Articles | Volume 17, issue 22
https://doi.org/10.5194/gmd-17-8115-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8115-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
18 Nov 2024
Methods for assessment of models |
| 18 Nov 2024
| 18 Nov 2024
Generalised drought index: a novel multi-scale daily approach for drought assessment
João António Martins Careto
CORRESPONDING AUTHOR
IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Rita Margarida Cardoso
IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Ana Russo
IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
CEF – Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Lisbon, Portugal
Daniela Catarina André Lima
IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Pedro Miguel Matos Soares
IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Related authors
No articles found.
Jan Wohland, Peter Hoffmann, Daniela C. A. Lima, Marcus Breil, Olivier Asselin, and Diana Rechid
Earth Syst. Dynam., 15, 1385–1400, https://doi.org/10.5194/esd-15-1385-2024, https://doi.org/10.5194/esd-15-1385-2024, 2024
Short summary
Short summary
We evaluate how winds change when humans grow or cut down forests. Our analysis draws from climate model simulations with extreme scenarios where Europe is either fully forested or covered with grass. We find that the effect of land use change on wind energy is very important: wind energy potentials are twice as high above grass as compared to forest in some locations. Our results imply that wind profile changes should be better incorporated in climate change assessments for wind energy.
João P. A. Martins, Sara Caetano, Carlos Pereira, Emanuel Dutra, and Rita M. Cardoso
Nat. Hazards Earth Syst. Sci., 24, 1501–1520, https://doi.org/10.5194/nhess-24-1501-2024, https://doi.org/10.5194/nhess-24-1501-2024, 2024
Short summary
Short summary
Over Europe, 2022 was truly exceptional in terms of extreme heat conditions, both in terms of temperature anomalies and their temporal and spatial extent. The satellite all-sky land surface temperature (LST) is used to provide a climatological context to extreme heat events. Where drought conditions prevail, LST anomalies are higher than 2 m air temperature anomalies. ERA5-Land does not represent this effect correctly due to a misrepresentation of vegetation anomalies.
Amélie Simon, Coline Poppeschi, Sandra Plecha, Guillaume Charria, and Ana Russo
Ocean Sci., 19, 1339–1355, https://doi.org/10.5194/os-19-1339-2023, https://doi.org/10.5194/os-19-1339-2023, 2023
Short summary
Short summary
In the coastal northeastern Atlantic and for three subregions (the English Channel, Bay of Brest and Bay of Biscay) over the period 1982–2022, marine heatwaves are more frequent and longer and extend over larger areas, while the opposite is seen for marine cold spells. This result is obtained with both in situ and satellite datasets, although the satellite dataset underestimates the amplitude of these extremes.
Florian Knutzen, Paul Averbeck, Caterina Barrasso, Laurens M. Bouwer, Barry Gardiner, José M. Grünzweig, Sabine Hänel, Karsten Haustein, Marius Rohde Johannessen, Stefan Kollet, Joni-Pekka Pietikaeinen, Karolina Pietras-Couffignal, Joaquim G. Pinto, Diana Rechid, Efi Rousi, Ana Russo, Laura Suarez-Gutierrez, Julian Wendler, Elena Xoplaki, and Daniel Gliksman
EGUsphere, https://doi.org/10.5194/egusphere-2023-1463, https://doi.org/10.5194/egusphere-2023-1463, 2023
Short summary
Short summary
With a team of 20 authors from different countries, we tried to compile the impacts of drought and heat on European forests in the period 2018–2022. This is a research approach that transcends subject and country borders.
Margarida L. R. Liberato, Irene Montero, Célia Gouveia, Ana Russo, Alexandre M. Ramos, and Ricardo M. Trigo
Earth Syst. Dynam., 12, 197–210, https://doi.org/10.5194/esd-12-197-2021, https://doi.org/10.5194/esd-12-197-2021, 2021
Short summary
Short summary
Extensive, long-standing dry and wet episodes are frequent climatic extreme events (EEs) in the Iberian Peninsula (IP). A method for ranking regional extremes of persistent, widespread drought and wet events is presented, using different SPEI timescales. Results show that there is no region more prone to EE occurrences in the IP, the most extreme extensive agricultural droughts evolve into hydrological and more persistent extreme droughts, and widespread wet and dry EEs are anti-correlated.
Andreia Filipa Silva Ribeiro, Ana Russo, Célia Marina Gouveia, Patrícia Páscoa, and Jakob Zscheischler
Biogeosciences, 17, 4815–4830, https://doi.org/10.5194/bg-17-4815-2020, https://doi.org/10.5194/bg-17-4815-2020, 2020
Short summary
Short summary
This study investigates the impacts of compound dry and hot extremes on crop yields, namely wheat and barley, over two regions in Spain dominated by rainfed agriculture. We provide estimates of the conditional probability of crop loss under compound dry and hot conditions, which could be an important tool for responsible authorities to mitigate the impacts magnified by the interactions between the different hazards.
Andreia F. S. Ribeiro, Ana Russo, Célia M. Gouveia, Patrícia Páscoa, and Carlos A. L. Pires
Nat. Hazards Earth Syst. Sci., 19, 2795–2809, https://doi.org/10.5194/nhess-19-2795-2019, https://doi.org/10.5194/nhess-19-2795-2019, 2019
Short summary
Short summary
This work investigates the dependence between drought hazard and yield anomalies of rainfed cropping systems in the Iberian Peninsula using the copula theory. The applied methodology allows us to estimate the likelihood of wheat and barley loss under drought conditions, and a dependence among extreme values is suggested. From the decision-making point of view this study aims to contribute to the mitigation of drought-related crop failure.
Catarina Alonso, Celia M. Gouveia, Ana Russo, and Patrícia Páscoa
Nat. Hazards Earth Syst. Sci., 19, 2727–2743, https://doi.org/10.5194/nhess-19-2727-2019, https://doi.org/10.5194/nhess-19-2727-2019, 2019
Short summary
Short summary
A vulnerability assessment method is proposed to identify the most vulnerable regions over Portugal. Two methods were compared, namely a subjective categorical method and an automatic method, based on drought indicators, vegetation indices and soil variables. Both methods present similar results, and both identify Minho (Alentejo) as having low (extreme) vulnerability. The automatic method has advantages, as it is fully statistical and presents results without prior knowledge of the region.
Patrícia Páscoa, Célia M. Gouveia, Ana C. Russo, Roxana Bojariu, Sergio M. Vicente-Serrano, and Ricardo M. Trigo
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-264, https://doi.org/10.5194/hess-2018-264, 2018
Revised manuscript not accepted
Related subject area
Hydrology
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Prediction of hysteretic matric potential dynamics using artificial intelligence: application of autoencoder neural networks
Regionalization in global hydrological models and its impact on runoff simulations: a case study using WaterGAP3 (v 1.0.0)
STORM v.2: A simple, stochastic rainfall model for exploring the impacts of climate and climate change at and near the land surface in gauged watersheds
Fluvial flood inundation and socio-economic impact model based on open data
RoGeR v3.0.5 – a process-based hydrological toolbox model in Python
Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments
An open-source refactoring of the Canadian Small Lakes Model for estimates of evaporation from medium-sized reservoirs
EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions
Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software
HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications
Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community
HydroFATE (v1): a high-resolution contaminant fate model for the global river system
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications
GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers
mesas.py v1.0: a flexible Python package for modeling solute transport and transit times using StorAge Selection functions
rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment
GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model
Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)
The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Customized deep learning for precipitation bias correction and downscaling
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024, https://doi.org/10.5194/gmd-17-7751-2024, 2024
Short summary
Short summary
We develop an operational forecast system, Coastlines-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model has relatively low computational requirements, and results compare well with near-real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and wave predictions can improve in accuracy.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
Short summary
Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Geosci. Model Dev., 17, 7083–7103, https://doi.org/10.5194/gmd-17-7083-2024, https://doi.org/10.5194/gmd-17-7083-2024, 2024
Short summary
Short summary
Geoscientists commonly use various potential evapotranpiration (PET) formulas for environmental studies, which can be prone to errors and sensitive to climate change. PyEt, a tested and open-source Python package, simplifies the application of 20 PET methods for both time series and gridded data, ensuring accurate and consistent PET estimations suitable for a wide range of environmental applications.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024, https://doi.org/10.5194/gmd-17-6949-2024, 2024
Short summary
Short summary
The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of the dynamic SWP.
Jenny Kupzig, Nina Kupzig, and Martina Flörke
Geosci. Model Dev., 17, 6819–6846, https://doi.org/10.5194/gmd-17-6819-2024, https://doi.org/10.5194/gmd-17-6819-2024, 2024
Short summary
Short summary
Valid simulation results from global hydrological models (GHMs) are essential, e.g., to studying climate change impacts. Adapting GHMs to ungauged basins requires regionalization, enabling valid simulations. In this study, we highlight the impact of regionalization of GHMs on runoff simulations using an ensemble of regionalization methods for WaterGAP3. We have found that regionalization leads to temporally and spatially varying uncertainty, potentially reaching up to inter-model differences.
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
Short summary
Short summary
STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
Short summary
Short summary
River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
Short summary
Short summary
The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
Short summary
Short summary
This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
Short summary
Short summary
This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
Short summary
Short summary
The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
Short summary
Short summary
Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
Short summary
Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
Short summary
Short summary
We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
Short summary
Short summary
The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
Short summary
Short summary
Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
Short summary
Short summary
We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
Short summary
Short summary
Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
Short summary
Short summary
We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
Short summary
Short summary
Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
Short summary
Short summary
Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
Short summary
Short summary
This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
Short summary
Short summary
We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Hannes Müller Schmied, Tim Trautmann, Sebastian Ackermann, Denise Cáceres, Martina Flörke, Helena Gerdener, Ellen Kynast, Thedini Asali Peiris, Leonie Schiebener, Maike Schumacher, and Petra Döll
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-213, https://doi.org/10.5194/gmd-2023-213, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Assessing water availability and water use at the global scale is challenging but essential for a range of purposes. We describe the newest version of the global hydrological model WaterGAP which has been used for numerous water resources assessments since 1996. We show the effects of new model features and model evaluations against observed streamflow and water storage anomalies as well as water abstractions statistics. The publically available model output for several variants is described.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
Short summary
Short summary
We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
Short summary
Short summary
Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
Short summary
Short summary
Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
Short summary
Short summary
Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
Short summary
Short summary
NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
Short summary
Short summary
We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
Short summary
Short summary
Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
Short summary
Short summary
DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
Short summary
Short summary
Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
Short summary
Short summary
Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
Short summary
Short summary
Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
Short summary
Short summary
In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
Short summary
Short summary
We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
Short summary
Short summary
During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
Short summary
Short summary
It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
Short summary
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
Short summary
Short summary
This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
Short summary
Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
Short summary
Short summary
Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
Short summary
Short summary
Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
Short summary
Short summary
A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
Short summary
Short summary
Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
Short summary
Short summary
The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
Short summary
Short summary
A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
Short summary
Short summary
A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
Short summary
Short summary
We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Cited articles
Abramowitz, M. and Stegun, I. A. (Eds.): Handbook of mathematical functions with formulas, graphs, and mathematical tables, vol. 55, US Government printing office, https://books.google.pt/books?hl=pt-PT&lr=&id=ZboM5tOFWtsC&oi=fnd&pg=PR3&ots=dWhHSE67Vt&sig=mpXMahEU9bNMBD7sBTAWdMtA_3o&redir_esc=y#v=onepage&q&f=false (last access: 21 October 2024). 1968.
Ahmad, M. I., Sinclair, C. D., and Werritty, A.: Log-logistic flood frequency analysis, J. Hydrol., 98, 205–224, https://doi.org/10.1016/0022-1694(88)90015-7, 1988.
Akhtari, R., Morid, S., Mahdian, M. H., and Smakhtin, V.: Assessment of areal interpolation methods for spatial analysis of SPI and EDI drought indices, Int. J. Climatol., 29, 135–145, https://doi.org/10.1002/joc.1691, 2009.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: FAO Irrigation and drainage paper No 56 Rome: Food and Agriculture Organisation of the United Nations 56 156, http://www.climasouth.eu/sites/default/files/FAO\,%2056.pdf (last access: 29 July 2023), 1998.
Alley, W. M.: The Palmer drought severity index: limitations and assumptions, J. Appl. Meteorol. Clim., 23, 1100–1109, https://doi.org/10.1175/1520-0450(1984)023<1100:TPDSIL>2.0.CO;2, 1984.
Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi, M., Ahrens, B., Alias, A., Anders, I., Bastin, S., Belušić, D., Berthou, S., Brisson, E., Cardoso, R. M., Chan, S. C., Christensen, O. B., Fernández, J., Fita, L., Frisius, T., Gašparac, G., Giorgi, F., Goergen, K., Haugen, J, E., Hodnebrog, Ø., Kartsios, S., Katragkou, E., Kendon, E. J., Keuler, K., Lavin-Gullon, A., Lenderink, G., Leutwyler, D., Lorenz, T., Maraun, D., Mercogliano, P., Milovac, J., Panitz, H. J., Raffa, M, Remedio, A. R., Schär, C., Soares, P. M. M., Srnec, L., Steensen, B. MM., Stocchi, Tölle, Truhetz, Temprado, J. V., de Vries, H., Warrach.-Sagi, K., Wulfmeyer, V., and Zander, M. J. The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation, Clim. Dynam., 57, 275–302, https://doi.org/10.1007/S00382-021-05708-W, 2021.
Beguería, S., Vicente-Serrano, S. M., Reig, F., and Latorre, B.: Standardised precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring, Int. J. Climatol., 34, 3001–3023, https://doi.org/10.1002/joc.3887, 2014.
Cardoso, R. M., Soares, P. M., Lima, D. C., and Miranda, P. M.: Mean and extreme temperatures in a warming climate: EURO CORDEX and WRF regional climate high-resolution projections for Portugal, Clim. Dynam., 52, 129–157, https://doi.org/10.1007/s00382-018-4124-4, 2019.
Careto, J. A. M., Soares, P. M. M., Cardoso, R. M., Herrera, S., and Gutiérrez, J. M.: Added value of EURO-CORDEX high-resolution downscaling over the Iberian Peninsula revisited – Part 1: Precipitation, Geosci. Model Dev., 15, 2635–2652, https://doi.org/10.5194/gmd-15-2635-2022, 2022a.
Careto, J. A. M., Soares, P. M. M., Cardoso, R. M., Herrera, S., and Gutiérrez, J. M.: Added value of EURO-CORDEX high-resolution downscaling over the Iberian Peninsula revisited – Part 2: Max and min temperature, Geosci. Model Dev., 15, 2653–2671, https://doi.org/10.5194/gmd-15-2653-2022, 2022b.
Carvalho, D., Cardoso Pereira, S., and Rocha, A.: Future surface temperature changes for the Iberian Peninsula according to EURO-CORDEX climate projections, Clim. Dynam., 56, 123–138, https://doi.org/10.1007/s00382-020-05472-3, 2021.
Chen, M., Shi, W., Xie, P., Silva, V. B., Kousky, V. E., Wayne Higgins, R., and Janowiak, J. E.: Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res.-Atmos., 113, 1–13, https://doi.org/10.1029/2007JD009132, 2008.
Christian, J. I., Martin, E. R., Basara, J. B., Furtado, J. C., Otkin, J. A., Lowman, L. E., Hunt, E. D., Mishra, V., and Xiao, X.: Global projections of flash drought show increased risk in a warming climate, Commun. Earth Environ., 4, 165, https://doi.org/10.1038/s43247-023-00826-1, 2023.
Coppola, E., Sobolowski, S., Pichelli, E., Raffaele, F., Ahrens, B., Anders, I., Ban, N., Bastin, S., Belda, M., Belusic, D., Caldas-Alvarez, A., Cardoso, R. M., Davolio, S., Dobler, A., Fernadez, J., Fita, L., Fumiere, Q., Giorgi, G., Goergen, K., Güttler, I., Halenka, T., Heinzeller, D., Hodnebrog, Ø., Jacob, D., Kartsios, S., Katragkou, E., Kendon, E., Khodayar, S., Kunstmann, H., Knist, S., Lavín-Gullón, A., Lind, P., Lorenz, T., Maraun, D., Marelle, L., van Meijgaard, E., Milovac, J., Myhre, G., Panitz, H.-J., Piazza, M., Raffa, M., Raub, T, Rockel, B., Schär, C., Sieck., Soares, P. M. M., Somot, S., Srnec, L., Stocchi, P., Tölle, M. H., Truhertz, Vautard, R., de Vries, H., and Warrach-Sagi, K.: A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean, Clim. Dynam., 55, 3–34, https://doi.org/10.1007/s00382-018-4521-8, 2020.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J., and Jones, P. D.: An ensemble version of the E-OBS temperature and precipitation data sets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018.
Dai, A.: Drought under global warming: a review, Wires Clim. Change, 2, 45–65, https://doi.org/10.1002/wcc.81, 2011.
Droogers, P., and Allen, R. G.: Estimating reference evapotranspiration under inaccurate data conditions, Irrig. Drain., 16, 33–45, https://doi.org/10.1023/A:1015508322413, 2002.
Edwards, D. C.: Characteristics of 20th Century drought in the United States at multiple timescales, Air Force Inst of Tech Wright-Patterson Afb Oh, https://apps.dtic.mil/sti/pdfs/ADA325595.pdf (last access: 16 October 2023), 1997.
Farmer, W., Strzepek, K., Schlosser, C. A., Droogers, P., and Gao, X.: A method for calculating reference evapotranspiration on daily timescales, MIT Joint Program on the Science and Policy of Global Change, https://dspace.mit.edu/bitstream/handle/1721.1/61773/MITJPSPGC_Rpt195.pdf?sequence=_1&isAllowed=_y (last access: 16 October 2023), 2011.
FCiênciasID Associação para a Investigação e Desenvolvimento de Ciências (Fciências.ID): Land Use Changes and Atmosphere Feedbacks in a Changing Climate, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/2021.01280.CEECIND/CP1650/CT0006, 2022.
FCiênciasID Associação para a Investigação e Desenvolvimento de Ciências (Fciências.ID): COMPLEX – Compound Extreme Events and their relevance for human life and the environment under a climate change perspective, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/2022.01167.CEECIND/CP1722/CT0006, 2023.
FCiênciasID Associação para a Investigação e Desenvolvimento de Ciências: Análise de eventos secos, quentes e fogos combinados ou consecutivos e dos seus impactos na qualidade do ar ao nível Europeu numa perspetiva de alterações climáticas, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/2022.09185.PTDC, 2023–2024.
Fonseca, D., Carvalho, M. J., Marta-Almeida, M., Melo-Gonçalves, P., and Rocha, A.: Recent trends of extreme temperature indices for the Iberian Peninsula, Phys. Chem. Earth, 94, 66–76, https://doi.org/10.1016/j.pce.2015.12.005, 2016.
Fritsch, F. N. and Butland, J.: A method for constructing local monotone piecewise cubic interpolants, SIAM J. Sci. Stat. Comp., 5, 300–304, https://doi.org/10.1137/0905021, 1984.
García-Herrera, R., Hernández, E., Barriopedro, D., Paredes, D., Trigo, R. M., Trigo, I. F., and Mendes, M. A.: The outstanding 2004/05 drought in the Iberian Peninsula: associated atmospheric circulation, J. Hydrometeorol., 8, 483–498, https://doi.org/10.1175/JHM578.1, 2007.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., and Wargan, K.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Giorgi, F., Jones, C., and Asrar, G. R.: Addressing climate information needs at the regional level: the CORDEX framework, World Meteorological Organisation (WMO) Bulletin, 58, 175, http://wcrp.ipsl.jussieu.fr/cordex/documents/CORDEX_giorgi_WMO.pdf (last access: 29 July 2023), 2009.
Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016.
Guttman, N. B.: Comparing the palmer drought index and the Standardised precipitation index 1, JAWRA J. Am. Water Resour. Assoc., 34, 113–121, https://doi.org/10.1111/j.1752-1688.1998.tb05964.x, 1998.
Guttman, N. B.: Accepting the Standardised precipitation index: a calculation algorithm 1, JAWRA J. Am. Water Resour. Assoc., 35, 311–322, https://doi.org/10.1111/j.1752-1688.1999.tb03592.x, 1999.
Hayes, M. J., Svoboda, M. D., Wiihite, D. A., and Vanyarkho, O. V.: Monitoring the 1996 drought using the Standardised precipitation index, B. Am. Meteorol. Soc., 80, 429–438, https://doi.org/10.1175/1520-0477(1999)080<0429:MTDUTS>2.0.CO;2, 1999.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., and Simmons, A.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Herrera, S., Cardoso, R. M., Soares, P. M., Espírito-Santo, F., Viterbo, P., and Gutiérrez, J. M.: Iberia01: a new gridded dataset of daily precipitation and temperatures over Iberia, Earth Syst. Sci. Data, 11, 1947–1956, https://doi.org/10.5194/essd-11-1947-2019, 2019a.
Herrera, S., Cardoso, R. M., Soares, P. M. M., Espírio-Santo, F., Viterbo, P., and Gutiérrez, J. M.: Iberia01: Daily gridded (0.1° resolution) dataset of precipitation and temperatures over the Iberian Peninsula, DIGITAL.CSIC [data set], https://doi.org/10.20350/digitalCSIC/8641, 2019b.
Herrera, S., Soares, P. M., Cardoso, R. M., and Gutiérrez, J. M.: Evaluation of the EURO-CORDEX regional climate models over the Iberian Peninsula: Observational uncertainty analysis, J. Geophys. Res.-Atmos., 125, e2020JD032880, https://doi.org/10.1029/2020JD032880, 2020.
Hosking, J. R.: The theory of probability weighted moments, pp. 3–16, New York, USA, IBM Research Division, TJ Watson Research Center, https://dominoweb.draco.res.ibm.com/reports/RC12210.pdf (last access: 13 March 2023), 1986.
Hosking, J. R.: L-moments: analysis and estimation of distributions using linear combinations of order statistics, J. Roy. Stat. Soc. B, 52, 105–124, https://doi.org/10.1111/j.2517-6161.1990.tb01775.x, 1990.
Hu, Q. and Willson, G. D.: Effect of temperature anomalies on the Palmer drought severity index in the central United States, Int. J. Climatol., 20, 1899–1911, https://doi.org/10.1002/1097-0088(200012)20:15<1899::AID-JOC588>3.0.CO;2-M, 2000.
Hunt, E. D., Svoboda, M., Wardlow, B., Hubbard, K., Hayes, M., and Arkebauer, T.: Monitoring the effects of rapid onset of drought on non-irrigated maise with agronomic data and climate-based drought indices, Agr. Forest Meteorol., 191, 1–11, https://doi.org/10.1016/j.agrformet.2014.02.001, 2014.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colettem A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menit, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner N., Kotlarski, S., Kriiegsmann, A., Martin, E., van Mejigaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K, Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014.
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A., Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin, E. L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M. A., García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K., Gómez-Navarro, J. J., Alemán, J. J. G., Gutiérrez, C., Gutiérrez, J. M., Güttler, I., Haensler, A., Halenka, T., Jerez, S., Jiménez-Guerrero, P., Jones, R. G., Keuler, K., Kjellström, E., Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P., Montávez, J. P., Navarra, A., Nikulin, G., de Noblet-Ducoudré, N., Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli, E., Pietikäinen, J.-P., Prein, A. F., Preuschmann, S., Rechid, D., Rockel, B., Romera, R., Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Lund, Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change, 20, 1–20, https://doi.org/10.1007/s10113-020-01606-9, 2020.
Jain, S. K., Keshri, R., Goswami, A., and Sarkar, A.: Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India, Nat. Hazards, 54, 643–656, https://doi.org/10.1007/s11069-009-9493-x, 2010.
Jain, V. K., Pandey, R. P., Jain, M. K., and Byun, H. R.: Comparison of drought indices for appraisal of drought characteristics in the Ken River Basin, Weather and Climate Extremes, 8, 1–11, https://doi.org/10.1016/j.wace.2015.05.002, 2015.
Jia, Y., Zhang, B., and Ma, B.: Daily SPEI reveals long-term change in drought characteristics in Southwest China, Chinese Geogr. Sci., 28, 680–693, https://doi.org/10.1007/s11769-018-0973-3, 2018.
Klein-Tank, A. M., Wijngaard, J. B., Können, G. P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., and Heino, R.: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment, Int. J. Climatol., 22, 1441–1453, https://doi.org/10.1002/joc.773, 2002.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., and Miyaoka, K.: The JRA-55 reanalysis: General specifications and basic characteristics, J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Lai, C., Zhong, R., Wang, Z., Wu, X., Chen, X., Wang, P., and Lian, Y.: Monitoring hydrological drought using long-term satellite-based precipitation data, Sci. Total Environ., 649, 1198–1208, https://doi.org/10.1016/j.scitotenv.2018.08.245, 2019.
Li, J., Wang, Z., Wu, X., Xu, C. Y., Guo, S., and Chen, X.: Toward monitoring short-term droughts using a novel daily scale, Standardised antecedent precipitation evapotranspiration index, J. Hydrometeorol., 21, 891–908, https://doi.org/10.1175/JHM-D-19-0298.1, 2020.
Lima, D. C., Lemos, G., Bento, V. A., Nogueira, M., and Soares, P. M.: A multi-variable constrained ensemble of regional climate projections under multi-scenarios for Portugal–Part I: An overview of impacts on means and extremes, Climate Services, 30, 100351, https://doi.org/10.1016/j.cliser.2023.100351, 2023a.
Lima, D. C., Bento, V. A., Lemos, G., Nogueira, M., and Soares, P. M.: A multi-variable constrained ensemble of regional climate projections under multi-scenarios for Portugal – Part II: Sectoral climate indices. Climate Services, 30, 100377, https://doi.org/10.1016/j.cliser.2023.100377, 2023b.
Ma, B., Zhang, B., Jia, L., and Huang, H.: Conditional distribution selection for SPEI-daily and its revealed meteorological drought characteristics in China from 1961 to 2017, Atmos. Res., 246, 105108, https://doi.org/10.1016/j.atmosres.2020.105108, 2020.
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought frequency and duration to timescales, in: Proceedings of the 8th Conference on Applied Climatology, Vol. 17, 179–183, https://climate.colostate.edu/pdfs/relationshipofdroughtfrequency.pdf (last access: 30 July 2023) 1993.
Meyer, S. J., Hubbard, K. G., and Wilhite, D. A.: A crop-specific drought index for corn: I. Model development and validation, Agronomy J., 85, 388–395, https://doi.org/10.2134/agronj1993.00021962008500020040x, 1993.
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391, 202–216, https://doi.org/10.1016/j.jhydrol.2010.07.012, 2010.
Moemken, J., Koerner, B., Ehmele, F., Feldmann, H., and Pinto, J. G.: Recurrence of drought events over Iberia. Part II: Future changes using regional climate projections, Tellus, 74, 262, https://doi.org/10.16993/tellusa.52, 2022.
Monish, N. T. and Rehana, S.: Suitability of distributions for standard precipitation and evapotranspiration index over meteorologically homogeneous zones of India, J. Earth Syst. Sci., 129, 1–19, https://doi.org/10.1007/s12040-019-1271-x, 2020.
Onuşluel Gül, G., Gül, A., and Najar, M.: Historical evidence of climate change impact on drought outlook in river basins: analysis of annual maximum drought severities through daily SPI definitions, Nat. Hazards, 110, 1389–1404, https://doi.org/10.1007/s11069-021-04995-0, 2021.
Palmer, W. C.: Meteorological drought, Vol. 30, US Department of Commerce, Weather Bureau, https://books.google.pt/books?hl=en&lr=&id=kyYZgnEk-L8C&oi=fnd&pg=PA6&dq=Palmer,+W.+C.,+(1965)+Meteorological+drought&ots=U4hAdh1Fom&sig=b_uhe6PC7FvMeofO1D9S59q7l64&redir_esc=y#v=onepage&q=Palmer%2C%20W.%20C.%2C%20(1965)%20Meteorological%20drought&f=false (last access: 16 October 2023), 1965.
Pascoa, P., Russo, A., Gouveia, C. M., Soares, P. M., Cardoso, R. M., Careto, J. A., and Ribeiro, A. F.: A high-resolution view of the recent drought trends over the Iberian Peninsula, Weather and Climate Extremes, 32, 100320, https://doi.org/10.1016/j.wace.2021.100320, 2021.
Patel, N. R., Chopra, P., and Dadhwal, V. K.: Analyzing spatial patterns of meteorological drought using Standardised precipitation index, Meteorol. Appl., 14, 329–336, https://doi.org/10.1002/met.33, 2007.
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007.
Perkins, S. E., Pitman, A. J., Holbrook, N. J., and McAneney, J.: Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions, J. Climate, 20, 4356–4376, https://doi.org/10.1175/JCLI4253.1, 2007.
Pichelli, E., Coppola, E., Sobolowski, S., Ban, N., Giorgi, F., Stocchi, P., Alias, A., Belušić, D., Berthou, S., Caillaud, C. Cardoso, R.M. Chan, S., Christensen, O. B., Dobler, A., de Vries, H., Goergen, K., Kendon, E. J., Keuler, K., Lenderink, G., Lorenz, T., Mishra, A. N., Panitz, H.-J., Schär, C., Soares, P. M. M., Truhetz, H., and Vergara-Temprado, J.: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution part 2: historical and future simulations of precipitation, Clim. Dynam., 56, 3581–3602, https://doi.org/10.1007/s00382-021-05657-4, 2021.
Pohl, F., Rakovec, O., Rebmann, C., Hildebrandt, A., Boeing, F., Hermanns, F., Attinger, S., Samaniego, L., and Kumar, R.: Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS sites, Sci. Data, 10, 281, https://doi.org/10.1038/s41597-023-02192-1, 2023.
Rhee, J., Im, J., and Carbone, G. J.: Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data, Remote Sens. Environ., 114, 2875–2887, https://doi.org/10.1016/j.rse.2010.07.005, 2010.
Rios-Entenza, A., Soares, P. M., Trigo, R. M., Cardoso, R. M., and Miguez-Macho, G.: Moisture recycling in the Iberian Peninsula from a regional climate simulation: Spatiotemporal analysis and impact on the precipitation regime, J. Geophys. Res.-Atmos., 119, 5895–5912, https://doi.org/10.1002/2013JD021274, 2014.
Sánchez, E., Domínguez, M., Romera, R., López de la Franca, N., Gaertner, M.A., Gallardo, C. and Castro, M.: Regional modeling of dry spells over the Iberian Peninsula for present climate and climate change conditions: A letter, Climatic Change, 107, 625–634, https://doi.org/10.1007/s10584-011-0114-9, 2011.
Santos, J., Corte-Real, J., and Leite, S.: Atmospheric large-scale dynamics during the 2004/2005 winter drought in portugal, Int. J. Climatol., 27, 571–586, https://doi.org/10.1002/joc.1425, 2007.
Seguí, P. Q., Martin, E., Sánchez, E., Zribi, M., Vennetier, M., Vicente-Serrano, S. M., and Vidal, J. P.: Drought: observed trends, future projections. The Mediterranean under climate change, IRD Editions, 123–131, hal-01401386, 2016.
Shan, B., Verhoest, N. E. C., and De Baets, B.: Identification of compound drought and heatwave events on a daily scale and across four seasons, Hydrol. Earth Syst. Sci., 28, 2065–2080, https://doi.org/10.5194/hess-28-2065-2024, 2024.
Soares, P. M. and Cardoso, R. M.: A simple method to assess the added value using high-resolution climate distributions: application to the EURO-CORDEX daily precipitation, Int. J. Climatol., 38, 1484–1498, https://doi.org/10.1002/joc.5261, 2018.
Soares, P. M. and Lima, D. C.: Water scarcity down to earth surface in a Mediterranean climate: The extreme future of soil moisture in Portugal, J. Hydrol., 615, 128731, https://doi.org/10.1016/j.jhydrol.2022.128731, 2022.
Soares, P. M., Cardoso, R. M., Lima, D. C., and Miranda, P. M.: Future precipitation in Portugal: high-resolution projections using WRF model and EURO-CORDEX multi-model ensembles, Clim. Dynam., 49, 2503–2530, https://doi.org/10.1007/s00382-016-3455-2, 2017.
Soares, P. M., Lemos, G., and Lima, D. C.: Critical analysis of CMIPs past climate model projections in a regional context: The Iberian climate, Int. J. Climatol., 43, 2250–2270, https://doi.org/10.1002/joc.7973, 2022.
Soares, P. M., Careto, J. A., Russo, A., and Lima, D. C.: The future of Iberian droughts: a deeper analysis based on multi-scenario and a multi-model ensemble approach, Nat. Hazards, 117, 2001–2028, https://doi.org/10.1007/s11069-023-05938-7, 2023.
Singh, V. P., Guo, H., and Yu, F. X.: Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome, Stoch. Hydrol. Hydraul., 7, 163–177, https://doi.org/10.1007/BF01585596, 1993.
Spinoni, J., Vogt, J. V., Naumann, G., Barbosa, P., and Dosio, A.: Will drought events become more frequent and severe in Europe?, Int. J. Climatol., 38, 1718–1736, https://doi.org/10.1002/joc.5291, 2018.
Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., and Stahl, K.: Candidate distributions for climatological drought indices (SPI and SPEI), Int. J. Climatol., 35, 4027–4040, https://doi.org/10.1002/joc.4267, 2015.
Svoboda, M. D. and Fuchs, B. A.: Handbook of drought indicators and indices (Vol. 2), Geneva, Switzerland: World Meteorological Organisation, https://www.droughtmanagement.info/literature/GWP_Handbook_of_Drought_Indicators_and_Indices_2016.pdf (last access: 16 October 2023), 2016.
Thiemig, V., Gomes, G. N., Skøien, J. O., Ziese, M., Rauthe-Schöch, A., Rustemeier, E., Rehfeldt, K., Walawender, J. P., Kolbe, C., Pichon, D., Schweim, C., and Salamon, P.: EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe, Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, 2022.
Thornthwaite, C. W.: An approach toward a rational classification of climate, Geograph. Rev., 38, 55–94, https://doi.org/10.2307/210739, 1948.
Tsakiris, G. and Vangelis, H. J. E. W.: Establishing a drought index incorporating evapotranspiration, European water, 9, 3–11, 2005.
Umran Komuscu, A.: Using the SPI to analyze spatial and temporal patterns of drought in Turkey, Drought Network News (1994–2001), 49, https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=_1048&context=_droughtnetnews (last access: 30 July 2023), 1999.
Universidade de Lisboa Faculdade de Ciências (FCUL): Atmosphere-land-ocean interactions over southwestern Africa under a changing climate, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/2022.03183.CEECIND/CP1715/CT0004, 2023.
Universidade de Lisboa Instituto Dom Luiz: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Base, Universidade de Lisboa Instituto Dom Luiz, Portugal, UIDB/50019/2020, https://doi.org/10.54499/UIDB/50019/2020, 2020–2024a.
Universidade de Lisboa Instituto Dom Luiz: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Programático, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/UIDP/50019/2020, 2020–2024b.
Universidade de Lisboa Instituto Superior de Agronomia: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Base, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/UIDB/00239/2020, 2020–2024a.
Universidade de Lisboa Instituto Superior de Agronomia: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Programático, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/UIDP/00239/2020, 2020–2024b.
Universidade de Lisboa Instituto Superior de Agronomia and Universidade de Lisboa Centro de Estudos Florestais: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Base, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/UIDB/00239/2020, 2020–2024a.
Universidade de Lisboa Instituto Superior de Agronomia and Universidade de Lisboa Centro de Estudos Florestais: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Programático, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/UIDP/00239/2020, 2020–2024b.
Van der Schrier, G., Jones, P. D., and Briffa, K. R.: The sensitivity of the PDSI to the Thornthwaite and Penman-Monteith parameterisations for potential evapotranspiration, J. Geophys. Res.-Atmos., 116, 1–16, https://doi.org/10.1029/2010JD015001, 2011.
Vicente-Serrano, S. M.: Differences in spatial patterns of drought on different timescales: an analysis of the Iberian Peninsula, Water Resour. Manage., 20, 37–60, https://doi.org/10.1007/s11269-006-2974-8, 2006.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A multiscalar drought index sensitive to global warming: the Standardised precipitation evapotranspiration index, J. Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2010.
Vicente-Serrano, S. M., Gouveia, C., Camarero, J. J., Beguería, S., Trigo, R., López-Moreno, J. I., Azorín-Molna, C., Poasho, E., Lorenzo-Lacruz, J., Revuelto, J., Morán-Tejeda, E., and Sanchez-Lorenzo, A.: Response of vegetation to drought time-scales across global land biomes, P. Natl. Acad. Sci. USA, 110, 52–57, https://doi.org/10.1073/pnas.1207068110, 2013.
Wan, L., Bento, V. A., Qu, Y., Qiu, J., Song, H., Zhang, R., Wu, X., Xu, F., Lu, J., and Wang, Q.: Drought characteristics and dominant factors across China: Insights from high-resolution daily SPEI dataset between 1979 and 2018, Sci. Total Environ., 901, 166362, https://doi.org/10.1016/j.scitotenv.2023.166362, 2023.
Wang, Q., Wu, J., Lei, T., He, B., Wu, Z., Liu, M., Mo, X., Geng, G., Li, X., Zhou, H., and Liu, D.: Temporal-spatial characteristics of severe drought events and their impact on agriculture on a global scale, Quatern. Int., 349, 10–21, https://doi.org/10.1016/j.quaint.2014.06.021, 2014.
Wang, Q., Shi, P., Lei, T., Geng, G., Liu, J., Mo, X., Li X, Zhou, H., and Wu, J.: The alleviating trend of drought in the Huang-Huai-Hai Plain of China based on the daily SPEI, Int. J. Climatol., 35, 3760–3769, https://doi.org/10.1002/joc.4244, 2015.
Wang, Q., Wu, J., Li, X., Zhou, H., Yang, J., Geng, G., An, X., Liu, L., and Tang, Z.: A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model, Int. J. Biometeorol., 61, 685–699, https://doi.org/10.1007/s00484-016-1246-4, 2017.
Wang, Q., Zeng, J., Qi, J., Zhang, X., Zeng, Y., Shui, W., Xu, Z., Zhang, R., Wu, X., and Cong, J.: A multi-scale daily SPEI dataset for drought characterization at observation stations over mainland China from 1961 to 2018, Earth Syst. Sci. Data, 13, 331–341, https://doi.org/10.5194/essd-13-331-2021, 2021.
Wang, Q., Zhang, R., Qi, J., Zeng, J., Wu, J., Shui, W., Wu, X., and Li, J.: An improved daily Standardised precipitation index dataset for mainland China from 1961 to 2018, Sci. Data, 9, 124, doi:s41597-022-01201-z, 2022.
Wells, N., Goddard, S., and Hayes, M. J.: A self-calibrating Palmer drought severity index, J. Climate, 17, 2335–2351, https://doi.org/10.1175/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2, 2004.
Wilhite, D. A.: Drought as a natural hazard: concepts and definitions, Drought Mitigation Center Faculty Publications, University of Nebraska, Lincoln, https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=_1068&context=_droughtfacpub (last access: 30 July 2023), 2000.
Wilhite, D. A. and Glantz, M. H.: Understanding: the drought phenomenon: the role of definitions, Water Int., 10, 111–120, https://doi.org/10.1080/02508068508686328, 1985.
Williams, D. N., Taylor, K. E., Cinquini, L., Evans, B., Kawamiya, M., Lautenschlager, M., Lawrencce, B., Middleton, D., and Contributors, E. S. G. F.: The Earth System Grid Federation: Software framework supporting CMIP5 data analysis and dissemination, ClIVAR Exchanges, 56, 40–42, https://centaur.reading.ac.uk/25732/1/WilEA11_CE.pdf (last access: 21 October 2024), 2011.
Wu, H., Hayes, M. J., Weiss, A., and Hu, Q. I.: An evaluation of the Standardised Precipitation Index, the China-Z Index and the statistical Z-Score, Int. J. Climatol., 21, 745–758, https://doi.org/10.1002/joc.658, 2001.
Xie, P., Chen, M., Yang, S., Yatagai, A., Hayasaka, T., Fukushima, Y., and Liu, C.: A gauge-based analysis of daily precipitation over East Asia, J. Hydrometeorol., 8, 607–626, https://doi.org/10.1175/JHM583.1, 2007.
Zhang, Y. and Li, Z.: Uncertainty analysis of Standardised precipitation index due to the effects of probability distributions and parameter errors, Front. Earth Sci., 8, 76, https://doi.org/10.3389/feart.2020.00076, 2020.
Zhang, R., Bento, V. A., Qi, J., Xu, F., Wu, J., Qiu, J., Li, J., Shui, W., and Wang, Q.: The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018, Big Earth Data, 7, 860–885, https://doi.org/10.1080/20964471.2022.2148331, 2023.
Zhang, X., Duan, Y., Duan, J., Jian, D., and Ma, Z.: A daily drought index based on evapotranspiration and its application in regional drought analyses, Sci. China Earth Sci., 65, 317–336, https://doi.org/10.1007/s11430-021-9822-y, 2022a.
Zhang, X., Duan, Y., Duan, J., Chen, L., Jian, D., Lv, M., Yang, Q., and Ma, Z.: A daily drought index-based regional drought forecasting using the Global Forecast System model outputs over China, Atmos. Res., 273, 106166, https://doi.org/10.1016/j.atmosres.2022.106166, 2022b.
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M. D., Maraun, D., Ramos, A., Ridder, N. N., Thiery, W., and Vignotto, E.: A typology of compound weather and climate events, Nature Rev. Earth Environ., 1, 333–347, 2020.
Short summary
This study proposes a new daily drought index, the generalised drought index (GDI). The GDI not only identifies the same events as established indices but is also capable of improving their results. The index is empirically based and easy to compute, not requiring fitting the data to a probability distribution. The GDI can detect flash droughts and longer-term events, making it a versatile tool for drought monitoring.
This study proposes a new daily drought index, the generalised drought index (GDI). The GDI not...
