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Middelkoop, H.; Daamen, K.; Gellens, D.; Grabs, Wolfgang; Kwadijk, Jaap; Herbert, L.R.; Parmet, B.; Schädler, Bruno; Schulla, Joerg; Wilke, K.

doi:10.1023/a:1010784727448

Cited by 503 publications

(105 citation statements)

References 22 publications

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“…The delta-change approach and the linear transformation are the two most common transfer methods and have been widely used (Gellens and Roulin, 1998;Graham et al, 2007a, b;Lettenmaier et al, 1999;Middelkoop et al, 2001;Moore et al, 2008;Shabalova et al, 2003), because they are straightforward and easy to implement due to their simplicity. Yet, our validation of these correction approaches with the differential split-sample test showed that these two methods result in large deviations and are the least reliable under changed conditions.…”

Section: Discussionmentioning

confidence: 99%

Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

Teutschbein

Seibert

2013

Hydrol. Earth Syst. Sci.

335

251

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Abstract. In hydrological climate-change impact studies, regional climate models (RCMs) are commonly used to transfer large-scale global climate model (GCM) data to smaller scales and to provide more detailed regional information. Due to systematic and random model errors, however, RCM simulations often show considerable deviations from observations. This has led to the development of a number of correction approaches that rely on the assumption that RCM errors do not change over time. It is in principle not possible to test whether this underlying assumption of error stationarity is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well correction methods perform for conditions different from those used for calibration with the relatively simple differential split-sample test.For five Swedish catchments, precipitation and temperature simulations from 15 different RCMs driven by ERA40 (the 40 yr reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF)) were corrected with different commonly used bias correction methods. We then performed differential split-sample tests by dividing the data series into cold and warm respective dry and wet years. This enabled us to cross-evaluate the performance of different correction procedures under systematically varying climate conditions. The differential split-sample test identified major differences in the ability of the applied correction methods to reduce model errors and to cope with nonstationary biases. More advanced correction methods performed better, whereas large deviations remained for climate model simulations corrected with simpler approaches. Therefore, we question the use of simple correction methods such as the widely used delta-change approach and linear transformation for RCM-based climate-change impact studies. Instead, we recommend using higher-skill correction methods such as distribution mapping.

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Section: Discussionmentioning

confidence: 99%

Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

Teutschbein

Seibert

2013

Hydrol. Earth Syst. Sci.

335

251

View full text Add to dashboard Cite

show abstract

“…Music and Caya, 2007;Middelkoop et al, 2001;Christensen et al, 2004), prediction at different lead times (e.g. Yuan et al, 2015;Arnell and Gosling, 2016;Scoccimarro et al, 2016), and extreme event attribution (e.g.…”

Section: Introductionmentioning

confidence: 99%

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Schiemann

Vidale

Shaffrey

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Limited spatial resolution is one of the factors that may hamper applications of global climate models (GCMs), in particular over Europe with its complex coastline and orography. In this study, the representation of European mean and extreme precipitation is evaluated in simulations with an atmospheric GCM (AGCM) at different resolutions between about 135 and 25 km grid spacing in the mid-latitudes. The continent-wide root-mean-square error in mean precipitation in the 25 km model is about 25 % smaller than in the 135 km model in winter. Clear improvements are also seen in autumn and spring, whereas the model's sensitivity to resolution is very small in summer. Extreme precipitation is evaluated by estimating generalised extreme value distributions (GEVs) of daily precipitation aggregated over river basins whose surface area is greater than 50 000 km 2 . GEV location and scale parameters are measures of the typical magnitude and of the interannual variability of extremes, respectively. Median model biases in both these parameters are around 10 % in summer and around 20 % in the other seasons. For some river basins, however, these biases can be much larger and take values between 50 % and 100 %. Extreme precipitation is better simulated in the 25 km model, especially during autumn when the median GEV parameter biases are more than halved, and in the North European Plains, from the Loire in the west to the Vistula in the east. A sensitivity experiment is conducted showing that these resolution sensitivities in both mean and extreme precipitation are in many areas primarily due to the increase in resolution of the model orography. The findings of this study illustrate the improved capability of a global high-resolution model in simulating European mean and extreme precipitation.

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“…The studies in western North America found that Pacific climate variability is responsible for 10%-60% of trends in snow water equivalent (Mote, 2006). According to the International Commission for the Hydrology of the Rhine Basin (CHR), the impact assessment of climate changes on river-flow conditions in the Rhine Basin of West Europe, and higher temperature will increase the flood risk, while low flows will adversely affect inland navigation and reduce water availability for agriculture and industry (Middelkoop et al, 2001). The Qilian Mountain is the main water resource of Zhangye City, in which agriculture plays an important role in economic and social development.…”

Section: Climate Change and Eco-hydrological Responsesmentioning

confidence: 99%

Hydrological services by mountain ecosystems in Qilian Mountain of China: A review

Sun

Lyu

et al. 2016

Chin. Geogr. Sci.

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Hydrological service is a hot issue in the current researches of ecosystem service, particularly in the upper reaches of mountain rivers in dry land areas, where the Qilian Mountain is a representative one. The Qilian Mountain, where forest, shrubland and grassland consist of its main ecosystems, can provide fresh water and many other ecosystem services, through a series of eco-hydrological process such as precipitation interception, soil water storage, and fresh water provision. Thus, monitoring water regulation and assessing the hydrological service of the Qilian Mountain are meaningful and helpful for the healthy development of the lower reaches of arid and semi-arid areas. In recent 10 years, hydrological services have been widely researched in terms of scale and landscape pattern, including water conservation, hydrological responses to afforestation and their ecological effects. This study, after analyzing lots of current models and applications of geographical information system (GIS) in hydrological services, gave a scientific and reasonable evaluation of mountain ecosystem in eco-hydrological services, by employing the combination of international forefronts and contentious issues into the Qilian Mountain. Assessments of hydrological services at regional or larger scales are limited compared with studies within watershed scale in the Qilian Mountain. In our evaluation results of forest ecosystems, it is concluded that long-term observation and dynamic monitoring of different types of ecosystem are indispensable, and the hydrological services and the potential variation in water supplement on regional and large scales should be central issues in the future research.

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Untitled

Cited by 503 publications

References 22 publications

Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Hydrological services by mountain ecosystems in Qilian Mountain of China: A review

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