Articles | Volume 13, issue 6
https://doi.org/10.5194/essd-13-2701-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-2701-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Jun 2021
Data description paper |
| 15 Jun 2021
| 15 Jun 2021
Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions
Karlsruhe Institute of Technology (KIT), Campus Alpin, Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Tanja C. Portele
Karlsruhe Institute of Technology (KIT), Campus Alpin, Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Patrick Laux
Karlsruhe Institute of Technology (KIT), Campus Alpin, Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Augsburg University, Institute of Geography, Alter Postweg 118, 86159 Augsburg, Germany
Harald Kunstmann
Karlsruhe Institute of Technology (KIT), Campus Alpin, Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Augsburg University, Institute of Geography, Alter Postweg 118, 86159 Augsburg, Germany
Related authors
No articles found.
Ling Zhang, Lu Li, Zhongshi Zhang, Joël Arnault, Stefan Sobolowski, Xiaoling Chen, Jianzhong Lu, Anthony Musili Mwanthi, Pratik Kad, Mohammed Abdullahi Hassan, Tanja Portele, Harald Kunstmann, and Zhengkang Zuo
Hydrol. Earth Syst. Sci., 29, 4109–4132, https://doi.org/10.5194/hess-29-4109-2025, https://doi.org/10.5194/hess-29-4109-2025, 2025
Short summary
Short summary
To address challenges related to unreliable hydrological simulations, we present an enhanced hydrological simulation with a refined climate model and a more comprehensive hydrological model. The model with the two parts outperforms that without, especially in migrating bias in peak flow and dry-season flow. Our findings highlight the enhanced hydrological simulation capability, with the refined climate and lake module contributing 24 % and 76 % improvement, respectively.
Yinchi Zhang, Wanling Xu, Chao Deng, Shao Sun, Miaomiao Ma, Jianhui Wei, Ying Chen, Harald Kunstmann, and Lu Gao
EGUsphere, https://doi.org/10.5194/egusphere-2025-2438, https://doi.org/10.5194/egusphere-2025-2438, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Most studies of compound extremes assume stable climate conditions. We use high-resolution regional climate modeling and non-stationary statistical methods to assess future changes in southeastern China. Our results show that non-stationary models better capture shifts in the risk of compound extremes, highlighting that traditional methods may underestimate future threats.
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025, https://doi.org/10.5194/hess-29-2023-2025, 2025
Short summary
Short summary
Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather events, yet current long-term forecast products are often embedded with uncertainties. We develop a deep-learning-based modelling framework to improve 30 d rainfall and streamflow forecasts by combining advanced neural networks and physical models. With the flow forecast error reduced by up to 33 %, the framework has the potential to enhance water management and disaster prevention.
Maximilian Graf, Andreas Wagner, Julius Polz, Llorenç Lliso, José Alberto Lahuerta, Harald Kunstmann, and Christian Chwala
Atmos. Meas. Tech., 17, 2165–2182, https://doi.org/10.5194/amt-17-2165-2024, https://doi.org/10.5194/amt-17-2165-2024, 2024
Short summary
Short summary
Commercial microwave links (CMLs) can be used for rainfall retrieval. The detection of rainy periods in their attenuation time series is a crucial processing step. We investigate the usage of rainfall data from MSG SEVIRI for this task, compare this approach with existing methods, and introduce a novel combined approach. The results show certain advantages for SEVIRI-based methods, particularly for CMLs where existing methods perform poorly. Our novel combination yields the best performance.
Patrick Olschewski, Qi Sun, Jianhui Wei, Yu Li, Zhan Tian, Laixiang Sun, Joël Arnault, Tanja C. Schober, Brian Böker, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-95, https://doi.org/10.5194/hess-2024-95, 2024
Preprint under review for HESS
Short summary
Short summary
There are indications that typhoon intensities may increase under global warming. However, further research on these projections and their uncertainties is necessary. We study changes in typhoon intensity under SSP5-8.5 for seven events affecting the Pearl River Delta using Pseudo-Global Warming and a storyline approach based on 16 CMIP6 models. Results show intensified wind speed, sea level pressure drop and precipitation levels for six events with amplified increases for individual storylines.
Patrick Olschewski, Mame Diarra Bousso Dieng, Hassane Moutahir, Brian Böker, Edwin Haas, Harald Kunstmann, and Patrick Laux
Nat. Hazards Earth Syst. Sci., 24, 1099–1134, https://doi.org/10.5194/nhess-24-1099-2024, https://doi.org/10.5194/nhess-24-1099-2024, 2024
Short summary
Short summary
We applied a multivariate and dependency-preserving bias correction method to climate model output for the Greater Mediterranean Region and investigated potential changes in false-spring events (FSEs) and heat–drought compound events (HDCEs). Results project an increase in the frequency of FSEs in middle and late spring as well as increases in frequency, intensity, and duration for HDCEs. This will potentially aggravate the risk of crop loss and failure and negatively impact food security.
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci., 28, 761–780, https://doi.org/10.5194/hess-28-761-2024, https://doi.org/10.5194/hess-28-761-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) often cause high economic loss due to heavy winds and rainfall, particularly in densely populated regions such as the Pearl River Delta (China). This study provides a reference to set up regional climate models for TC simulations. They contribute to a better TC process understanding and assess the potential changes and risks of TCs in the future. This lays the foundation for hydrodynamical modelling, from which the cities' disaster management and defence could benefit.
Dragan Petrovic, Benjamin Fersch, and Harald Kunstmann
Nat. Hazards Earth Syst. Sci., 24, 265–289, https://doi.org/10.5194/nhess-24-265-2024, https://doi.org/10.5194/nhess-24-265-2024, 2024
Short summary
Short summary
The influence of model resolution and settings on the reproduction of heat waves in Germany between 1980–2009 is analyzed. Outputs from a high-resolution model with settings tailored to the target region are compared to those from coarser-resolution models with more general settings. Neither the increased resolution nor the tailored model settings are found to add significant value to the heat wave simulation. The models exhibit a large spread, indicating that the choice of model can be crucial.
Mohsen Soltani, Bert Hamelers, Abbas Mofidi, Christopher G. Fletcher, Arie Staal, Stefan C. Dekker, Patrick Laux, Joel Arnault, Harald Kunstmann, Ties van der Hoeven, and Maarten Lanters
Earth Syst. Dynam., 14, 931–953, https://doi.org/10.5194/esd-14-931-2023, https://doi.org/10.5194/esd-14-931-2023, 2023
Short summary
Short summary
The temporal changes and spatial patterns in precipitation events do not show a homogeneous tendency across the Sinai Peninsula. Mediterranean cyclones accompanied by the Red Sea and Persian troughs are responsible for the majority of Sinai's extreme rainfall events. Cyclone tracking captures 156 cyclones (rainfall ≥10 mm d-1) either formed within or transferred to the Mediterranean basin precipitating over Sinai.
Efi Rousi, Andreas H. Fink, Lauren S. Andersen, Florian N. Becker, Goratz Beobide-Arsuaga, Marcus Breil, Giacomo Cozzi, Jens Heinke, Lisa Jach, Deborah Niermann, Dragan Petrovic, Andy Richling, Johannes Riebold, Stella Steidl, Laura Suarez-Gutierrez, Jordis S. Tradowsky, Dim Coumou, André Düsterhus, Florian Ellsäßer, Georgios Fragkoulidis, Daniel Gliksman, Dörthe Handorf, Karsten Haustein, Kai Kornhuber, Harald Kunstmann, Joaquim G. Pinto, Kirsten Warrach-Sagi, and Elena Xoplaki
Nat. Hazards Earth Syst. Sci., 23, 1699–1718, https://doi.org/10.5194/nhess-23-1699-2023, https://doi.org/10.5194/nhess-23-1699-2023, 2023
Short summary
Short summary
The objective of this study was to perform a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. A combination of favorable large-scale conditions and locally dry soils were related with the intensity and persistence of the events. We also showed that such extremes have become more likely due to anthropogenic climate change and might occur almost every year under +2 °C of global warming.
Dragan Petrovic, Benjamin Fersch, and Harald Kunstmann
Nat. Hazards Earth Syst. Sci., 22, 3875–3895, https://doi.org/10.5194/nhess-22-3875-2022, https://doi.org/10.5194/nhess-22-3875-2022, 2022
Short summary
Short summary
The influence of model resolution and settings on drought reproduction in Germany between 1980–2009 is investigated here. Outputs from a high-resolution model with settings tailored to the target region are compared to those from coarser-resolution models with more general settings. Gridded observational data sets serve as reference. Regarding the reproduction of drought characteristics, all models perform on a similar level, while for trends, only the modified model produces reliable outputs.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
Short summary
Short summary
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Bernd Schalge, Gabriele Baroni, Barbara Haese, Daniel Erdal, Gernot Geppert, Pablo Saavedra, Vincent Haefliger, Harry Vereecken, Sabine Attinger, Harald Kunstmann, Olaf A. Cirpka, Felix Ament, Stefan Kollet, Insa Neuweiler, Harrie-Jan Hendricks Franssen, and Clemens Simmer
Earth Syst. Sci. Data, 13, 4437–4464, https://doi.org/10.5194/essd-13-4437-2021, https://doi.org/10.5194/essd-13-4437-2021, 2021
Short summary
Short summary
In this study, a 9-year simulation of complete model output of a coupled atmosphere–land-surface–subsurface model on the catchment scale is discussed. We used the Neckar catchment in SW Germany as the basis of this simulation. Since the dataset includes the full model output, it is not only possible to investigate model behavior and interactions between the component models but also use it as a virtual truth for comparison of, for example, data assimilation experiments.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Cited articles
Abatzoglou, J. T. and Brown, T. J.: A comparison of statistical downscaling
methods suited for wildfire applications, Int. J.
Climatol., 32, 772–780, https://doi.org/10.1002/joc.2312, 2012. a, b
Ahmed, K. F., Wang, G., Silander, J., Wilson, A. M., Allen, J. M., Horton, R.,
and Anyah, R.: Statistical downscaling and bias correction of climate model
outputs for climate change impact assessment in the U.S. northeast, Global
Planet. Change, 100, 320–332, https://doi.org/10.1016/j.gloplacha.2012.11.003,
2013. a
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018. a
Alidoost, F., Stein, A., Su, Z., and Sharifi, A.: Multivariate copula quantile
mapping for bias correction of reanalysis air temperature data, J.
Spatial Sci., 66, 299–315, https://doi.org/10.1080/14498596.2019.1601138, 2019. a
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model:
Procedures, Data Sources and Analysis, NOAA Technical Memorandum NESDIS
NGDC-24. National Geophysical Data Center, NOAA, https://doi.org/10.7289/V5C8276M,
2009. a
Andrade, C. W. L., Montenegro, S. M. G. L., Montenegro, A. A. A., Lima, J. R.
D. S., Srinivasan, R., and Jones, C. A.: Climate change impact assessment on
water resources under RCP scenarios: A case study in Mundaú
River Basin, Northeastern Brazil, Int. J. Climatol., 41, E1045–E1061,
https://doi.org/10.1002/joc.6751, 2021. a
Anghileri, D., Monhart, S., Zhou, C., Bogner, K., Castelletti, A., Burlando,
P., and Zappa, M.: The Value of Subseasonal Hydrometeorological Forecasts to
Hydropower Operations: How Much Does Preprocessing Matter?, Water Resour.
Res., 55, 10159–10178, https://doi.org/10.1029/2019WR025280, 2019. a
Block, P.: Tailoring seasonal climate forecasts for hydropower operations, Hydrol. Earth Syst. Sci., 15, 1355–1368, https://doi.org/10.5194/hess-15-1355-2011, 2011. a
Boé, J., Terray, L., Habets, F., and Martin, E.: Statistical and
dynamical downscaling of the Seine basin climate for hydro-meteorological
studies, Int. J. Climatol., 27, 1643–1655,
https://doi.org/10.1002/joc.1602, 2007. a, b, c
Bolson, J., Martinez, C., Breuer, N., Srivastava, P., and Knox, P.: Climate
information use among southeast US water managers: beyond barriers and toward
opportunities, Reg. Environ. Change, 13, 141–151,
https://doi.org/10.1007/s10113-013-0463-1, 2013. a
Brier, G. W.: Verification of forecasts expressed in terms of probability,
Mon. Weather Rev., 78, 1–3,
https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2, 1950. a
Briley, L. J., Ashley, W. S., Rood, R. B., and Krmenec, A.: The role of
meteorological processes in the description of uncertainty for climate change
decision-making, Theor. Appl. Climatol., 127, 643–654,
https://doi.org/10.1007/s00704-015-1652-2, 2017. a
Cannon, A. J.: Multivariate quantile mapping bias correction: an N-dimensional
probability density function transform for climate model simulations of
multiple variables, Clim. Dynam., 50, 31–49,
https://doi.org/10.1007/s00382-017-3580-6, 2018. a
Casati, B., Wilson, L. J., Stephenson, D. B., Nurmi, P., Ghelli, A., Pocernich,
M., Damrath, U., Ebert, E. E., Brown, B. G., and Mason, S.: Forecast
verification: current status and future directions, Meteorol.
Appl., 15, 3–18, https://doi.org/10.1002/met.52, 2008. a
Chen, J., Brissette, F. P., Chaumont, D., and Braun, M.: Finding appropriate
bias correction methods in downscaling precipitation for hydrologic impact
studies over North America, Water Resour. Res., 49, 4187–4205,
https://doi.org/10.1002/wrcr.20331, 2013. a
Crochemore, L., Ramos, M.-H., and Pappenberger, F.: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, 2016. a
Digna, R., Castro-Gama, M., van der Zaag, P., Mohamed, Y., Corzo, G., and
Uhlenbrook, S.: Optimal Operation of the Eastern Nile System Using Genetic
Algorithm, and Benefits Distribution of Water Resources Development, Water,
10, 921, https://doi.org/10.3390/w10070921, 2018. a
Domínguez-Castro, F., García-Herrera, R., and Vicente-Serrano,
S. M.: Wet and dry extremes in Quito (Ecuador) since the 17th century,
Int. J. Climatol., 38, 2006–2014, https://doi.org/10.1002/joc.5312,
2018. a
Dutra, E., Di Giuseppe, F., Wetterhall, F., and Pappenberger, F.: Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index, Hydrol. Earth Syst. Sci., 17, 2359–2373, https://doi.org/10.5194/hess-17-2359-2013, 2013. a
ECMWF: ERA5-Land hourly data from 1981 to present, Tech. rep., ECMWF,
https://doi.org/10.24381/cds.e2161bac, 2019. a, b
ECMWF: ERA5-Land: data documentation, Tech. rep., 2020. a
Elagib, N. A. and Elhag, M. M.: Major climate indicators of ongoing drought in
Sudan, J. Hydrol., 409, 612–625,
https://doi.org/10.1016/j.jhydrol.2011.08.047, 2011. a, b
Emerton, R. E., Stephens, E. M., and Cloke, H. L.: What is the most useful
approach for forecasting hydrological extremes during El Niño?,
Environ. Res. Commun., 1, 031002,
https://doi.org/10.1088/2515-7620/ab114e, 2019. a
Gerlitz, L., Vorogushyn, S., and Gafurov, A.: Climate informed seasonal
forecast of water availability in Central Asia: State-of-the-art and decision
making context, Water Security, 10, 100061,
https://doi.org/10.1016/j.wasec.2020.100061, 2020. a
Gubler, S., Sedlmeier, K., Bhend, J., Avalos, G., Coelho, C. A. S.,
Escajadillo, Y., Jacques-Coper, M., Martinez, R., Schwierz, C., de Skansi,
M., and Spirig, C.: Assessment of ECMWF SEAS5 Seasonal Forecast Performance
over South America, Weather Forecast., 35, 561–584,
https://doi.org/10.1175/WAF-D-19-0106.1, 2019. a
Gutmann, E., Pruitt, T., Clark, M. P., Brekke, L., Arnold, J. R., Raff, D. A.,
and Rasmussen, R. M.: An intercomparison of statistical downscaling methods
used for water resource assessments in the United States, Water Resour.
Res., 50, 7167–7186, https://doi.org/10.1002/2014WR015559, 2014. a
Haiden, T., Janousek, M., Bidlot, J., Buizza, R., Ferranti, L., Prates, F., and
Vitart, F.: Evaluation of ECMWF forecasts, including the 2018 upgrade,
Tech. rep., ECMWF Tech. Memo. 831, 2018. a
Hartmann, H. C., Pagano, T. C., Sorooshian, S., and Bales, R.: Confidence
Builders: Evaluating Seasonal Climate Forecasts from User Perspectives,
B. Am. Meteorol. Soc., 83, 683–698,
https://doi.org/10.1175/1520-0477(2002)083<0683:CBESCF>2.3.CO;2, 2002. a
Hermanson, L., Ren, H.-L., Vellinga, M., Dunstone, N. D., Hyder, P., Ineson,
S., Scaife, A. A., Smith, D. M., Thompson, V., Tian, B., and Williams, K. D.:
Different types of drifts in two seasonal forecast systems and their
dependence on ENSO, Clim. Dynam., 51, 1411–1426,
https://doi.org/10.1007/s00382-017-3962-9, 2018. a
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for
Ensemble Prediction Systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a
Hersbach, H., De Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C.,
Abdalla, S., Balmaseda, A., Balsamo, G., Bechtold, P., Berrisford, P.,
Bidlot, J., De Boisséson, E., Bonavita, M., Browne, P., Buizza, R.,
Dahlgren, P., Dee, D., Dragani, R., Diamantakis, M., Flemming, J., Forbes,
R., Geer, A., Haiden, T., Hólm, E., Haimberger, L., Hogan, R.,
Horányi, A., Janisková, M., Laloyaux, P., Lopez, P.,
Muñoz-Sabater, J., Peubey, C., Radu, R., Richardson, D., Thépaut,
J.-N., Vitart, F., Yang, X., Zsótér, E., and Zuo, H.:
Operational global reanalysis: progress, future directions and synergies
with NWP including updates on the ERA5 production status, Tech. rep., ECMWF,
ERA Report Series 27, https://doi.org/10.21957/tkic6g3wm, 2018. a
Hersbach, H., Bell, B., Berrisford, P., Horányi, A., Munoz-Sabater, J.,
Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.:
Global reanalysis: goodbye ERA-Interim, hello ERA5, ECMWF Newsletter, 159,
Spring 2019. a
Hwang, S. and Graham, W. D.: Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation, Hydrol. Earth Syst. Sci., 17, 4481–4502, https://doi.org/10.5194/hess-17-4481-2013, 2013. a
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and Monge-Sanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019. a
Khajehei, S., Ahmadalipour, A., and Moradkhani, H.: An effective
post-processing of the North American multi-model ensemble (NMME)
precipitation forecasts over the continental US, Clim. Dynam., 51,
457–472, https://doi.org/10.1007/s00382-017-3934-0, 2018. a
Khalili, A. and Rahimi, J.: High-resolution spatiotemporal distribution of
precipitation in Iran: a comparative study with three global-precipitation
datasets, Theor. Appl. Climatol., 118, 211–221,
https://doi.org/10.1007/s00704-013-1055-1, 2014. a
Kidus, A. E.: Long-term potential impact of Great Ethiopian Renaissance Dam
(GERD) on the downstream eastern Nile High Aswan Dam (HAD), Sustainable
Water Resources Management, 5, 1973–1980, https://doi.org/10.1007/s40899-019-00351-0,
2019. a
Lafon, T., Dadson, S., Buys, G., and Prudhomme, C.: Bias correction of daily
precipitation simulated by a regional climate model: a comparison of
methods, Int. J. Climatol., 33, 1367–1381,
https://doi.org/10.1002/joc.3518, 2013. a, b
Lehner, B. and Grill, G.: Global river hydrography and network routing:
baseline data and new approaches to study the world's large river systems,
Hydrol. Process., 27, 2171–2186, https://doi.org/10.1002/hyp.9740, 2013. a
Lemos, M. C., Finan, T. J., Fox, R. W., Nelson, D. R., and Tucker, J.: The Use
of Seasonal Climate Forecasting in Policymaking: Lessons from Northeast
Brazil, Clim. Change, 55, 479–507, https://doi.org/10.1023/A:1020785826029, 2002. a
Lorenz, C. and Kunstmann, H.: The Hydrological Cycle in Three State-of-the-Art
Reanalyses: Intercomparison and Performance Analysis, J.
Hydrometeorol., 13, 1397–1420, https://doi.org/10.1175/JHM-D-11-088.1, 2012. a
Lorenz, C., Kunstmann, H., Devaraju, B., Tourian, M. J., Sneeuw, N., Riegger,
J., and Kunstmann, H.: Large-scale runoff from landmasses: a global
assessment of the closure of the hydrological and atmospheric water
balances, J. Hydrometeorol., 15, 2111–2139,
https://doi.org/10.1175/JHM-D-13-0157.1, 2014. a
Lorenz, C., Montzka, C., Jagdhuber, T., Laux, P., and Kunstmann, H.: Long-Term
and High-Resolution Global Time Series of Brightness Temperature from
Copula-Based Fusion of SMAP Enhanced and SMOS Data, Remote Sensing, 10,
1842, https://doi.org/10.3390/rs10111842, 2018. a
Lorenz, C., Portele, T. C., Laux, P., and Kunstmann, H.: Seasonal Water
Resources Management for Semiarid Areas: Bias-corrected and spatially
disaggregated seasonal forecasts for the Catamayo-Chira Basin
(Ecuador/Peru) [dataset], World Data Center for Climate (WDCC) at DKRZ,
https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD, 2020a. a, b
Lorenz, C., Portele, T. C., Laux, P., and Kunstmann, H.: Seasonal Water
Resources Management for Semiarid Areas: Bias-corrected and spatially
disaggregated seasonal forecasts for the Karun Basin (Iran) [dataset], World Data
Center for Climate (WDCC) at DKRZ,
https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD, 2020b. a, b
Lorenz, C., Portele, T. C., Laux, P., and Kunstmann, H.: Seasonal Water
Resources Management for Semiarid Areas: Bias-corrected and spatially
disaggregated seasonal forecasts for the Rio São Francisco Basin
(Brazil) [dataset], World Data Center for Climate (WDCC) at DKRZ,
https://doi.org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD, 2020c. a, b
Lorenz, C., Portele, T. C., Laux, P., and Kunstmann, H.: Seasonal Water
Resources Management for Semiarid Areas: Bias-corrected and spatially
disaggregated seasonal forecasts for the Tekeze-Atbara and Blue Nile Basins
(Ethiopia/Eritrea/Sudan) [dataset], World Data Center for Climate (WDCC) at DKRZ,
https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD, 2020d. a, b
Magnusson, L., Alonso-Balmaseda, M., Corti, S., Molteni, F., and Stockdale, T.:
Evaluation of forecast strategies for seasonal and decadal forecasts in
presence of systematic model errors, Clim. Dynam., 41, 2393–2409,
https://doi.org/10.1007/s00382-012-1599-2, 2013. a
Mahto, S. S. and Mishra, V.: Does ERA‐5 Outperform Other Reanalysis Products
for Hydrologic Applications in India?, J. Geophys. Res.-Atmos., 124, 9423–9441, https://doi.org/10.1029/2019JD031155, 2019. a
Manzanas, R., Gutiérrez, J., Fernández, J., van Meijgaard, E.,
Calmanti, S., Magariño, M., Cofiño, A., and Herrera, S.:
Dynamical and statistical downscaling of seasonal temperature forecasts in
Europe: Added value for user applications, Climate Services, 9, 44–56,
https://doi.org/10.1016/j.cliser.2017.06.004, 2018a. a
Manzanas, R., Lucero, A., Weisheimer, A., and Gutiérrez, J. M.: Can bias
correction and statistical downscaling methods improve the skill of seasonal
precipitation forecasts?, Clim. Dynam., 50, 1161–1176,
https://doi.org/10.1007/s00382-017-3668-z, 2018b. a
Manzanas, R., Gutiérrez, J. M., Bhend, J., Hemri, S., Doblas-Reyes,
F. J., Torralba, V., Penabad, E., and Brookshaw, A.: Bias adjustment and
ensemble recalibration methods for seasonal forecasting: a comprehensive
intercomparison using the C3S dataset, Clim. Dynam., 53, 1287–1305,
https://doi.org/10.1007/s00382-019-04640-4, 2019. a
Marengo, J. A., Chou, S. C., Kay, G., Alves, L. M., Pesquero, J. F., Soares,
W. R., Santos, D. C., Lyra, A. A., Sueiro, G., Betts, R., Chagas, D. J.,
Gomes, J. L., Bustamante, J. F., and Tavares, P.: Development of regional
future climate change scenarios in South America using the Eta CPTEC/HadCM3
climate change projections: climatology and regional analyses for the Amazon,
São Francisco and the Paraná River basins, Clim. Dynam., 38,
1829–1848, https://doi.org/10.1007/s00382-011-1155-5, 2012. a
Marengo, J. A., Alves, L. M., Alvala, R. C., Cunha, A. P., Brito, S., and
Moraes, O. L.: Climatic characteristics of the 2010-2016 drought in the
semiarid Northeast Brazil region, Anais da Academia Brasileira de
Ciências, 90, 1973–1985, https://doi.org/10.1590/0001-3765201720170206, 2018. a, b
Martins, E. S. P. R., Coelho, C. A. S., Haarsma, R., Otto, F. E. L., King,
A. D., Jan van Oldenborgh, G., Kew, S., Philip, S., Vasconcelos Júnior,
F. C., and Cullen, H.: A Multimethod Attribution Analysis of the Prolonged
Northeast Brazil Hydrometeorological Drought (2012–16), B.
Am. Meteorol. Soc., 99, S65–S69,
https://doi.org/10.1175/BAMS-D-17-0102.1, 2018. a, b
Masih, I., Maskey, S., Mussá, F. E. F., and Trambauer, P.: A review of droughts on the African continent: a geospatial and long-term perspective, Hydrol. Earth Syst. Sci., 18, 3635–3649, https://doi.org/10.5194/hess-18-3635-2014, 2014. a
Ning, L., Riddle, E. E., and Bradley, R. S.: Projected Changes in Climate
Extremes over the Northeastern United States, J. Climate, 28,
3289–3310, https://doi.org/10.1175/JCLI-D-14-00150.1, 2015. a
Nyaupane, N., Thakur, B., Kalra, A., and Ahmad, S.: Evaluating Future Flood
Scenarios Using CMIP5 Climate Projections, Water, 10, 1866,
https://doi.org/10.3390/w10121866, 2018. a
Patt, A. and Gwata, C.: Effective seasonal climate forecast applications:
examining constraints for subsistence farmers in Zimbabwe, Global
Environ. Change, 12, 185–195, https://doi.org/10.1016/S0959-3780(02)00013-4,
2002. a
Patt, A., Suarez, P., and Gwata, C.: Effects of seasonal climate forecasts and
participatory workshops among subsistence farmers in Zimbabwe, P. Natl. Acad. Sci., 102, 12623–12628,
https://doi.org/10.1073/pnas.0506125102, 2005. a
Ratri, D. N., Whan, K., and Schmeits, M.: A Comparative Verification of Raw
and Bias-Corrected ECMWF Seasonal Ensemble Precipitation Reforecasts in Java
(Indonesia), J. Appl. Meteorol. Climatol., 58, 1709–1723,
https://doi.org/10.1175/JAMC-D-18-0210.1, 2019. a
Rayner, S., Lach, D., and Ingram, H.: Weather Forecasts are for Wimps: Why
Water Resource Managers Do Not Use Climate Forecasts, Clim. Change, 69,
197–227, https://doi.org/10.1007/s10584-005-3148-z, 2005. a, b
Ritchie, J. W., Abawi, G. Y., Dutta, S. C., Harris, T. R., and Bange, M.: Risk
management strategies using seasonal climate forecasting in irrigated cotton
production: a tale of stochastic dominance, Aust. J. Agr. Resour. Ec., 48, 65–93,
https://doi.org/10.1111/j.1467-8489.2004.00236.x, 2008. a
Schepen, A., Zhao, T., Wang, Q. J., and Robertson, D. E.: A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments, Hydrol. Earth Syst. Sci., 22, 1615–1628, https://doi.org/10.5194/hess-22-1615-2018, 2018. a
Siegmund, J., Bliefernicht, J., Laux, P., and Kunstmann, H.: Toward a seasonal
precipitation prediction system for West Africa: Performance of CFSv2 and
high-resolution dynamical downscaling, J. Geophys. Res.-Atmos., 120, 7316–7339, https://doi.org/10.1002/2014JD022692, 2015. a
Tall, A., Mason, S. J., van Aalst, M., Suarez, P., Ait-Chellouche, Y., Diallo,
A. A., and Braman, L.: Using Seasonal Climate Forecasts to Guide Disaster
Management: The Red Cross Experience during the 2008 West Africa Floods,
Int. J. Geophys., 2012, 1–12, https://doi.org/10.1155/2012/986016,
2012. a, b
Teutschbein, C. and Seibert, J.: Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?, Hydrol. Earth Syst. Sci., 17, 5061–5077, https://doi.org/10.5194/hess-17-5061-2013, 2013. a
Thober, S., Kumar, R., Sheffield, J., Mai, J., Schäfer, D., and
Samaniego, L.: Seasonal Soil Moisture Drought Prediction over Europe Using
the North American Multi-Model Ensemble (NMME), J. Hydrometeorol.,
16, 2329–2344, https://doi.org/10.1175/JHM-D-15-0053.1, 2015. a
Thrasher, B., Maurer, E. P., McKellar, C., and Duffy, P. B.: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping, Hydrol. Earth Syst. Sci., 16, 3309–3314, https://doi.org/10.5194/hess-16-3309-2012, 2012. a, b, c
Thrasher, B., Xiong, J., Wang, W., Melton, F., Michaelis, A., and Nemani, R.:
Downscaled Climate Projections Suitable for Resource Management, Eos,
Transactions American Geophysical Union, 94, 321–323,
https://doi.org/10.1002/2013EO370002, 2013. a
Torres, R. R., Lapola, D. M., and Gamarra, N. L. R.: Future Climate Change in
the Caatinga, in: Caatinga, Springer International Publishing,
Cham, 383–410, https://doi.org/10.1007/978-3-319-68339-3_15, 2017. a
Tryhorn, L. and DeGaetano, A.: A comparison of techniques for downscaling
extreme precipitation over the Northeastern United States, Int. J. Climatol., 31, 1975–1989, https://doi.org/10.1002/joc.2208, 2011. a
Urraca, R., Huld, T., Gracia-Amillo, A., Martinez-de Pison, F. J., Kaspar, F.,
and Sanz-Garcia, A.: Evaluation of global horizontal irradiance estimates
from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data,
Solar Energ., 164, 339–354, https://doi.org/10.1016/j.solener.2018.02.059, 2018. a
Vaghefi, S. A., Keykhai, M., Jahanbakhshi, F., Sheikholeslami, J., Ahmadi, A.,
Yang, H., and Abbaspour, K. C.: The future of extreme climate in Iran,
Sci. Rep.-UK, 9, 1464, https://doi.org/10.1038/s41598-018-38071-8, 2019. a
van den Besselaar, E. J. M., van der Schrier, G., Cornes, R. C., Iqbal, A. S.,
and Klein Tank, A. M. G.: SA-OBS: A Daily Gridded Surface Temperature and
Precipitation Dataset for Southeast Asia, J. Climate, 30,
5151–5165, https://doi.org/10.1175/JCLI-D-16-0575.1, 2017. a
Vandal, T., Kodra, E., and Ganguly, A. R.: Intercomparison of machine learning
methods for statistical downscaling: the case of daily and extreme
precipitation, Theor. Appl. Climatol., 137, 557–570,
https://doi.org/10.1007/s00704-018-2613-3, 2019. a
Voisin, N., Schaake, J. C., and Lettenmaier, D. P.: Calibration and
Downscaling Methods for Quantitative Ensemble Precipitation Forecasts,
Weather Forecast., 25, 1603–1627, https://doi.org/10.1175/2010WAF2222367.1,
2010. a, b, c, d
Washington, R., Harrison, M., Conway, D., Black, E., Challinor, A., Grimes, D.,
Jones, R., Morse, A., Kay, G., and Todd, M.: African Climate Change: Taking
the Shorter Route, B. Am. Meteorol. Soc., 87,
1355–1366, https://doi.org/10.1175/BAMS-87-10-1355, 2006. a
Wessel, P. and Smith, W. H. F.: A global, self-consistent, hierarchical,
high-resolution shoreline database, J. Geophys. Res.-Sol.
Ea., 101, 8741–8743, https://doi.org/10.1029/96JB00104, 1996.
a
Westrick, K. J. and Mass, C. F.: An Evaluation of a High-Resolution
Hydrometeorological Modeling System for Prediction of a Cool-Season Flood
Event in a Coastal Mountainous Watershed, J. Hydrometeorol., 2,
161–180, https://doi.org/10.1175/1525-7541(2001)002<0161:AEOAHR>2.0.CO;2, 2001. a
Wheeler, K. G., Hall, J. W., Abdo, G. M., Dadson, S. J., Kasprzyk, J. R.,
Smith, R., and Zagona, E. A.: Exploring Cooperative Transboundary River
Management Strategies for the Eastern Nile Basin, Water Resour. Res.,
54, 9224–9254, https://doi.org/10.1029/2017WR022149, 2018. a
Wheeler, K. G., Jeuland, M., Hall, J. W., Zagona, E., and Whittington, D.:
Understanding and managing new risks on the Nile with the Grand Ethiopian
Renaissance Dam, Nat. Commun., 11, 5222,
https://doi.org/10.1038/s41467-020-19089-x, 2020. a
White, C. J., Carlsen, H., Robertson, A. W., Klein, R. J., Lazo, J. K., Kumar,
A., Vitart, F., Coughlan de Perez, E., Ray, A. J., Murray, V., Bharwani, S.,
MacLeod, D., James, R., Fleming, L., Morse, A. P., Eggen, B., Graham, R.,
Kjellström, E., Becker, E., Pegion, K. V., Holbrook, N. J., McEvoy, D.,
Depledge, M., Perkins-Kirkpatrick, S., Brown, T. J., Street, R., Jones, L.,
Remenyi, T. A., Hodgson-Johnston, I., Buontempo, C., Lamb, R., Meinke, H.,
Arheimer, B., and Zebiak, S. E.: Potential applications of
subseasonal-to-seasonal (S2S) predictions, Meteorol. Appl., 24,
315–325, https://doi.org/10.1002/met.1654, 2017. a
Wood, A., Maurer, E. P., Kumar, A., and Lettenmaier, D. P.: Long-range
experimental hydrologic forecasting for the eastern United States, J. Geophys. Res., 107, 4429, https://doi.org/10.1029/2001JD000659, 2002. a, b, c, d
Xue, Y., Chen, M., Kumar, A., Hu, Z.-Z., and Wang, W.: Prediction Skill and
Bias of Tropical Pacific Sea Surface Temperatures in the NCEP Climate
Forecast System Version 2, J. Climate, 26, 5358–5378,
https://doi.org/10.1175/JCLI-D-12-00600.1, 2013. a
Yitayew, M. and Melesse, A. M.: Critical Water Resources Issues in the Nile
River Basin, in: Nile River Basin, pp. 401–416, Springer Netherlands,
Dordrecht, https://doi.org/10.1007/978-94-007-0689-7_20, 2011. a
Yuan, X., Wood, E. F., Luo, L., and Pan, M.: A first look at Climate Forecast
System version 2 (CFSv2) for hydrological seasonal prediction, Geophys.
Res. Lett., 38, L13402, https://doi.org/10.1029/2011GL047792, 2011. a
Zhao, T., Bennett, J. C., Wang, Q. J., Schepen, A., Wood, A. W., Robertson,
D. E., and Ramos, M.-H.: How Suitable is Quantile Mapping For Postprocessing
GCM Precipitation Forecasts?, J. Climate, 30, 3185–3196,
https://doi.org/10.1175/JCLI-D-16-0652.1, 2017. a
Short summary
Semi-arid regions depend on the freshwater resources from the rainy seasons as they are crucial for ensuring security for drinking water, food and electricity. Thus, forecasting the conditions for the next season is crucial for proactive water management. We hence present a seasonal forecast product for four semi-arid domains in Iran, Brazil, Sudan/Ethiopia and Ecuador/Peru. It provides a benchmark for seasonal forecasts and, finally, a crucial contribution for improved disaster preparedness.
Semi-arid regions depend on the freshwater resources from the rainy seasons as they are crucial...
Altmetrics
Final-revised paper
Preprint