Rodrigo Manzanas

Rodrigo Manzanas
Universidad de Cantabria | UNICAN · Department of Applied Mathematics and Computation Sciences

PhD

About

63
Publications
21,087
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1,124
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Introduction
I am an Assistant Professor in the Department of Applied Mathematics and Computer Science at the University of Cantabria, where my research focuses on the analysis of climate variability, the development of climate services, the evaluation of global and regional dynamical climate models and the statistical downscaling of seasonal forecasts and climate change projections.
Additional affiliations
October 2019 - present
University of Cantabria
Position
  • Professor (Assistant)
March 2019 - October 2019
Intergovernmental Panel on Climate Change (IPCC) Working Group I
Position
  • Science Officer
February 2019 - October 2019
University of Cantabria
Position
  • Professor
Education
October 2010 - October 2012
University of Cantabria
Field of study
January 2009 - September 2016
University of Cantabria
Field of study
October 2002 - February 2008
University of Salamanca
Field of study

Publications

Publications (63)
Preprint
Full-text available
The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has adopted the FAIR Guiding Principles. The Atlas chapter of Working Group I (WGI) is presented as a test case. Here, we describe the application of these principles in the Atlas, the challenges faced during its implementation, and those that remain for the f...
Article
Full-text available
Plain Language Summary Statistical downscaling methods aim to improve the limited spatial resolution of current climate models by linking a set of key large‐scale predictor variables (e.g., geopotential, winds, etc.) to the predictand of interest (e.g., precipitation). Recently, the Experiment 1 of the COST action VALUE carried out the most compreh...
Preprint
Deep Learning (DL) has recently emerged as an innovative tool to downscale climate variables from large-scale atmospheric fields under the perfect prognosis (PP) approach. Different Convolutional Neural Networks (CNN) have been applied under present-day conditions with promising results, but little is known about their suitability for extrapolating...
Article
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Historical and future projected changes in climatic patterns over the largest irrigated basin in the world, the Indus River Basin (IRB), threaten agricultural production and food security in Pakistan, in particular for vulnerable farming communities. To build a more detailed understanding of the impacts of climate change on agriculture s in the IRB...
Article
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Central Malawi has intensely been subjected to different climate-related shocks such as floods, dry spells, and droughts, resulting in decreases in crop yields. Due to their recurrence arising from the effects of climate change, drought characterization, monitoring, and prediction are crucial in guiding agriculture-water users and planners to prepa...
Article
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In response to the COVID-19 pandemic, governments declared severe restrictions throughout 2020, presenting an unprecedented scenario of reduced anthropogenic emissions of air pollutants derived mainly from traffic sources. To analyze the effect of these restrictions derived from COVID-19 pandemic on air quality levels, relative changes in NO, NO2,...
Article
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Internal variability, multiple emission scenarios, and different model responses to anthropogenic forcing are ultimately behind a wide range of uncertainties that arise in climate change projections. Model weighting approaches are generally used to reduce the uncertainty related to the choice of the climate model. This study compares three multi-mo...
Article
Extreme precipitation occurring on consecutive days may substantially increase the risk of related impacts, but changes in such events have not been studied at a global scale. Here we use a unique global dataset based on in situ observations and multi-model historical and future simulations to analyse the changes in the frequency of extreme precipi...
Article
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In a recent paper, Baño-Medina et al. (Configuration and Intercomparison of deep learning neural models for statistical downscaling. preprint, 2019) assessed the suitability of deep convolutional neural networks (CNNs) for downscaling of temperature and precipitation over Europe using large-scale ‘perfect’ reanalysis predictors. They compared the r...
Article
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Climate change has significantly impacted the hydrological cycle in the rivers of Pakistan and the streamflow regimes of different rivers have witnessed noticeable flow alteration. These changes in streamflow affect aquatic biodiversity (e.g., freshwater fish species) and the productivity of freshwater ecosystems. This study, therefore, evaluates t...
Technical Report
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The book provides the outcome of a collaborative work FAO, the Department of Agriculture of Sri Lanka, the Department of Meteorology of Sri Lanka, the University of Peradeniya in Sri Lanka, the University of Cantabria in Spain and the University of Milan in Italy. Future climate change impacts on these crops were evaluated using the Modelling Sys...
Article
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Pakistan is among the most vulnerable regions to climate change impacts, in particular the agricultural areas found in the worlds’ largest contiguous irrigation system, the Indus River Basin (IRB). This study assesses the impacts of two climate change scenarios (Representative Concentration Pathways-RCPs 4.5 and 8.5) on soil evaporation and transpi...
Article
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The global hydrological cycle is vulnerable to changing climatic conditions, especially in developing regions, which lack abundant resources and management of freshwater resources. This study evaluates the impacts of climate change on the hydrological regime of the Chirah and Dhoke Pathan sub catchments of the Soan River Basin (SRB), in Pakistan, b...
Article
Climate change affects natural systems, leading to increased acceleration of global water cycle and substantial impacts on the productivity of tropical rivers and the several ecosystem functions they provide. However, the anticipated impacts of climate change in terms of frequency and intensity of extreme events (e.g., droughts and floods) on hydro...
Article
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Precipitation is of primary importance in hydrological modeling and streamflow prediction. However, lack of gauge stations for long-term precipitation data, particularly in the data-scarce Chitral River Basin (CRB) of Pakistan and other parts in the developing world, is a hindrance to understand surface water hydrology. Therefore, this study aims t...
Article
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Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advan...
Article
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Statistical downscaling (SD) and bias adjustment (BA) methods are routinely used to produce regional to local climate change projections from coarse global model outputs. The suitability of these techniques depends on the particular application of interest and, especially , on the required spatial resolution. Whereas SD is appropriate for local (e....
Article
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Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to proce...
Preprint
Full-text available
Abstract. Several sets of reference regions have been proposed in the literature for the regional synthesis of observed and model-projected climate change information. A popular example is the set of reference regions introduced in the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX) bas...
Article
Full-text available
The increasing demand for high-resolution climate information has attracted growing attention to statistical downscaling (SDS) methods, due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness for purpose for m...
Article
Full-text available
Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatiotemporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation...
Article
Full-text available
The present paper is a follow-on of the work presented in Manzanas et al. (Clim Dyn 53(3–4):1287–1305, 2019) which provides a comprehensive intercomparison of alternatives for the post-processing (statistical adjustment, calibration and downscaling) of seasonal forecasts for a particularly interesting region, Southeast Asia. To answer the questions...
Article
Full-text available
Despite its systematic presence in state‐of‐the‐art seasonal forecasts, the model drift (leadtime‐dependent bias) has been seldom studied to‐date. To fill this gap, this work analyzes its spatio‐temporal distribution, and its sensitivity to the ensemble size in temperature and precipitation forecasts. Our results indicate that model continues to dr...
Technical Report
Full-text available
This document summarizes the main achievements of the project AMICAF-Paraguay, relevant for policymakers, and formulates specific policy recommendations in the main fields covered by the project: adaptation to climate change in agriculture, water resources and economy, as well as data collection, national programs and research.
Article
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Plain Language Summary The Coordinated Regional Climate Downscaling Experiment (CORDEX) provides spatially detailed climate change projections for different regions across the world. These projections are obtained through numerical models that solve the governing equations of the atmosphere over spatial domains, which typically cover continental ar...
Preprint
Full-text available
Abstract. Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatio-temporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple...
Conference Paper
Full-text available
To answer the fundamental question of "adapt to what" within the context of climate-smart agriculture investments, staff and partners of the Food and Agriculture Organization of the United Nations (FAO) have developed and applied a set of 69 agronomic weather indices to identify trends in the frequency and intensity of intra-seasonal weather events...
Preprint
Full-text available
The increasing demand for high-resolution climate information has attracted a growing attention for statistical down-scaling methods (SD), due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness-for-purpose fo...
Article
Full-text available
This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA) -e.g. quantile mapping- to more sophisticated ensemble recalibration (RC) methods -e.g. non-homogeneous Gaussian regression,- which build on the temporal correspondence between the climate...
Article
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Climate predictions, from three weeks to a decade into the future, can provide invaluable information for climate-sensitive socioeconomic sectors, such as renewable energy, agriculture, or insurance. However, communicating and interpreting these predictions is not straightforward. Barriers hindering user uptake include a terminology gap between cli...
Article
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Having an effective way of dealing with data provenance is a necessary condition to ensure reproducibility, helping to build trust and credibility in research outcomes and the data products delivered. METACLIP (METAdata for CLImate Products) is a language-independent framework envisaged to tackle the problem of climate product provenance descriptio...
Article
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Extreme precipitation often persists for multiple days with variable duration but has usually been examined at fixed duration. Here we show that considering extreme persistent precipitation by complete event with variable duration, rather than a fixed temporal period, is a necessary metric to account for the complexity of changing precipitation. Ob...
Article
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This work assesses the suitability of a first simple attempt for process-conditioned bias correction in the context of seasonal forecasting. To do this, we focus on the northwestern part of Peru and bias correct one- and four-month lead seasonal predictions of boreal winter (DJF) precipitation from ECMWF System4 forecasting system for the period 19...
Article
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Climate-driven sectoral applications commonly require different types of climate data (e.g. observations, reanalysis, climate change projections) from different providers. Data access, harmonization and post-processing (e.g. bias correction) are time-consuming error-prone tasks requiring different specialized software tools at each stage of the dat...
Conference Paper
Full-text available
Climate driven sectoral applications in a variety of domains (such as hydrology, agriculture, energy, or health) typically require elaborated data processing workflows involving multiple data access, collocation, harmonization and postprocessing (e.g. bias correction) steps. This is a time-consuming and error-prone task which, in many cases, is perf...
Presentation
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An important limitation of bias correction (BC) methods is that they can introduce arbitrary temporal changes which can deteriorate the interannual variability of the raw predictions. To partially alleviate these problems, Maraun et al. (2017) advocated the development of process-informed BC methods, combining the statistical modeling with the know...
Article
Full-text available
VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process-based, etc.). Here we describe the participating methods and first results from the first experiment, using "perfect" reanalysis (and reanalysis-driven r...
Article
Full-text available
Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs o...
Article
Interest in seasonal forecasting is growing fast in many environmental and socio-economic sectors due to the huge potential of these predictions to assist in decision making processes. The practical application of seasonal forecasts, however, is still hampered to some extent by the lack of tools for an effective communication of uncertainty to non-...
Article
Full-text available
Within the FP7 EUPORIAS project we have assessed the utility of dynamical and statistical downscaling to provide seasonal forecast for impact modelling in eastern Africa. An ensemble of seasonal hindcasts was generated by the global climate model (GCM) EC-EARTH and then downscaled by four regional climate models and by two statistical methods over...
Article
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This work tests the suitability of statistical downscaling (SD) approaches to generate local seasonal forecasts of daily maximum temperature and precipitation for a set of selected stations in Senegal for the July–August–September season during the period 1979–2000. Two-month lead raw daily maximum temperature and precipitation from the five models...
Article
Full-text available
Seasonal climate forecasts (SCFs) have significant potential to support shorter-term agricultural decisions and longer-term climate adaptation plans, but uptake in Europe has to date been low. Under the European Union funded project, European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales (EUPORIAS) we have developed t...
Article
Full-text available
Sectorial applications of seasonal forecasting require data for a reduced number of variables from different datasets, mainly (gridded) observations, reanalysis, and predictions from state-of-the-art seasonal forecast systems (such as NCEP/CFSv2, ECMWF/System4 or UKMO/GloSea5). Whilst this information can be obtained directly from the data provider...
Article
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This work describes the results of a comprehensive intercomparison experiment of dynamical and statistical downscaling methods performed in the framework of the SPECS (http://www.specs-fp7.eu) and EUPORIAS (http://www.euporias.eu) projects for seasonal forecasting over Europe, a region which exhibits low-to-moderate seasonal forecast skill. We cons...
Article
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This is the second in a pair of papers in which the performance of Statistical Downscaling Methods (SDMs) is critically re-assessed with respect to their robust applicability in climate change studies. Whereas Part I focused on temperatures (Gutiérrez et al. 2013), the present manuscript deals with precipitation and considers an ensemble of twelve...
Thesis
Full-text available
Seasonal climate predictions have a great number of applications and can help decision-making in many important socioeconomic sectors. However, the low spatial resolution (around hundreds of km) of the numerical models which are currently used for seasonal forecasting is insufficient for most of impact studies. Therefore, some kind of post-process...
Article
Full-text available
This work shows that local-scale climate projections obtained by means of statistical down-scaling are sensitive to the choice of reanalysis used for calibration. To this aim, a Generalized Linear Model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested separately with two distinct...
Poster
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The systematic drift (bias dependence on the forecast lead-time) present in state-of-the-art coupled general circulation models is an inherent feature of global seasonal forecasts. Usually, anomalies (relative to the model climatology) obtained from an ensemble of hindcasts are used to correct this drift. However, this procedure has not been system...
Article
Full-text available
Inter-annual variability and trends of annual/seasonal precipitation totals in Ghana are analyzed considering different gridded observational (gauge- and/or satellite-based) and reanalysis products. A quality-controlled dataset formed by fourteen gauges from the Ghana Meteorological Agency (GMet) is used as reference for the period 1961-2010. First...
Article
Full-text available
The skill of seasonal precipitation forecasts is assessed worldwide —grid point by grid point— for the forty-year period 1961-2000, considering the ENSEMBLES multi-model hindcast and applying a tercile-based probabilistic approach in terms of the ROC Skill Score (ROCSS). Although predictability varies with region, season and lead-time, results indi...
Technical Report
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This report briefly describes how SENAMHI (http://www.senamhi.gob.pe/) obtained local projections of precipitation/maximum/minimum temperature (daily data up to 2065) for 265/105/102 selected stations over Peru, needed for the completion of tasks within the Component I of the AMICAF project. In particular, an analog- (regression-) based technique w...
Article
Full-text available
The performance of statistical downscaling (SD) techniques is critically reassessed with respect to their robust applicability in climate change studies. To this end, in addition to standard accuracy measures and distributional similarity scores, the authors estimate the robustness of the methods under warming climate conditions working with anomal...