Evaluation of the rainfall component of a weather generator for climate impact studies

Elshamy, Mohamed; Wheater, H. S.; Gedney, Nicola; Huntingford, Chris

doi:10.1016/j.jhydrol.2005.09.017

Cited by 31 publications

(21 citation statements)

References 25 publications

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“…Examples of using weather generators to bridge the spatial gap between GCMs and hydrological impacts are widespread (e.g. Wilks, 1992;Katz, 1996;Semenov and Barrow, 1997;Fowler et al, 2000Fowler et al, , 2005Elshamy et al, 2006;Hewitson and Crane, 2006;Kilsby et al, 2007;te Linde et al, 2010;Fatichi et al, 2011;Lu et al, 2015;Kim et al, 2015).…”

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confidence: 99%

Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin

Murawski

Bürger

Vorogushyn

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. To understand past flood changes in the Rhine catchment and in particular 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. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis.

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confidence: 99%

Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin

Murawski

Bürger

Vorogushyn

et al. 2016

Hydrol. Earth Syst. Sci.

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“…To retain consistency across all variables, PP6 monthly precipitation totals and wet day frequencies are used instead of the CRU monthly series in the disaggregation process. The monthly wet day frequency of the CRU TS 2Ð1 is based on point values, which has been shown by Elshamy et al (2006) to be lower than the areal estimate and, thus, is always lower than that derived from PP6 data. The disaggregated daily series of all variables except rainfall are then used in the simulations.…”

Section: A2 Temporal Disaggregationmentioning

confidence: 84%

“…Elshamy et al (2006) evaluated the performance of the rainfall component of the Geng model and showed that regional calibration of the parameter estimation equations is required. In addition, they constrained the rainfall generator to produce the same monthly total rainfall as provided in the input data and improved the simulation of rainfall amounts by simplifying the estimation of the amount model parameters.…”

Section: A2 Temporal Disaggregationmentioning

confidence: 99%

“…In addition, they constrained the rainfall generator to produce the same monthly total rainfall as provided in the input data and improved the simulation of rainfall amounts by simplifying the estimation of the amount model parameters. In this study, a single disaggregation of the monthly meteorological variables mentioned above is performed using the calibrated coefficients for the Nile Basin as provided by Elshamy et al (2006). Longwave radiation, wind speed and surface pressure are not disaggregated and are kept at their monthly or climatological monthly values in the calculations.…”

Section: A2 Temporal Disaggregationmentioning

confidence: 99%

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Performance assessment of a GCM land surface scheme using a fine‐scale calibrated hydrological model: an evaluation of MOSES for the Nile Basin

Elshamy

Wheater

2009

Hydrological Processes

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Abstract:Land surface schemes (LSSs) represent the interface between land surface and the atmosphere in general circulation models (GCMs). Errors in LSS-simulated heat and moisture fluxes can result from inadequate representation of hydrological features and the derivation of effective surface parameters for large heterogeneous GCM gridboxes from small-scale observations. Previous assessments of LSS performance have generally compared simulated heat and moisture fluxes to observations over a defined experimental domain for a limited period. A different approach has been evaluated in this study, which uses a fineresolution calibrated hydrological model of the study basin to provide a quasi-observed runoff series for direct comparison with simulated runoff from a selected LSS at GCM scale. The approach is tested on two GCM gridboxes covering two contrasting regions within the Nile Basin. Performance is mixed; output from the LSS is generally compatible with that of the fine-resolution model for one gridbox while it cannot reproduce the runoff dynamics for the other. The results also demonstrate the high sensitivity of runoff and evapotranspiration to radiation and precipitation inputs and show the importance of subtle issues such as temporal disaggregation of climatic inputs. We conclude that the use of a fine-resolution calibrated model to evaluate a LSS has several advantages, can be generalized to other areas to improve the performance of global models and provides useful data that can be used to constrain LSS parameterizations.

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“…An increase in temperature was observed in the country especially during the last few decades which accelerate snow melting on glaciers.The increased rate of glacial melting in Himalayas has caused vast lakes to develop, and if these lacks outburst, these can inundate towns and villages below. There is high level of confidence that recent regional changes (rising tendency) in temperature have discernable impacts on precipitation, evaporation, stream flow, runoff and other elements of hydrological cycles (Elshamy et al, 2006). Evaporation rate and water requirement of crops are very high and continuously increasing due to increase in temperature especially the southern parts of the country.…”

Section: Erinc Aridity Indexmentioning

confidence: 99%

Classification and Assessment of Aridity Over Pakistan Provinces (1960-2009)

Haider

Adnan²

2014

Int. J. Environ.

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Due to rapid growth of population, massive deforestation and anthropogenic activities, noticeable change in climate conditions is being observed in Pakistan. Increased aridity due to climate change is a growing environmental problem of the agricultural country like Pakistan. It is essential to assess and monitor aridity to combat the probable land degradation and drought desertification. Identification of arid and semi arid regions on climatic basis is the first essential step in any project of land reclamation for agricultural and other purposes.A geographic information system is used in this paper for the assessment of aridity in Pakistan from long term climatic data of fifty years This study will be a good predicator for agricultural scientist, agronomist and hydrologist to plan according the climate of the region.

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Evaluation of the rainfall component of a weather generator for climate impact studies

Cited by 31 publications

References 25 publications

Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin

Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin

Performance assessment of a GCM land surface scheme using a fine‐scale calibrated hydrological model: an evaluation of MOSES for the Nile Basin

Classification and Assessment of Aridity Over Pakistan Provinces (1960-2009)

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