Assessing uncertainty of hydrological and ecological parameters originating from the application of an ensemble of ten global-regional climate model projections in a coastal ecosystem of the lagoon of Venice, Italy

Pesce, Marco; Critto, Andrea; Torresan, Silvia; Giubilato, Elisa; Pizzol, Lisa; Marcomini, Antonio

doi:10.1016/j.ecoleng.2019.04.011

Cited by 16 publications

(5 citation statements)

References 62 publications

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“…The results were consistent with those obtained by Reference [45] applying the SWAT model for simulating the effect of climate change on hydrological and ecological parameters in the same case study. However, while conclusions for autumn are similar to those reached by other authors [26,46,47] for similar catchments in Europe and the United States (US), for spring and summer, the results differ.…”

Section: Discussionsupporting

confidence: 91%

Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

et al. 2019

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With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 different combinations of a global climate model (GCM)-regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO 3 , NH 4 , PO 4 ) in mid-(2041-2070) and long-term (2071-2100) periods with respect to the baseline . BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between different GCM-RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources.Sustainability 2019, 11, 4764 2 of 34 stemming from climate change make the assessment of climate change impacts on water resources particularly challenging [8].Uncertainty plays a prominent role in climate change science and climate change impact science, with hydrology and water resources research in particular [8][9][10][11]. According to Parker et al. [12] and Hawkins et al. [13], it can be attributed to a number of reasons including (i) scenario uncertainty, arising from our limited understanding about the path of greenhouse gasses emissions and socio-economic development; (ii) internal climate variability, due to the inherent variability of the climate system components, processes, and their interaction; (iii) model uncertainty, caused by the different formulations used to represent climatic processes in climate and impact models.A proper understanding of the type, sources, and effects of uncertainty is needed to achieve the goals of reliability and sustainability in water system management and planning under changing conditions [14,15]. Uncertainty quantification is vital to facilitate a risk-based approach to decision-making, where the range of possible futures is considered [16,17], and costs and benefits of adaptation are estimated accordingly. For this reason, uncertainties should be communicated as an inevitable component of each climate impact assessment study in a form which is also understandable by a non-scientific community to avoid misjudged information and to prevent overconfidence in impact projections [18].A promising way to evaluate and deal with uncertainty is represented by ensemble modelin...

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Section: Discussionsupporting

confidence: 91%

Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

et al. 2019

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“…Bias corrected HighResMIP climate projections as described in Section 2.3 are used to drive the calibrated SWAT in order to simulate the future daily streamflow from 1985 to 2050. Thus, the projected hydro-climatic changes driving the simulation of SWAT are based on multi-GCM ensemble experiments, which help to consider the climate projection uncertainties associated with the different GCM configurations and improve reliability in the assessment of hydro-climatic impacts [58,59]. The simulated streamflow will then be used to calculate the 66 extreme indicators as listed in Tables 2 and 3.…”

Section: Model Setup and Input Datamentioning

confidence: 99%

Hydrological Extremes and Responses to Climate Change in the Kelantan River Basin, Malaysia, Based on the CMIP6 HighResMIP Experiments

Tan

Liang

Samat

et al. 2021

Water

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This study introduces a hydro-climatic extremes assessment framework that combines the latest climate simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) HighResMIP with the Soil and Water Assessment (SWAT) model, and examines the influence of the different climate model resolutions. Sixty-six hydrological and environmental flow indicators from the Indicators of Hydrologic Alteration (IHA) were computed to assess future extreme flows in the Kelantan River Basin (KRB), Malaysia, which is particularly vulnerable to flooding. Results show that the annual precipitation, streamflow, maximum and minimum temperatures are projected to increase by 6.9%, 9.9%, 0.8 °C and 0.9 °C, respectively, by the 2021–2050 period relative to the 1985–2014 baseline. Monthly precipitation and streamflow are projected to increase especially for the Southwest Monsoon (June–September) and the early phase of the Northeast Monsoon (December) periods. The magnitudes of the 1-, 3-, 7-, 30- and 90-day minima flows are projected to increase by 7.2% to 8.2% and the maxima flows by 10.4% to 28.4%, respectively. Lastly, changes in future hydro-climatic extremes are frequently quite different between the high-resolution and low-resolution models, e.g., the high-resolution models projected an increase of 11.8% in mean monthly flow in November-December-January compared to 3.2% for the low-resolution models.

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“…Model response uncertainty is explanation as the different climate change model may output different responses for the same forced information. The internal uncertainty explains as the natural variability of the precipitation and temperature etc., which describes the natural process in the atmosphere, ocean, and their couple uncertainties (Pesce et al 2019). The inherently chaotic internal processes in the climate system are cascading to the hydrological processes (Lafaysse et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Estimating the Effect of Climate Internal Variability and Source of Uncertainty in Climate-Hydrological Projections in a Representative Watershed of Northeastern China

Cai

Zhu

Zhao

et al. 2021

Preprint

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The decomposition and quantification of uncertainty sources in ensembles of climate-hydrological simulation chains is a key issue in climate impact researches. The mainly objectives of this study partitioning climate internal variability (CIV) and uncertainty sources in the climate-hydrological projections simulation process, the climate simulation process formed by six downscaled GCMs under two emission scenarios called GCMs-ES simulation chain, the hydrological simulation process add one calibrate Soil and Water Assessment Tool (SWAT) model called GCMs-ES-HM simulation chain. The CIV and external forcing of climate projections are investigated in each GCMs-ES simulation chain. The CIV of precipitation and ET are large in rainy season, and the single-to-noise ratio (SNR) are also relatively high in rainy season. Furthermore, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The CIV and GCMs are the dominate contributors of runoff in rainy season. It worth noting the CIV can propagate from precipitation and ET to runoff projections. In additional, the hydrological model parameters are the third uncertainty contributor of runoff, which embody the hydrological model simulate process play important role in hydrological projections in future. The findings of this study advised that the uncertainty is complex in hydrological, hence, it is meaning and necessary to estimate the uncertainty in climate simulation process, the uncertainty analysis results can provide effectively efforts to reduce uncertainty and then give some positive suggestions to stakeholders for adaption countermeasure under climate change.

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Assessing uncertainty of hydrological and ecological parameters originating from the application of an ensemble of ten global-regional climate model projections in a coastal ecosystem of the lagoon of Venice, Italy

Cited by 16 publications

References 62 publications

Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

Hydrological Extremes and Responses to Climate Change in the Kelantan River Basin, Malaysia, Based on the CMIP6 HighResMIP Experiments

Estimating the Effect of Climate Internal Variability and Source of Uncertainty in Climate-Hydrological Projections in a Representative Watershed of Northeastern China

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