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Analysis of persistence in the flood timing and the role of catchment wetness on flood generation in a large river basin in India

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Abstract

Mahanadi River Basin (MRB) is one of the largest tropical pluvial river basin systems in India contributing the major source of freshwater to more than 71 million people in east-central India. Being located in the monsoon “core” region (18–28° N latitude and 73–82° E longitude) and its proximity to Bay of Bengal, Mahanadi River Basin (MRB) system is highly vulnerable to tropical depression-induced severe storms and extreme precipitation-induced fluvial floods during southwest monsoon as reflected in several successive and catastrophic flood episodes in recent years (2001, 2003, 2006, 2008, 2011, 2013, 2014, 2016, 2019). While previous studies so far focused on analyzing either flood trends or frequency and show the role of precipitation in flood generating mechanism over MRB using both instrumental records and climate model simulations, this study for the first time examines space-time coherence in floods and the role catchment wetness in flood response (i.e., magnitude and the timing of floods) over the basin. We examine the incidence of flooding in three different time windows: 1970–2016 (whole 47 years), 1970–2006, and 2007–2016 (post-2007s) using monsoonal maxima peak discharge (MMPD) and peak over threshold (POT) series at 24 stream gauges spatially distributed over the basin. Our analysis reveals the mean dates of floods for most of the gauges are temporally clustered during the month of August irrespective of the type of flood series and the choice of time frames. Further, we observe sensitiveness of runoff responses (flood magnitude, FM and the flood timing, FT) to lagged d-day mean catchment wetness (CW), suggesting a physical association between them. We also note FT is more strongly correlated (as manifested by statistically significant correlations) to CW rather than FM. Overall, we observe, the correlation of CW versus FT is negative, where the flood timing is relatively irregular. The outcomes of the study help to improve the predictability of floods, which can, in turn, enhance existing flood warning techniques.

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References

  • Albertson JD, Kiely G (2001) On the structure of soil moisture time series in the context of land surface models. J Hydrol 243:101–119

    Google Scholar 

  • Amengual A, Romero R, Alonso S (2008) Hydrometeorological ensemble simulations of flood events over a small basin of Majorca Island, Spain. Q J R Meteorol Soc 134:1221–1242

    Google Scholar 

  • Beck HE, De JR, Schellekens J et al (2009) Improving curve number based storm runoff estimates using soil moisture proxies. IEEE J Sel Top Appl EARTH Obs Remote Sens 2:250–259

    Google Scholar 

  • Berghuijs WR, Woods RA, Hutton CJ, Sivapalan M (2016) Dominant flood generating mechanisms across the United States. Geophys Res Lett 43:4382–4390

    Google Scholar 

  • Best J (2019) Anthropogenic stresses on the world’s big rivers. Nat Geosci 12:7–21

    Google Scholar 

  • Blöschl G, Hall J, Parajka J, Perdigão RAP, Merz B, Arheimer B, Aronica GT, Bilibashi A, Bonacci O, Borga M, Čanjevac I, Castellarin A, Chirico GB, Claps P, Fiala K, Frolova N, Gorbachova L, Gül A, Hannaford J, Harrigan S, Kireeva M, Kiss A, Kjeldsen TR, Kohnová S, Koskela JJ, Ledvinka O, Macdonald N, Mavrova-Guirguinova M, Mediero L, Merz R, Molnar P, Montanari A, Murphy C, Osuch M, Ovcharuk V, Radevski I, Rogger M, Salinas JL, Sauquet E, Šraj M, Szolgay J, Viglione A, Volpi E, Wilson D, Zaimi K, Živković N (2017) Changing climate shifts timing of European floods. Science 357:588–590

    Google Scholar 

  • Burn DH, Whitfield PH (2018) Changes in flood events inferred from centennial length streamflow data records. Adv Water Resour 121:333–349

    Google Scholar 

  • Burn DH, Whitfield PH, Sharif M (2016) Identification of changes in floods and flood regimes in Canada using a peak over threshold approach. Hydrol Process 30:3303–3314

    Google Scholar 

  • Chowdhury MR, Ward N (2004) Hydro-meteorological variability in the greater Ganges-Brahmaputra-Meghna basins. Int J Climatol 24:1495–1508

    Google Scholar 

  • Chung D, Dorigo W, De Jeu R, et al (2018) ESA climate change initiative phase II - soil moisture. Product Specification Document (PSD) D1.2.1 Version 4.2. pp 1-50

  • CRED (Centre for Research on the Epidemiology of Disasters) (2018) Review of disaster events. Université catholique de Louvain, Ottignies-Louvain-la-Neuve https://www.cred.be/. Accessed April 2019

    Google Scholar 

  • CWC (Central Water Commission) (2014) Mahanadi Basin. CWC and NRSC, New Delhi, p 110

    Google Scholar 

  • Dhakal N, Jain S, Gray A, Dandy M, Stancioff E (2015) Nonstationarity in seasonality of extreme precipitation: a nonparametric circular statistical approach and its application. Water Resour Res 51:4499–4515

    Google Scholar 

  • Di Baldassarre G, Montanari A, Lins H et al (2010) Flood fatalities in Africa: from diagnosis to mitigation. Geophys Res Lett 37:1–5

    Google Scholar 

  • Dick GS, Anderson RS, Sampson DE (1997) Controls on flash flood magnitude and hydrograph shape, upper Blue Hills badlands, Utah. Geology 25:45–48

    Google Scholar 

  • Do HX, Westra S, Leonard M (2017) A global-scale investigation of trends in annual maximum streamflow. Journal of hydrology 552:28–43

    Google Scholar 

  • Dorigo W, Wagner W, Albergel C, Albrecht F, Balsamo G, Brocca L, Chung D, Ertl M, Forkel M, Gruber A, Haas E, Hamer PD, Hirschi M, Ikonen J, de Jeu R, Kidd R, Lahoz W, Liu YY, Miralles D, Mistelbauer T, Nicolai-Shaw N, Parinussa R, Pratola C, Reimer C, van der Schalie R, Seneviratne SI, Smolander T, Lecomte P (2017) ESA CCI soil moisture for improved Earth system understanding: state-of-the-art and future directions. Remote Sens Environ 203:185–215

    Google Scholar 

  • Dsouza CJ, Joy KJ, Bhadbhade N, et al (2017a) Mahanadi River Basin: a situation analysis. Forum for Policy Dialogue on Water Conflicts in India pp 1–78

  • Dsouza C, Samuel A, Bhagat S, Joy KJ (2017b) Water allocations and use in the Mahanadi River basin - a study of the agricultural and industrial sectors. Forum for Policy Dialogue on Water Conflicts in India pp 1–168

  • Ettrick TM, Mawdlsey JA, Metcalfe AV (1987) The influence of antecedent catchment conditions on seasonal flood risk. Water Resour Res 23:481–488

    Google Scholar 

  • FAO (Food and Agriculture Organization of the United Nations) (2001) Report of the FAO Asia-Pacific Conference on Early Warning, Prevention, Preparedness and Management of Disasters in Food and Agriculture. Food and Agricultural Organization of United Nations. http://www.fao.org/3/AC120E/AC120e00.htm. Accessed April 2019

  • FAO (Food and Agriculture Organization of the United Nations) (2015) Aquastat report: India. Food and Agricultural Organization of United Nations. http://www.fao.org/aquastat/en/. Accessed April 2019

  • Fisher NI, Lewis T, Embleton BJJ (1993) Statistical analysis of spherical data. Cambridge university press pp 329

  • Ganguli P, Ganguly AR (2016) Space-time trends in U.S. Meteorological droughts. J Hydrol.: Regional Studies 8:235–259

    Google Scholar 

  • Ganguli P, Kumar D, Ganguly AR (2017) US power production at risk from water stress in a changing climate. Sci Rep 7:11983

    Google Scholar 

  • Georgakakos KP (2006) Analytical results for operational flash flood guidance. J Hydrol 317:81–103

    Google Scholar 

  • Ghosh S, Raje D, Mujumdar PP (2010) Mahanadi streamflow: climate change impact assessment and adaptive strategies. Curr Sci 98:1084–1091

    Google Scholar 

  • Gosain AK, Rao S, Basuray D (2006) Climate change impact assessment on hydrology of Indian river basins. Curr Sci 90:346–353

    Google Scholar 

  • Grillakis MG, Koutroulis AG, Komma J, Tsanis IK, Wagner W, Blöschl G (2016) Initial soil moisture effects on flash flood generation – a comparison between basins of contrasting hydro-climatic conditions. J Hydrol 541:206–217

    Google Scholar 

  • Gruber A, Dorigo WA, Crow W, Wagner W (2017) Triple collocation-based merging of satellite soil moisture retrievals. IEEE Trans Geosci Remote Sens 55:6780–6792

    Google Scholar 

  • Helsel DR, Hirsch RM (2002) Statistical methods in water resources techniques of water resources investigations. U.S. Geological Survey, Book 4, chapter A3, pp 522

  • Hlavcova H, Kohnova S, Kubes R, Szolgay J, Zvolensky M (2005) An empirical method for estimating future flood risks for flood warnings. Hydrol Earth Syst Sci 9:431–448

    Google Scholar 

  • Ivancic TJ, Shaw SB (2015) Examining why trends in very heavy precipitation should not be mistaken for trends in very high river discharge. Clim Chang 133:681–693

    Google Scholar 

  • Jarvis A, Reuter HI, Nelson A, Guevara E (2008) Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database. CGIAR CSI Consort Spat Inf:1–9. https://doi.org/10.1167/iovs.10-6319

    Google Scholar 

  • Javelle P, Fouchier C, Arnaud P, Lavabre J (2010) Flash flood warning at ungauged locations using radar rainfall and antecedent soil moisture estimations. J Hydrol 394:267–274

    Google Scholar 

  • Jena PP, Chatterjee C, Pradhan G, Mishra A (2014) Are recent frequent high floods in Mahanadi basin in eastern India due to increase in extreme rainfalls? J Hydrol 517:847–862

    Google Scholar 

  • Kaur S, Diwakar SK, Das AK (2017) Long term rainfall trend over meteorological sub divisions and districts of India. Mausam 68:439–450

    Google Scholar 

  • Khan U, Ajami H, Tuteja NK, Sharma A, Kim S (2018) Catchment scale simulations of soil moisture dynamics using an equivalent cross-section based hydrological modelling approach. J Hydrol 564:944–966

    Google Scholar 

  • Komma J, Reszler C, Blöschl G, Haiden T (2007) Ensemble prediction of floods? Catchment non-linearity and forecast probabilities. Nat Hazards Earth Syst Sci 7:431–444

    Google Scholar 

  • Koster RD, Mahanama SPP, Livneh B, Lettenmaier DP, Reichle RH (2010) Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow. Nat Geosci 3:613–616

    Google Scholar 

  • Laaha G, Blöschl G (2006) Seasonality indices for regionalizing low flows. Hydrol Process 20:3851–3878

    Google Scholar 

  • Liu YY, Dorigo WA, Parinussa RM, de Jeu RAM, Wagner W, McCabe MF, Evans JP, van Dijk AIJM (2012) Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens Environ 123:280–297

    Google Scholar 

  • Livneh B, Rosenberg EA, Lin C, Nijssen B, Mishra V, Andreadis KM, Maurer EP, Lettenmaier DP (2013) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: update and extensions. J Clim 26:9384–9392

    Google Scholar 

  • Mahapatra R (2006) Disaster dossier: the impact of climate change on Orissa. Infochange Environ 9

  • Marchi L, Borga M, Preciso E, Gaume E (2010) Characterisation of selected extreme flash floods in Europe and implications for flood risk management. J Hydrol 394:118–133

    Google Scholar 

  • Mardia KV (1972) Statistics of directional data. Academic Press https://www.elsevier.com/books/statistics-of-directional-data/mardia/978-0-12-471150-1. Accessed October 2018

    Google Scholar 

  • Merz R, Blöschl G (2009) Process controls on the statistical flood moments - a data based analysis. Hydrol Process 23:675–696

    Google Scholar 

  • Merz B, Dung NV, Apel H, Gerlitz L, Schröter K, Steirou E, Vorogushyn S (2018) Spatial coherence of flood-rich and flood-poor periods across Germany. J Hydrol 559:813–826

    Google Scholar 

  • Mizumura K (1985) Estimation of hydraulic data by spline functions. J Hydraul Eng 111:1219–1225

    Google Scholar 

  • Moftakhari HR, Salvadori G, AghaKouchak A, Sanders BF, Matthew RA (2017) Compounding effects of sea level rise and fluvial flooding. Proc Natl Acad Sci 114:9785–9790

    Google Scholar 

  • Mohan TS, Rajeevan M (2017) Past and future trends of hydroclimatic intensity over the Indian monsoon region. J Geophys Res: Atmos 122:896–909

    Google Scholar 

  • Mondal A, Mujumdar PP (2012) On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow. Water Resour Res 48:1–18

    Google Scholar 

  • Mora C, Dousset B, Caldwell IR, Powell FE, Geronimo RC, Bielecki CR, Counsell CWW, Dietrich BS, Johnston ET, Louis LV, Lucas MP, McKenzie MM, Shea AG, Tseng H, Giambelluca TW, Leon LR, Hawkins E, Trauernicht C (2017) Global risk of deadly heat. Nat Clim Chang 7:501–506

    Google Scholar 

  • Mujumdar PP, Ghosh S (2008) Modeling GCM and scenario uncertainty using a possibilistic approach: application to the Mahanadi River, India. Water Resour Res 44:1–15

    Google Scholar 

  • Nasta P, Sica B, Chirico G et al (2013) Analysis of near-surface soil moisture spatial and temporal dynamics in an experimental catchment in Southern Italy. Procedia Environ Sci 19:188–197

    Google Scholar 

  • Nayak HP, Osuri KK, Sinha P, Nadimpalli R, Mohanty UC, Chen F, Rajeevan M, Niyogi D (2018) High-resolution gridded soil moisture and soil temperature datasets for the indian monsoon region. Sci Data 5:180264

    Google Scholar 

  • NDMA (National Disaster Management Authority) (2019) Major disasters in India. https://ndma.gov.in/en/disaster-data-statistics.html. Accessed April 2019

  • Newson R (2002) Parameters behind “nonparametric” statistics: Kendall’s tau, Somers’ D and median differences. Stata J 2:45–64

    Google Scholar 

  • Norbiato D, Borga M, Merz R, Blöschl G, Carton A (2009) Controls on event runoff coefficients in the eastern Italian Alps. J Hydrol 375:312–325

    Google Scholar 

  • NRSC-ISRO (2011) Surface water resources. India-WRIS, Jodhpur http://india-wris.nrsc.gov.in/wrpinfo/index.php?title=Surface_water_resources. Accessed April 2019

    Google Scholar 

  • NRSC-ISRO (2012) Morga Dam D00376. India-WRIS, Jodhpur http://india-wris.nrsc.gov.in/wrpinfo/index.php?title=Morga_Dam_D00376. Accessed April 2019

    Google Scholar 

  • Orth R, Seneviratne SI (2013) Propagation of soil moisture memory to streamflow and evapotranspiration in Europe. Hydrol Earth Syst Sci 17:3895–3911

    Google Scholar 

  • OSDMA (Odisha State Disaster Management Authority) (2019) Floods is Orissa. http://www.osdma.org/ViewDetails.aspx?vchglinkid=GL002&vchplinkid=PL006. Accessed April 2019

  • Panda DK, Kumar A, Ghosh S, Mohanty RK (2013) Streamflow trends in the Mahanadi River basin (India): linkages to tropical climate variability. J Hydrol 495:135–149

    Google Scholar 

  • Pattanayak S, Nanjundiah RS, Kumar DN (2017) Linkage between global sea surface temperature and hydroclimatology of a major river basin of India before and after 1980. Environ Res Lett 12:124002

    Google Scholar 

  • Petrow T, Merz B (2009) Trends in flood magnitude, frequency and seasonality in Germany in the period 1951-2002. J Hydrol 371:129–141

    Google Scholar 

  • Pewsey A, Neuhäuser M, Ruxton GD (2013) Circular statistics in R. Oxford University Press pp 182

  • Price DT, McKenney DW, Nalder IA et al (2000) A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric For Meteorol 101:81–94

    Google Scholar 

  • Quamar MF, Bera SK (2017) Pollen analysis of modern tree bark samples from the Manendragarh Forest Range of the Koriya district, Chhattisgarh, India. Grana 56:137–146

    Google Scholar 

  • Raje D, Mujumdar PP (2009) A conditional random field–based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin. Water Resour Res 45. https://doi.org/10.1029/2008WR007487

  • Rakhecha PR (2002) Highest floods in India. The extremes of the extremes: extraordinary floods (Proceedings of a symposia held at Reykjavik, Iceland, July 2000), IAHS Publ. no. 271

  • Rao PG (1993) Climatic changes and trends over a major river basin in India. Clim Res 2:215–223

    Google Scholar 

  • Rao PG (1995) Effect of climate change on streamflows in the Mahanadi River Basin, India. Water Int 20:205–212

    Google Scholar 

  • Rao PG, Kumar KK (1992) Climatic shifts over Mahanadi river basin. Curr Sci 63:192–196

    Google Scholar 

  • Raynaud D, Thielen J, Salamon P, Burek P, Anquetin S, Alfieri L (2015) A dynamic runoff co-efficient to improve flash flood early warning in Europe: evaluation on the 2013 central European floods in Germany. Meteorol Appl 22:410–418

    Google Scholar 

  • Sahoo B, Bhaskaran PK (2018) Multi-hazard risk assessment of coastal vulnerability from tropical cyclones – a GIS based approach for the Odisha coast. J Environ Manag 206:1166–1178

    Google Scholar 

  • Saini R, Wang G, Pal JS (2016) Role of soil moisture feedback in the development of extreme summer drought and flood in the United States. J Hydrometeorol 17:2191–2207

    Google Scholar 

  • Sakazume R, Ryo M, Saavedra O (2015) Consideration of antecedent soil moisture for predicting flood characteristics. J Japan Soc Civ Eng Ser B1 (Hydraulic Eng) 71:I_97–I_102

    Google Scholar 

  • Samantaray AK, Singh G, Ramadas M, Panda RK (2019) Drought hotspot analysis and risk assessment using probabilistic drought monitoring and severity–duration–frequency analysis. Hydrol Process 33:432–449

    Google Scholar 

  • Sathyanadh A, Karipot A, Ranalkar M, Prabhakaran T (2016) Evaluation of soil moisture data products over Indian region and analysis of spatio-temporal characteristics with respect to monsoon rainfall. J Hydrol 542:47–62

    Google Scholar 

  • Seneviratne SI, Koster RD, Guo Z, Dirmeyer PA, Kowalczyk E, Lawrence D, Liu P, Mocko D, Lu CH, Oleson KW, Verseghy D (2006) Soil moisture memory in AGCM simulations: analysis of global land–atmosphere coupling experiment (GLACE) data. J Hydrometeorol 7:1090–1112

    Google Scholar 

  • Sharma PJ, Patel PL, Jothiprakash V (2018a) Changes in monthly hydro-climatic indices for middle Tapi basin, India. 23rd Int. Conf. on Hydraulics, Water Resources and Coastal Engineering HYDRO 2018, NIT Patna, pp 1-14

  • Sharma A, Wasko C, Lettenmaier DP (2018b) If precipitation extremes are increasing, why aren’t floods? Water Resour Res 54:8545–8551

    Google Scholar 

  • Singh D, Tsiang M, Rajaratnam B, Diffenbaugh NS (2014) Observed changes in extreme wet and dry spells during the South Asian summer monsoon season. Nat Clim Chang 4:456–461

    Google Scholar 

  • Stephens E, Day JJ, Pappenberger F, Cloke H (2015) Precipitation and floodiness. Geophys Res Lett 42:10316–10323

    Google Scholar 

  • Svensson C, Kundzewicz WZ, Maurer T (2005) Trend detection in river flow series: 2. Flood and low-flow index series. Hydrol Sci J 50:811–824

    Google Scholar 

  • Tian P, Zhao GJ, Li J, Tian K (2011) Extreme value analysis of streamflow time series in Poyang Lake Basin, China. Water Sci Eng 4:121–132

    Google Scholar 

  • Van den Dool H, Huang J, Fan Y (2003) Performance and analysis of the constructed analogue method applied to US soil moisture over 1981–2001. J Geophys Res Atmospheres 108(D16):8617

    Google Scholar 

  • Van Steenbergen N, Willems P (2013) Increasing river flood preparedness by real-time warning based on wetness state conditions. J Hydrol 489:227–237

    Google Scholar 

  • Vivoni ER, Entekhabi D, Bras RL, Ivanov VY, van Horne MP, Grassotti C, Hoffman RN (2006) Extending the predictability of hydrometeorological flood events using radar rainfall nowcasting. J Hydrometeorol 7:660–677

    Google Scholar 

  • Vormoor K, Lawrence D, Schlichting L, Wilson D, Wong WK (2016) Evidence for changes in the magnitude and frequency of observed rainfall vs. snowmelt driven floods in Norway. J Hydrol 538:33–48

    Google Scholar 

  • Wasko C, Sharma A (2017a) Global assessment of flood and storm extremes with increased temperatures. Sci Rep 7:1–8

    Google Scholar 

  • Wasko C, Sharma A (2017b) Continuous rainfall generation for a warmer climate using observed temperature sensitivities. J Hydrol 544:575–590

    Google Scholar 

  • Woldemeskel F, Sharma A (2016) Should flood regimes change in a warming climate? The role of antecedent moisture conditions. Geophys Res Lett 43:7556–7563

    Google Scholar 

  • Xiao Y, Wan J, Hewings GJD (2013) Flooding and the Midwest economy: assessing the Midwest floods of 1993 and 2008. GeoJournal 78:245–258

    Google Scholar 

  • Yang L, Tian F, Smith JA, Hu H (2014) Urban signatures in the spatial clustering of summer heavy rainfall events over the Beijing metropolitan region. Journal of Geophysical Research : Atmospheres J Geophys Res Atmos 119:1203–1217

    Google Scholar 

  • Ye S, Li H-Y, Leung LR, Guo J, Ran Q, Demissie Y, Sivapalan M (2017) Understanding flood seasonality and its temporal shifts within the contiguous United States. J Hydrometeorol 18:1997–2009

    Google Scholar 

  • Yin J, Gentine P, Zhou S, Sullivan SC, Wang R, Zhang Y, Guo S (2018) Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat Commun 9:4389

    Google Scholar 

  • Zehe E, Blöschl G (2004) Predictability of hydrologic response at the plot and catchment scales: role of initial conditions. Water Resour Res 40:1–21

    Google Scholar 

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Acknowledgments

This study forms a part of the sub-project “Impact of Climate Change on Flood Risk” conducted under the CoE in Climate Change at IIT Kharagpur. We are also thankful to the Central Water Commission (CWC), Government of India, for providing the data sets for this research. We especially thank the European Space Agency (ESA)-Climate Change Initiative for their support in data extraction. We are thankful to Philip Buttinger, TU Wien, for clarifying technical queries pertaining to ECV-SM global soil moisture combined data product.

Funding

The Department of Science and Technology (DST), Government of India, provided financial support. This study was organized as a part of the Center of Excellence (CoE) in Climate Change studies activity established at IIT Kharagpur and funded by DST, Government of India.

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Ganguli, P., Nandamuri, Y.R. & Chatterjee, C. Analysis of persistence in the flood timing and the role of catchment wetness on flood generation in a large river basin in India. Theor Appl Climatol 139, 373–388 (2020). https://doi.org/10.1007/s00704-019-02964-z

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