An Integrated Statistical Method to Generate Potential Future Climate Scenarios to Analyse Droughts

Collados‐Lara, Antonio‐Juan; Pulido-Velázquez, David; Pardo‐Igúzquiza, Eulogio

doi:10.3390/w10091224

Cited by 35 publications

(41 citation statements)

References 52 publications

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“…The RCP 8.5 appeared from little effort to lessen emissions and indicate a failure to limit warming by 2100 [47]. The output of physical models could be treated using statistical techniques to generate future climate scenarios [48]. During the simulation of current climate, the global and regional climate models showed many types of biases [49].…”

Section: Introductionmentioning

confidence: 99%

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

Hayat

Akbar

Tahir

et al. 2019

Water

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Upper Indus Basin (UIB) supplies more than 70% flow to the downstream agricultural areas during summer due to the melting of snow and glacial ice. The estimation of the stream flow under future climatic projections is a pre-requisite to manage water resources properly. This study focused on the simulation of snowmelt-runoff using Snowmelt-Runoff Model (SRM) under the current and future Representative Concentration Pathways (RCP 2.6, 4.5 and 8.5) climate scenarios in the two main tributaries of the UIB namely the Astore and the Hunza River basins. Remote sensing data from Advanced Land Observation Satellite (ALOS) and Moderate Resolution Imaging Spectroradiometer (MODIS) along with in-situ hydro-climatic data was used as input to the SRM. Basin-wide and zone-wise approaches were used in the SRM. For the zone-wise approach, basin areas were sliced into five elevation zones and the mean temperature for the zones with no weather stations was estimated using a lapse rate value of −0.48 °C to −0.76 °C/100 m in both studied basins. Zonal snow cover was estimated for each zone by reclassifying the MODIS snow maps according to the zonal boundaries. SRM was calibrated over 2000–2001 and validated over the 2002–2004 data period. The results implied that the SRM simulated the river flow efficiently with Nash-Sutcliffe model efficiency coefficient of 0.90 (0.86) and 0.86 (0.86) for the basin-wide (zone-wise) approach in the Astore and Hunza River Basins, respectively, over the entire simulation period. Mean annual discharge was projected to increase by 11–58% and 14–90% in the Astore and Hunza River Basins, respectively, under all the RCP mid- and late-21st-century scenarios. Mean summer discharge was projected to increase between 10–60% under all the RCP scenarios of mid- and late-21st century in the Astore and Hunza basins. This study suggests that the water resources of Pakistan should be managed properly to lessen the damage to human lives, agriculture, and economy posed by expected future floods as indicated by the climatic projections.

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Section: Introductionmentioning

confidence: 99%

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

Hayat

Akbar

Tahir

et al. 2019

Water

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“…First, the data from SWICCA were selected because there was made a pre-processing of filtering the models that best fit in the European area. Despite this, in the literature it is stated that for the Mediterranean area it is very difficult to find reliable data or with enough skill to work with them with confidence (Barranco et al, 2018;Collados-Lara et al, 2018), especially if these are hydrological data (Suárez-Almiñana et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…In this sense, one of the most reputed methods in literature is the quantile mapping, maybe because it is relatively simple to apply with good results, both for meteorological and flow variables (Grillakis et al, 2017;Manne et al, 2017;Teutschbein and Seibert, 2012). It is based on the distribution function which tries to keep the mean and standard deviation of the reference series (Collados-Lara et al, 2018). In this case, it is a feasible approach since the observations are of similar spatial resolution as the EMs data (Maraun, 2013).…”

Section: Bias Correctionmentioning

confidence: 99%

“…In this sense, there are some issues that have to be addressed, as the correct way to handle the projections, taking into account their inherent uncertainty that normally determines its use in practice (Lemos and Rood, 2010). Some authors recommend working with the ensemble, since increasing the number of ensemble members reduce the sampling uncertainty (Collados-Lara et al, 2018;Thompson et al, 2017). On the other hand, working only with one ensemble member more fitted to the historical data in the reference period is not advisable, since the results can lead to erroneous conclusions due to the extreme values (Collados-Lara et al, 2018).…”

mentioning

confidence: 99%

“…Some authors recommend working with the ensemble, since increasing the number of ensemble members reduce the sampling uncertainty (Collados-Lara et al, 2018;Thompson et al, 2017). On the other hand, working only with one ensemble member more fitted to the historical data in the reference period is not advisable, since the results can lead to erroneous conclusions due to the extreme values (Collados-Lara et al, 2018). Another option is differentiating between the Representative Concentration Pathways (RCP) implied in the study (Barranco et al, 2018;Marcos-Garcia et al, 2017) in order to consider the impacts related to the emission scenarios.…”

mentioning

confidence: 99%

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Methodology based on modelling processes and the characterisation of natural flows for risk assessment and water management under the influence of climate change

Suárez-Almiñana¹,

Solera²,

Madrigal³

et al. 2019

Preprint

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Abstract. Climate change and its possible effects on water resources has become an increasingly near threat. Therefore, the study of these impacts in highly regulated systems and those suffering extreme events is essential to deal with them effectively. This paper responds to the need of an effective methodology that integrates the climate change projections into water planning and management to guide complex basin decision-making through drought risk and management assessments. In this study is presented an adaptive method based on a model chain and correction processes, where the main outcomes are the impacts on future natural inflows, a drought risk indicator and the simulation of the future water storage of the water resources system (WRS) under consideration. The proposed methodology was applied in the Júcar River Basin (JRB) due to its complexity and the multiannual drought events it goes through. The results shown a decreasing tendency of future inflows to the basin, and the drought risk indicator shows a high probability (≈ 80 %) of being under 50 % of total capacity of the WRS in the near future, but the uncertainty is considerable from the middle century onwards, indicating that an improvement in the skill of climate projections is required. Thus, this paper also highlights the difficulties of developing this type of methods, since the conclusions on climate change impact assessment depend on partial decisions taken during the methodological processes. However, the main results call for action in the JRB and the tool developed can be considered as a feasible option to facilitate and support decision-making in future water planning and management.

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Application of deep learning algorithms to correct bias in CMIP6 simulations of surface air temperature over the Indian monsoon core region

Sabarinath,

Naga Rajesh,

Gunthe

et al. 2023

Intl Journal of Climatology

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Indian subcontinent witnessed a rise in surface air temperature (SAT) in recent decades, during the summer months of March, April and May. The monsoon core region (MCR) of India experiences a hot and humid climate, with temperatures typically highest in May and June before the onset of the monsoon. Global climate model (GCM) simulations of SAT are very much essential to understand the future climate of Indian MCR. Biases in GCMs simulations are due to insufficient knowledge of parameterizations and various assumptions that are made to simulate the complex interactions between land, ocean and atmosphere. The objective of this study is to correct the bias in the Coupled Model Intercomparison Project Phase 6 (CMIP6)–GCM simulations of SAT during March, April and May months over MCR for the historical period 1985–2014 and shared socio‐economic pathways (SSPs) SSP2‐4.5 and SSP5‐8.5 for the period 2015–2100. SAT dataset of fifth‐generation reanalysis (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) is used as reference dataset to perform bias correction for the historical period. Preliminary investigation of both SAT datasets has shown that there exists considerable warm bias (1.47°C) over the MCR. Bias correction is performed using a one‐dimensional convolutional neural network (CNN‐1D) and a convolutional long short‐term memory network (CNN‐LSTM) deep learning algorithm. The performance of these algorithms is evaluated with the statistical metrics such as root‐mean‐square error (RMSE), normalized root‐mean‐square error, Nash–Sutcliffe efficiency, mean absolute error, percent bias, correlation coefficient and dynamic time warping. RMSE and percent bias were decreased to 0.35°C and 0.8% with CNN‐LSTM algorithm. The CNN‐LSTM algorithm also preserves the year‐to‐year variability of SAT. Hence, CNN‐LSTM algorithm is found to be suitable for the bias correction of GCM simulations of SAT with encouraging results.

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An Integrated Statistical Method to Generate Potential Future Climate Scenarios to Analyse Droughts

Cited by 35 publications

References 52 publications

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

Methodology based on modelling processes and the characterisation of natural flows for risk assessment and water management under the influence of climate change

Application of deep learning algorithms to correct bias in CMIP6 simulations of surface air temperature over the Indian monsoon core region

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