Grappling with uncertainties in physical climate impact projections of water resources

Dankers, Rutger; Kundzewicz, Zbigniew W.

doi:10.1007/s10584-020-02858-4

Cited by 11 publications

(4 citation statements)

References 83 publications

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“…Hattermann et al 2018;Thober et al 2018;Vetter et al 2017), hydrological model uncertainty can still be significant. Dankers and Kundzewicz (2020) suggest that the main sources of hydrological bias are related to evaporation and snow processes. River flows in most British catchments are rainfall-dominated, but snowmelt can be significant for some upland catchments (Kay 2016); a snow module is applied here to account for snow processes, albeit in a relatively simple way.…”

Section: Discussionmentioning

confidence: 99%

“…Appropriate representation of PE is important, particularly under climate change (Kay et al 2013). In contrast to many hydrological impact studies (Dankers & Kundzewicz 2020), here the future PE includes the effect of stomatal closure under higher concentrations of atmospheric carbon dioxide (Robinson et al 2021). The use of RCM PE for both SIMRCM and SIMCPM means that the differences in impacts are essentially due to precipitation differences, possibly with some effect from temperature differences in some catchments (although CPM and RCM winter temperature projections are broadly similar; Kendon et al 2021b Fig.…”

Section: Discussionmentioning

confidence: 99%

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Differences in hydrological impacts using regional climate model and nested convection-permitting model data

Kay

2022

Climatic Change

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Assessing the potential impacts of climate change on river flows is critically important for adaptation. Data from global or nested regional climate models (GCMs/RCMs) are frequently used to drive hydrological models, but now there are also very high-resolution convection-permitting models (CPMs). Here, data from the first CPM climate ensemble for the UK, along with the RCM ensemble within which the CPM is nested, are used to drive a grid-based hydrological model. The performance for simulating baseline (1981–2000) river flows is compared between the RCM and the CPM, and the projections of future changes in seasonal mean flows and peak flows are compared across Britain (1981–2000 to 2061–2080). The baseline performance assessment shows that (before bias correction) the CPM generally performs better than the RCM, and bias correction of precipitation makes both the RCM and CPM perform more similarly to use of observation-based driving data. The analysis of future changes in flows shows that the CPM almost always gives higher flow changes than the RCM. If reliable, these differences in flow projections suggest that adaptation planning for high flows based on use of regional data may be insufficient, although planning for low flows may be slightly over-cautious. However, the availability of CPM data only for one RCM/GCM is a limitation for use in adaptation as it under-samples the uncertainty range. There are significant challenges to the wider application of CPM ensembles, including the high computational and data storage demands.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Differences in hydrological impacts using regional climate model and nested convection-permitting model data

Kay

2022

Climatic Change

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“…(Giuntoli et al, 2015;Knutti and Sedláček, 2012;Mockler et al, 2016;Wang et al, 2014). To simulate and predict the climate change, and to understand some factors that lead to the uncertainty of GCMs, model evaluation is a key step in the development and application of any model of the environment (Chen et al, 2012;Dankers and Kundzewicz, 2020;Eyring et al, 2016b). A number of previous studies have assessed the effectiveness of runoff simulations using global model output archived in the CMIP3 and CMIP5.…”

Section: Evaluation and Comparison Of Cmip6 And Cmip5 Models Performa...mentioning

confidence: 99%

Evaluation and comparison of CMIP6 and CMIP5 models performance in simulating the runoff

Guo¹,

Zhan

Ning³

et al. 2022

Preprint

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This study evaluates and compares the performance of Coupled Model Intercomparison Project Phase 6 (CMIP6) and CMIP5 in simulating the runoff on global scale and eight large-scale basins, over the period 1981–2005 using percent bias (PBIAS), correlation coefficient (CC), root mean square error (RMSE), Theil-Sen median trend, and the Taylor diagram. The CMIP models are ranked by comprehensive rating index (MR), which is determined by PBIAS, CC and RMSE three metrics. LORA, GRUN and ERA5-Land were selected as reference data sets. LORA was used as the main reference data to evaluate the historical runoff results of CMIP from 1981 to 2012 for three aspects: trend, PBIAS and uncertainty. Results reveal that: (i) CMIP6 models have obviously overvalued on the global and basins (except Amazon and Lena basin), this phenomenon was more prominent in arid and semi-arid areas ( Murray-Darling and Nile basin). (ii) Compared with CMIP5 models, CMIP6 models have less uncertainty on the global scale, but it has not made outstanding progress on the basin scale. (iii) CMIP6 multi‐model ensemble mean (CMIP6_MMEs) has better simulation effect than most individual models, which reduces the uncertainty among different models to some extent. (iv) There were differences in trends and PBIAS between the three reference data sets at both the global and basin scale. However, the interannual fluctuations of the three data sets were basically the same and have high correlation coefficient (except for ERA5 in the world and Nile basin), which shows that LORA data set has high reliability. The global comprehensive rating metric (GR) of CMIP6_MMEs was better than CMIP5_MMEs in all metrics, but this result was not found in eight basins. This shows that CMIP6 models has better effect in simulating global runoff and related diagnostic indicators. Implying further improvements are needs for the runoff simulation capability at the basin scale.

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“…One paper is devoted to a systematic evaluation of global water models in the Arctic region, though impact assessment is not included (Gädeke et al 2020). The focus of three remaining papers is on other specific research questions, such as testing a new calibration method for a large region in Africa (Chawanda et al 2020), selection of climate ensemble members based on simulated streamflow (Kiesel et al 2020), and reviewing sources of uncertainty in climate impact projections (Dankers and Kundzewicz, 2020), but all three have indirect relations to the main topic of this SI.…”

Section: Introductionmentioning

confidence: 99%

How evaluation of hydrological models influences results of climate impact assessment—an editorial

2020

Self Cite

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This paper introduces the Special Issue (SI) “How evaluation of hydrological models influences results of climate impact assessment.” The main objectives were as follows: (a) to test a comprehensive model calibration/validation procedure, consisting of five steps, for regional-scale hydrological models; (b) to evaluate performance of global-scale hydrological models; and (c) to reveal whether the calibration/validation methods and the model evaluation results influence climate impacts in terms of the magnitude of the change signal and the uncertainty range. Here, we shortly describe the river basins and large regions used as case studies; the hydrological models, data, and climate scenarios used in the studies; and the applied approaches for model evaluation and for analysis of projections for the future. After that, we summarize the main findings. The following general conclusions could be drawn. After successful comprehensive calibration and validation, the regional-scale models are more robust and their projections for the future differ from those of the model versions after the conventional calibration and validation. Therefore, climate impacts based on the former models are more trustworthy than those simulated by the latter models. Regarding the global-scale models, using only models with satisfactory or good performance on historical data and weighting them based on model evaluation results is a more reliable approach for impact assessment compared to the ensemble mean approach that is commonly used. The former method provides impact results with higher credibility and reduced spreads in comparison to the latter approach. The studies for this SI were performed in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).

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Grappling with uncertainties in physical climate impact projections of water resources

Cited by 11 publications

References 83 publications

Differences in hydrological impacts using regional climate model and nested convection-permitting model data

Differences in hydrological impacts using regional climate model and nested convection-permitting model data

Evaluation and comparison of CMIP6 and CMIP5 models performance in simulating the runoff

How evaluation of hydrological models influences results of climate impact assessment—an editorial

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