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Updated: Sep 24, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

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Published on: July 24, 2016

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Employing sensitivity analysis to catchments having scanty data.

Sakiba Nabi1, Manzoor Ahmad Ahanger2, Abdul Qayoom Dar2

  • 1Department of Civil Engineering, National Institute of Technology Srinagar, Srinagar, Jammu And Kashmir, 190006, India. sakiba_09phd17@nitsri.ac.in.

Environmental Science and Pollution Research International
|May 9, 2022
PubMed
Summary

Sensitivity analysis (SA) in hydrological models can now be performed in data-sparse regions using a novel "minimum continuous data period" approach. This method effectively captures parameter importance even with limited data, improving rainfall-runoff modeling.

Keywords:
Data-sparseHydrological modellingMinimum continuous data periodMorrisSensitivity analysis

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Area of Science:

  • Hydrology
  • Environmental Modeling
  • Data Science

Background:

  • Sensitivity analysis (SA) is crucial for hydrological model calibration and optimization.
  • Data scarcity in many regions hinders the application of SA.
  • Lack of continuous data is a major constraint for SA implementation.

Purpose of the Study:

  • To introduce the concept of a "minimum continuous data period" for SA in data-sparse regions.
  • To determine the minimum data duration required for reliable SA in hydrological models.
  • To assess the feasibility of using sub-catchment data for catchment-wide SA.

Main Methods:

  • Analysis of sensitivity profiles using data from two data-sufficient sub-catchments.
  • Evaluation of data at various timescales to identify the minimum required period for SA.
  • Comparison of SA results from a minimum data period with those from longer datasets.

Main Results:

  • A minimum continuous data period of 2 years replicated the actual sensitivity profile by an average of 77.5%.
  • The most sensitive and insensitive parameters were identified with 100% accuracy using the minimum data period.
  • The study demonstrated that sub-catchment data can effectively represent the sensitivity profile of a larger, data-sparse catchment.

Conclusions:

  • The proposed "minimum continuous data period" method enables SA in data-scarce environments.
  • This approach significantly enhances the applicability of SA for hydrological model calibration and optimization.
  • The findings support the use of sub-catchment data to overcome data limitations in hydrological modeling.