Statistical Downscaling Using Local Polynomial Regression for Rainfall Predictions – A Case Study

George, Jany; Janaki, Letha; Gomathy, Jairaj Parameswaran

doi:10.1007/s11269-015-1154-0

Cited by 18 publications

(12 citation statements)

References 13 publications

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“…, Goyal and Ojha , George et al . , Ahmed et al . , Okkan and Kirdemir , utilized several artificial intelligence models (e.g., support vector regression, ANN, genetic programing) for downscaling GCM outputs.…”

Section: Resultsmentioning

confidence: 99%

“…While this is the first study to use ELM for statistical downscaling of GCM outputs to monthly precipitation over Minab Basin, it is worthwhile to relate the performance of the applied methods in the current research with those presented in other studies that closely relate to this study. Chen et al [18], Goyal and Ojha [19], George et al [47], Ahmed et al [48], Okkan and Kirdemir [49], utilized several artificial intelligence models (e.g., support vector regression, ANN, genetic programing) for downscaling GCM outputs. Hashmi et al [5] utilized Gene Expression Programming for simulating watershed precipitation and they found R 2 of 0.5 in the test period.…”

Section: Kahnoojmentioning

confidence: 99%

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Statistical downscaling of global climate model outputs to monthly precipitation via extreme learning machine: A case study

Alizamir

Moghadam

Monfared

et al. 2018

Env Prog and Sustain Energy

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The present article explores the impacts of climate change on precipitation at station scale in the Minab basin, Iran. The data used for evaluation were large‐scale input (predictor) parameters extracted from the reanalysis data set of the National Center for Environmental Prediction and National Center for Atmospheric Research to downscale monthly precipitation. In this research, four approaches were applied to downscale precipitation, including an implementation on extreme learning machine (ELM) for single‐hidden layer feedforward neural network, artificial neural network, genetic programming, and quantile mapping. The results indicated that the ELM approach outperformed all other approaches in downscaling the large‐scale global climate model atmospheric variables to monthly precipitation at station scale. © 2018 American Institute of Chemical Engineers Environ Prog, 37: 1853–1862, 2018

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Section: Resultsmentioning

confidence: 99%

Section: Kahnoojmentioning

confidence: 99%

Statistical downscaling of global climate model outputs to monthly precipitation via extreme learning machine: A case study

Alizamir

Moghadam

Monfared

et al. 2018

Env Prog and Sustain Energy

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“…Moreover, even though integrated long-term transformative adaptation strategy makes the action more affordable, these investments are rewarding after a long time beyond political mandates. Long term adaptation planning operates with periods of approximately 50-100 years and represent a difficult challenge owing to the uncertainty associated with future climate, as well as because of the socioeconomic evolution of complex urban environments (George et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…To successfully tackle the impacts of CC in urban systems, climate projection data with a suitable spatial scale are vital. For example, while water management studies require an inter-regional approach, UHIs or stormwater related challenges are by their nature local (George et al, 2016). Local stakeholders often have very fine scale information regarding vulnerabilities to changing climate, while at the same time local decision makers have a key responsibility to deliver spacespecific adaptation measures to address the environmental, social and economic implications of CC (Carter et al, 2015).…”

Section: Linking Climate Data and Urban Planningmentioning

confidence: 99%

“…Currently, we are witnessing two parallel tendencies. The first one is a push towards localised city / district level planning (George et al, 2016), and a second one is a need for long-term adaptation planning (Davoudi et al, 2012). The lack of information that can be used as a basis for impact studies addressing these two requirements represents a certain gap.…”

Section: Linking Climate Data and Urban Planningmentioning

confidence: 99%

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Climate change and impacts in the urban systems

Šmíd¹

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Urban systems are not only major drivers of climate change, but also impact hotspots. The processes of global warming and urban population growth make our urban agglomerations vulnerable to chain reactions triggered by climate related hazards. Hence, the reliable and costeffective assessment of future climate impact is of high importance. Two major approaches emerge from the literature: i) detailed spatially explicit assessments, and ii) more holistic approaches consistently assessing multiple cities. In this multidisciplinary thesis both approaches were addressed. Firstly, we discuss the underlying reasons and main challenges of the applicability of downscaling procedures of climate projections in the process of urban planning. While the climate community has invested significant effort to provide downscaling techniques yielding localised information on future climate extreme events, these methods are not widely exploited in the process of urban planning. The first part of this research attempts to help bridge the gap between the communities of urban planners and climatologists. First, we summarize the rationale for such cooperation, supporting the argument that the spatial scale represents an important linkage between urban and climate science in the process of designing an urban space. Secondly, we introduce the main families of downscaling techniques and their application on climate projections, also providing the references to profound studies in the field. Thirdly, special attention is given to previous works focused on the utilization of downscaled ensembles of climate simulations in urban agglomerations. Finally, we identify three major challenges of the wider utilization of climate projections and downscaling techniques, namely: (i) the scale mismatch between data needs and data availability, (ii) the terminology, and (iii) the IT bottleneck. The practical implications of these issues are discussed in the context of urban studies. The second part of this work is devoted to the assessment of impacts of extreme temperatures across the European capital cities. In warming Europe, we are witnessing a growth in urban population with aging trend, which will make the society more vulnerable to extreme heat waves. In the period 1950-2015 the occurrence of extreme heat waves increased across European capitals. As an example, Moscow was hit by the strongest heat wave of the present era, killing more than ten thousand people. Here we focus on larger metropolitan areas of European capitals. By using an ensemble of eight EURO-CORDEX models under the RCP8.5 scenario, we calculate a suite of temperature based climate indices. We introduce a ranking procedure based on ensemble predictions using the mean of metropolitan grid cells for each capital, and socioeconomic variables as a proxy to quantify the future impact. Results show that all the investigated European metropolitan areas will be more vulnerable to extreme heat in the coming decades. Based on the impact ranking, the results reveal that in near, but mainly in di...

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Development of high‐resolution monthly precipitation datasets for geomorphological applications in the Tianshan Mountains from 2000 to 2050

Fan,

Xu,

Chen

et al. 2024

Earth Surf Processes Landf

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Precipitation has a fundamental influence on landscape evolution and erosion rates. Accurate predictions of precipitation changes are crucial for landscape evolution modelling and erosion modelling. The Tianshan Mountains are known as the ‘Water Tower in Central Asia’, where precipitation is the main water supply for the surrounding lakes and rivers and also supports the population and economic development in the plain areas. This study developed a global climate model (GCM) and digital elevation model (DEM)‐based downscaling method and simulated high‐resolution monthly precipitation in Tianshan from 2000 to 2050, with a resolution of 90 m. The datasets developed from 30 surface observation stations had a high degree of precision and simulated the precipitation processes well in the historical period. The simulation results indicated that precipitation will increase at a rate of 11 mm/decade under the SSP245 scenario and 5 mm/decade under the RCP4.5 scenario from 2022 to 2050. Spatially, precipitation was projected to increase in the central and eastern regions and decrease in the western region. In the Kaidu, Urumqi and Manas river basins in the central Tianshan Mountains, precipitation was projected to increase at a rate of 20 mm/decade. The datasets developed here have important theoretical and practical implications for water resource management, and sustainable development in Central Asia.

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Statistical Downscaling Using Local Polynomial Regression for Rainfall Predictions – A Case Study

Cited by 18 publications

References 13 publications

Statistical downscaling of global climate model outputs to monthly precipitation via extreme learning machine: A case study

Statistical downscaling of global climate model outputs to monthly precipitation via extreme learning machine: A case study

Climate change and impacts in the urban systems

Development of high‐resolution monthly precipitation datasets for geomorphological applications in the Tianshan Mountains from 2000 to 2050

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