Hydrological Modeling with Respect to Impact of Land-Use and Land-Cover Change on the Runoff Dynamics in Godavari River Basin Using the HEC-HMS Model
Bibliographic record
Abstract
Hydrological modeling and the hydrological response to land-use/land-cover changes induced by human activities have gained enormous research interest over the last few decades. The study presented here analyzes the spatial and qualitative changes in the rainfall–runoff that have resulted from the land-cover changes between 1985–2014 in the Godavari River Basin using the Hydrologic Engineering Centre-Hydrologic Modeling System(HEC-HMS) model and remote sensing—GIS (geographic information system) techniques. The purpose of this paper is to analyze the dynamics of land-use/land-cover (LULC) changes for the years 1985, 1995, 2005, and 2014 for the Godavari Basin. The findings reveal an increase of 0.64% of built-up land, a decrease of 0.92% in shrubland, and an increase of 0.56% in waterbodies between 1985–2014. The LULC change detection results between the years 1985–2014 indicated a drastic change in the cropland, forest, built-up land, and water bodies among all of the other classes. The urbanization and agricultural activities are the major reasons for the increase of cropland, built-up land, and water bodies, at the expense of decreases in shrubland and forest. The study had an overall classification accuracy of 92% and an overall Kappa coefficient of 0.9. The HEC-HMS model is used to simulate the hydrology of the Godavari Basin. The analyses carried out were mainly focussed on the impact of LULC changes on the streamflow pattern. The surface runoff was simulated for the year 2014 to quantify the changes that have taken place due to changes in LULC. The observed and the simulated peak streamflow was found to be the same i.e., 56,780 m3/s on 9 September 2014. In the validation part, the linear regression method was used to correlate the observed and simulated streamflow data at the prominent gauge station of the Badrachalam outlet for the Godavari River Basin and give a correlation coefficient value of 0.83. It was found that the HEC-HMS model is compatible and works better for the rainfall–runoff modeling, as it takes into account the various parameters that are influencing the process. The hydrological modeling that was carried out using the HEC-HMS model has brought out the significant impact of LULCC on rainfall–runoff at the Pranhita sub-basinscale, indicating the model’s ability to successfully accommodate all of the environmental and landscape variables. The study indicates that deforestation at the cost of urbanization and cropland expansions leads to decreases in the overall evapotranspiration (ET) and infiltration, with an increase in runoff. The results of the study show that the integration of remote sensing, GIS, and the hydrological model (HEC-HMS) can solve hydrological problems in a river basin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".