Skip to main content

Advertisement

SpringerLink
Log in

Development and application of travel time based gridded runoff and sediment yield model

  • Original Paper
  • Published:
International Journal of Environmental Science and Technology Aims and scope Submit manuscript

Abstract

The present study proposes a Geographic Information System-based runoff and sediment yield model. The model derives the study watershed characteristics using the Digital Elevation Model. The model calculates the runoff (using the NRCS curve number method) and sediment yield (using MUSLE) on each cell (pixel) of the DEM and routes these hydrological parameters over the overland flow cells into the drainage channels to the watershed outlet using Time-Area histogram method. The backend and frontend of the model is developed in Python and HTML + JavaScript code, respectively. The model develops the runoff hydrograph and sediment graph at the watershed outlet. The performance of the developed model has been assessed by comparing the model output with the observed runoff and sediment yield measured at the outlet of forest micro-watershed located in Shivalik foot hills of North-West India. The statistical analysis reveals the reasonably well performance of the developed model in simulating runoff and sediment yield as is corroborated by low values of PBIAS, MAPE, MBE and RMSE and high values of correlation coefficient and model efficiency.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

References

  • Abedin SJH, Stephen H (2019) GIS framework for spatiotemporal mapping of urban flooding. Geosciences 9:77. https://doi.org/10.3390/geosciences9020077

    Article  Google Scholar 

  • Abrahams AD, Parsons AJ (1991) Resistance to overland flow on desert pavement and its implications for sediment transport modeling. Water Resour Res 27:1827–1836

    Article  Google Scholar 

  • Alam MJ, Meah MA, Noor MS (2011) Numerical modeling of ground water flow and the effect of boundary conditions for the Hsieh aquifer. Asian J MathStat 4:33–44

    Google Scholar 

  • Ali KF, Boer DH (2010) Spatially distributed erosion and sediment yield modeling in the upper Indus River basin. Water Resour Res 46:W08504. https://doi.org/10.1029/2009WR008762

    Article  Google Scholar 

  • Arekhi S, Shabani A, Alavipanah SK (2011) Evaluation of integrated KW-GIUH and MUSLE models to predict sediment yield using geographic information system (GIS) (Case study: Kengir watershed, Iran). Afr J Agric Res 6:4185–4198

    Google Scholar 

  • Arnold JG, Allen PM, Bernhardt G (1993) A comprehensive surface-groundwater flow model. J Hydrol 142:47–69

    Article  Google Scholar 

  • Aubert D, Loumagne C, Oudin L (2003) Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model. J Hydrol 280:145–161. https://doi.org/10.1016/S0022-1694(03)00229-4

    Article  Google Scholar 

  • Bagarello V, Ferro V (1999) Assessment of soil erosion. In: van Lier HN, Pereira LS, Steiner FR (eds) CIGR Handbook of Agricultural Engineering (Volume I), Land and Water Engineering. American Society of Agricultural & Biological Engineers. USA, pp 153–183

    Google Scholar 

  • Bansal ML, Singh S, Singh TP, Kumar R (1991) Statistical methods for research workers. 3rd edition. pp 131–35. Kalyani Publishers, Ludhiana, India

  • Bationo A, Kihara J, Vanlauwe B, Waswa B, Kimetu J (2007) Soil organic carbon dynamics, functions and management in West African agro-ecosystems. Agric Syst 94:13–25. https://doi.org/10.1016/j.agsy.2005.08.011

    Article  Google Scholar 

  • Bergström S (1991) Principles and confidence in hydrological modelling. Nordic Hydrol 22:123–136. https://doi.org/10.2166/nh.1991.0009

    Article  Google Scholar 

  • Bhardwaj A, Kaushal MP (2009) Two-dimensional physically based finite element runoff model for small agricultural watershed: I. Model Development Hydrol Process 23:397–407

    Article  Google Scholar 

  • Billen G, Garnier J, Rousseau V (2005) Nutrient fluxes and water quality in the drainage network of the Scheldt basin over the last 50 years. Hydrobiologia 540:47–67

    Article  CAS  Google Scholar 

  • Brevik EC (2012) Soils and Human Health-An Overview. In: Brevik EC, Burgess LC (eds) Soils and Human Health. CRC Press, Boca Raton, USA, pp 29–56

    Chapter  Google Scholar 

  • Brito MG, Costa CN, Almeida JA, Vendas D, Verdial PH (2005) Characterization of maximum infiltration areas using GIS tools. EngGeol 85:14–18. https://doi.org/10.1016/j.enggeo.2005.09.022

    Article  Google Scholar 

  • Brocca L, Mellone F, Moramarco T, Morbidelli R (2010) Spatial and temporal variability of soil moisture and its estimation across scales. Water Resour Res 46:W02516. https://doi.org/10.1029/2009WR008016

    Article  Google Scholar 

  • Callow JN, Van Niel KP, Boggs GS (2007) How does modifying a DEM to reflect known hydrology affect subsequent terrain analysis? J Hydrol 332:30–39

    Article  Google Scholar 

  • Chen Y, Shen C, Chiu Y (2018) Dynamic modeling of sediment budget in Shihmen Reservoir watershed in Taiwan. Water 10:1808. https://doi.org/10.3390/w10121808

    Article  Google Scholar 

  • Cho Y (2016) Development and evaluation of a watershed-scale hybrid hydrologic model. Ph.D. Dissertation. Purdue University, West Lafayette, Indiana.

  • Cho Y, Engel BA (2017) Spatially distributed long-term hydrologic simulation using a continuous SCS CN method-based hybrid hydrologic model. Hydrol Process 32:904–922

    Article  Google Scholar 

  • Cho J, Bosch D, Lowrance R, Strickland T, Vellidis G (2009) Effect of spatial distribution of rainfall on temporal and spatial uncertainty of SWAT output. Trans ASAE 52:1545–1555

    Article  Google Scholar 

  • Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill Inc, Singapore, p 149

    Google Scholar 

  • Cirilo JA, Verçosa LFM, Gomes MMA, Feitoza MAB, Ferraz GF, Silva BM (2020) Development and application of a rainfall-runoff model for semi-arid regions. Brazilian J Water Resour. 25:e15

    Google Scholar 

  • Cronshey R (1986)Urban hydrology for small watersheds. Technical Report US Department of Agriculture, Soil Conservation Service, Engineering Division: Washington, DC

  • Devi GK, Ganasri BP, Dwarakish GS (2015) A review on hydrological models. Aquatic Procedia 4:1001–1007

    Article  Google Scholar 

  • Domnita M (2012) Runoff modeling using GIS: Application torrential basins in the Apuseni Mountains. Ph.D. Dissertation. Cluj Napoca, Romania.

  • Drishya J, Kumar DS (2018) Automated calibration of a two-dimensional overland flow model by estimating Manning’s roughness coefficient using genetic algorithm. J Hydroinform 20:440–456

    Article  Google Scholar 

  • Fallah M, Ataollah K, Omidvar E (2016) Watershed prioritization in order to implement soil and water conservation practices. Environ Earth Sci 75:1248. https://doi.org/10.1007/s12665-016-6035-1

    Article  Google Scholar 

  • Fortin FP, Turcotte P, Massicotte S, Moussa R, Fitzback J, Villeneu JP (2001) Distributed watershed model compatible with remote sensing and GIS data. I: Description of model. J HydrolEng 6:91–99

    Google Scholar 

  • Gallant JC, Wilson JP (2000) Primary topographic attributes in Terrain Analysis: Principles and Applications. PP. 51–86. John Wiley, Hoboken, New Jersey.

  • Ganasri BP, Ramesh H (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS - a case study of Nethravathi Basin. Geosci Front 7:953–961

    Article  Google Scholar 

  • Gashu K, Muchie Y (2018) Rethink the interlink between land degradation and livelihood of rural communities in Chilga district, Northwest Ethiopia. J Ecol Environ 42:1–7. https://doi.org/10.1186/s41610-018-0077-0

    Article  Google Scholar 

  • Gassman PW, Williams JR, Wang X, Saleh A, Osei E, Hauck LM, Izaurralde C, Flowers JD (2009) The Agricultural Policy Environmental EXtender (APEX) Model: An Emerging Tool for Landscape and Watershed Environmental Analyses. Technical Report 09-TR 49. Centre for Agricultural and Rural Development Iowa State University. Ames, Iowa.

  • Hansen JR, Refsgaard JC, Hansen S, Ernstsen V (2007) Problems with heterogeneity in physically based agricultural catchment models. J Hydrol 342:1–16. https://doi.org/10.1016/j.jhydrol.2007.04.016

    Article  Google Scholar 

  • He C (2003) Integration of geographic information systems and simulation model for watershed management. Environ Model Soft 18:809–813. https://doi.org/10.1016/S1364-8152(03)00080-X

    Article  Google Scholar 

  • Hickey R (2000) Slope Angle and Slope Length Solutions for GIS. Cartography 29:1–8

    Article  Google Scholar 

  • Horn BKP (1981) Hill Shading and the Reflectance Map. Proc IEEE69:14–47

  • Huang PC, Lee KT (2016) Efficient DEM-based overland flow routing using integrated recursive algorithms. Hydrol Process 31:1000–1017

    Google Scholar 

  • Jajarmizadeh M, Harun S, Solarpour M (2012) A review on theoretical considerations and types of models in hydrology. J Environ Sci Technol 5:249–261

    Article  Google Scholar 

  • Jensen SK, Domingue JO (1988) Extracting topographic structure from digital elevation data for geographic information system analysis. PhotogrammEng Remote Sens 54:1593–1600

    Google Scholar 

  • Jinkang D, Shunping X, Youpeng X, Xu C, Singh VP (2007) Development and testing of a simple physically-based distributed rainfall-runoff model for storm runoff simulation in humid forested basins. J Hydrol. 336:334–346

    Article  Google Scholar 

  • Kamphorst EC, Jetenet L, Guerif J, Pitkanen J, Iversen BV, Douglas JT, Paz A (2000) Predicting depressional storage from soil surface roughness. Soil Sci Soc Am J 64:1749–1758. https://doi.org/10.2136/sssaj2000.6451749x

    Article  CAS  Google Scholar 

  • Kite GW (1995) The SLURP model. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publications, Colorado, USA, pp 521–562

    Google Scholar 

  • Lai Z, Li S, Lu G, Pan Z, Fei G (2015) Watershed delineation using hydrographic features and a DEM in plain river network region. Hydrol Process 30:276–288. https://doi.org/10.1002/hyp.10612

    Article  Google Scholar 

  • Li H, Wigmosta MS, Wu H, Huang M, Ke Y, Coleman AM, Leung LR (2012) A physically based runoff routing model for land surface and earth system models. J Hydrometeorol 14:808–828

    Article  Google Scholar 

  • Li L, Yang J, Wu J (2019) A method of watershed delineation for flat terrain using Sentinel-2A imagery and DEM: A case study of the Taihu basin. ISPRS Int J Geo-Inf 8:528. https://doi.org/10.3390/ijgi8120528

    Article  CAS  Google Scholar 

  • McCool DK, Williams JD (2008) Soil erosion by water. In: Jorgensen SE, Fath BD (eds) Encyclopaedia of ecology. Elsevier, Netherlands, pp 3284–90

    Chapter  Google Scholar 

  • Meisels A, Raizman S, Karnieli A (1995) Skeletonizing a DEM into a drainage network. ComputGeosci21:187–96.

  • Merritt WS, Letcher RA, Jakeman AJ (2006) A review of erosion and sediment transport models. Environ Model Soft 18:761–769

    Article  Google Scholar 

  • Mishra SK, Singh VP (2003) Soil Conservation Service Curve Number (SCS-CN) Methodology. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 1132–1136

    Book  Google Scholar 

  • Myttenaere AD, Golden B, Grand BL, Rossi F (2016) Mean absolute percentage error for regression models. Neurocomputing 192:38–48

    Article  Google Scholar 

  • Narimani R, Erfanian M, Nazarnejad H, Mohmadzadeh A (2017) Evaluating the impact of management scenarios and land use changes on annual surface runoff and sediment yield using the GeoWEPP: a case study from the Lighvanchai watershed. Iran Environ Earth Sci 76:353

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part 1: a discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  • Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2011) Soil and Water Assessment Tool Theoretical Documentation Version 2009. Grassland, Soil and Water Research Laboratory, Agricultural Research Service and Blackland Research Centre, Agricultural Experiment Station, College Station, Texas.

  • Noor H, Khalaj MR (2018) Improving MUSLE performance for sediment yield prediction at micro-watershed level using seasonal classified data. Water PractTechnol 13:505–512. https://doi.org/10.2166/wpt.2018.061

    Article  Google Scholar 

  • Nor NIA, Harun S, Kassim AHM (2007) Radial basis function modeling of hourly streamflow hydrograph. J HydrolEng 12:113–123

    Google Scholar 

  • Noto LV, Loggia GL (2007) Derivation of a distributed unit hydrograph integrating GIS and remote sensing. J Hydrol 12:639–650

    Google Scholar 

  • Novotny V, Olem H (1994) Water quality: prevention, identification, and management of diffuse pollution. Van Nostrand Reinhold, New york

    Google Scholar 

  • O’Callaghan JF, Mark DM (1984) The extraction of drainage networks from digital elevation data. Comput Gr Image Process 28:323–344

    Article  Google Scholar 

  • Ogden FL, Raj-Pradhan N, Downer CW, Zahner JA (2011) Relative importance of impervious area, drainage density, width function, and subsurface storm drainage on flood runoff from an urbanized catchment. Water Resour Res 47:W12503

    Article  Google Scholar 

  • Panuska JC, Moore ID, Kramer LA (1991) Terrain analysis: Integration into Agricultural Nonpoint Source (AGNPS) Pollution Model. J Soil Water Cons 46:59–64

    Google Scholar 

  • Planchon O, Darboux F (2002) A fast, simple and versatile algorithm to fill the depressions of digital elevation models. CATENA 46:159–176

    Article  Google Scholar 

  • Planchon O, Esteves M, Silvera N, Lapetite JM (2001) Microrelief induced by tillage: Measurement and modeling of surface storage capacity. CATENA 46:141–157. https://doi.org/10.1016/S0341-8162(01)00163-1

    Article  Google Scholar 

  • Ponce VM, Hawkins RH (1996) Runoff curve number: has it reached maturity? J Hydrol Eng 1:11–19. https://doi.org/10.1061/(ASCE)1084-0699(1996)1:1(11)

    Article  Google Scholar 

  • Pongsai S, Schmidt VD, Shrestha RP, Clemente RS, Eiumnoh A (2010) Calibration and validation of the Modified universal soil loss equation for estimating sediment yield on sloping plots: a case study in Khun Satan catchment of northern Thailand. Can J Soil Sci 90:585–596

    Article  Google Scholar 

  • Qin CZ, Zhan L (2012) Parallelizing flow-accumulation calculations on graphics processing units-From iterative DEM pre-processing algorithm to recursive multiple-flow-direction algorithm. ComputGeosci 43:7–16

    Google Scholar 

  • Rai RK, Upadhayay A, Singh VP (2010) Effect of variable roughness on runoff. J Hydrol 382:111–127. https://doi.org/10.1016/j.jhydrol.2009.12.022

    Article  Google Scholar 

  • Renschler CS (2003) Designing geo-spatial interfaces to scale process models: the GeoWEPP approach. Hydrol Process 17:1005–1017

    Article  Google Scholar 

  • Revilla-Romero B, Beck HE, Burek P, Salamon P, de Roo A, Thielen J (2015) Filling the gaps: Calibrating a rainfall-runoff model using satellite-derived surface water extent. Remote Sens Environ 171:118–131. https://doi.org/10.1016/j.rse.2015.10.022

    Article  Google Scholar 

  • Sabu MS (1999) Runoff and sediment yield simulation of micro-watersheds using artificial neural networks. Thesis, Punjab Agricultural University, Ludhiana, M.Tech

    Google Scholar 

  • Saha A, Ghosh P, Mitra B (2018) GIS based soil erosion estimation using RUSLE model: a case study of upper Kangsabati watershed, West Bengal, India. Int J Environ Sci Nat Res 13:555871. https://doi.org/10.19080/IJESNR.2018.13.555871

    Article  Google Scholar 

  • Schmidt JA (1991) A mathematical model to simulate rainfall erosion. CATENA 19:101–109

    Google Scholar 

  • Shahzad F, Gloaguen R (2011) TecDEM: A MATLAB based toolbox for tectonic geomorphology, Part 1: drainage network preprocessing and stream profile analysis. ComputGeosci 37:250–260

    Google Scholar 

  • Sivakumar MVK (2007) Interactions between climate and desertification. Agric for Meteor 142:143–155

    Article  Google Scholar 

  • Sivapalan M (2003) Prediction in ungauged basins: A grand challenge for theoretical hydrology. Hydrol Process 17:3163–3170

    Article  Google Scholar 

  • Sorrell RC, Hamilton DA (2003) Computing flood discharges for small ungauged watersheds. Geological and Land Management Division, Michigan Department of Environmental Quality: Lansing, MI

  • Stisen S, Sandhlot I (2010) Evaluation of remote-sensing-based rainfall products through predictive capability in hydrological runoff modeling. Hydrol Process 24:879–891. https://doi.org/10.1002/hyp.7529

    Article  Google Scholar 

  • Su C, Wang X, Feng C, Huang Z, Zhang X (2015) An integrated algorithm for depression filling and assignment of drainage directions over flat surfaces in digital elevation models. Earth Sci Inf 8:1–11

    Article  CAS  Google Scholar 

  • Thomann RV (1982) Verification of water quality models. J Environ Engg 108:923–940

    CAS  Google Scholar 

  • Tyagi JV, Mishra SK, Singh R, Singh VP (2008) SCS-CN based time-distributed sediment yield model. J Hydrol 352:388–403

    Article  Google Scholar 

  • Wallis C, Wallace D, Tarboton DG, Schreuders K (2009) Hydrologic terrain processing using parallel computing. Proc 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. 2540–2545, Cairns, Australia.

  • Wheater HS (2002) Progress in and prospects for fluvial flood modelling. Philos Trans R Soc A 360:1409–1431

    Article  CAS  Google Scholar 

  • Williams JR, Singh VP (1995) The EPIC model. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, CO, USA, pp 909–1000

    Google Scholar 

  • Wohl E, Barros A, Brunsell N, Chappell NA, Coe M, Giambelluca T, Ogden F (2012) The hydrology of the humid tropics. Nat Clim Change 2:655–662. https://doi.org/10.1038/nclimate1556

    Article  Google Scholar 

  • World Research Institute (2015) World’s 15 Countries with the Most People Exposed to River Floods. https://www.wri.org/blog/2015/03/world-s-15-countries-most-people-exposed-river-floods (Accessed on 25th January 2021)

  • Xevi E, Christiaens K, Espinao A, Sewnandan W, Mallants D, Sorensen H, Feyen J (1997) Calibration, validation and sensitivity analysis of the MIKE-SHE model using the Neuenkirchen catchment as a case study. Water ResourManag. 11:219–42

    Google Scholar 

  • Xu Y, Wang S, Bai X, Shu D, Tian Y (2018) Runoff response to climate change and human activities in a typical karst watershed. SW China. PLoS ONE. 13:e0193073

    Article  CAS  Google Scholar 

  • Yao Y, Shi X (2015) Alternating scanning orders and combining algorithms to improve the efficiency of flow accumulation calculation. Int J Geogr Inf Sci 29:1–26

    Article  Google Scholar 

  • Yen BC (1991) Channel flow resistance: centennial of Manning’s formula. Water Resources Publications, Littleton, USA, pp 43–46

    Google Scholar 

  • Yildirim AA, Watson D, Tarboton DG, Wallace R (2015) A virtual tile approach to raster-based calculations of large digital elevation models in a shared-memory system. Comput. Geosci 82:78–88

    Article  Google Scholar 

  • Yong-He L, Wan-Chang Z, Jing-Wen X (2009) Another fast and simple DEM depression-filling algorithm based on priority queue structure. Atmos Oceanic Sci Lett 2:214–219

    Article  Google Scholar 

  • Zhang Y, Degroote J, Wolter C, Sugumaran R (2009) Integration of modified universal soil loss equation (MUSLE) into a GIS framework to assess soil erosion risk. Land Degrad Develop 20:84–91. https://doi.org/10.1002/ldr.893

    Article  Google Scholar 

  • ZhangG LR, CaoY SR, Zhang XC (2010) Impacts of sediment load on manning coefficient in supercritical shallow flow on steep slopes. Hydrol Process 24:3909–3914. https://doi.org/10.1002/hyp.7892

    Article  Google Scholar 

  • Zheng C, Hill MC, Cao G, Ma R (2012) MT3DMS: Model use, calibration, and validation. Trans ASABE 55:1549–1559. https://doi.org/10.13031/2013.422

    Article  Google Scholar 

Download references

Acknowledgements

The first author thankfully acknowledges the Department of Science and Technology, Government of India, New Delhi, for providing support in the form of Junior Research Fellowship-INSPIRE under grant number: DST/INSPIRE/03/2015/002269. The authors are thankful to Director, Punjab Agricultural University-Regional Research Station, Ballowal Saunkhri, for providing necessary laboratory facilities and hydrological data used in the present study.

Funding

Punjab Agricultural University, Ludhiana, India.

Author information

Authors and Affiliations

  1. Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana, 141004, India

    A. Yousuf & A. Bhardwaj

Authors
  1. A. Yousuf
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Bhardwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Yousuf.

Ethics declarations

Conflicts of interest

Authors declare that there exists no conflict of interest.

Code availability

The model developed and code is not open access as of now.

Additional information

Editorial respnsibility: J Aravind.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOC 683 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yousuf, A., Bhardwaj, A. Development and application of travel time based gridded runoff and sediment yield model. Int. J. Environ. Sci. Technol. 19, 9801–9816 (2022). https://doi.org/10.1007/s13762-021-03661-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13762-021-03661-z

Keywords

Search

Navigation