Articles | Volume 26, issue 4
https://doi.org/10.5194/hess-26-975-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-975-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Feb 2022
Research article |
| 21 Feb 2022
| 21 Feb 2022
Remote sensing-aided rainfall–runoff modeling in the tropics of Costa Rica
Saúl Arciniega-Esparza
CORRESPONDING AUTHOR
Hydrogeology Group, Faculty of Engineering, Universidad Nacional
Autónoma de México, Mexico City, 04510, Mexico
Christian Birkel
Department of Geography and Water and Global Change Observatory,
University of Costa Rica, San José, Costa Rica
Northern Rivers Institute, University of Aberdeen, Aberdeen,
Scotland
Andrés Chavarría-Palma
Department of Geography and Water and Global Change Observatory,
University of Costa Rica, San José, Costa Rica
Berit Arheimer
Swedish Meteorological and Hydrological Institute, Norrköping,
Sweden
José Agustín Breña-Naranjo
Institute of Engineering, Universidad Nacional Autónoma de
México, Mexico City, Mexico
Instituto Mexicano de Tecnología del Agua, Jiutepec, Morelos,
Mexico
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Mercury (Hg) is a toxic pollutant that vegetation removes from the atmosphere. We examined how rainfall seasonality regulates Hg sequestration in understudied tropical dry and rainforests. Dry forests showed strong seasonality, with Hg uptake peaking in the wet season, while high water availability kept stable uptake in rainforests. Vegetation absorbed Hg in both, but higher litterfall enhanced Hg transfer to rainforest soils, emphasizing tropical forests as major yet vulnerable global Hg sinks.
Hanwu Zheng, Doerthe Tetzlaff, Christian Birkel, Songjun Wu, Tobias Sauter, and Chris Soulsby
Hydrol. Earth Syst. Sci., 30, 1143–1163, https://doi.org/10.5194/hess-30-1143-2026, https://doi.org/10.5194/hess-30-1143-2026, 2026
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Ecohydrological processes in heavily managed catchments are often inadequately represented in models. We applied a tracer-aided model STARR (Spatially distributed Tracer-Aided Rainfall-Runoff) in an ET (evapotranspiration)-dominated region (the Middle Spree, NE Germany) with major management impacts. Water isotopes were useful in identifying runoff contributions and partitioning ET even at sparse resolution. Trade-offs between discharge- and isotope-based calibrations could be partially mitigated by integrating more process-based conceptualizations into the model.
Ann-Marie Ring, Dörthe Tetzlaff, Christian Birkel, and Chris Soulsby
Hydrol. Earth Syst. Sci., 29, 6663–6683, https://doi.org/10.5194/hess-29-6663-2025, https://doi.org/10.5194/hess-29-6663-2025, 2025
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During summer drought, a clear sub-daily cycling of atmospheric water vapour isotopes (δv) and plant xylem water isotopes (δxyl) was observed. δv daytime depletion was driven by evaporation and local atmospheric factors (entrainment). δxyl daytime enrichment was consistent with high vapor pressure deficit and stomatal regulation of transpiration. This sub-daily dataset provides unique insights on sub-daily cycling of stable water isotopes and can help constrain ecohydrological models.
Maria Elenius, Charlotta Pers, Sara Schützer, Göran Lindström, and Berit Arheimer
Hydrol. Earth Syst. Sci., 29, 4307–4325, https://doi.org/10.5194/hess-29-4307-2025, https://doi.org/10.5194/hess-29-4307-2025, 2025
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Simulations of peatland rewetting in Sweden under various conditions of climate, local hydrology, and rewetting practices showed insubstantial changes in landscape flow extremes due to mixing with runoff from various landcovers. The impacts on local hydrological extremes are governed by groundwater levels prior to rewetting and reduced tree cover; hence wetland allocation and management practices are crucial if the purpose is to reduce flow extremes in peatland streams.
Cong Jiang, Doerthe Tetzlaff, Songjun Wu, Christian Birkel, Hjalmar Laudon, and Chris Soulsby
EGUsphere, https://doi.org/10.5194/egusphere-2025-2533, https://doi.org/10.5194/egusphere-2025-2533, 2025
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We used a modelling approach supported by stable water isotopes to explore how forest management – such as conifer, broadleaf, and mixed tree–crop systems – affects water distribution and drought resilience in a drought-sensitive region of Germany. By representing forest type, density, and rooting depth, the model helps quantify and show how land use choices affect water availability and supports better land and water management decisions.
Alban de Lavenne, Vazken Andréassian, Louise Crochemore, Göran Lindström, and Berit Arheimer
Hydrol. Earth Syst. Sci., 26, 2715–2732, https://doi.org/10.5194/hess-26-2715-2022, https://doi.org/10.5194/hess-26-2715-2022, 2022
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A watershed remembers the past to some extent, and this memory influences its behavior. This memory is defined by the ability to store past rainfall for several years. By releasing this water into the river or the atmosphere, it tends to forget. We describe how this memory fades over time in France and Sweden. A few watersheds show a multi-year memory. It increases with the influence of groundwater or dry conditions. After 3 or 4 years, they behave independently of the past.
Aaron J. Neill, Christian Birkel, Marco P. Maneta, Doerthe Tetzlaff, and Chris Soulsby
Hydrol. Earth Syst. Sci., 25, 4861–4886, https://doi.org/10.5194/hess-25-4861-2021, https://doi.org/10.5194/hess-25-4861-2021, 2021
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Structural changes (cover and height of vegetation plus tree canopy characteristics) to forests during regeneration on degraded land affect how water is partitioned between streamflow, groundwater recharge and evapotranspiration. Partitioning most strongly deviates from baseline conditions during earlier stages of regeneration with dense forest, while recovery may be possible as the forest matures and opens out. This has consequences for informing sustainable landscape restoration strategies.
Cited articles
Andersson, J. C. M., Pechlivanidis, I. G., Gustafsson, D., Donnelly, C., and
Arheimer, B.: Key factors for improving large-scale hydrological model performance, Eur. Water, 49, 77–88, 2015.
Andersson, J. C. M., Ali, A., Arheimer, B., Gustafsson, D., and Minoungou, B.: Providing peak river flow statistics and forecasting in the Niger River
basin, Phys. Chem. Earth, 100, 3–12, https://doi.org/10.1016/j.pce.2017.02.010, 2017.
Archfield, S. A., Clark, M., Arheimer, B., Hay, L. E., McMillan, H., Kiang, J. E., Seibert, J., Hakala, K., Bock, A., Wagener, T., Farmer, W. H., Andréassian, V., Attinger, S., Viglione, A., Knight, R., Markstrom, S., and Over, T.: Accelerating advances in continental domain hydrologic
modeling, Water Resour. Res., 51, 10078–10091, https://doi.org/10.1002/2015WR017498, 2015.
Arciniega-Esparza, S. and Birkel, C.: Hydrological simulations for Costa Rica from 1985 to 2019 using HYPE CR 1.0 (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.4029572, 2020.
Arciniega-Esparza, S., Breña-Naranjo, J. A., and Troch, P. A.: On the
connection between terrestrial and riparian vegetation: The role of storage
partitioning in water-limited catchments, Hydrol. Process., 31, 489–494, https://doi.org/10.1002/hyp.11071, 2017.
Arheimer, B., Hjerdt, N., and Lindström, G.: Artificially Induced Floods
to Manage Forest Habitats Under Climate Change, Fronti. Environ. Sci., 6, 1–8, https://doi.org/10.3389/fenvs.2018.00102, 2018.
Arheimer, B., Pimentel, R., Isberg, K., Crochemore, L., Andersson, J. C. M., Hasan, A., and Pineda, L.: Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation, Hydrol. Earth Syst. Sci., 24, 535–559, https://doi.org/10.5194/hess-24-535-2020, 2020.
Bamler, R.: The SRTM mission: A world-wide 30 m resolution DEM from SAR
interferometry in 11 days, Photogrammetric Week, https://earthexplorer.usgs.gov/ (last access: 20 April 2019), 1999.
Bayissa, Y., Tadesse, T., Demisse, G., and Shiferaw, A.: Evaluation of
satellite-based rainfall estimates and application to monitor meteorological
drought for the Upper Blue Nile Basin, Ethiopia, Remote Sens., 9, 669,
https://doi.org/10.3390/rs9070669, 2017.
Beck, H. E., de Roo, A., and van Dijk, A. I. J. M.: Global Maps of Streamflow Characteristics Based on Observations from Several Thousand Catchments, J. Hydrometeorol., 16, 1478–1501, https://doi.org/10.1175/JHM-D-14-0155.1, 2015.
Berg, P., Donnelly, C., and Gustafsson, D.: Near-real-time adjusted reanalysis forcing data for hydrology, Hydrol. Earth Syst. Sci., 22, 989–1000, https://doi.org/10.5194/hess-22-989-2018, 2018.
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006.
Beven, K.: Rainfall-runoff modelling: The primer, 2nd Edn., Wiley, Chichester, UK, 2012.
Birkel, C., Soulsby, C., and Tetzlaff, D.: Modelling the impacts of land-cover change on streamflow dynamics of a tropical rainforest headwater
catchment, Hydrolog. Sci. J., 57, 1543–1561, https://doi.org/10.1080/02626667.2012.728707, 2012.
Birkel, C., Duvert, C., Correa, A., Munksgaard, N. C., Maher, D. T., and
Hutley, L. B.: Tracer-Aided Modeling in the Low-Relief, Wet-Dry Tropics
Suggests Water Ages and DOC Export Are Driven by Seasonal Wetlands and Deep
Groundwater, Water Resour. Res., 56, e2019WR026175, https://doi.org/10.1029/2019WR026175, 2020.
Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., Kirches, G., Zülkhe, M., Kalogirou, V., and Arino, O.: Consistent Global Land Cover Maps for Climate Modeling Communities: Current Achievements of the ESA's Land Cover CCI, in: ESA Living Planet Symposium, http://maps.elie.ucl.ac.be/CCI/viewer/index.php (last access: 5 May 2019), 2013.
Brocca, L., Massari, C., Pellarin, T., Filippucci, P., Ciabatta, L., Camici,
S., Kerr, Y. H., and Fernández-Prieto, D.: River flow prediction in data
scarce regions: soil moisture integrated satellite rainfall products outperform rain gauge observations in West Africa, Sci. Rep., 10, 12517,
https://doi.org/10.1038/s41598-020-69343-x, 2020.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015.
Dal Molin, M., Schirmer, M., Zappa, M., and Fenicia, F.: Understanding
dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: The case study of the Thur catchment,
Hydrol. Earth Syst. Sci., 24, 1319–1345, https://doi.org/10.5194/hess-24-1319-2020,
2020.
Dehaspe, J., Birkel, C., Tetzlaff, D., Sánchez-Murillo, R., Durán-Quesada, A. M., and Soulsby, C.: Spatially distributed tracer-aided modelling to explore water and isotope transport, storage and mixing in a pristine, humid tropical catchment, Hydrol. Process., 32, 3206–3224, https://doi.org/10.1002/hyp.13258, 2018.
Esquivel-Hernández, G., Sánchez-Murillo, R., Birkel, C., Good, S. P., and Boll, J.: Hydroclimatic and ecohydrological resistance/resilience conditions across tropical biomes of Costa Rica, Ecohydrology, 10, 1–12,
https://doi.org/10.1002/eco.1860, 2017.
Frumau, K. F. A., Bruijnzeel, L. A. S., and Tobón, C.: Precipitation
measurement and derivation of precipitation inclination in a windy mountainous area in northern Costa Rica, Hydrol. Process., 25, 499–509, https://doi.org/10.1002/hyp.7860, 2011.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – A new environmental record for monitoring extremes, Scient. Data, 2, 1–21, https://doi.org/10.1038/sdata.2015.66, 2015.
Garcia, F., Folton, N., and Oudin, L.: Which objective function to calibrate
rainfall–runoff models for low-flow index simulations?, Hydrolog. Sci. J., 62, 1149–1166, https://doi.org/10.1080/02626667.2017.1308511, 2017.
Genereux, D. P. and Jordan, M.: Interbasin groundwater flow and groundwater
interaction with surface water in a lowland rainforest, Costa Rica: A review, J. Hydrol., 320, 385–399, https://doi.org/10.1016/j.jhydrol.2005.07.023, 2006.
Genereux, D. P., Wood, S. J., and Pringle, C. M.: Chemical tracing of interbasin groundwater transfer in the lowland rainforest of Costa Rica, J. Hydrol., 258, 163–178, https://doi.org/10.1016/S0022-1694(01)00568-6, 2002.
Getirana, A., Jung, H. C., Arsenault, K., Shukla, S., Kumar, S., Peters-Lidard, C., Maigari, I., and Mamane, B.: Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa, Water Resour. Res., 56, e2019WR026259, https://doi.org/10.1029/2019WR026259, 2020.
Gibbs, H. K., Ruesch, A. S., Achard, F., Clayton, M. K., Holmgren, P.,
Ramankutty, N. and Foley, J. A.: Tropical forests were the primary sources
of new agricultural land in the 1980s and 1990s, P. Natl. Acad. Sci. USA, 107, 16732–16737, https://doi.org/10.1073/pnas.0910275107, 2010.
Giorgi, F.: Climate change hot-spots, Geophys. Res. Lett., 33, L08707, https://doi.org/10.1029/2006GL025734, 2006.
Gómez-Delgado, F., Roupsard, O., Le Maire, G., Taugourdeau, S., Pérez, A., Van Oijen, M., Vaast, P., Rapidel, B., Harmand, J. M., Voltz,
M., Bonnefond, J. M., Imbach, P., and Moussa, R.: Modelling the hydrological
behaviour of a coffee agroforestry basin in Costa Rica, Hydrol. Earth Syst.
Sci., 15, 369–392, https://doi.org/10.5194/hess-15-369-2011, 2011.
Goshime, D. W., Absi, R., and Ledésert, B.: Evaluation and Bias Correction of CHIRP Rainfall Estimate for Rainfall-Runoff Simulation over Lake Ziway Watershed, Ethiopia, Hydrology, 6, 1–22, https://doi.org/10.3390/hydrology6030068, 2019.
Grillakis, M., Koutroulis, A., Tsanis, I., Grillakis, M., Koutroulis, A., and Tsanis, I.: Improving Seasonal Forecasts for Basin Scale Hydrological Applications, Water, 10, 1593, https://doi.org/10.3390/W10111593, 2018.
Guimberteau, M., Drapeau, G., Ronchail, J., Sultan, B., Polcher, J., Martinez, J.-M., Prigent, C., Guyot, J.-L., Cochonneau, G., Espinoza, J. C., Filizola, N., Fraizy, P., Lavado, W., De Oliveira, E., Pombosa, R., Noriega,
L., and Vauchel, P.: Discharge simulation in the sub-basins of the Amazon
using ORCHIDEE forced by new datasets, Hydrol. Earth Syst. Sci., 16, 911–935, https://doi.org/10.5194/hess-16-911-2012, 2012.
Gurtz, J., Baltensweiler, A., and Lang, H.: Spatially distributed hydrotope-based modelling of evapotranspiration and runoff in mountainous
basins, Hydrol. Process., 13, 2751–2768, https://doi.org/10.1002/(SICI)1099-1085(19991215)13:17<2751::AID-HYP897>3.0.CO;2-O, 1999.
Hengl, T., De Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S. and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, PLoS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Her, Y. and Seong, C.: Responses of hydrological model equifinality, uncertainty, and performance to multi-objective parameter calibration, J.
Hydroinform., 20, 864–885, https://doi.org/10.2166/hydro.2018.108, 2018.
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret,
U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut,
R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S.,
Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A
decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1198–1255, https://doi.org/10.1080/02626667.2013.803183, 2013.
Infante-Corona, J. A., Lakhankar, T., Pradhanang, S., and Khanbilvardi, R.:
Remote sensing and ground-based weather forcing data analysis for streamflow
simulation, Hydrology, 1, 89–111, https://doi.org/10.3390/hydrology1010089, 2014.
Kirchner, J. W.: Catchments as simple dynamical systems: Catchment
characterization, rainfall-runoff modeling, and doing hydrology backward,
Water Resour. Res., 45, W02429, https://doi.org/10.1029/2008WR006912, 2009.
Kling, H. and Gupta, H.: On the development of regionalization relationships
for lumped watershed models: The impact of ignoring sub-basin scale variability, J. Hydrol., 373, 337–351, https://doi.org/10.1016/j.jhydrol.2009.04.031, 2009.
Kruskal, W. H. and Wallis, W. W.: Use of Ranks in One-Criterion Variance
Analysis, J. Am. Stat. Assoc., 47, 283–621, 1952.
Kumar, R., Livneh, B., and Samaniego, L.: Toward computationally efficient
large-scale hydrologic predictions with a multiscale regionalization scheme,
Water Resour. Res., 49, 5700–5714, https://doi.org/10.1002/wrcr.20431, 2013.
Kwon, M., Kwon, H. H., and Han, D.: A hybrid approach combining conceptual
hydrological models, support vector machines and remote sensing data for
rainfall-runoff modeling, Remote Sens., 12, 1801, https://doi.org/10.3390/rs12111801, 2020.
Lin, P., Rajib, M. A., Yang, Z. L., Somos-Valenzuela, M., Merwade, V., Maidment, D. R., Wang, Y., and Chen, L.: Spatiotemporal Evaluation of
Simulated Evapotranspiration and Streamflow over Texas Using the WRF-Hydro-RAPID Modeling Framework, J. Am. Water Resour. Assoc., 54, 40–54, https://doi.org/10.1111/1752-1688.12585, 2018.
Lindström, G.: Lake water levels for calibration of the S-HYPE model,
Hydrol. Res., 47, 672–682, https://doi.org/10.2166/nh.2016.019, 2016.
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergström, S.: Development and test of the distributed HBV-96 model, J.
Hydrol., 201, 272–288, 1997.
Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., and Berit, A.:
Development and testing of the HYPE (Hydrological Predictions for the
Environment) water quality model for different spatial scales, Hydrol. Res., 4, 295–319, https://doi.org/10.2166/nh.2010.007, 2010.
Liu, Z., Shao, Q., and Liu, J.: The performances of MODIS-GPP and -ET products in China and their sensitivity to input data (FPAR/LAI), Remote
Sens., 7, 135–152, https://doi.org/10.3390/rs70100135, 2015.
Maggioni, V., Meyers, P. C., and Robinson, M. D.: A review of merged
high-resolution satellite precipitation product accuracy during the Tropical
Rainfall Measuring Mission (TRMM) era, J. Hydrometeorol., 17, 1101–1117,
https://doi.org/10.1175/JHM-D-15-0190.1, 2016.
Maldonado, T., Alfaro, E., Fallas-López, B., and Alvarado, L.: Seasonal
prediction of extreme precipitation events and frequency of rainy days over
Costa Rica, Central America, using Canonical Correlation Analysis, Adv. Geosci., 33, 41–52, https://doi.org/10.5194/adgeo-33-41-2013, 2013.
Mann, H. B. and Whitney, D. R.: On a test of whether one of two random variables is stochastically larger than the other, Ann. Math. Stat., 18,
50–60, 1947.
Massari, C., Brocca, L., Tarpanelli, A., and Moramarco, T.: Data assimilation of satellite soil moisture into rainfall-runoff modelling: A complex recipe?, Remote Sens., 7, 11403–11433, https://doi.org/10.3390/rs70911403, 2015.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters,
A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated
from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Monteiro, E. S. V., Fonte, C. C., and de Lima, J. L. M. P.: Analyzing the
potential of OpenStreetMap data to improve the accuracy of SRTM 30 DEM on
derived basin delineation, slope, and drainage networks, Hydrology, 5, 1–27, https://doi.org/10.3390/hydrology5030034, 2018.
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011.
Mu, Q., Zhao, M., and Running, S. W.: MODIS Global Terrestrial
Evapotranspiration (ET) Product (MOD16A2/A3), Algorithm Theor. Basis Doc,
https://modis-land.gsfc.nasa.gov/pdf/MOD16ATBD.pdf (last access: 11 May 2019), 2013.
Muñoz, E., Busalacchi, A. J., Nigam, S., and Ruiz-Barradas, A.: Winter
and summer structure of the Caribbean low-level jet, J. Climate, 21, 1260–1276, https://doi.org/10.1175/2007JCLI1855.1, 2008.
Neteler, M., Bowman, M. H., Landa, M., and Metz, M.: GRASS GIS: A multi-purpose open source GIS, Environ. Model. Softw., 31, 124–130,
https://doi.org/10.1016/j.envsoft.2011.11.014, 2012.
OACG: Hydrology for Costa Rica, https://zaul-ae.gitbook.io/oacg-hidrologia/v/english/, last access: 21 May 2021.
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of satellite-derived global land surface moisture, J. Geophys. Res.-Earth, 113, F01002, https://doi.org/10.1029/2007JF000769, 2008.
Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, H., Arora, V. K., Haverd, V., Jain, A. K., Kato, E., Lienert, S., Lombardozzi, D., Nabel, J. E. M. S., Ottlé, C., Poulter, B., Zaehle, S., and Running, S. W.: Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling, Hydrol. Earth Syst. Sci., 24, 1485–1509, https://doi.org/10.5194/hess-24-1485-2020, 2020.
Pechlivanidis, G. I., and Arheimer, B.: Large-scale hydrological modelling
by using modified PUB recommendations: The India-HYPE case, Hydrol. Earth
Syst. Sci., 19, 4559–4579, https://doi.org/10.5194/hess-19-4559-2015, 2015.
Pechlivanidis, G. I., Bosshard, T., Spångmyr, H., Lindström, G.,
Gustafsson, D., and Arheimer, B.: Uncertainty in the Swedish Operational
Hydrological Forecasting Systems, in: ASCE proceedings: Vulnerability,
Uncertainty, and Risk, 253–262, https://doi.org/10.1061/9780784413609.026, 2014.
Pugliese, A., Persiano, S., Bagli, S., Mazzoli, P., Parajka, J., Arheimer, B., Capell, R., Montanari, A., Blöschl, G., and Castellarin, A.: A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations, Hydrol. Earth Syst. Sci., 22, 4633–4648, https://doi.org/10.5194/hess-22-4633-2018, 2018.
Rajib, A., Evenson, G. R., Golden, H. E., and Lane, C. R.: Hydrologic model
predictability improves with spatially explicit calibration using remotely
sensed evapotranspiration and biophysical parameters, J. Hydrol., 567, 668–683, https://doi.org/10.1016/j.jhydrol.2018.10.024, 2018a.
Rajib, A., Merwade, V., and Yu, Z.: Rationale and Efficacy of Assimilating
Remotely Sensed Potential Evapotranspiration for Reduced Uncertainty of Hydrologic Models, Water Resour. Res., 54, 4615–4637, https://doi.org/10.1029/2017WR021147, 2018b.
Rakovec, O., Kumar, R., Attinger, S. and Samaniego, L.: Improving the realism of hydrologic model functioning through multivariate parameter estimation, Water Resour. Res., 52, 7779–7792, https://doi.org/10.1002/2016WR019430, 2016.
Raphael Tshimanga, M. and Hughes, D. A.: Basin-scale performance of a
semidistributed rainfall-runoff model for hydrological predictions and water
resources assessment of large rivers: The Congo River, Water Resour. Res.,
50, 1174–188, https://doi.org/10.1111/j.1752-1688.1969.tb04897.x, 2014.
Reager, J. T., Thomas, A. C., Sproles, E. A., Rodell, M., Beaudoing, H. K.,
Li, B., and Famiglietti, J. S.: Assimilation of GRACE terrestrial water storage observations into a land surface model for the assessment of regional flood potential, Remote Sens., 7, 14663–14679, https://doi.org/10.3390/rs71114663, 2015.
Rojas-Serna, C., Lebecherel, L., Perrin, C., Andréassian, V., and Oudin,
L.: How should a rainfall-runoff model be parameterized in an almost ungauged catchment? A methodology tested on 609 catchments, Water Resour. Res., 52, 4765–4784, https://doi.org/10.1002/2015WR018549, 2016.
Sáenz, F. and Durán-Quesada, A. M.: A climatology of low level wind
regimes over Central America using a weather type classification approach,
Front. Earth Sci., 3, 1–18, https://doi.org/10.3389/feart.2015.00015, 2015.
Santos, L., Thirel, G., and Perrin, C.: Technical note: Pitfalls in using log-transformed flows within the KGE criterion, Hydrol. Earth Syst. Sci., 22, 4583–4591, https://doi.org/10.5194/hess-22-4583-2018, 2018.
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.:
Catchment classification: Empirical analysis of hydrologic similarity based
on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15,
2895–2911, https://doi.org/10.5194/hess-15-2895-2011, 2011.
Seibert, J.: HBV light version 2 User's Manual, Department of Earth
Sciences, Hydrology, Sweden, https://www.geo.uzh.ch/dam/jcr:c8afa73c-ac90-478e-a8c7-929eed7b1b62/HBV_manual_2005.pdf (last access: 5 December 2019), 2005.
Sheffield, J., Wood, E. F., Pan, M., Beck, H., Coccia, G., Serrat-Capdevila,
A., and Verbist, K.: Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions, Water Resour. Res., 54, 9724–9758, https://doi.org/10.1029/2017WR022437, 2018.
Shepard, D.: A two-dimensional interpolation function for irregularly-spaced
data, in: Proceedings of the 1968 23rd ACM National Conference, ACM 1968,
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.154.6880&rep=rep1&type=pdf (last access: 11 September 2021), 1968.
Silvestro, F., Gabellani, S., Rudari, R., Delogu, F., Laiolo, P., and Boni, G.: Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data, Hydrol. Earth Syst. Sci., 19, 1727–1751, https://doi.org/10.5194/hess-19-1727-2015, 2015.
SMHI: HYPE model description, 1–113, https://hypeweb.smhi.se/wp-content/uploads/2020/03/hype_model_description.pdf (last access: 3 July 2021), 2018.
Sood, A. and Smakhtin, V.: Revue des modèles hydrologiques globaux, Hydrolog. Sci. J., 60, 549–565, https://doi.org/10.1080/02626667.2014.950580, 2015.
Tang, R., Li, Z. L., and Chen, K. S.: Validating MODIS-derived land surface
evapotranspiration with in situ measurements at two AmeriFlux sites in a
semiarid region, J. Geophys. Res.-Atmos., 116, 1–14,
https://doi.org/10.1029/2010JD014543, 2011.
Tanouchi, H., Olsson, J., Lindström, G., Kawamura, A., and Amaguchi, H.:
Improving Urban Runoff in Multi-Basin Hydrological Simulation by the HYPE
Model Using EEA Urban Atlas: A Case Study in the Sege River Basin, Sweden,
Hydrology, 6, 28, https://doi.org/10.3390/hydrology6010028, 2019.
Todini, E.: Hydrological catchment modelling: Past, present and future, Hydrol. Earth Syst. Sci., 11, 468–482, https://doi.org/10.5194/hess-11-468-2007, 2007.
Troch, P. A., Martinez, G. F., Pauwels, V. R. N., Durcik, M., Sivapalan, M.,
Harman, C., Brooks, P. D., Gupta, H., and Huxman, T.: Climate and vegetation
water use efficiency at catchment scales, Hydrol. Process., 23, 2409–2414,
https://doi.org/10.1002/hyp.7358, 2009.
Ullah, W., Wang, G., Ali, G., Fiifi, D., Hagan, T., Bhatti, A. S., and Lou,
D.: Comparing Multiple Precipitation Products against In-Situ Observations
over Different Climate Regions of Pakistan, Remote Sens., 11, 628,
https://doi.org/10.3390/rs11060628, 2019.
Velpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S., and Verdin, J. P.: A
comprehensive evaluation of two MODIS evapotranspiration products over the
conterminous United States: Using point and gridded FLUXNET and water
balance ET, Remote Sens. Environ., 139, 35–49, https://doi.org/10.1016/j.rse.2013.07.013, 2013.
Walsh, R. P. D. and Lawler, D. M.: Rainfall seasonality spatial patterns and change through time, Weather, 36, 201–208, 1981.
Wang, L. and Liu, H.: An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling, Int. J. Geogr. Inf. Sci., 20, 193–213, https://doi.org/10.1080/13658810500433453, 2006.
Waylen, P. R., Caviedes, C. N., and Quesada, M. E.: Interannual variability
of monthly precipitation in Costa Rica, J. Climate, 9, 2606–2613, 1996.
Weerasinghe, I., Bastiaanssen, W., Mul, M., Jia, L., and van Griensven, A.: Can we trust remote sensing evapotranspiration products over Africa?, Hydrol. Earth Syst. Sci., 24, 1565–1586, https://doi.org/10.5194/hess-24-1565-2020, 2020.
Westerberg, I. K. and Birkel, C.: Observational uncertainties in hypothesis
testing: Investigating the hydrological functioning of a tropical catchment,
Hydrol. Process., 29, 4863–4879, https://doi.org/10.1002/hyp.10533, 2015.
Westerberg, I. K. and McMillan, H. K.: Uncertainty in hydrological signatures, Hydrol. Earth Syst. Sci., 19, 3951–3968,
https://doi.org/10.5194/hess-19-3951-2015, 2015.
Westerberg, I. K., Gong, L., Beven, K. J., Seibert, J., Semedo, A., Xu, C. Y., and Halldin, S.: Regional water balance modelling using flow-duration
curves with observational uncertainties, Hydrol. Earth Syst. Sci., 18,
2993–3013, https://doi.org/10.5194/hess-18-2993-2014, 2014.
Wohl, E., Barros, A., Brunsell, N., Chappell, N. A., Coe, M., Giambelluca, T., Goldsmith, S., Harmon, R., Hendrickx, J. M. H., Juvik, J., McDonnell, J.,
and Ogden, F.: The hydrology of the humid tropics, Nat. Clim. Change, 2,
655–662, https://doi.org/10.1038/nclimate1556, 2012.
Wörner, V., Kreye, P., and Meon, G.: Effects of bias-correcting climate
model data on the projection of future changes in high flows, Hydrology, 6, 46, https://doi.org/10.3390/hydrology6020046, 2019.
Xiong, L. and Zeng, L.: Impacts of introducing remote sensing soil moisture
in calibrating a distributed hydrological model for streamflow simulation,
Water, 11, 666, https://doi.org/10.3390/w11040666, 2019.
Zambrano-Bigiarini, M., Nauditt, A., Birkel, C., Verbist, K., and Ribbe, L.:
Temporal and spatial evaluation of satellite-based rainfall estimates across
the complex topographical and climatic gradients of Chile, Hydrol. Earth
Syst. Sci., 21, 1295–1320, https://doi.org/10.5194/hess-21-1295-2017, 2017.
Zhang, R., Liu, J., Gao, H., and Mao, G.: Can multi-objective calibration of
streamflow guarantee better hydrological model accuracy?, J. Hydroinform., 20, 687–698, https://doi.org/10.2166/hydro.2018.131, 2018.
Ziegler, A. D., Giambelluca, T. W., Plondke, D., Leisz, S., Tran, L. T., Fox, J., Nullet, M. A., Vogler, J. B., Minh Troung, D., and Tran Duc, V.: Hydrological consequences of landscape fragmentation in mountainous northern
Vietnam: Buffering of Hortonian overland flow, J. Hydrol., 337, 52–67, https://doi.org/10.1016/j.jhydrol.2007.01.031, 2007.
Short summary
In the humid tropics, a notoriously data-scarce region, we need to find alternatives in order to reasonably apply hydrological models. Here, we tested remotely sensed rainfall data in order to drive a model for Costa Rica, and we evaluated the simulations against evapotranspiration satellite products. We found that our model was able to reasonably simulate the water balance and streamflow dynamics of over 600 catchments where the satellite data helped to reduce the model uncertainties.
In the humid tropics, a notoriously data-scarce region, we need to find alternatives in order to...
