Statistical downscaling of global circulation models to assess future climate changes in the Black Volta Basin of Ghana

ABSTRACT Climate changes can have different impacts on water resources. Strategies to adapt to climate changes depend on impact studies. In this context, this study aimed to estimate the impact that changes in precipitation, projected by Global Circulation Models (GCMs) in the fifth report by the Intergovernmental Panel on Climate Change (IPCC-AR5) may cause on reservoir yield (Q90) of large reservoirs (Castanhão and Banabuiú), located in the Jaguaribe River Basin, Ceará. The rainfall data are from 20 GCMs using two greenhouse gas scenarios (RCP4.5 and RCP8.5). The precipitation projections were used as input data for the rainfall-runoff model (SMAP) and, after the reservoirs’ inflow generation, the reservoir yields were simulated in the AcquaNet model, for the time periods of 2040-2069 and 2070-2099. The results were analyzed and presented a great divergence, in sign (increase or decrease) and in the magnitude of change of Q90. However, most Q90 projections indicated reduction in both reservoirs, for the two periods, especially at the end of the 21th century.

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Temperature Changes In Poland In 21st Century – Results Of Global Simulation And Regional Downscaling

Quaestiones Geographicae ◽

10.1515/quageo-2015-0023 ◽

2015 ◽

Vol 34 (3) ◽

pp. 85-99 ◽

Cited By ~ 1

Author(s):

Michał Pilarski

Keyword(s):

Spatial Scale ◽

Statistical Downscaling ◽

Information Transfer ◽

Climate Changes ◽

Dynamical Downscaling ◽

Small Scale ◽

Regional Models ◽

Global Circulation Models ◽

The World ◽

Scale Variable

Abstract The main source of information about future climate changes are the results of numerical simulations performed in scientific institutions around the world. Present projections from global circulation models (GCMs) are too coarse and are only usefulness for the world, hemisphere or continent spatial analysis. The low horizontal resolution of global models (100–200 km), does not allow to assess climate changes at regional or local scales. Therefore it is necessary to lead studies concerning how to detail the GCMs information. The problem of information transfer from the GCMs to higher spatial scale solve: dynamical and statistical downscaling. The dynamical downscaling method based on “nesting” global information in a regional models (RCMs), which solve the equations of motion and the thermodynamic laws in a small spatial scale (10–50 km). However, the statistical downscaling models (SDMs) identify the relationship between large-scale variable (predictor) and small-scale variable (predictand) implementing linear regression. The main goal of the study was to compare the global model scenarios of thermal condition in Poland in XXI century with the more accurate statistical and dynamical regional models outcomes. Generally studies confirmed usefulness of statistical downscaling to detail information from GCMs. Basic results present that regional models captured local aspects of thermal conditions variability especially in coastal zone.

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Projecting Future Climate Change Scenarios Using Three Bias-Correction Methods

Advances in Meteorology ◽

10.1155/2014/704151 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 12

Author(s):

Donghyuk Kum ◽

Kyoung Jae Lim ◽

Chun Hwa Jang ◽

Jichul Ryu ◽

Jae E. Yang ◽

...

Keyword(s):

Climate Change ◽

Bias Correction ◽

Interdisciplinary Studies ◽

Future Climate ◽

Future Climate Change ◽

Climate Change Scenarios ◽

Quantile Mapping ◽

Global Circulation ◽

Global Circulation Models ◽

The Individual

We performed bias correction in future climate change scenarios to provide better accuracy of models through adaptation to future climate change. The proposed combination of the change factor (CF) and quantile mapping (QM) methods combines the individual advantages of both methods for adjusting the bias in global circulation models (GCMs) and regional circulation models (RCMs). We selected a study site in Songwol-dong, Seoul, Republic of Korea, to test and assess our proposed method. Our results show that the combined CF + QM method delivers better performance in terms of correcting the bias in GCMs/RCMs than when both methods are applied individually. In particular, our proposed method considerably improved the bias-corrected precipitation by capturing both the high peaks and amounts of precipitation as compared to that from the CF-only and QM-only methods. Thus, our proposed method can provide high-accuracy bias-corrected precipitation data, which could prove to be highly useful in interdisciplinary studies across the world.

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Here be dragons: important spatial uncertainty driven by climate data in forecasted distribution of an endangered insular reptile

10.1101/2021.06.14.448338 ◽

2021 ◽

Author(s):

Nicolas Dubos ◽

Stephane Augros ◽

Gregory Deso ◽

Jean-Michel Probst ◽

Jean-Cyrille Notter ◽

...

Keyword(s):

Climate Change ◽

Bias Correction ◽

Future Climate ◽

Climate Data ◽

Spatial Overlap ◽

Global Circulation ◽

Sample Bias ◽

Environmental Suitability ◽

Global Circulation Models ◽

Baseline Climate

The effect of future climate change is poorly documented in the tropics, especially in mountainous areas. Yet, species living in these environments are predicted to be strongly affected. Newly available high-resolution environmental data and statistical methods enable the development of forecasting models. Nevertheless, the uncertainty related to climate models can be strong, which can lead to ineffective conservation actions. Predicted studies aimed at providing conservation guidelines often account for a range of future climate predictions (climate scenarios and global circulation models). However, very few studies considered potential differences related to baseline climate data and/or did not account for spatial information (overlap) in uncertainty assessments. We modelled the environmental suitability for Phelsuma borbonica, an endangered reptile native to Reunion Island. Using two metrics of species range change (difference in overall suitability and spatial overlap), we quantified the uncertainty related to the modelling technique (n = 10), sample bias correction, climate change scenario, global circulation models (GCM) and baseline climate (CHELSA versus Worldclim). Uncertainty was mainly driven by GCMs when considering overall suitability, while for spatial overlap the uncertainty related to baseline climate became more important than that of GCMs. The uncertainty driven by sample bias correction and variable selection was much higher when assessed based on spatial overlap. The modelling technique was a strong driver of uncertainty in both cases. We eventually provide a consensus ensemble prediction map of the environmental suitability of P. borbonica to identify the areas predicted to be the most suitable in the future with the highest certainty. Predictive studies aimed at identifying priority areas for conservation in the face of climate change need to account for a wide panel of modelling techniques, GCMs and baseline climate data. We recommend the use of multiple approaches, including spatial overlap, when assessing uncertainty in species distribution models.

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Statistical Downscaling of Daily Temperature and Rainfall Data from Global Circulation Models, in Borkena River Catchment, Ethiopia

Journal of Water Resources and Ocean Science ◽

10.11648/j.wros.20200905.11 ◽

2020 ◽

Vol 9 (5) ◽

pp. 87

Author(s):

Kasye Shitu ◽

Shimelis Berhanu

Keyword(s):

Statistical Downscaling ◽

Rainfall Data ◽

Daily Temperature ◽

River Catchment ◽

Global Circulation ◽

Global Circulation Models

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Streamflow estimation for six UK catchments under future climate scenarios

Hydrology Research ◽

10.2166/nh.2009.086 ◽

2009 ◽

Vol 40 (2-3) ◽

pp. 96-112 ◽

Cited By ~ 17

Author(s):

K. P. Chun ◽

H. S. Wheater ◽

C. J. Onof

Keyword(s):

Time Series ◽

Climate Models ◽

Climate Model ◽

Future Climate ◽

Rainfall Time Series ◽

Climate Scenarios ◽

Large Variability ◽

Global Circulation ◽

Global Circulation Models ◽

Rainfall Runoff Model

Possible changes in streamflow in response to climate variation are crucial for anthropological and ecological systems. However, estimates of precipitation under future climate scenarios are notoriously uncertain. In this article, rainfall time series are generated by the generalized linear model (GLM) approach in which stochastic time series are generated using alternative climate model output variables and potential evaporation series estimated by a temperature method. These have been input to a conceptual rainfall–runoff model (pd4-2par) to simulate the daily streamflows for six UK catchments for a set of climate scenarios using seven global circulation models (GCMs) and regional circulation models (RCMs). The performance of the combined methodology in reproducing observed streamflows is generally good. Results of future climate scenarios show significant variability between different catchments, and very large variability between different climate models. It is concluded that the GLM methodology is promising, and can readily be extended to support distributed hydrological modelling.

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Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq

Ecological Processes ◽

10.1186/s13717-020-00259-0 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Nabaz R. Khwarahm

Keyword(s):

Climate Change ◽

Habitat Suitability ◽

Environmental Variables ◽

Climate Changes ◽

Future Climate ◽

Annual Precipitation ◽

Future Climate Change ◽

Climate Change Scenarios ◽

Kurdistan Region ◽

Oak Tree

Abstract Background The oak tree (Quercus aegilops) comprises ~ 70% of the oak forests in the Kurdistan Region of Iraq (KRI). Besides its ecological importance as the residence for various endemic and migratory species, Q. aegilops forest also has socio-economic values—for example, as fodder for livestock, building material, medicine, charcoal, and firewood. In the KRI, Q. aegilops has been degrading due to anthropogenic threats (e.g., shifting cultivation, land use/land cover changes, civil war, and inadequate forest management policy) and these threats could increase as climate changes. In the KRI and Iraq as a whole, information on current and potential future geographical distributions of Q. aegilops is minimal or not existent. The objectives of this study were to (i) predict the current and future habitat suitability distributions of the species in relation to environmental variables and future climate change scenarios (Representative Concentration Pathway (RCP) 2.6 2070 and RCP8.5 2070); and (ii) determine the most important environmental variables controlling the distribution of the species in the KRI. The objectives were achieved by using the MaxEnt (maximum entropy) algorithm, available records of Q. aegilops, and environmental variables. Results The model demonstrated that, under the RCP2.6 2070 and RCP8.5 2070 climate change scenarios, the distribution ranges of Q. aegilops would be reduced by 3.6% (1849.7 km2) and 3.16% (1627.1 km2), respectively. By contrast, the species ranges would expand by 1.5% (777.0 km2) and 1.7% (848.0 km2), respectively. The distribution of the species was mainly controlled by annual precipitation. Under future climate change scenarios, the centroid of the distribution would shift toward higher altitudes. Conclusions The results suggest (i) a significant suitable habitat range of the species will be lost in the KRI due to climate change by 2070 and (ii) the preference of the species for cooler areas (high altitude) with high annual precipitation. Conservation actions should focus on the mountainous areas (e.g., by establishment of national parks and protected areas) of the KRI as climate changes. These findings provide useful benchmarking guidance for the future investigation of the ecology of the oak forest, and the categorical current and potential habitat suitability maps can effectively be used to improve biodiversity conservation plans and management actions in the KRI and Iraq as a whole.

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d4PDF: large-ensemble and high-resolution climate simulations for global warming risk assessment

Progress in Earth and Planetary Science ◽

10.1186/s40645-020-00367-7 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Masayoshi Ishii ◽

Nobuhito Mori

Keyword(s):

Risk Assessment ◽

Global Warming ◽

High Resolution ◽

Climate Changes ◽

Future Climate ◽

Atmospheric Models ◽

Large Ensemble ◽

Past Climate ◽

The Past ◽

Climate Simulations

Abstract A large-ensemble climate simulation database, which is known as the database for policy decision-making for future climate changes (d4PDF), was designed for climate change risk assessments. Since the completion of the first set of climate simulations in 2015, the database has been growing continuously. It contains the results of ensemble simulations conducted over a total of thousands years respectively for past and future climates using high-resolution global (60 km horizontal mesh) and regional (20 km mesh) atmospheric models. Several sets of future climate simulations are available, in which global mean surface air temperatures are forced to be higher by 4 K, 2 K, and 1.5 K relative to preindustrial levels. Nonwarming past climate simulations are incorporated in d4PDF along with the past climate simulations. The total data volume is approximately 2 petabytes. The atmospheric models satisfactorily simulate the past climate in terms of climatology, natural variations, and extreme events such as heavy precipitation and tropical cyclones. In addition, data users can obtain statistically significant changes in mean states or weather and climate extremes of interest between the past and future climates via a simple arithmetic computation without any statistical assumptions. The database is helpful in understanding future changes in climate states and in attributing past climate events to global warming. Impact assessment studies for climate changes have concurrently been performed in various research areas such as natural hazard, hydrology, civil engineering, agriculture, health, and insurance. The database has now become essential for promoting climate and risk assessment studies and for devising climate adaptation policies. Moreover, it has helped in establishing an interdisciplinary research community on global warming across Japan.

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Urban flood hazard analysis in present and future climate after statistical downscaling: a case study in Ha Tinh city, Vietnam

Urban Water Journal ◽

10.1080/1573062x.2021.1877744 ◽

2021 ◽

Vol 18 (4) ◽

pp. 257-274

Author(s):

T. T. A. Le ◽

N. T. Lan-Anh ◽

V. Daskali ◽

B. Verbist ◽

K. C. Vu ◽

...

Keyword(s):

Statistical Downscaling ◽

Flood Hazard ◽

Hazard Analysis ◽

Future Climate ◽

Urban Flood

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Optimising predictor domains for spatially coherent precipitation downscaling

Hydrology and Earth System Sciences ◽

10.5194/hess-17-4189-2013 ◽

2013 ◽

Vol 17 (10) ◽

pp. 4189-4208 ◽

Cited By ~ 30

Author(s):

S. Radanovics ◽

J.-P. Vidal ◽

E. Sauquet ◽

A. Ben Daoud ◽

G. Bontron

Keyword(s):

Statistical Downscaling ◽

Prediction Models ◽

Target Location ◽

Weather Prediction ◽

Target Zones ◽

Southeastern Part ◽

Global Circulation Models ◽

Downscaling Method ◽

Starting Point ◽

East West

Abstract. Statistical downscaling is widely used to overcome the scale gap between predictors from numerical weather prediction models or global circulation models and predictands like local precipitation, required for example for medium-term operational forecasts or climate change impact studies. The predictors are considered over a given spatial domain which is rarely optimised with respect to the target predictand location. In this study, an extended version of the growing rectangular domain algorithm is proposed to provide an ensemble of near-optimum predictor domains for a statistical downscaling method. This algorithm is applied to find five-member ensembles of near-optimum geopotential predictor domains for an analogue downscaling method for 608 individual target zones covering France. Results first show that very similar downscaling performances based on the continuous ranked probability score (CRPS) can be achieved by different predictor domains for any specific target zone, demonstrating the need for considering alternative domains in this context of high equifinality. A second result is the large diversity of optimised predictor domains over the country that questions the commonly made hypothesis of a common predictor domain for large areas. The domain centres are mainly distributed following the geographical location of the target location, but there are apparent differences between the windward and the lee side of mountain ridges. Moreover, domains for target zones located in southeastern France are centred more east and south than the ones for target locations on the same longitude. The size of the optimised domains tends to be larger in the southeastern part of the country, while domains with a very small meridional extent can be found in an east–west band around 47° N. Sensitivity experiments finally show that results are rather insensitive to the starting point of the optimisation algorithm except for zones located in the transition area north of this east–west band. Results also appear generally robust with respect to the archive length considered for the analogue method, except for zones with high interannual variability like in the Cévennes area. This study paves the way for defining regions with homogeneous geopotential predictor domains for precipitation downscaling over France, and therefore de facto ensuring the spatial coherence required for hydrological applications.

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