Skip to main content

Advertisement

Log in

Biophysical and biochemical features’ feedback associated with a flood episode in a tropical river basin model

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Global climate change scenarios such as frequent and extreme floods disturb the river basins by destructing the vegetation resulting in rehabilitation procedures being more costly. Thus, understanding the recovery and regeneration of vegetation followed by extreme flood events is critical for a successful rehabilitation process. Spatial and temporal variation of biochemical and biophysical features derived from remote sensing technology in vegetation can be incorporated to understand the recovery and regeneration of vegetation. The present study explores the flood impact on vegetation caused by major river basins in Sri Lanka (a model tropical river basin) by comparing pre-flood and post-flood cases. The study utilized enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), the fraction of photosynthetically active radiation (FPAR), and gross primary productivity (GPP) of the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A remarkable decline in EVI, LAI, FPAR, GPP, and vegetation condition index was observed in the post-flood case. Notably, coupled GPP-EVI and GPP-LAI portrayed dependency of features and showed a significant impact triggered by the flood episode by narrowing the feature in post-flood events. EVI depicted the highest regeneration (0.333) while GPP presented the lowest regeneration (0.093) after the flood event. Further, it was revealed that 1.18 years have been on the regeneration. The regeneration of GPP and LAI remained low comparatively justifying the magnitude and impact of the flood event. The study revealed successful implications of vegetation indices on flood basin management of small to large tropical river basins.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

Data analyzed during the current study are available from the corresponding author on reasonable request.

Not applicable.

References

  • Aerts, J. C. (2018). A review of cost estimates for flood adaptation. Water, 10(11), 1646.

    Article  Google Scholar 

  • Aich, V., Liersch, S., Vetter, T., Andersson, J., Müller, E. N., & Hattermann, F. F. (2015). Climate or land use? Attribution of changes in river flooding in the Sahel zone. Water, 7(6), 2796–2820.

    Article  Google Scholar 

  • Aljahdali, M. O., Munawar, S., & Khan, W. R. (2021). Monitoring mangrove forest degradation and regeneration: Landsat time series analysis of moisture and vegetation indices at Rabigh Lagoon. Red Sea. Forests, 12(1), 52.

    Google Scholar 

  • Alongi, D. M. (2014). Carbon cycling and storage in mangrove forests. Annual Review of Marine Science, 6, 195–219.

    Article  Google Scholar 

  • Ampitiyawatta, A. D., & Guo, S. (2009). Precipitation trends in the Kalu Ganga basin in Sri Lanka. The Journal of Agricultural Science, 4(1), 10–18.

    Google Scholar 

  • Ashraf, M. A. (2012). Waterlogging stress in plants: A review. African Journal of Agricultural Research, 7(13), 1976–1981.

    Google Scholar 

  • Ball, M. C. (1988). Ecophysiology of mangroves. Trees, 2(3), 129–142.

    Article  Google Scholar 

  • Ballesteros, J. A., Stoffel, M., Bodoque, J. M., Bollschweiler, M., Hitz, O., & Díez-Herrero, A. (2010). Changes in wood anatomy in tree rings of Pinus pinaster Ait. following wounding by flash floods. Tree-Ring Research66(2), 93–103.

  • Ballesteros-Cánovas, J. A., Stoffel, M., St George, S., & Hirschboeck, K. (2015a). A review of flood records from tree rings. Progress in Physical Geography, 39(6), 794–816.

    Article  Google Scholar 

  • Ballesteros-Cánovas, J. A., Czajka, B., Janecka, K., Lempa, M., Kaczka, R. J., & Stoffel, M. (2015b). Flash floods in the Tatra Mountain streams: Frequency and triggers. Science of the Total Environment, 511, 639–648.

    Article  Google Scholar 

  • Ballesteros-Cánovas, J. A., Koul, T., Bashir, A., Del Pozo, J. M. B., Allen, S., Guillet, S., ... & Stoffel, M. (2020). Recent flood hazards in Kashmir put into context with millennium-long historical and tree-ring records. Science of The Total Environment722, 137875.

  • Baniya, B., Tang, Q., Xu, X., Haile, G. G., & Chhipi-Shrestha, G. (2019a). Spatial and temporal variation of drought based on satellite derived vegetation condition index in Nepal from 1982–2015. Sensors, 19(2), 430.

    Article  Google Scholar 

  • Baniya, M. B., Asaeda, T., Fujino, T., Jayasanka, S. M., Muhetaer, G., & Li, J. (2019b). Mechanism of riparian vegetation growth and sediment transport interaction in floodplain: A dynamic riparian vegetation model (DRIPVEM) approach. Water, 12(1), 77.

    Article  Google Scholar 

  • Bendix, J. (1998). Impact of a flood on southern California riparian vegetation. Physical Geography, 19(2), 162–174.

    Article  Google Scholar 

  • Bhuiyan, C., Singh, R. P., & Kogan, F. N. (2006). Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 8(4), 289–302.

    Article  Google Scholar 

  • Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66(3), 273–285.

    Article  Google Scholar 

  • Bowles, D. E. (2022). Resiliency and Recovery of Aquatic Vegetation Following Scouring Floods in Two First-Magnitude Springs, Missouri, USA. Hydrobiology, 1(2), 164–182.

    Article  Google Scholar 

  • Bünemann, E. K., Bongiorno, G., Bai, Z., Creamer, R. E., De Deyn, G., de Goede, R., ... & Brussaard, L. (2018). Soil quality–A critical review. Soil Biology and Biochemistry120, 105–125.

  • Burman, P. K. D., Sarma, D., Williams, M., Karipot, A., & Chakraborty, S. (2017). Estimating gross primary productivity of a tropical forest ecosystem over north-east India using LAI and meteorological variables. Journal of Earth System Science, 126(7), 1–16.

    Google Scholar 

  • Canfield, R. H. (1941). Application of the line interception method in sampling range vegetation. Journal of Forestry, 39(4), 388–394.

    Google Scholar 

  • Cetin, M. (2015). Determining the bioclimatic comfort in Kastamonu City. Environmental Monitoring and Assessment, 187(10), 1–10.

    Article  Google Scholar 

  • Cetin, M. (2019). The effect of urban planning on urban formations determining bioclimatic comfort area’s effect using satellitia imagines on air quality: A case study of Bursa city. Air Quality, Atmosphere & Health, 12(10), 1237–1249.

    Article  CAS  Google Scholar 

  • Cetin, M. (2020). The changing of important factors in the landscape planning occur due to global climate change in temperature, Rain and climate types: A case study of Mersin City. Turkish Journal of Agriculture-Food Science and Technology, 8(12), 2695–2701.

    Article  Google Scholar 

  • Damasceno-Junior, G. A., Semir, J., Santos, F. A. M. D., & Leitão-Filho, H. D. F. (2004). Tree mortality in a riparian forest at Rio Paraguai, Pantanal, Brazil, after an extreme flooding. Acta Botanica Brasilica, 18(4), 839–846.

    Article  Google Scholar 

  • de Resende, A. F., Schöngart, J., Streher, A. S., Ferreira-Ferreira, J., Piedade, M. T. F., & Silva, T. S. F. (2019). Massive tree mortality from flood pulse disturbances in Amazonian floodplain forests: The collateral effects of hydropower production. Science of the Total Environment, 659, 587–598.

    Article  Google Scholar 

  • De Silva, A. L. C., & De Costa, W. A. J. M. (2012). Growth and radiation use efficiency of sugarcane under irrigated and rain-fed conditions in Sri Lanka. Sugar Tech, 14(3), 247–254.

    Article  CAS  Google Scholar 

  • Department of Irrigation. (2003). Inundation Maps of the Kalu Ganga basin during the Flood in May 2003. https://www.irrigation.gov.lk/images/pdf/downloads/Flood/intro_2003_kalu.pdf

  • Department of Meteorology. (2021). Climate of Sri Lanka; seasonal monsoons. Retrieved October 14, 2022, from https://meteo.gov.lk/index.php?option=com_content&view=article&id=94&Itemid=310&lang=en

  • Deshapriya, L., Sothy, M., HengYuthin, L. S., & Hazarika, M. (2015). Mapping paddy area in Kandal and Prey Veng provinces in Cambodia using multi-temporal MODIS images. 1–6.

  • Di, L., Yu, E., Shrestha, R., & Lin, L. (2018). DVDI: A new remotely sensed index for measuring vegetation damage caused by natural disasters. In IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, (9067–9069). IEEE.

  • Domenikiotis, C., Spiliotopoulos, M., Tsiros, E., & Dalezios, N. R. (2004). Early cotton yield assessment by the use of the NOAA/AVHRR derived vegetation condition index (VCI) in Greece. International Journal of Remote Sensing, 25(14), 2807–2819.

    Article  Google Scholar 

  • Dong, T., Wu, B., Meng, J., Du, X., & Shang, J. (2016). Sensitivity analysis of retrieving fraction of absorbed photosynthetically active radiation (FPAR) using remote sensing data. Acta Ecologica Sinica, 36(1), 1–7.

    Article  Google Scholar 

  • Duveneck, M. J., Scheller, R. M., White, M. A., Handler, S. D., & Ravenscroft, C. (2014). Climate change effects on northern Great Lake (USA) forests: A case for preserving diversity. Ecosphere, 5(2), 1–26.

    Article  Google Scholar 

  • Džubáková, K., Molnar, P., Schindler, K., & Trizna, M. (2015). Monitoring of riparian vegetation response to flood disturbances using terrestrial photography. Hydrology and Earth System Sciences, 19(1), 195–208.

    Article  Google Scholar 

  • Edirisinghe, J., Wijesuriya, W., & Bogahawatte, C. (2010). Profit efficiency of smallholder rubber farmers in Kegalle, Kalutara and Ratnapura districts. Journal of the Rubber Research Institute of Sri Lanka, 90, 64–77.

    Article  Google Scholar 

  • Fischer, S., Greet, J., Walsh, C. J., & Catford, J. A. (2021). Flood disturbance affects morphology and reproduction of woody riparian plants. Scientific Reports, 11(1), 1–14.

    Article  Google Scholar 

  • Fonseca, L. D., Dalagnol, R., Malhi, Y., Rifai, S. W., Costa, G. B., Silva, T. S., ... & Borma, L. S. (2019). Phenology and seasonal ecosystem productivity in an Amazonian floodplain forest. Remote Sensing11(13), 1530.

  • Friedman, J. M., Osterkamp, W. R., & Lewis, W. M., Jr. (1996). Channel narrowing and vegetation development following a Great Plains flood. Ecology, 77(7), 2167–2181.

    Article  Google Scholar 

  • Fukao, T., Barrera-Figueroa, B. E., Juntawong, P., & Peña-Castro, J. M. (2019). Submergence and waterlogging stress in plants: A review highlighting research opportunities and understudied aspects. Frontiers in Plant Science, 10, 340.

    Article  Google Scholar 

  • Gallay, I., Olah, B., Gallayová, Z., & Lepeška, T. (2021). Monetary valuation of flood protection ecosystem service based on hydrological modelling and avoided damage costs. An Example from the Čierny Hron River Basin, Slovakia. Water13(2), 198.

  • Garibaldi, L. A., Semmartin, M., & Chaneton, E. J. (2007). Grazing-induced changes in plant composition affect litter quality and nutrient cycling in flooding Pampa grasslands. Oecologia, 151(4), 650–662.

    Article  Google Scholar 

  • Garssen, A. G., Baattrup-Pedersen, A., Voesenek, L. A., Verhoeven, J. T., & Soons, M. B. (2015). Riparian plant community responses to increased flooding: A meta-analysis. Global Change Biology, 21(8), 2881–2890.

    Article  Google Scholar 

  • Gitelson, A. A., Peng, Y., Arkebauer, T. J., & Schepers, J. (2014). Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production. Remote Sensing of Environment, 144, 65–72.

    Article  Google Scholar 

  • Gleyzer, A., Denisyuk, M., Rimmer, A., & Salingar, Y. (2004). A fast recursive GIS algorithm for computing strahler stream order in braided and nonbraided networks 1. JAWRA Journal of the American Water Resources Association, 40(4), 937–946.

    Article  Google Scholar 

  • Guo, W., Zhou, Z., Chen, J., Zheng, X., & Ye, X. (2022). Effects of extreme flooding on aquatic vegetation cover in Shengjin Lake. China. Hydrological Processes, 36(2), e14459.

    Article  Google Scholar 

  • Hettiwaththa, H. W. Y. J., & Abeygunawardana, R. A. B. (2018). measuring flood risk in RATNAPURA town area in Sri Lanka. Proceedings of The International Conference on Climate Change2(2), 31–41. https://doi.org/10.17501/iccc.2018.2203

  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.

    Article  Google Scholar 

  • Ingrisch, J., & Bahn, M. (2018). Towards a comparable quantification of resilience. Trends in Ecology & Evolution, 33(4), 251–259.

    Article  Google Scholar 

  • Japan international cooperation agency. (2009). Comprehensive study on disaster management in Sri Lanka final report. https://openjicareport.jica.go.jp/pdf/11931938_01.pdf

  • Kabenge, M., Elaru, J., Wang, H., & Li, F. (2017). Characterizing flood hazard risk in data-scarce areas, using a remote sensing and GIS-based flood hazard index. Natural Hazards, 89(3), 1369–1387.

    Article  Google Scholar 

  • Kala, J., Decker, M., Exbrayat, J. F., Pitman, A. J., Carouge, C., Evans, J. P., & Abaramowitz, G. (2014). Influence of leaf area index prescriptions on simulations of heat, moisture, and carbon fluxes. Journal of Hydrometeorology, 15(1), 489–503.

    Article  Google Scholar 

  • Kamble, D. B., Gautam, S., Bisht, H., Rawat, S., & Kundu, A. (2019). Drought assessment for kharif rice using standardized precipitation index (SPI) and vegetation condition index (VCI). Journal of Agrometeorology, 21(2), 182–187.

    Article  Google Scholar 

  • Kamran, M., Ahmad, S., Ahmad, I., Hussain, I., Meng, X., Zhang, X., Javed, T., Ullah, M., Ding, R., Xu, P., & Gu, W. (2020). Paclobutrazol application favors yield improvement of maize under semiarid regions by delaying leaf senescence and regulating photosynthetic capacity and antioxidant system during grain-filling Stage. Agronomy, 10(2), 187.

    Article  CAS  Google Scholar 

  • Kanchanamala, D. P. H. M., Nandalal, K. D. W., & Herath, H. M. H. K. (2016). Impact of catchment scale on rainfall runoff modeling: Kalu Ganga river catchment upto Ratnapura. Journal of the Institution of Engineers, Sri Lanka, 49(2), 1–7.

    Article  Google Scholar 

  • Kath, J., Le Brocque, A. F., Reardon-Smith, K., & Apan, A. (2019). Remotely sensed agricultural grassland productivity responses to land use and hydro-climatic drivers under extreme drought and rainfall. Agricultural and Forest Meteorology, 268, 11–22.

    Article  Google Scholar 

  • Kilicoglu, C., Cetin, M., Aricak, B., & Sevik, H. (2020). Site selection by using the multi-criteria technique—a case study of Bafra. Turkey. Environmental Monitoring and Assessment, 192(9), 1–12.

    Google Scholar 

  • Kim, Y., Shahzad, R., & Lee, I. J. (2020). Regulation of flood stress in plants. In Plant life under changing environment, 157–173. Academic Press.

  • Kogan, F. N. (1998). Global drought and flood-watch from NOAA polar-orbitting satellites. Advances in Space Research, 21(3), 477–480.

    Article  Google Scholar 

  • Kominoski, J. S., Fernandez, M., Breault, P., Sclater, V., & Rothermel, B. B. (2021). Fire severity and post-fire hydrology drive nutrient cycling and plant community recovery in intermittent wetlands. Ecosystems, 1–14.

  • Kozlowski, T. T. (2000). Responses of woody plants to human-induced environmental stresses: Issues, problems, and strategies for alleviating stress. Critical Reviews in Plant Sciences, 19(2), 91–170.

    Article  Google Scholar 

  • Kozlowski, T. T. (2002). Physiological-ecological impacts of flooding on riparian forest ecosystems. Wetlands, 22(3), 550–561.

    Article  Google Scholar 

  • Kretz, L., Seele, C., van der Plas, F., Weigelt, A., & Wirth, C. (2020). Leaf area and pubescence drive sedimentation on leaf surfaces during flooding. Oecologia, 193(3), 535–545.

    Article  Google Scholar 

  • Kumar, A., Chen, F., Barlage, M., Ek, M. B., & Niyogi, D. (2014). Assessing impacts of integrating MODIS vegetation data in the weather research and forecasting (WRF) model coupled to two different canopy-resistance approaches. Journal of Applied Meteorology and Climatology, 53(6), 1362–1380.

    Article  Google Scholar 

  • Land Processes Distributed Active Archive Center (LP DAAC). (2021). http://lpdaac.usgs.gov 

  • Liao, C. T., & Lin, C. H. (2001). Physiological adaptation of crop plants to flooding stress. Proceedings of the National Science Council, Republic of China. Part B, Life Sciences, 25(3), 148–157.

  • Liu, Z., Shao, Q., & Liu, J. (2015). The performances of MODIS-GPP and-ET products in China and their sensitivity to input data (FPAR/LAI). Remote Sensing, 7(1), 135–152.

    Article  CAS  Google Scholar 

  • Lugo, A. E. (2008). Visible and invisible effects of hurricanes on forest ecosystems: An international review. Australian Ecology, 33(4), 368–398.

    Article  Google Scholar 

  • Mancuso, S., & Shabala, S. (Eds.). (2010). Waterlogging signalling and tolerance in plants (pp. 1–294). Springer.

    Google Scholar 

  • McDowell, N. G., Coops, N. C., Beck, P. S., Chambers, J. Q., Gangodagamage, C., Hicke, J. A., Huang, C., Kennedy, R., Krofcheck, D. J., Litvak, M., Meddens, A. J. H., Muss, J., & Negro´ n-Juarez, R., Peng, C., Schwantes, A.M., Swenson, J.J., Vernon, L.J., Williams, A.P., Xu, C., Zhao, M., Running, S.W., & Allen, C. D. (2015). Global satellite monitoring of climate-induced vegetation disturbances. Trends in Plant Science, 20(2), 114–123.

    Article  CAS  Google Scholar 

  • Mendis, I. U., & Udomsade, J. (2005). Factors affecting adoption of recommended crop management practices in paddy cultivation in Kalutara district, Sri Lanka. Kasetsart Journal of Social Sciences, 26(1), 91–102.

    Google Scholar 

  • Mohammadi, A., Costelloe, J. F., & Ryu, D. (2017). Application of time series of remotely sensed normalized difference water, vegetation and moisture indices in characterizing flood dynamics of large-scale arid zone floodplains. Remote Sensing of Environment, 190, 70–82.

    Article  Google Scholar 

  • Murthy, I. K., Gupta, M., Tomar, S., Munsi, M., Tiwari, R., Hegde, G. T., & Ravindranath, N. H. (2013). Carbon sequestration potential of agroforestry systems in India. J Earth Sci Climate Change, 4(1), 1–7.

    Article  Google Scholar 

  • Nandalal, H. K., & Ratnayake, U. R. (2010). Event based modeling of a watershed using HEC-HMS. Engineer: Journal of the Institution of Engineers, Sri Lanka, 43(2).

  • Nandalal, H. K., & Ratnayake, U. R. (2011). Flood risk analysis using fuzzy models. Journal of Flood Risk Management, 4(2), 128–139.

    Article  Google Scholar 

  • Nandalal, K. D. W. (2009). Use of a hydrodynamic model to forecast floods of Kalu River in Sri Lanka. Journal of Flood Risk Management, 2(3), 151–158.

    Article  Google Scholar 

  • Natho, S., & Thieken, A. H. (2018). Implementation and adaptation of a macro-scale method to assess and monitor direct economic losses caused by natural hazards. International Journal of Disaster Risk Reduction, 28, 191–205.

    Article  Google Scholar 

  • Newman, G., Sansom, G. T., Yu, S., Kirsch, K. R., Li, D., Kim, Y., ... & Musharrat, S. (2022). A framework for evaluating the effects of green infrastructure in mitigating pollutant transferal and flood events in Sunnyside, Houston, TX. Sustainability14(7), 4247.

  • Nguyen, L. T., Osanai, Y., Lai, K., Anderson, I. C., Bange, M. P., Tissue, D. T., & Singh, B. K. (2018). Responses of the soil microbial community to nitrogen fertilizer regimes and historical exposure to extreme weather events: Flooding or prolonged drought. Soil Biology and Biochemistry, 118, 227–236.

    Article  CAS  Google Scholar 

  • Noble, R. E., & Murphy, P. K. (1975). Short term effects of prolonged backwater flooding on understory vegetation. Castanea, 228–238.

  • Ogilvie, A., Belaud, G., Delenne, C., Bailly, J. S., Bader, J. C., Oleksiak, A., Ferry, L., & Martin, D. (2015). Decadal monitoring of the Niger Inner Delta flood dynamics using MODIS optical data. Journal of Hydrology, 523, 368–383.

    Article  Google Scholar 

  • Panditharathne, D. L. D., Abeysingha, N. S., Nirmanee, K. G. S., & Mallawatantri, A. (2019). Application of revised universal soil loss equation (Rusle) model to assess soil erosion in “Kalu Ganga” river basin in Sri Lanka. Applied and Environmental Soil Science, 2019, 1–15.

    Article  Google Scholar 

  • Parent, C., Capelli, N., Berger, A., Crèvecoeur, M., & Dat, J. F. (2008). An overview of plant responses to soil waterlogging. Plant Stress, 2(1), 20–27.

    Google Scholar 

  • Parolin, P., & Wittmann, F. (2010). Struggle in the flood: Tree responses to flooding stress in four tropical floodplain systems. AoB Plants, 2010.

  • Patel, P. K., Singh, A. K., Tripathi, N., Yadav, D., & Hemantaranjan, A. (2014). Flooding: Abiotic constraint limiting vegetable productivity. Advances in Plants and Agriculture Research, 1(3), 00016.

    Google Scholar 

  • Phillips, J. D., & Van Dyke, C. (2016). Principles of geomorphic disturbance and recovery in response to storms. Earth Surface Processes and Landforms, 41(7), 971–979.

    Article  Google Scholar 

  • Phompila, C., Lewis, M., Ostendorf, B., & Clarke, K. (2015). MODIS EVI and LST temporal response for discrimination of tropical land covers. Remote Sensing, 7, 6026–6040. https://doi.org/10.3390/rs70506026

    Article  Google Scholar 

  • Quesada-Román, A., Ballesteros-Cánovas, J. A., Granados-Bolaños, S., Birkel, C., & Stoffel, M. (2022). Improving regional flood risk assessment using flood frequency and dendrogeomorphic analyses in mountain catchments impacted by tropical cyclones. Geomorphology, 396, 108000.

    Article  Google Scholar 

  • Quiring, S. M., & Ganesh, S. (2010). Evaluating the utility of the vegetation condition index (VCI) for monitoring meteorological drought in Texas. Agricultural and Forest Meteorology, 150(3), 330–339.

    Article  Google Scholar 

  • Robertson, M. J., Bonnett, G. D., Hughes, R. M., Muchow, R. C., & Campbell, J. A. (1998). Temperature and leaf area expansion of sugarcane: Integration of controlled-environment, field and model studies. Functional Plant Biology, 25(7), 819–828.

    Article  Google Scholar 

  • Samarasinghea, S. M. J. S., Nandalalb, H. K., Weliwitiyac, D. P., Fowzed, J. S. M., Hazarikad, M. K., & Samarakoond, L. (2010). Application of remote sensing and GIS for flood risk analysis: A case study at Kalu-Ganga river, Sri Lanka. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, 38(8), 110–115.

    Google Scholar 

  • Sánchez-Rodríguez, A. R., Chadwick, D. R., Tatton, G. S., Hill, P. W., & Jones, D. L. (2018). Comparative effects of prolonged freshwater and saline flooding on nitrogen cycling in an agricultural soil. Applied Soil Ecology, 125, 56–70.

    Article  Google Scholar 

  • Sandhu, H. S., Gilbert, R. A., McCray, J. M., Perdomo, R., Eiland, B., Powell, G., & Montes, G. (2012). Relationships among leaf area index, visual growth rating, and sugarcane yield. Journal American Society of Sugar Cane Technologists, 32, 1–14.

    Google Scholar 

  • Sarmah, S., Singha, M., Wang, J., Dong, J., Burman, P. K. D., Goswami, S., Ge, Y., Ilyas, S. N., & Niu, S. (2021). Mismatches between vegetation greening and primary productivity trends in South Asia–A satellite evidence. International Journal of Applied Earth Observation and Geoinformation, 104, 102561.

    Article  Google Scholar 

  • Schulz, L., & Kingston, D. G. (2017). GCM-related uncertainty in river flow projections at the threshold for “dangerous” climate change: The Kalu Ganga river. Sri Lanka. Hydrological Sciences Journal, 62(14), 2369–2380.

    Article  Google Scholar 

  • Selvarajah, J., & Jayathilaka, D. (2016). Study on flood inundation areas in Rathnapura municipal council. International research symposium, National Building Research Organization, Sri Lanka. https://www.researchgate.net/publication/329916377_Study_on_flood_inundation_areas_in_Rathnapura_Municipal_Council.

  • Sims, D. A., Rahman, A. F., Cordova, V. D., El-Masri, B. Z., Baldocchi, D. D., Bolstad, P. V., Flanagan, L. B., Goldstein, A. H., Hollinger, D. Y., Misson, L., Monson, R. K., Oechel, W. C., Schmid, H. P., Wofsy, S. C., & Xu, L. K. (2008). A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS, Remote Sens. Environ., 112, 1633–1646.

    Google Scholar 

  • Sims, N. C., & Colloff, M. J. (2012). Remote sensing of vegetation responses to flooding of a semi-arid floodplain: Implications for monitoring ecological effects of environmental flows. Ecological Indicators, 18, 387–391.

    Article  Google Scholar 

  • Singh, R. P., Roy, S., & Kogan, F. (2003). Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. International Journal of Remote Sensing, 24(22), 4393–4402.

    Article  Google Scholar 

  • Smethurst, C. F., Garnet, T., & Shabala, S. (2005). Nutrition and chlorophyll fluorescence responses of lucerne (Medicago sativa) to waterlogging subsequent recovery. Plant and Soil, 270(1–2), 31–45.

    Article  CAS  Google Scholar 

  • Striker, G. G. (2012). Flooding stress on plants: Anatomical, morphological and physiological responses. Botany, 1, 3–28.

    Google Scholar 

  • Stromberg, J. C., Richter, B. D., Patten, D. T., & Wolden, L. G. (1993). Response of a Sonoran riparian forest to a 10-year return flood. The Great Basin Naturalist, 118–130.

  • Tichavský, R., Koutroulis, A., Chalupová, O., Chalupa, V., & Šilhán, K. (2020). Flash flood reconstruction in the Eastern Mediterranean: Regional tree ring-based chronology and assessment of climate triggers on the island of Crete. Journal of Arid Environments, 177, 104135.

    Article  Google Scholar 

  • Tripathi, G., Pandey, A. C., Parida, B. R., & Kumar, A. (2020). Flood inundation mapping and impact assessment using multi-temporal optical and SAR satellite data: A case study of 2017 Flood in Darbhanga district, Bihar. India. Water Resources Management, 34(6), 1871–1892.

    Article  Google Scholar 

  • Tucker, C. J., & Sellers, P. J. (1986). Satellite remote sensing of primary production. International Journal of Remote Sensing, 7(11), 1395–1416.

    Article  Google Scholar 

  • Van Auken, O. W., & Ford, A. L. (2017). Flood caused changes to the Upper Guadalupe River riparian forests of central Texas. Phytologia, 99, 226–237.

    Google Scholar 

  • Van Cleve, K., & Yarie, J. (1986). Interaction of temperature, moisture, and soil chemistry in controlling nutrient cycling and ecosystem development in the taiga of Alaska. In Forest ecosystems in the Alaskan taiga, pp 160–189. Springer, New York, NY.

  • van der Valk, A. G., Squires, L., & Welling, C. H. (1994). Assessing the impacts of an increase in water level on wetland vegetation. Ecological Applications, 4(3), 525–534.

    Article  Google Scholar 

  • van Meerveld, H. J., Jones, J. P., Ghimire, C. P., Zwartendijk, B. W., Lahitiana, J., Ravelona, M., & Mulligan, M. (2021). Forest regeneration can positively contribute to local hydrological ecosystem services: Implications for forest landscape restoration. Journal of Applied Ecology, 58(4), 755–765.

    Article  Google Scholar 

  • Varol, T., Canturk, U., Cetin, M., Ozel, H. B., & Sevik, H. (2021). Impacts of climate change scenarios on European ash tree (Fraxinus excelsior L.) in Turkey. Forest Ecology and Management491, 119199.

  • Viedma, O., Meliá, J., Segarra, D., & Garcia-Haro, J. (1997). Modeling rates of ecosystem recovery after fires by using Landsat TM data. Remote Sensing of Environment, 61(3), 383–398.

    Article  Google Scholar 

  • Visser, E. J. W., Voesenek, L. A. C. J., Vartapetian, B. B., & Jackson, M. (2003). Flooding and plant growth. Annals of Botany, 91(2), 107–109.

    Article  CAS  Google Scholar 

  • Vormberg, A., Effenberger, F., Muellerleile, J., & Cuntz, H. (2017). Universal features of dendrites through centripetal branch ordering. PLoS Computational Biology, 13(7), e1005615.

    Article  Google Scholar 

  • Wang, B., Chen, Y., & Lü, C. (2015). Evaluating flood inundation impact on wetland vegetation FPAR of the Macquarie Marshes. Australia. Environmental Earth Sciences, 74(6), 4989–5000.

    Article  Google Scholar 

  • Watham, T., Patel, N. R., Kushwaha, S. P. S., Dadhwal, V. K., & Kumar, A. S. (2017). Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data. International Journal of Remote Sensing, 38(18), 5069–5090.

    Article  Google Scholar 

  • Welivitiya, W. D. D. P., Jayasinghe, J. M. H. C. B, Musheen, M. K., Saputhanthri, S. V., & Samaranayake, T. D. N. T. (2012). Development of flood hazard zonation map for “Kalu Ganga” basin by GIS modeling. SAARC Workshop on FLOOD; Risk Management in South Asia, 130–135.

  • Wen, Y., Liu, X., Pei, F., Li, X., & Du, G. (2018). Non-uniform time-lag effects of terrestrial vegetation responses to asymmetric warming. Agricultural and Forest Meteorology, 252, 130–143.

    Article  Google Scholar 

  • Wickramagamage, P. (2011). Evolution of the Kalu Ganga-Bolgoda Ganga flood plain system, Sri Lanka. Journal of the Geological Society of Sri Lanka, 14(1), 41–53.

    Google Scholar 

  • Yamamoto, K., & Sayama, T. (2021). Impact of climate change on flood inundation in a tropical river basin in Indonesia. Progress in Earth and Planetary Science, 8(1), 1–15.

    Article  Google Scholar 

  • Ye, C., Butler, O. M., Chen, C., Liu, W., Du, M., & Zhang, Q. (2020). Shifts in characteristics of the plant-soil system associated with flooding and revegetation in the riparian zone of Three Gorges Reservoir, China. Geoderma, 361, 114015.

  • Zhang, B., Di, L., Yu, G., Shao, Y., Shrestha, R., & Kang, L. (2013, August). A web service based application serving vegetation condition indices for flood crop loss assessment.In 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp 215–220. IEEE.

  • Zhang, Y., Li, Z., Ge, W., Wang, J., Guo, X., Wang, T., & Li, W. (2022). Assessment of the impact of floods on terrestrial plant biodiversity. Journal of Cleaner Production, 339, 130722.

    Article  Google Scholar 

  • Zhang, Y., Song, C., Band, L. E., & Sun, G. (2019). No proportional increase of terrestrial gross carbon sequestration from the greening Earth. Journal of Geophysical Research: Bio-Geosciences, 124(8), 2540–2553.

    Article  CAS  Google Scholar 

  • Zhao, D., Reddy, K. R., Kakani, V. G., Read, J. J., & Koti, S. (2007). Canopy reflectance in cotton for growth assessment and lint yield prediction. European Journal of Agronomy, 26(3), 335–344.

    Article  CAS  Google Scholar 

  • Zhao, W., & Kinouchi, T. (2022). Uncertainty quantification in intensity-duration-frequency curves under climate change: Implications for flood-prone tropical cities. Atmospheric Research, 270, 106070.

    Article  Google Scholar 

Download references

Acknowledgements

The authors convey the sincere gratitude to Dr. Nimal S. Abeysingha for providing some guidance for this present study.

Author information

Authors and Affiliations

Authors

Contributions

B. K. A. Bellanthudawa: conceptualization, data analysis, manuscript preparation. S.H. Ahmed: data extraction. N. M. S. K. Nawalage: manuscript preparation. K. M. N. Kendaragama: resources. M.M.T.D. Neththipola: data extraction, D. Halwatura: manuscript preparation.

Corresponding author

Correspondence to B. K. A. Bellanthudawa.

Ethics declarations

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bellanthudawa, B.K.A., Nawalage, N.M.S.K., Halwatura, D. et al. Biophysical and biochemical features’ feedback associated with a flood episode in a tropical river basin model. Environ Monit Assess 195, 504 (2023). https://doi.org/10.1007/s10661-023-11121-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-023-11121-z

Keywords

Navigation