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4. Long-term thermal sensitivity of Earth's tropical forests

Author

Sullivan, M.J.P., Lewis, S.L., Affum-Baffoe, K., Castilho, C., Costa, F., Sanchez, A.C., Ewango, C.E.N., Hubau, W., Marimon, B., Monteagudo-Mendoza, A., Qie, L., Sonké, B., Martinez, R.V., Baker, T.R., Brienen, R.J.W., Feldpausch, T.R., Galbraith, D., Gloor, M., Malhi, Y., Aiba, S.-I., Alexiades, M.N., Almeida, E.C., de Oliveira, E.A., Dávila, E.Á., Loayza, P.A., Andrade, A., Vieira, S.A., Aragão, L.E.O.C., Araujo-Murakami, A., Arets, E.J.M.M., Arroyo, L., Ashton, P., Aymard C, G., Baccaro, F.B., Banin, L.F., Baraloto, C., Camargo, P.B., Barlow, J., Barroso, J., Bastin, J.-F., Batterman, S.A., Beeckman, H., Begne, S.K., Bennett, A.C., Berenguer, E., Berry, N., Blanc, L., Boeckx, P., Bogaert, J., Bonal, D., Bongers, F., Bradford, M., Brearley, F.Q., Brncic, T., Brown, F., Burban, B., Camargo, J.L., Castro, W., Céron, C., Ribeiro, S.C., Moscoso, V.C., Chave, J., Chezeaux, E., Clark, C.J., de Souza, F.C., Collins, M., Comiskey, J.A., Valverde, F.C., Medina, M.C., da Costa, L., Dančák, M., Dargie, G.C., Davies, S., Cardozo, N.D., de Haulleville, T., de Medeiros, M.B., Del Aguila Pasquel, J., Derroire, G., Di Fiore, A., Doucet, J.-L., Dourdain, A., Droissant, V., Duque, L.F., Ekoungoulou, R., Elias, F., Erwin, T., Esquivel-Muelbert, A., Fauset, S., Ferreira, J., Llampazo, G.F., Foli, E., Ford, A., Gilpin, M., Hall, J.S., Hamer, K.C., Hamilton, A.C., Harris, D.J., Hart, T.B., Hédl, R., Herault, B., Herrera, R., Higuchi, N., Hladik, A., Coronado, E.H., Huamantupa-Chuquimaco, I., Huasco, W.H., Jeffery, K.J., Jimenez-Rojas, E., Kalamandeen, M., Djuikouo, M.N.K., Kearsley, E., Umetsu, R.K., Kho, L.K., Killeen, T., Kitayama, K., Klitgaard, B., Koch, A., Labrière, N., Laurance, W., Laurance, S., Leal, M.E., Levesley, A., Lima, A.J.N., Lisingo, J., Lopes, A.P., Lopez-Gonzalez, G., Lovejoy, T., Lovett, J.C., Lowe, R., Magnusson, W.E., Malumbres-Olarte, J., Manzatto, ÂG., Marimon B.H., Jr, Marshall, A.R., Marthews, T., de Almeida Reis, S.M., Maycock, C., Melgaço, K., Mendoza, C., Metali, F., Mihindou, V., Milliken, W., Mitchard, E.T.A., Morandi, P.S., Mossman, H.L., Nagy, L., Nascimento, H., Neill, D., Nilus, R., Vargas, P.N., Palacios, W., Camacho, N.P., Peacock, J., Pendry, C., Peñuela Mora, M.C., Pickavance, G.C., Pipoly, J., Pitman, N., Playfair, M., Poorter, L., Poulsen, J.R., Poulsen, A.D., Preziosi, R., Prieto, A., Primack, R.B., Ramírez-Angulo, H., Reitsma, J., Réjou-Méchain, M., Correa, Z.R., de Sousa, T.R., Bayona, L.R., Roopsind, A., Rudas, A., Rutishauser, E., Abu Salim, K., Salomão, R.P., Schietti, J., Sheil, D., Silva, R.C., Espejo, J.S., Valeria, C.S., Silveira, M., Simo-Droissart, M., Simon, M.F., Singh, J., Soto Shareva, Y.C., Stahl, C., Stropp, J., Sukri, R., Sunderland, T., Svátek, M., Swaine, M.D., Swamy, V., Taedoumg, H., Talbot, J., Taplin, J., Taylor, D., Ter Steege, H., Terborgh, J., Thomas, R., Thomas, S.C., Torres-Lezama, A., Umunay, P., Gamarra, L.V., van der Heijden, G., van der Hout, P., van der Meer, P., van Nieuwstadt, M., Verbeeck, H., Vernimmen, R., Vicentini, A., Vieira, I.C.G., Torre, E.V., Vleminckx, J., Vos, V., Wang, O., White, L.J.T., Willcock, S., Woods, J.T., Wortel, V., Young, K., Zagt, R., Zemagho, L., Zuidema, P.A., Zwerts, J.A., Phillips, O.L., Sullivan, M.J.P., Lewis, S.L., Affum-Baffoe, K., Castilho, C., Costa, F., Sanchez, A.C., Ewango, C.E.N., Hubau, W., Marimon, B., Monteagudo-Mendoza, A., Qie, L., Sonké, B., Martinez, R.V., Baker, T.R., Brienen, R.J.W., Feldpausch, T.R., Galbraith, D., Gloor, M., Malhi, Y., Aiba, S.-I., Alexiades, M.N., Almeida, E.C., de Oliveira, E.A., Dávila, E.Á., Loayza, P.A., Andrade, A., Vieira, S.A., Aragão, L.E.O.C., Araujo-Murakami, A., Arets, E.J.M.M., Arroyo, L., Ashton, P., Aymard C, G., Baccaro, F.B., Banin, L.F., Baraloto, C., Camargo, P.B., Barlow, J., Barroso, J., Bastin, J.-F., Batterman, S.A., Beeckman, H., Begne, S.K., Bennett, A.C., Berenguer, E., Berry, N., Blanc, L., Boeckx, P., Bogaert, J., Bonal, D., Bongers, F., Bradford, M., Brearley, F.Q., Brncic, T., Brown, F., Burban, B., Camargo, J.L., Castro, W., Céron, C., Ribeiro, S.C., Moscoso, V.C., Chave, J., Chezeaux, E., Clark, C.J., de Souza, F.C., Collins, M., Comiskey, J.A., Valverde, F.C., Medina, M.C., da Costa, L., Dančák, M., Dargie, G.C., Davies, S., Cardozo, N.D., de Haulleville, T., de Medeiros, M.B., Del Aguila Pasquel, J., Derroire, G., Di Fiore, A., Doucet, J.-L., Dourdain, A., Droissant, V., Duque, L.F., Ekoungoulou, R., Elias, F., Erwin, T., Esquivel-Muelbert, A., Fauset, S., Ferreira, J., Llampazo, G.F., Foli, E., Ford, A., Gilpin, M., Hall, J.S., Hamer, K.C., Hamilton, A.C., Harris, D.J., Hart, T.B., Hédl, R., Herault, B., Herrera, R., Higuchi, N., Hladik, A., Coronado, E.H., Huamantupa-Chuquimaco, I., Huasco, W.H., Jeffery, K.J., Jimenez-Rojas, E., Kalamandeen, M., Djuikouo, M.N.K., Kearsley, E., Umetsu, R.K., Kho, L.K., Killeen, T., Kitayama, K., Klitgaard, B., Koch, A., Labrière, N., Laurance, W., Laurance, S., Leal, M.E., Levesley, A., Lima, A.J.N., Lisingo, J., Lopes, A.P., Lopez-Gonzalez, G., Lovejoy, T., Lovett, J.C., Lowe, R., Magnusson, W.E., Malumbres-Olarte, J., Manzatto, ÂG., Marimon B.H., Jr, Marshall, A.R., Marthews, T., de Almeida Reis, S.M., Maycock, C., Melgaço, K., Mendoza, C., Metali, F., Mihindou, V., Milliken, W., Mitchard, E.T.A., Morandi, P.S., Mossman, H.L., Nagy, L., Nascimento, H., Neill, D., Nilus, R., Vargas, P.N., Palacios, W., Camacho, N.P., Peacock, J., Pendry, C., Peñuela Mora, M.C., Pickavance, G.C., Pipoly, J., Pitman, N., Playfair, M., Poorter, L., Poulsen, J.R., Poulsen, A.D., Preziosi, R., Prieto, A., Primack, R.B., Ramírez-Angulo, H., Reitsma, J., Réjou-Méchain, M., Correa, Z.R., de Sousa, T.R., Bayona, L.R., Roopsind, A., Rudas, A., Rutishauser, E., Abu Salim, K., Salomão, R.P., Schietti, J., Sheil, D., Silva, R.C., Espejo, J.S., Valeria, C.S., Silveira, M., Simo-Droissart, M., Simon, M.F., Singh, J., Soto Shareva, Y.C., Stahl, C., Stropp, J., Sukri, R., Sunderland, T., Svátek, M., Swaine, M.D., Swamy, V., Taedoumg, H., Talbot, J., Taplin, J., Taylor, D., Ter Steege, H., Terborgh, J., Thomas, R., Thomas, S.C., Torres-Lezama, A., Umunay, P., Gamarra, L.V., van der Heijden, G., van der Hout, P., van der Meer, P., van Nieuwstadt, M., Verbeeck, H., Vernimmen, R., Vicentini, A., Vieira, I.C.G., Torre, E.V., Vleminckx, J., Vos, V., Wang, O., White, L.J.T., Willcock, S., Woods, J.T., Wortel, V., Young, K., Zagt, R., Zemagho, L., Zuidema, P.A., Zwerts, J.A., and Phillips, O.L.

Abstract

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (-9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth's climate.

Published
2020

5. Denial of long-term issues with agriculture on tropical peatlands will have devastating consequences

Author

Wijedasa, LS, Jauhiainen, J, Kononen, M, Lampela, M, Vasander, H, Leblanc, MC, Evers, S, Smith, TEL, Yule, CM, Varkkey, H, Lupascu, M, Parish, F, Singleton, I, Clements, GR, Aziz, SA, Harrison, ME, Cheyne, S, Anshari, GZ, Meijaard, E, Goldstein, JE, Waldron, S, Hergoualc'h, K, Dommain, R, Frolking, S, Evans, CD, Posa, MRC, Glaser, PH, Suryadiputra, N, Lubis, R, Santika, T, Padfield, R, Kurnianto, S, Hadisiswoyo, P, Lim, TW, Page, SE, Gauci, V, Van der Meer, PJ, Buckland, H, Garnier, F, Samuel, MK, Choo, LNLK, O'Reilly, P, Warren, M, Suksuwan, S, Sumarga, E, Jain, A, Laurance, WF, Couwenberg, J, Joosten, H, Vernimmen, R, Hooijer, A, Malins, C, Cochrane, MA, Perumal, B, Siegert, F, Peh, KSH, Corneau, LP, Verchot, L, Harvey, CF, Cobb, A, Jaafar, Z, Wosten, H, Manuri, S, Muller, M, Giesen, W, Phelps, J, Yong, DL, Silvius, M, Wedeux, BMM, Hoyt, A, Osaki, M, Hirano, T, Takahashi, H, Kohyama, TS, Haraguchi, A, Nugroho, NP, Coomes, DA, Quoi, LP, Dohong, A, Gunawan, H, Gaveau, DLA, Langner, A, Lim, FKS, Edwards, DP, Giam, X, Van der Werf, G, Carmenta, R, Verwer, CC, Gibson, L, Grandois, L, Graham, LLB, Regalino, J, Wich, SA, Rieley, J, Kettridge, N, Brown, C, Pirard, R, Moore, S, Capilla, BR, Ballhorn, U, Ho, HC, Hoscilo, A, Lohberger, S, Evans, TA, Yulianti, N, Blackham, G, Onrizal, Husson, S, Murdiyarso, D, Pangala, S, Cole, LES, Tacconi, L, Segah, H, Tonoto, P, Lee, JSH, Schmilewski, G, Wulffraat, S, Putra, EI, Cattau, ME, Clymo, RS, Morrison, R, Mujahid, A, Miettinen, J, Liew, SC, Valpola, S, Wilson, D, D'Arcy, L, Gerding, M, Sundari, S, Thornton, SA, Kalisz, B, Chapman, SJ, Su, ASM, Basuki, I, Itoh, M, Traeholt, C, Sloan, S, Sayok, AK, Andersen, R, Wijedasa, LS, Jauhiainen, J, Kononen, M, Lampela, M, Vasander, H, Leblanc, MC, Evers, S, Smith, TEL, Yule, CM, Varkkey, H, Lupascu, M, Parish, F, Singleton, I, Clements, GR, Aziz, SA, Harrison, ME, Cheyne, S, Anshari, GZ, Meijaard, E, Goldstein, JE, Waldron, S, Hergoualc'h, K, Dommain, R, Frolking, S, Evans, CD, Posa, MRC, Glaser, PH, Suryadiputra, N, Lubis, R, Santika, T, Padfield, R, Kurnianto, S, Hadisiswoyo, P, Lim, TW, Page, SE, Gauci, V, Van der Meer, PJ, Buckland, H, Garnier, F, Samuel, MK, Choo, LNLK, O'Reilly, P, Warren, M, Suksuwan, S, Sumarga, E, Jain, A, Laurance, WF, Couwenberg, J, Joosten, H, Vernimmen, R, Hooijer, A, Malins, C, Cochrane, MA, Perumal, B, Siegert, F, Peh, KSH, Corneau, LP, Verchot, L, Harvey, CF, Cobb, A, Jaafar, Z, Wosten, H, Manuri, S, Muller, M, Giesen, W, Phelps, J, Yong, DL, Silvius, M, Wedeux, BMM, Hoyt, A, Osaki, M, Hirano, T, Takahashi, H, Kohyama, TS, Haraguchi, A, Nugroho, NP, Coomes, DA, Quoi, LP, Dohong, A, Gunawan, H, Gaveau, DLA, Langner, A, Lim, FKS, Edwards, DP, Giam, X, Van der Werf, G, Carmenta, R, Verwer, CC, Gibson, L, Grandois, L, Graham, LLB, Regalino, J, Wich, SA, Rieley, J, Kettridge, N, Brown, C, Pirard, R, Moore, S, Capilla, BR, Ballhorn, U, Ho, HC, Hoscilo, A, Lohberger, S, Evans, TA, Yulianti, N, Blackham, G, Onrizal, Husson, S, Murdiyarso, D, Pangala, S, Cole, LES, Tacconi, L, Segah, H, Tonoto, P, Lee, JSH, Schmilewski, G, Wulffraat, S, Putra, EI, Cattau, ME, Clymo, RS, Morrison, R, Mujahid, A, Miettinen, J, Liew, SC, Valpola, S, Wilson, D, D'Arcy, L, Gerding, M, Sundari, S, Thornton, SA, Kalisz, B, Chapman, SJ, Su, ASM, Basuki, I, Itoh, M, Traeholt, C, Sloan, S, Sayok, AK, and Andersen, R

Abstract

Letter

Published
2017

6. NHI Toetsing, Ontwikkeling en toepassing van methode voor toetsing van NHI 2.1 inclusief vergelijking met NHI 2.0

Author

Hoogewoud, J., Veldhuizen, A.A., Prinsen, G., Kuijper, M.J.M., Huinink, J., Lourens, A., and Vernimmen, R.

Subjects
oppervlaktewater, groundwater, grondwater, surface water, hydrology, Wageningen Environmental Research, CWC - Integrated Water Resources Management, CWK - Integraal Waterbeheer, information systems, hydrologie, informatiesystemen
Abstract

Dit rapport beschrijft de achtergrond van de methode om NHI2.1 te toetsen aan de criteria die opgesteld zijn door Rijkswaterstaat Waterdienst geldend voor 2010 en bevat de resultaten van die toetsing en de vergelijking met resultaten van NHI2.0. Volgens de criteria is de berekende aan en afvoer van oppervlakte water verbeterd. Op enkele belangrijke meetpunten van de oppervlaktewaterverdeling zijn signifinante verbeteringen te zien.

Published
2011

8. Drought Forecasting System of the Netherlands

Author

Weerts, A. H., Berendrecht, W. L., Veldhuizen, A., Goorden, N., Vernimmen, R., Lourens, A., Prinsen, G., Mulder, M., Kroon, T., Stam, J., Weerts, A. H., Berendrecht, W. L., Veldhuizen, A., Goorden, N., Vernimmen, R., Lourens, A., Prinsen, G., Mulder, M., Kroon, T., and Stam, J.

Abstract

During periods of droughts the National Coordinating Committee for Water Distribution of the Netherlands has to decide how the available surface water is used and allocated between different users (agriculture, navigation, industry etc). To support this decision making, real-time information is needed about the availability of surface water, groundwater levels, saturation of the root zone, etc. This real-time information must give insight into the current state of the system as well as into its state in the near future (i.e. 10 days ahead). For this purpose, the National Hydrological Instrument (NHI), running on a daily time step and consisting of a nationwide distribution model and surface water model coupled with a MODFLOW-METASWAP model of the saturated-unsaturated zone of the whole of the Netherlands, driven by measured and forecasted precipitation and evaporation (ECMWF-DET and -EPS), is used to obtain insight into the actual and forecasted states of the surface, ground and soil water in the Netherlands. The tool also gives insight in the actual and forecasted water demands by the different actors. The whole system is operationalised within Delft-FEWS, an operational forecasting system to manage data and models in a real time environment. The surface water and groundwater models can be compared with surface water measurements (discharges and water levels) and groundwater level measurements in real-time. ECMWF reforecasts will be used to gain insight in the performance of the drought forecasting system.

Published
2009

9. Drought Forecasting System of the Netherlands

Author

Hydrologie, Landscape functioning, Geocomputation and Hydrology, Weerts, A. H., Berendrecht, W. L., Veldhuizen, A., Goorden, N., Vernimmen, R., Lourens, A., Prinsen, G., Mulder, M., Kroon, T., Stam, J., Hydrologie, Landscape functioning, Geocomputation and Hydrology, Weerts, A. H., Berendrecht, W. L., Veldhuizen, A., Goorden, N., Vernimmen, R., Lourens, A., Prinsen, G., Mulder, M., Kroon, T., and Stam, J.

Published
2009

11. Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia.

Author

Vernimmen, R. R. E., Hooijer, A., Mamenun, Aldrian, E., van Dijk, A. I. J. M., and Thompson, S.

Subjects
RAINFALL, DROUGHTS, EARTH stations, EMPIRICAL research, ERROR analysis in mathematics
Abstract

The accuracy of three satellite rainfall products (TMPA 3B42RT, CMORPH and PERSIANN) was investigated through comparison with grid cell average ground station rainfall data in Indonesia, with a focus on their ability to detect patterns of low rainfall that may lead to drought conditions. Each of the three products underestimated rainfall in dry season months. The CMORPH and PERSIANN data differed most from ground station data and were also very different from the TMPA 3B42RT data. It proved possible to improve TMPA 3B42RT estimates by applying a single empirical bias correction equation that was uniform in space and time. For the six regions investigated, this reduced the root mean square error for estimates of dry season rainfall totals by a mean 9% (from 44 to 40 mm) and for annual totals by 14% (from 77 to 66 mm). The resulting errors represent 10% and 3% of mean dry season and annual rainfall, respectively. The accuracy of these bias corrected TMPA 3B42RT data is considered adequate for use in real-time drought monitoring in Indonesia. Compared to drought monitoring with only ground stations, this use of satellite-based rainfall estimates offers important advantages in terms of accuracy, spatial coverage, timeliness and cost efficiency. [ABSTRACT FROM AUTHOR]

Published
2012
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12. DeltaDTM: A global coastal digital terrain model.

Author

Pronk M, Hooijer A, Eilander D, Haag A, de Jong T, Vousdoukas M, Vernimmen R, Ledoux H, and Eleveld M

Abstract

Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels, subsidence and changing extreme weather patterns. However, current freely available elevation datasets are not sufficiently accurate to model these risks. We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 1 arcsecond (∼30 m) and a vertical mean absolute error (MAE) of 0.45 m overall. DeltaDTM corrects CopernicusDEM with spaceborne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. Notably, our classification approach produces more accurate results than regression methods recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications., (© 2024. The Author(s).)

Published
2024
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13. Mapping deep peat carbon stock from a LiDAR based DTM and field measurements, with application to eastern Sumatra.

Author

Vernimmen R, Hooijer A, Akmalia R, Fitranatanegara N, Mulyadi D, Yuherdha A, Andreas H, and Page S

Abstract

Background: Reduction of carbon emissions from peatlands is recognized as an important factor in global climate change mitigation. Within the SE Asia region, areas of deeper peat present the greatest carbon stocks, and therefore the greatest potential for future carbon emissions from degradation and fire. They also support most of the remaining lowland swamp forest and its associated biodiversity. Accurate maps of deep peat are central to providing correct estimates of peat carbon stocks and to facilitating appropriate management interventions. We present a rapid and cost-effective approach to peat thickness mapping in raised peat bogs that applies a model of peat bottom elevation based on field measurements subtracted from a surface elevation model created from airborne LiDAR data., Results: In two raised peat bog test areas in Indonesia, we find that field peat thickness measurements correlate well with surface elevation derived from airborne LiDAR based DTMs (R 2 0.83-0.88), confirming that the peat bottom is often relatively flat. On this basis, we created a map of extent and depth of deep peat (> 3 m) from a new DTM that covers two-thirds of Sumatran peatlands, applying a flat peat bottom of 0.61 m +MSL determined from the average of 2446 field measurements. A deep peat area coverage of 2.6 Mha or 60.1% of the total peat area in eastern Sumatra is mapped, suggesting that deep peat in this region is more common than shallow peat and its extent was underestimated in earlier maps. The associated deep peat carbon stock range is 9.0-11.5 Pg C in eastern Sumatra alone., Conclusion: We discuss how the deep peat map may be used to identify priority areas for peat and forest conservation and thereby help prevent major potential future carbon emissions and support the safeguarding of the remaining forest and biodiversity. We propose rapid application of this method to other coastal raised bog peatland areas in SE Asia in support of improved peatland zoning and management. We demonstrate that the upcoming global ICESat-2 and GEDI satellite LiDAR coverage will likely result in a global DTM that, within a few years, will be sufficiently accurate for this application.

Published
2020
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