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Han Zhang, Chuanhao Wu, Wenjie Chen, and Guoru Huang

Abstract

Climate warming is expected to occur with an increased magnitude of extreme precipitation and sea level rise, which leads to an increased probability of waterlogging in coastal cities. In this paper, a combined probability model is developed to evaluate the impact of climate change on waterlogging in Guangzhou by using eight climate models with four emissions scenarios [Special Report on Emissions Scenarios (SRES) scenario A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5]. The copula method was applied to derive the bivariate distributions of extreme rainfall and tidal level. The uncertainty in the projected future temperature, extreme rainfall, sea level, and the combined extreme rainfall and tidal level probability were discussed. The results show that although there is a large uncertainty driven by both climate models and emissions scenarios in the projection of climate change, most modeling results predict an increase in temperature and extreme precipitation in Guangzhou during the future period of 2020–50, relative to the historical period of 1970–2000. Moreover, greater increases are projected for higher emissions scenarios. The sea level is projected to increase in the range of 11.40–23.37 cm during the period 2020–50, consistent with climate warming. Both simultaneous probability and waterlogging probability are projected to show an upward trend in the future period 2020–50, with the largest and smallest increases in the RCP4.5 and RCP2.6 scenarios, respectively. The results of this paper provide a new scientific reference for waterlogging control in Guangzhou under climate change conditions.

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Chuanhao Wu, Guoru Huang, Haijun Yu, Zhijing Chen, and Jingguang Ma

Abstract

One of the potential impacts of global warming is likely to be experienced through changes in flood frequency and magnitude, which poses a potential threat to the downstream reservoir flood control system. In this paper, the downscaling results of the multimodel dataset from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) were coupled with the Variable Infiltration Capacity (VIC) model to evaluate the impact of climate change on the Feilaixia reservoir flood control in the Beijiang River basin for the first time. Four emissions scenarios [A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5] were chosen. Results indicate that annual distribution and interannual variability of temperature and precipitation are well simulated by the downscaling results of the CMIP3 and CMIP5 multimodel dataset. The VIC model, which performs reasonably well in simulating runoff processes with high model efficiency and low relative error, is suitable for the study area. Overall, annual maximum 1-day precipitation in 2020–50 would increase under all the scenarios (relative to the baseline period 1970–2000). However, the spatial distribution patterns of changes in projected extreme precipitation are uneven under different scenarios. Extreme precipitation is most closely associated with extreme floods in the study area. There is a gradual increase in extreme floods in 2020–50 under any of the different emission scenarios. The increases in 500-yr return period daily discharge of the Feilaixia reservoir have been found to be from 4.35% to 9.18% in 2020–50. The reservoir would be likely to undergo more flooding in 2020–50.

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Chuanhao Wu, Pat J.-F. Yeh, Haichun Wu, Bill X. Hu, and Guoru Huang

Abstract

Recent studies have extended the applicability of the Budyko framework from the long-term mean to annual or shorter time scales. However, the effects of water storage change ΔS on the overall water balance estimated from the Budyko models (BM) at annual-to-monthly time scales were less investigated, particularly at the continental or global scales, due to the lack of large-scale ΔS data. Here, based on a 25-yr (1984–2008) global gridded terrestrial water budget dataset and by using an analytical error-decomposition framework, we analyzed the effects of ΔS in evapotranspiration (ET) predicted from BM at both grid and basin scales under diverse climates for the annual, wet-seasonal, dry-seasonal, and monthly time scales. Results indicated that the BM underperforms in the short dry (wet) seasons of predominantly humid (dry) basins, with lower accuracy under more humid climates (at annual, dry-seasonal, and monthly scales) and under more arid climates (at wet-seasonal scale). When the effects of ΔS are incorporated into BM, improvements can be found mostly at annual and dry-seasonal scales, but not notable at wet-seasonal and monthly scales. The magnitudes of ΔS are positively correlated with the errors in BM-predicted ET for most global regions at annual and monthly scales, especially under arid climates. Under arid climates, the variability of ET prediction errors is controlled mainly by the ΔS variability at annual and monthly time scales. In contrast, under humid climates the effect of ΔS on ET prediction errors is generally limited, particularly at the wet-seasonal scale due to the more dominant influences of other climatic factors (precipitation and potential ET) and catchment responses (runoff).

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Chuanhao Wu, Pat J.-F. Yeh, Yi-Ying Chen, Bill X. Hu, and Guoru Huang

Abstract

Anthropogenic forcing is anticipated to increase the magnitude and frequency of precipitation-induced extremes such as the increase in drought risks. However, the model-projected future changes in global droughts remain largely uncertain, particularly in the context of the Paris Agreement targets. Here, by using the standardized precipitation index (SPI), we present a multiscale global assessment of the precipitation-driven meteorological drought characteristics at the 1.5° and 2°C warming levels based on 28 CMIP5 global climate models (GCMs) under three representative concentration pathways scenarios (RCP2.6, RCP4.5, and RCP8.5). The results show large uncertainties in the timing reaching 1.5° and 2°C warming and the changes in drought characteristics among GCMs, especially at longer time scales and under higher RCP scenarios. The multi-GCM ensemble mean projects a general increase in drought frequency (Df) and area (Da) over North America, Europe, and northern Asia at both 1.5° and 2°C of global warming. The additional 0.5°C warming from 1.5° to 2°C is expected to result in a trend toward wetter climatic conditions for most global regions (e.g., North America, Europe, northern Asia, and northern Africa) due to the continuing increase in precipitation under the more intensified 2°C warming. In contrast, the increase in Df is projected only in some parts of southwest Asia, South America, southern Africa, and Australia. Our results highlight the need to consider multiple GCMs in drought projection studies under the context of the Paris Agreement targets to account for large model-dependent uncertainties.

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Chuanhao Wu, Pat J.-F. Yeh, Jiali Ju, Yi-Ying Chen, Kai Xu, Heng Dai, Jie Niu, Bill X. Hu, and Guoru Huang

Abstract

Drought projections are accompanied with large uncertainties due to varying estimates of future warming scenarios from different modeling and forcing data. Using the standardized precipitation index (SPI), this study presents a global assessment of uncertainties in drought characteristics (severity S and frequency Df) projections based on the simulations of 28 general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). A hierarchical framework incorporating a variance-based global sensitivity analysis was developed to quantify the uncertainties in drought characteristics projections at various spatial (global and regional) and temporal (decadal and 30-yr) scales due to 28 GCMs, three representative concentration pathway scenarios (RCP2.6, RCP4.5, RCP8.5), and two bias-correction (BC) methods. The results indicated that the largest uncertainty contribution in the globally projected S and Df is from the GCM uncertainty (>60%), followed by BC (<35%) and RCP (<16%) uncertainty. Spatially, BC reduces the spreads among GCMs particularly in Northern Hemisphere (NH), leading to smaller GCM uncertainty in the NH than the Southern Hemisphere (SH). In contrast, the BC and RCP uncertainties are larger in the NH than the SH, and the BC uncertainty can be larger than GCM uncertainty for some regions (e.g., southwest Asia). At the decadal and 30-yr time scales, the contributions for three uncertainty sources show larger variability in S than Df projections, especially in the SH. The GCM and BC uncertainties show overall decreasing trends with time, while the RCP uncertainty is expected to increase over time and even can be larger than BC uncertainty for some regions (e.g., northern Asia) by the end of this century.

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Jiali Ju, Heng Dai, Chuanhao Wu, Bill X. Hu, Ming Ye, Xingyuan Chen, Dongwei Gui, Haifan Liu, and Jin Zhang

Abstract

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. The results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

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