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8 results on '"Beck, H.E."'

1. Global high-resolution drought indices for 1981–2022

Author

Gebrechorkos, S.H., Peng, Jian, Dyer, E., Miralles, D.G., Vicente-Serrano, S.M., Funk, C., Beck, H.E., Asfaw, D.T., Singer, M.B., Dadson, S.J., Gebrechorkos, S.H., Peng, Jian, Dyer, E., Miralles, D.G., Vicente-Serrano, S.M., Funk, C., Beck, H.E., Asfaw, D.T., Singer, M.B., and Dadson, S.J.

Abstract

Droughts are among the most complex and devastating natural hazards globally. High-resolution datasets of drought metrics are essential for monitoring and quantifying the severity, duration, frequency, and spatial extent of droughts at regional and particularly local scales. However, current global drought indices are available only at a coarser spatial resolution (>50 km). To fill this gap, we developed four high-resolution (5 km) gridded drought records based on the standardized precipitation evaporation index (SPEI) covering the period 1981–2022. These multi-scale (1–48 months) SPEI indices are computed based on monthly precipitation (P) from the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS, version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8), and potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a) and hourly Potential Evapotranspiration (hPET). We generated four SPEI records based on all possible combinations of P and PET datasets: CHIRPS_GLEAM, CHIRPS_hPET, MSWEP_GLEAM, and MSWEP_hPET. These drought records were evaluated globally and exhibited excellent agreement with observation-based estimates of SPEI, root zone soil moisture, and vegetation health indices. The newly developed high-resolution datasets provide more detailed local information and can be used to assess drought severity for particular periods and regions and to determine global, regional, and local trends, thereby supporting the development of site-specific adaptation measures. These datasets are publicly available at the Centre for Environmental Data Analysis (CEDA; https://doi.org/10.5285/ac43da11867243a1bb414e1637802dec) (Gebrechorkos et al., 2023).

Published
2023

2. Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches

Author

Pan, M., Gleason, C.J., Durand, M., Lin, P., David, C.H., Wood, E.F., Yang, Y., Yamazaki, D., Allen, G.H., Beck, H.E., Pavelsky, T.M., and Frasson, R.

Abstract

Spatiotemporally continuous global river discharge estimates across the full spectrum of stream orders are vital to a range of hydrologic applications, yet they remain poorly constrained. Here we present a carefully designed modeling effort (Variable Infiltration Capacity land surface model and Routing Application for Parallel computatIon of Discharge river routing model) to estimate global river discharge at very high resolutions. The precipitation forcing is from a recently published 0.1° global product that optimally merged gauge-, reanalysis-, and satellite-based data. To constrain runoff simulations, we use a set of machine learning-derived, global runoff characteristics maps (i.e., runoff at various exceedance probability percentiles) for grid-by-grid model calibration and bias correction. To support spaceborne discharge studies, the river flowlines are defined at their true geometry and location as much as possible—approximately 2.94 million vector flowlines (median length 6.8 km) and unit catchments are derived from a high-accuracy global digital elevation model at 3-arcsec resolution (~90 m), which serves as the underlying hydrography for river routing. Our 35-year daily and monthly model simulations are evaluated against over 14,000 gauges globally. Among them, 35% (64%) have a percentage bias within ±20% (±50%), and 29% (62%) have a monthly Kling-Gupta Efficiency ≥0.6 (0.2), showing data robustness at the scale the model is assessed. This reconstructed discharge record can be used as a priori information for the Surface Water and Ocean Topography satellite mission's discharge product, thus named “Global Reach-level A priori Discharge Estimates for Surface Water and Ocean Topography”. It can also be used in other hydrologic applications requiring spatially explicit estimates of global river flows.

Published
2019
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3. Global-scale regionalization of hydrologic model parameters

Author

Beck, H.E., Van Dijk, A.I.J.M., de Roo, A., Gonzalez Miralles, D., McVicar, T.R., Schellekens, J., Bruijnzeel, L.A., Dynamic Earth and Resources, Earth and Climate, Earth Sciences, and Amsterdam Global Change Institute

Subjects
SDG 14 - Life Below Water
Abstract

Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments (10-10,000 km

Published
2016
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4. State of the climate in 2018

Author

Ades, M., Adler, R., Aldeco, L.S., Alejandra, G., Alfaro, E.J., Aliaga-Nestares, V., Allan, R.P., Allan, R., Alves, L.M., Amador, J.A., Andersen, J.K., Anderson, J., Arndt, D.S., Arosio, C., Arrigo, K., Azorin-Molina, C., Bardin, M.Y., Barichivich, J., Barreira, S., Baxter, S., Beck, H.E., Becker, A., Bell, G.D., Bellouin, N., Belmont, M., Benedetti, A., Benedict, I., Bernhard, G.H., Berrisford, P., Berry, D.I., Bettio, L., Bhatt, U.S., Biskaborn, B.K., Bissolli, P., Bjella, K.L., Bjerke, J.K., Blake, E.S., Blenkinsop, S., Blunden, J., Bock, O., Bosilovich, M.G., Boucher, O., Box, J.E., Boyer, T., Braathen, G., Bringas, F.G., Bromwich, D.H., Brown, A., Brown, R., Brown, T.J., Buehler, S.A., Cáceres, L., Calderón, B., Camargo, S.J., Campbell, J.D., Campos Diaz, D.A., Cappelen, J., Carrea, L., Carrier, S.B., Carter, B.R., Castro, A.Y., Cetinic, I., Chambers, D.P., Chen, L., Cheng, L., Cheng, V.Y.S., Christiansen, H.H., Christy, J.R., Chung, E.-S., Claus, F., Clem, K.R., Coelho, C.A.S., Coldewey-Egbers, M., Colwell, S., Cooper, O.R., Cosca, C., Covey, C., Coy, L., Dávila, C.P., Davis, S.M., de Eyto, E., de Jeu, R.A.M., De Laat, J., Decharme, B., Degasperi, C.L., Degenstein, D., Demircan, M., Derksen, C., Dhurmea, K.R., Di Girolamo, L., Diamond, H.J., Diaz, E., Diniz, F.A., Dlugokencky, E.J., Dohan, K., Dokulil, M.T., Dolman, A.J., Domingues, C.M., Domingues, R., Donat, M.G., Dorigo, W.A., Drozdov, D.S., Druckenmiller, M.L., Dunn, R.J.H., Durre, I., Dutton, G.S., Elkharrim, M., Elkins, J.W., Epstein, H.E., Espinoza, J.C., Famiglietti, J.S., Farrell, S.L., Fausto, R.S., Feely, R.A., Feng, Z., Fenimore, C., Fettweis, X., Fioletov, V.E., Flemming, J., Fogt, R.L., Forbes, B.C., Foster, M.J., Francis, S.D., Franz, B.A., Frey, R.A., Frith, S.M., Froidevaux, L., Ganter, C., Garforth, J., Gerland, S., Gilson, J., Gleason, K., Gobron, N., Goetz, S., Goldenberg, S.B., Goni, G., Gray, A., Groo, J.-U., Gruber, A., Gu, G., Guard, C.C.P., Gupta, S.K., Gutiérrez, D., Haas, Christian, Hagos, S., Hahn, S., Haimberger, L., Hall, B.D., Halpert, M.S., Hamlington, B.D., Hanna, E., Hanssen-Bauer, I., Harris, I., Hazeleger, W., He, Q., Heidinger, A.K., Heim, Jr., Hemming, D.L., Hendricks, Stefan, Hernández, R., Hersbach, H.E., Hidalgo, H.G., Ho, S.-P.B., Holmes, R.M., Hu, C., Huang, B., Hubbard, K., Hubert, D., Hurst, D.F., Ialongo, I., Ijampy, J.A., Inness, A., Isaac, V., Isaksen, K., Ishii, M., Jeffries, M.O., Jevrejeva, S., Jia, G., Jiménez, C., Jin, X., John, V., Johnsen, B., Johnson, G.C., Johnson, K.S., Johnson, B., Jones, P.D., Jumaux, G., Kabidi, K., Kaiser, J.W., Karaköylü, E.M., Karlsen, S.-R., Karnauskas, M., Kato, S., Kazemi, A.F., Kelble, C., Keller, L.M., Kennedy, J., Kholodov, A.L., Khoshkam, M., Kidd, R., Killick, R., Kim, H., Kim, S.-J., King, A.D., King, B.A., Kipling, Z., Klotzbach, P.J., Knaff, J.A., Korhonen, J., Korshunova, N.N., Kramarova, N.A., Kratz, D.P., Kruger, A., Kruk, M.C., Krumpen, Thomas, Labbé, L., Ladd, C., Lakatos, M., Lakkala, K., Lander, M.A., Landschützer, P., Landsea, C.W., Lareau, N.P., Lavado-Casimiro, W., Lazzara, M.A., Lee, T.C., Leuliette, E., L�heureux, M., Li, B., Li, T., Lieser, J.L., Lim, J.-Y., Lin, I.-I., Liu, H., Locarnini, R., Loeb, N.G., Long, C.S., López, L.A., Lorrey, A.M., Loyola, D., Lumpkin, R., Luo, J.-J., Luojus, K., Lyman, J.M., Malkova, G.V., Manney, G.L., Marchenko, S.S., Marengo, J.A., Marin, D., Marquardt Collow, A.B., Marra, J.J., Marszelewski, W., Martens, B., Martínez-Güingla, R., Massom, R.A., May, L., Mayer, M., Mazloff, M., McBride, C., McCabe, M., McClelland, J.W., McEvoy, D.J., McGree, S., McVicar, T.R., Mears, C.A., Meier, W., Meijers, A., Mekonnen, A., Mengistu Tsidu, G., Menzel, W.P., Merchant, C.J., Meredith, M.P., Merrifield, M.A., Miller, B., Miralles, D.G., Misevicius, N., Mitchum, G.T., Mochizuki, Y., Monselesan, D., Montzka, S.A., Mora, N., Morice, C., Mosquera-Vásquez, K., Mostafa, A.E., Mote, T., Mudryk, L., Mühle, J., Mullan, A.B., Müller, R., Myneni, R., Nash, E.R., Nauslar, N.J., Nerem, R.S., Newman, P.A., Nicolas, J.P., Nieto, J.J., Noetzli, J., Osborn, T.J., Osborne, E., Overland, J., Oyunjargal, L., Park, T., Pasch, R.J., Pascual Ramírez, R., Pastor Saavedra, M.A., Paterson, A.M., Pearce, P.R., Pelto, M.S., Perovich, D., Petropavlovskikh, I., Pezza, A.B., Phillips, C., Phillips, D., Phoenix, G., Pinty, B., Pitts, M., Po-Chedley, S., Polashenski, C., Preimesberger, W., Purkey, S.G., Quispe, N., Rajeevan, M., Rakotoarimalala, C.L., Ramos, A.M., Ramos, I., Randel, W., Raynolds, M.K., Reagan, J., Reid, P., Reimer, C., Rémy, S., Revadekar, J.V., Richardson, A.D., Richter-Menge, J., Ricker, Robert, Ripaldi, A., Robinson, D.A., Rodell, M., Rodriguez Camino, E., Romanovsky, V.E., Ronchail, J., Rosenlof, K.H., Rösner, B., Roth, C., Rozanov, A., Rusak, J.A., Rustemeier, E., Rutishäuser, T., Sallée, J.-B., Sánchez-Lugo, A., Santee, M.L., Sawaengphokhai, P., Sayouri, A., Scambos, T.A., Scanlon, T., Scardilli, A.S., Schenzinger, V., Schladow, S.G., Schmid, C., Schmid, M., Schoeneich, P., Schreck, III, Selkirk, H.B., Sensoy, S., Shi, L., Shiklomanov, A.I., Shiklomanov, N.I., Shimpo, A., Shuman, C.A., Siegel, D.A., Sima, F., Simmons, A.J., Smeets, C.J.P.P., Smith, A., Smith, S.L., Soden, B., Sofieva, V., Sparks, T.H., Spence, J., Spencer, R.G.M., Spillane, S., Srivastava, A.K., Stabeno, P.J., Stackhouse, Jr., Stammerjohn, S., Stanitski, D.M., Steinbrecht, W., Stella, J.L., Stengel, M., Stephenson, T.S., Strahan, S.E., Streeter, C., Streletskiy, D.A., Sun-Mack, S., Suslova, A., Sutton, A.J., Swart, S., Sweet, W., Takahashi, K.S., Tank, S.E., Taylor, M.A., Tedesco, M., Thackeray, S.J., Thompson, P.R., Timbal, B., Timmermans, M.-L., Tobin, S., Tømmervik, H., Tourpali, K., Trachte, K., Tretiakov, M., Trewin, B.C., Triñanes, J.A., Trotman, A.R., Tschudi, M., Tye, M.R., van As, D., van de Wal, R.S.W., van der A, R.J., van der Schalie, R., van der Schrier, G., van der Werf, G.R., van Heerwaarden, C., Van Meerbeeck, C.J., Verburg, P., Vieira, G., Vincent, L.A., Vömel, H., Vose, R.S., Walker, D.A., Walsh, J.E., Wang, B., Wang, H., Wang, L., Wang, M., Wang, R., Wang, S.-H., Wanninkhof, R., Watanabe, S., Weber, M., Webster, M., Weerts, A., Weller, R.A., Westberry, T.K., Weyhenmeyer, G.A., Widlansky, M.J., Wijffels, S.E., Wilber, A.C., Wild, J.D., Willett, K.M., Wong, T., Wood, E.F., Woolway, R.I., Xue, Y., Yin, X., Yu, L., Zambrano, E., Zeyaeyan, S., Zhang, H.-M., Zhang, P., Zhao, G., Zhao, L., Zhou, X., Zhu, Z., Ziemke, J.R., Ziese, M., Andersen, A., Griffin, J., Hammer, G., Love-Brotak, S.E., Misch, D.J., Riddle, D.B., Veasey, S.W., Ades, M., Adler, R., Aldeco, L.S., Alejandra, G., Alfaro, E.J., Aliaga-Nestares, V., Allan, R.P., Allan, R., Alves, L.M., Amador, J.A., Andersen, J.K., Anderson, J., Arndt, D.S., Arosio, C., Arrigo, K., Azorin-Molina, C., Bardin, M.Y., Barichivich, J., Barreira, S., Baxter, S., Beck, H.E., Becker, A., Bell, G.D., Bellouin, N., Belmont, M., Benedetti, A., Benedict, I., Bernhard, G.H., Berrisford, P., Berry, D.I., Bettio, L., Bhatt, U.S., Biskaborn, B.K., Bissolli, P., Bjella, K.L., Bjerke, J.K., Blake, E.S., Blenkinsop, S., Blunden, J., Bock, O., Bosilovich, M.G., Boucher, O., Box, J.E., Boyer, T., Braathen, G., Bringas, F.G., Bromwich, D.H., Brown, A., Brown, R., Brown, T.J., Buehler, S.A., Cáceres, L., Calderón, B., Camargo, S.J., Campbell, J.D., Campos Diaz, D.A., Cappelen, J., Carrea, L., Carrier, S.B., Carter, B.R., Castro, A.Y., Cetinic, I., Chambers, D.P., Chen, L., Cheng, L., Cheng, V.Y.S., Christiansen, H.H., Christy, J.R., Chung, E.-S., Claus, F., Clem, K.R., Coelho, C.A.S., Coldewey-Egbers, M., Colwell, S., Cooper, O.R., Cosca, C., Covey, C., Coy, L., Dávila, C.P., Davis, S.M., de Eyto, E., de Jeu, R.A.M., De Laat, J., Decharme, B., Degasperi, C.L., Degenstein, D., Demircan, M., Derksen, C., Dhurmea, K.R., Di Girolamo, L., Diamond, H.J., Diaz, E., Diniz, F.A., Dlugokencky, E.J., Dohan, K., Dokulil, M.T., Dolman, A.J., Domingues, C.M., Domingues, R., Donat, M.G., Dorigo, W.A., Drozdov, D.S., Druckenmiller, M.L., Dunn, R.J.H., Durre, I., Dutton, G.S., Elkharrim, M., Elkins, J.W., Epstein, H.E., Espinoza, J.C., Famiglietti, J.S., Farrell, S.L., Fausto, R.S., Feely, R.A., Feng, Z., Fenimore, C., Fettweis, X., Fioletov, V.E., Flemming, J., Fogt, R.L., Forbes, B.C., Foster, M.J., Francis, S.D., Franz, B.A., Frey, R.A., Frith, S.M., Froidevaux, L., Ganter, C., Garforth, J., Gerland, S., Gilson, J., Gleason, K., Gobron, N., Goetz, S., Goldenberg, S.B., Goni, G., Gray, A., Groo, J.-U., Gruber, A., Gu, G., Guard, C.C.P., Gupta, S.K., Gutiérrez, D., Haas, Christian, Hagos, S., Hahn, S., Haimberger, L., Hall, B.D., Halpert, M.S., Hamlington, B.D., Hanna, E., Hanssen-Bauer, I., Harris, I., Hazeleger, W., He, Q., Heidinger, A.K., Heim, Jr., Hemming, D.L., Hendricks, Stefan, Hernández, R., Hersbach, H.E., Hidalgo, H.G., Ho, S.-P.B., Holmes, R.M., Hu, C., Huang, B., Hubbard, K., Hubert, D., Hurst, D.F., Ialongo, I., Ijampy, J.A., Inness, A., Isaac, V., Isaksen, K., Ishii, M., Jeffries, M.O., Jevrejeva, S., Jia, G., Jiménez, C., Jin, X., John, V., Johnsen, B., Johnson, G.C., Johnson, K.S., Johnson, B., Jones, P.D., Jumaux, G., Kabidi, K., Kaiser, J.W., Karaköylü, E.M., Karlsen, S.-R., Karnauskas, M., Kato, S., Kazemi, A.F., Kelble, C., Keller, L.M., Kennedy, J., Kholodov, A.L., Khoshkam, M., Kidd, R., Killick, R., Kim, H., Kim, S.-J., King, A.D., King, B.A., Kipling, Z., Klotzbach, P.J., Knaff, J.A., Korhonen, J., Korshunova, N.N., Kramarova, N.A., Kratz, D.P., Kruger, A., Kruk, M.C., Krumpen, Thomas, Labbé, L., Ladd, C., Lakatos, M., Lakkala, K., Lander, M.A., Landschützer, P., Landsea, C.W., Lareau, N.P., Lavado-Casimiro, W., Lazzara, M.A., Lee, T.C., Leuliette, E., L�heureux, M., Li, B., Li, T., Lieser, J.L., Lim, J.-Y., Lin, I.-I., Liu, H., Locarnini, R., Loeb, N.G., Long, C.S., López, L.A., Lorrey, A.M., Loyola, D., Lumpkin, R., Luo, J.-J., Luojus, K., Lyman, J.M., Malkova, G.V., Manney, G.L., Marchenko, S.S., Marengo, J.A., Marin, D., Marquardt Collow, A.B., Marra, J.J., Marszelewski, W., Martens, B., Martínez-Güingla, R., Massom, R.A., May, L., Mayer, M., Mazloff, M., McBride, C., McCabe, M., McClelland, J.W., McEvoy, D.J., McGree, S., McVicar, T.R., Mears, C.A., Meier, W., Meijers, A., Mekonnen, A., Mengistu Tsidu, G., Menzel, W.P., Merchant, C.J., Meredith, M.P., Merrifield, M.A., Miller, B., Miralles, D.G., Misevicius, N., Mitchum, G.T., Mochizuki, Y., Monselesan, D., Montzka, S.A., Mora, N., Morice, C., Mosquera-Vásquez, K., Mostafa, A.E., Mote, T., Mudryk, L., Mühle, J., Mullan, A.B., Müller, R., Myneni, R., Nash, E.R., Nauslar, N.J., Nerem, R.S., Newman, P.A., Nicolas, J.P., Nieto, J.J., Noetzli, J., Osborn, T.J., Osborne, E., Overland, J., Oyunjargal, L., Park, T., Pasch, R.J., Pascual Ramírez, R., Pastor Saavedra, M.A., Paterson, A.M., Pearce, P.R., Pelto, M.S., Perovich, D., Petropavlovskikh, I., Pezza, A.B., Phillips, C., Phillips, D., Phoenix, G., Pinty, B., Pitts, M., Po-Chedley, S., Polashenski, C., Preimesberger, W., Purkey, S.G., Quispe, N., Rajeevan, M., Rakotoarimalala, C.L., Ramos, A.M., Ramos, I., Randel, W., Raynolds, M.K., Reagan, J., Reid, P., Reimer, C., Rémy, S., Revadekar, J.V., Richardson, A.D., Richter-Menge, J., Ricker, Robert, Ripaldi, A., Robinson, D.A., Rodell, M., Rodriguez Camino, E., Romanovsky, V.E., Ronchail, J., Rosenlof, K.H., Rösner, B., Roth, C., Rozanov, A., Rusak, J.A., Rustemeier, E., Rutishäuser, T., Sallée, J.-B., Sánchez-Lugo, A., Santee, M.L., Sawaengphokhai, P., Sayouri, A., Scambos, T.A., Scanlon, T., Scardilli, A.S., Schenzinger, V., Schladow, S.G., Schmid, C., Schmid, M., Schoeneich, P., Schreck, III, Selkirk, H.B., Sensoy, S., Shi, L., Shiklomanov, A.I., Shiklomanov, N.I., Shimpo, A., Shuman, C.A., Siegel, D.A., Sima, F., Simmons, A.J., Smeets, C.J.P.P., Smith, A., Smith, S.L., Soden, B., Sofieva, V., Sparks, T.H., Spence, J., Spencer, R.G.M., Spillane, S., Srivastava, A.K., Stabeno, P.J., Stackhouse, Jr., Stammerjohn, S., Stanitski, D.M., Steinbrecht, W., Stella, J.L., Stengel, M., Stephenson, T.S., Strahan, S.E., Streeter, C., Streletskiy, D.A., Sun-Mack, S., Suslova, A., Sutton, A.J., Swart, S., Sweet, W., Takahashi, K.S., Tank, S.E., Taylor, M.A., Tedesco, M., Thackeray, S.J., Thompson, P.R., Timbal, B., Timmermans, M.-L., Tobin, S., Tømmervik, H., Tourpali, K., Trachte, K., Tretiakov, M., Trewin, B.C., Triñanes, J.A., Trotman, A.R., Tschudi, M., Tye, M.R., van As, D., van de Wal, R.S.W., van der A, R.J., van der Schalie, R., van der Schrier, G., van der Werf, G.R., van Heerwaarden, C., Van Meerbeeck, C.J., Verburg, P., Vieira, G., Vincent, L.A., Vömel, H., Vose, R.S., Walker, D.A., Walsh, J.E., Wang, B., Wang, H., Wang, L., Wang, M., Wang, R., Wang, S.-H., Wanninkhof, R., Watanabe, S., Weber, M., Webster, M., Weerts, A., Weller, R.A., Westberry, T.K., Weyhenmeyer, G.A., Widlansky, M.J., Wijffels, S.E., Wilber, A.C., Wild, J.D., Willett, K.M., Wong, T., Wood, E.F., Woolway, R.I., Xue, Y., Yin, X., Yu, L., Zambrano, E., Zeyaeyan, S., Zhang, H.-M., Zhang, P., Zhao, G., Zhao, L., Zhou, X., Zhu, Z., Ziemke, J.R., Ziese, M., Andersen, A., Griffin, J., Hammer, G., Love-Brotak, S.E., Misch, D.J., Riddle, D.B., and Veasey, S.W.

Published
2019

5. Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

Author

van Dijk, A.I.J.M., Peña-Arancibia, J.L., Wood, E.F., Sheffield, J., Beck, H.E., Dynamic Earth and Resources, and Amsterdam Global Change Institute

Subjects
SDG 13 - Climate Action
Abstract

Key Points Global bimonthly streamflow forecasts show potentially valuable skill Initial catchment conditions are responsible for most skill Skill can be estimated from model performance and theoretical skill Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1° resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (

Published
2013
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6. The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society

Author

van Dijk, A.I.J.M., Beck, H.E., Crosbie, R.S., de Jeu, R.A.M., Liu, Y.Y., Podger, G.M., Timbal, B., Viney, N.R., Dynamic Earth and Resources, Earth and Climate, and Amsterdam Global Change Institute

Subjects
SDG 6 - Clean Water and Sanitation
Abstract

The "Millennium Drought" (2001-2009) can be described as the worst drought on record for southeast Australia. Adaptation to future severe droughts requires insight into the drivers of the drought and its impacts. These were analyzed using climate, water, economic, and remote sensing data combined with biophysical modeling. Prevailing El Ni~no conditions explained about two thirds of rainfall deficit in east Australia. Results for south Australia were inconclusive; a contribution from global climate change remains plausible but unproven. Natural processes changed the timing and magnitude of soil moisture, streamflow, and groundwater deficits by up to several years, and caused the amplification of rainfall declines in streamflow to be greater than in normal dry years. By design, river management avoided impacts on some categories of water users, but did so by exacerbating the impacts on annual irrigation agriculture and, in particular, river ecosystems. Relative rainfall reductions were amplified 1.5-1.7 times in dryland wheat yields, but the impact was offset by steady increases in cropping area and crop water use efficiency (perhaps partly due to CO2 fertilization). Impacts beyond the agricultural sector occurred (e.g., forestry, tourism, utilities) but were often diffuse and not well quantified. Key causative pathways from physical drought to the degradation of ecological, economic, and social health remain poorly understood and quantified. Combined with the multiple dimensions of multiyear droughts and the specter of climate change, this means future droughts may well break records in ever new ways and not necessarily be managed better than past ones. © 2013. American Geophysical Union. All Rights Reserved.

Published
2013
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8. Filling the gaps: calibrating a rainfall-runoff model using a satellite-derived surface water extent

Author

Revilla-Romero, B., Beck, H.E., Burek, P., Salamon, P., de roo, A., Thielen, J., Revilla-Romero, B., Beck, H.E., Burek, P., Salamon, P., de roo, A., and Thielen, J.

Abstract

Calibration is a crucial step in the application of hydrological models and is typically performed using in situ streamflow data. However, many rivers on the globe are ungauged or poorly gauged, or the gauged data are not readily available. In this study, we used remotely- sensed surface water extent from the Global Flood Detection System (GFDS) as a proxy for streamflow, and tested its value for calibration of the distributed rainfall-runoff routing model LISFLOOD. In a first step, we identified 30 streamflow gauging sites with a high likelihood of reliable GFDS data. Next, for each of these 30 sites, the model parameters related to groundwater and routing were independently calibrated against in situ and GFDS-derived streamflow time series, and against the raw GFDS surface water extent time series. We compared the performance of the three calibrated and the uncalibrated model simulations in terms of reproducing the in situ streamflow time series. Furthermore, we calculated the gain achieved by each scenario that used satellite-derive information relative to the reference uncalibrated scenario and the one that used in situ data. Results show that using the raw GFDS data as a proxy for streamflow for calibration improved the skill of the simulated streamflow in particular the high flows) for 21 of the 30 sites using correlation as a metric. Furthermore, we discuss a calibration strategy using a combination of in situ and satellite data for global hydrological models.

Published
2015

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