# Hydrological modeling of flood impacts under land use and land cover change: A systematic review of tools, trends, and challenges

## TL;DR

This paper reviews 78 studies to show how land use changes like urbanization increase flood risk and proposes a framework for better flood modeling.

## Contribution

Introduces a decision framework linking bibliometric trends to model selection for flood mitigation under land use changes.

## Key findings

- Urbanization and deforestation significantly increase surface runoff and flood frequency.
- Remote sensing and GIS tools improve LULC detection and flood prediction accuracy.
- Data scarcity and model calibration remain major challenges in hydrological modeling.

## Abstract

•A systematic review of 78 articles (2005–2025) using PRISMA and bibliometric mapping.•LULC changes, like urbanization and deforestation, strongly increase flood risk and runoff.•Introduces a framework linking bibliometric findings to model selection for flood mitigation.

A systematic review of 78 articles (2005–2025) using PRISMA and bibliometric mapping.

LULC changes, like urbanization and deforestation, strongly increase flood risk and runoff.

Introduces a framework linking bibliometric findings to model selection for flood mitigation.

Land use and land cover (LULC) change is a major anthropogenic factor influencing flood behavior and hydrological processes. This systematic review synthesizes two decades (2005–2025) of research on hydrological modeling approaches used to assess flood responses under LULC transitions. A total of 114 publications were retrieved from the Scopus database, and after applying PRISMA-based screening, 78 peer-reviewed studies were analyzed using bibliometric and content mapping. The review categorizes hydrological models by spatial scale, process representation, and sensitivity to LULC dynamics. Findings consistently indicate that urban expansion, deforestation, and vegetation loss intensify surface runoff, peak flow, and flood frequency. Despite advancements, significant challenges remain particularly related to data scarcity, model calibration, and the limited integration of socio-economic variables. Emerging tools such as Remote Sensing (RS), Geographic Information Systems (GIS), and machine learning especially within platforms like Google Earth Engine (GEE) enhance LULC detection accuracy and flood prediction capability. The study proposes an integrated decision framework linking bibliometric trends with model selection strategies, enabling researchers to align model choice with data availability and landscape characteristics. Overall, this review emphasizes the importance of interdisciplinary, data-driven modeling to strengthen flood resilience in rapidly transforming land systems.

Image, graphical abstract