Short summary
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,Short summary
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,
G. G. Oliveira, O. C. Pedrollo, and N. M. R. Castro
Hydrol. Earth Syst. Sci., 19, 3585–3604,Short summary
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,
C. Onyutha and P. Willems
Hydrol. Earth Syst. Sci., 19, 2227–2246,Short summary
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,
M. J. van den Berg, L. Delobbe, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 18, 5331–5344,
H. Seyyedi, E. N. Anagnostou, E. Beighley, and J. McCollum
Hydrol. Earth Syst. Sci., 18, 5077–5091,Short summary
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,
A. G. Yilmaz, I. Hossain, and B. J. C. Perera
Hydrol. Earth Syst. Sci., 18, 4065–4076,
F. Oriani, J. Straubhaar, P. Renard, and G. Mariethoz
Hydrol. Earth Syst. Sci., 18, 3015–3031,
J. Oh, T. Sinha, and A. Sankarasubramanian
Hydrol. Earth Syst. Sci., 18, 2885–2898,
Hydrol. Earth Syst. Sci., 18, 2485–2492,
S. K. Sigaroodi, Q. Chen, S. Ebrahimi, A. Nazari, and B. Choobin
Hydrol. Earth Syst. Sci., 18, 1995–2006,
P. Gagnon and A. N. Rousseau
Hydrol. Earth Syst. Sci., 18, 1695–1704,
F. Uboldi, A. N. Sulis, C. Lussana, M. Cislaghi, and M. Russo
Hydrol. Earth Syst. Sci., 18, 981–995,
E. P. Maurer and D. W. Pierce
Hydrol. Earth Syst. Sci., 18, 915–925,
Y. Sun, Z. Hou, M. Huang, F. Tian, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 17, 4995–5011,
M. Liu, A. Bárdossy, and E. Zehe
Hydrol. Earth Syst. Sci., 17, 4685–4699,
S. Hwang and W. D. Graham
Hydrol. Earth Syst. Sci., 17, 4481–4502,
Addor, N. and Fischer, E. M.: The influence of natural variability and interpolation errors on bias characterization in RCM simulations, J. Geophys. Res.-Atmos., 120, 10180–10195, https://doi.org/https://doi.org/10.1002/2014JD022824, 2015.
Arnell, N. W., Liu, C., Compagnucci, R. da Cunha, L., Hanaki, K., Howe, C., Mailu, G., Shiklomanov, I., and Stakhiv, E.: Hydrology and water resources, in: Climate Change 2001: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, edited by: McCarthy, J. J., Canziani, O. F., Leary, N. A., Dokken, D. J., and White, K. S., Cambridge University Press, Cambridge, 191–233, 2001.
Baigorria, G. A., Jones, J. W., Shin, D.-W., Mishra, A., and O'Brien, J. J.: Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs, Clim. Res., 34, 211–222, https://doi.org/10.3354/cr00703, 2007.
Bates, B., Kundzewicz, Z. W., Wu, S., and Palutikof, J.: Climate change and water, Intergovernmental Panel on Climate Change (IPCC), 2008.
Block, P. J., Souza Filho, F. A., Sun, L., and Kwon, H. H.: A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1, J. Am. Water Resour. Assoc., 45, 828–843, 2009.
Bromwich, D. H., Otieno, F. O., Hines, K. M., Manning, K. W., and Shilo, E.: Comprehensive evaluation of polar weather research and forecasting model performance in the Antarctic, J. Geophys. Res.-Atmos., 118, 274–292, 2013.
Chen, J., Brissette, F. P., and Leconte, R.: Uncertainty of downscaling method in quantifying the impact of climate change on hydrology, J. Hydrol., 401, 190–202, 2011a.
Chen, J., Brissette, F. P., Poulin, A., and Leconte, R.: Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed, Water Resour. Res., 47, W12509, https://doi.org/10.1029/2011WR01060, 2011b.
Chen, J., Brissette, F. P., Chaumont, D., and Braun, M.: Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America, Water Resour. Res., 49, 4187–4205, 2013.
Collins, M., Booth, B. B., Harris, G. R., Murphy, J. M., Sexton, D. M., and Webb, M. J.: Towards quantifying uncertainty in transient climate change, Clim. Dynam., 27, 127–147, 2006.
Collins, M., Booth, B. B., Bhaskaran, B., Harris, G. R., Murphy, J. M., Sexton, D. M., and Webb, M. J.: Climate model errors, feedbacks and forcings: a comparison of perturbed physics and multi-model ensembles, Clim. Dynam., 36, 1737–1766, 2011.
Dee, D., Källén, E., Simmons, A., and Haimberger, L.: Comments on “Reanalyses suitable for characterizing long-term trends”, B. Am. Meteorol. Soc., 92, 65–70, 2011.
Déqué, M., Rowell, D., Lüthi, D., Giorgi, F., Christensen, J., Rockel, B., Jacob, D., Kjellström, E., De Castro, M., and van den Hurk, B.: An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections, Climatic Change, 81, 53–70, 2007.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions “Should we apply bias correction to global and regional climate model data?”, Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012.
Feddersen, H. and Andersen, U.: A method for statistical downscaling of seasonal ensemble predictions, Tellus A, 57, 398–408, 2005.
Good, P. and Lowe, J.: Emergent behavior and uncertainty in multimodel climate projections of precipitation trends at small spatial scales, J. Climate, 19, 5554–5569, 2006.
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in regional climate predictions, B. Am. Meteorol. Soc., 90, 1095–1107, 2009.
Ines, A. V. and Hansen, J. W.: Bias correction of daily GCM rainfall for crop simulation studies, Agr. Forest Meteorol., 138, 44–53, 2006.
Jakeman, A. and Hornberger, G.: How much complexity is warranted in a rainfall-runoff model?, Water Resour. Res., 29, 2637–2649, 1993.
Jakeman, A., Littlewood, I., and Whitehead, P.: An assessment of the dynamic response characteristics of streamflow in the Balquhidder catchments, J. Hydrol., 145, 337–355, 1993.
Johnson, F. and Sharma, A.: Accounting for interannual variability: A comparison of options for water resources climate change impact assessments, Water Resour. Res., 47, W04508, https://doi.org/10.1029/2010WR009272, 2011.
Jones, P., Kilsby, C., Harpham, C., Glenis, V., and Burton, A.: UK Climate Projections science report: Projections of future daily climate for the UK from the Weather Generator, University of Newcastle, UK, 2009.
Kew, S. F., Selten, F. M., Lenderink, G., and Hazeleger, W.: Robust assessment of future changes in extreme precipitation over the Rhine basin using a GCM, Hydrol. Earth Syst. Sci., 15, 1157–1166, https://doi.org/10.5194/hess-15-1157-2011, 2011.
Kim, H. and Lee, S.: Assessment of a seasonal calibration technique using multiple objectives in rainfall–runoff analysis, Hydrol. Process., 28, 2159–2173, 2014.
Kotlarski, S., Block, A., Böhm, U., Jacob, D., Keuler, K., Knoche, R., Rechid, D., and Walter, A.: Regional climate model simulations as input for hydrological applications: evaluation of uncertainties, Adv. Geosci., 5, 119–125, https://doi.org/10.5194/adgeo-5-119-2005, 2005.
Leander, R. and Buishand, T. A.: Resampling of regional climate model output for the simulation of extreme river flows, J. Hydrol., 332, 487–496, 2007.
Leander, R., Buishand, T. A., van den Hurk, B. J., and de Wit, M. J.: Estimated changes in flood quantiles of the river Meuse from resampling of regional climate model output, J. Hydrol., 351, 331–343, 2008.
Lenderink, G., Buishand, A., and van Deursen, W.: Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach, Hydrol. Earth Syst. Sci., 11, 1145–1159, https://doi.org/10.5194/hess-11-1145-2007, 2007.
Letcher, R., Schreider, S. Y., Jakeman, A., Neal, B., and Nathan, R.: Methods for the analysis of trends in streamflow response due to changes in catchment condition, Environmetrics, 12, 613-630, 2001.
Littlewood, I. G.: Improved unit hydrograph characterisation of the daily flow regime (including low flows) for the River Teifi, Wales: towards better rainfall-streamflow models for regionalisation, Hydrol. Earth Syst. Sci., 6, 899–911, https://doi.org/10.5194/hess-6-899-2002, 2002.
Maraun, D., Wetterhall, F., Ireson, A., Chandler, R., Kendon, E., Widmann, M., Brienen, S., Rust, H., Sauter, T., and Themeßl, M.: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314, 2010.
Meehl, G. A., Arblaster, J. M., and Tebaldi, C.: Understanding future patterns of increased precipitation intensity in climate model simulations, Geophys. Res. Lett., 32, L18719, https://doi.org/10.1029/2005GL023680, 2005.
Meehl, G. A., Covey, C., Taylor, K. E., Delworth, T., Stouffer, R. J., Latif, M., McAvaney, B., and Mitchell, J. F.: The WCRP CMIP3 multimodel dataset: A new era in climate change research, B. Am. Meteorol. Soc., 88, 1383–1394, 2007.
Murphy, J., Sexton, D., Jenkins, G., Boorman, P., Booth, B., Brown, K., Clark, R., Collins, M., Harris, G., and Kendon, E.: UKCP09 Climate change projections, Met Office Hadley Centre, Exeter, 2009.
Murphy, J. M., Sexton, D. M., Barnett, D. N., Jones, G. S., Webb, M. J., Collins, M., and Stainforth, D. A.: Quantification of modelling uncertainties in a large ensemble of climate change simulations, Nature, 430, 768–772, 2004.
Palmer, T. and Räisänen, J.: Quantifying the risk of extreme seasonal precipitation events in a changing climate, Nature, 415, 512–514, 2002.
Piani, C., Haerter, J., and Coppola, E.: Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192, 2010.
Schmidli, J., Frei, C., and Vidale, P. L.: Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods, Int. J. Climatol., 26, 679–689, 2006.
Solomon, S.: Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC, Cambridge University Press, 2007.
Stainforth, D. A., Aina, T., Christensen, C., Collins, M., Faull, N., Frame, D., Kettleborough, J., Knight, S., Martin, A., and Murphy, J.: Uncertainty in predictions of the climate response to rising levels of greenhouse gases, Nature, 433, 403–406, 2005.
Stocker, D. Q.: Climate change 2013: The physical science basis, Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Summary for Policymakers, IPCC, 2013.
Sun, F., Roderick, M. L., Lim, W. H., and Farquhar, G. D.: Hydroclimatic projections for the Murray-Darling Basin based on an ensemble derived from Intergovernmental Panel on Climate Change AR4 climate models, Water Resour. Res., 47, W00G02, https://doi.org/10.1029/2010WR009829, 2011.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2012.
Tebaldi, C., Hayhoe, K., Arblaster, J. M., and Meehl, G. A.: Going to the extremes, Climatic Change, 79, 185–211, 2006.
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, J. Hydrol., 456, 12–29, 2012.
Thorne, P. and Vose, R.: Reanalyses suitable for characterizing long-term trends: Are they really achievable?, B. Am. Meteorol. Soc., 91, 353–361, 2010.
Webb, M., Senior, C., Sexton, D., Ingram, W., Williams, K., Ringer, M., McAvaney, B., Colman, R., Soden, B., and Gudgel, R.: On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles, Clim. Dynam., 27, 17–38, 2006.
Weisheimer, A. and Palmer, T.: Changing frequency of occurrence of extreme seasonal temperatures under global warming, Geophys. Res. Lett., 32, L20721, https://doi.org/10.1029/2005GL023365, 2005.
Wood, E. F., Roundy, J. K., Troy, T. J., Van Beek, L., Bierkens, M. F., Blyth, E., de Roo, A., Döll, P., Ek, M., and Famiglietti, J.: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011.
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.
A primary advantage of using model ensembles for climate change impact studies is to represent...