Evaluating the performance of climate models in reproducing the hydrological characteristics of rainfall events

Song, Jung‐Hun; Her, Younggu; Shin, Satbyeol; Cho, Jaepil; Paudel, Rajendra; Khare, Yogesh; Obeysekera, Jayantha; Martinez, Christopher J.

doi:10.1080/02626667.2020.1750616

Cited by 12 publications

(8 citation statements)

References 60 publications

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“…Furthermore, they define several classes of dry spell lengths (Table 4), based on the percentiles of dry spell length calculated using the historical period of the study. Dry spells are not to be confused with the terms dry events (Willems, 2013;Willems and Vrac, 2011) or inter-events (Sørup et al, 2017;Thorndahl et al, 2017) used in the statistical downscaling methods. This is due to the definition of dry spells comprising consecutive dry days (≥ 2 d).…”

Section: Research Indicatorsmentioning

confidence: 99%

Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

Tabari

Paz

Buekenhout

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. General circulation models (GCMs) are the primary tools for evaluating the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Apart from the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small- and large-scale variables. This study compares four statistical downscaling methods of bias correction (BC), the change factor of mean (CFM), quantile perturbation (QP) and an event-based weather generator (WG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000 for the weather station of Uccle located in Belgium. A set of drought-related aspects is analysed, i.e. dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 28-member ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, each forced by four future scenarios of SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability in the climate. A performance comparison of the downscaling methods reveals that the QP method outperforms the others in reproducing the magnitude and monthly pattern of the observed indicators. While all methods show a good agreement on downscaling total precipitation, their results differ quite largely for the frequency and length of dry spells. Using the downscaling methods, dry day frequency is projected to increase significantly in the summer months, with a relative change of up to 19 % for SSP5–8.5. At the same time, total precipitation is projected to decrease significantly by up to 33 % in these months. Total precipitation also significantly increases in winter, as it is driven by a significant intensification of extreme precipitation rather than a dry day frequency change. Lastly, extreme dry spells are projected to increase in length by up to 9 %.

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Section: Research Indicatorsmentioning

confidence: 99%

Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

Tabari

Paz

Buekenhout

et al. 2021

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Under RCP 8.5, for instance, the best GCM projected the overall rainfall depth and intensity to decrease by 10% in the 2030s and 20% in the 2070s, while the median of the GCM projections predicted the frequency to decrease by 20% and the intensity to increase by more than 30% in both the future periods. Such results demonstrate that we may reach opposite conclusions about future rainfall depending on the selection of GCM projection, which necessitates a comprehensive and detailed investigation of GCM projection accuracy in the hydrologic analysis of climate change (Her et al, 2019;Knutti et al, 2010;Song et al, 2020).…”

Section: Discussionmentioning

confidence: 97%

“…Studies show that not all GCMs could reproduce the characteristics of local weather variables of interest including the depths, intensities, frequencies, and pause periods of rainfall events (Hidalgo & Alfaro, 2015; Murawski et al, 2016; Song et al, 2020). It is often desirable to have a single representative future projection in a climate change study for the brevity of analysis and documentation, while the use of multiple GCM projections enables the quantification of uncertainty.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of impacts of climate change on natural and managed wetland basins

Shin

Her

Khare

2023

J American Water Resour Assoc

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Low floodplain wetlands such as the Western Everglades in South Florida are vulnerable to extreme weather events, and their water quality and ecosystem functions vary greatly depending on changes in water levels and discharges. The future (i.e., the mid and late 21st century) climate is projected to result in increased frequency and magnitude of extreme events, which could negatively affect the hydroecological function of the wetlands. Wetland management practices have commonly been implemented to protect wetlands and their functions, but it is not clear whether the current management practices can still be effective in projected climate change scenarios. The main goal of this study was to evaluate the impacts of climate change on the runoff and total phosphorus (TP) of natural (L28 Gap) and managed (L28) wetland watersheds in the Western Everglades. For the assessment, we employed future climate projections made using 29 general circulation models (GCMs) and the Watershed Assessment Model (WAM), a watershed loading model. The WAM was calibrated and validated for the baseline period (2000–2014), and the bias‐corrected climate projections were incorporated into the model to project the runoff discharge and TP loads for the near‐future (2030–2044) and far‐future (2070–2084) periods in two carbon emission scenarios. The modeling results show that the natural wetland watershed would be more vulnerable to projected climate change than the managed wetland watershed. The impact of projected climate change scenarios on daily runoff and TP loads was modulated by water control facilities and practices in the managed watershed, highlighting the significance of watershed management practices for improved water quality under projected climate change. This study demonstrates how the local natural and managed wetland watersheds distinctly respond to the global‐scale changes and emphasizes the role of water management practices in wetland basins, which are expected to help develop effective climate change adaptation plans for improved sustainability of wetland systems.

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“…However, because of large uncertainties associated with climate projections, for estimating site-specific precipitation, regenerated or projected rainfall data is recommended to be supplemented with site-specific observations, particularly for sensitive structures such as nuclear facilities (Kumar et al 2013 ; Song et al 2020 ; Salimian et al 2021 ). As indicated earlier, observational data at new nuclear sites are generally limited between 10 and 15 years at the time of finalization of designs.…”

Section: Introductionmentioning

confidence: 99%

Climate change indications in short-period rainfall records from coastal India using trend analysis: a proposed framework

Dauji

2023

Arab J Geosci

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Climate change has reportedly resulted in higher magnitude and frequency of extreme weather variables in many parts of India. Such studies pertaining to rainfall were based on long-period records, gridded rainfall data, or climate model projections, which have inherent uncertainties. As long-period records might not be available for many locations, gridded data or climate model analysis could be supplemented with the examination of short-period observations (< 20 years) for climate change effects. Detecting climate change effect from short-period high-resolution data has not yet been reported in the literature. For such exercise, this study proposes a framework based on trend analysis along with a demonstration case at a coastal site in India. Using the proposed analysis, indications of climate change are identified in short-period (January 1997 to December 2013: 17 years) high-resolution (hourly) rainfall data, which are subsequently corroborated with results from long-period (60-year) annual data from the site. This proof-of-concept study opens up an intriguing possibility of detecting climate change indications from short-period data, subject to validation on a larger scale with observations from more weather stations. The results could aid judicious decision-making regarding safety margins required in hydraulic design, to account for climate change effects. The methodology, if corroborated with more studies, might prove useful for detecting climate change effects from other short-period hydro-meteorological data as well.

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Evaluating the performance of climate models in reproducing the hydrological characteristics of rainfall events

Cited by 12 publications

References 60 publications

Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

Evaluation of impacts of climate change on natural and managed wetland basins

Climate change indications in short-period rainfall records from coastal India using trend analysis: a proposed framework

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