EconPapers    
Economics at your fingertips  
 

Capability of LISEM to estimate flood hydrographs in a watershed with predominance of long-duration rainfall events

Marcelle Martins Vargas (), Samuel Beskow (), Carlos Rogério Mello (), Maíra Martim Moura (), Maria Cândida Moitinho Nunes (), Lessandro Coll Faria () and Leandro Sanzi Aquino ()
Additional contact information
Marcelle Martins Vargas: Federal University of Pelotas
Samuel Beskow: Federal University of Pelotas
Carlos Rogério Mello: Federal University of Lavras
Maíra Martim Moura: Federal University of Pelotas
Maria Cândida Moitinho Nunes: Federal University of Pelotas
Lessandro Coll Faria: Federal University of Pelotas
Leandro Sanzi Aquino: Federal University of Pelotas

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 109, issue 1, No 26, 593-614

Abstract: Abstract Process-based hydrological models are of great importance to understand hydrological processes and support decision making. The LImburg Soil Erosion Model (LISEM) requires information on soil and land-use-related attributes to represent the transformation of rainfall into runoff for isolated rainfall events. This study aimed at evaluating LISEM for estimation of direct surface runoff (DSR) hydrographs in a watershed in Southern Brazil under the predominance of long-duration rainfall events, dominated by Argisols and with availability of a high-density rain gauge network. In addition, this study sought to: (i) suggest and evaluate a procedure for definition of initial soil moisture from antecedent 5-day rainfall depth; (ii) reduce the degree of subjectivity involved in the determination of some vegetation-related parameters by using remote sensing; and (iii) recommend a validation procedure. The saturated soil hydraulic conductivity and the Manning’s surface roughness coefficient were calibrated considering 11 rainfall–runoff events, whereas the validation was performed for 4 events from the average calibrated parameters. The Nash–Sutcliffe coefficient was used to assess both calibration and validation, resulting in average values of 0.64 and 0.58, respectively. It can be inferred from the results that the use of remote sensing to derive some LISEM parameters, along with the suggested schemes for definition of initial soil moisture and validation, was effective and provided sound results even for long-duration rainfall events. The results of this study and its methodological procedures can serve as a basis for other professionals who intend to use LISEM for both conducting detailed analyses of DSR hydrographs and supporting water resources management.

Keywords: Heavy rainfall events; Direct surface runoff; Hydrological modeling; Validation procedure (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-021-04850-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:109:y:2021:i:1:d:10.1007_s11069-021-04850-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-021-04850-2

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:nathaz:v:109:y:2021:i:1:d:10.1007_s11069-021-04850-2