Assessing Impacts of Land Use and Land Cover (LULC) Change on Stream Flow and Runoff in Rur Basin, Germany

Shukla, Saurabh; Meshesha, Tesfa Worku; Sen, Indra Sekhar; Bol, Roland; Bogena, Heye; Wang, Junye

doi:10.3390/su15129811

Cited by 4 publications

(2 citation statements)

References 71 publications

(85 reference statements)

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“…In general, human activities characterized by urban development, agricultural activities and deforestation systematically leads to LULC changes, which then results in environmental changes that impacts on the earth-atmosphere interactions and sustainable development [107]. The increasing population and related human activities coupled with climate change have progressively led to the decline in per capita water availability [108]. The decline is estimated to have a 6-fold increase over the past century and is predicted to rise annually at a rate of 1% [109].…”

Section: Insights Into Lulc Change and Land-water Sustainabilitymentioning

confidence: 99%

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Ouma,

Nkwae,

Odirile

et al. 2024

Sustainability

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For sustainable water resource management within dam catchments, accurate knowledge of land-use and land-cover change (LULCC) and the relationships with dam water variability is necessary. To improve LULCC prediction, this study proposes the use of a random forest regression (RFR) model, in comparison with logistic regression–cellular automata (LR-CA) and artificial neural network–cellular automata (ANN-CA), for the prediction of LULCC (2019–2030) in the Gaborone dam catchment (Botswana). RFR is proposed as it is able to capture the existing and potential interactions between the LULC intensity and their nonlinear interactions with the change-driving factors. For LULCC forecasting, the driving factors comprised physiographic variables (elevation, slope and aspect) and proximity-neighborhood factors (distances to water bodies, roads and urban areas). In simulating the historical LULC (1986–2019) at 5-year time steps, RFR outperformed ANN-CA and LR-CA models with respective percentage accuracies of 84.9%, 62.1% and 60.7%. Using the RFR model, the predicted LULCCs were determined as vegetation (−8.9%), bare soil (+8.9%), built-up (+2.49%) and cropland (−2.8%), with water bodies exhibiting insignificant change. The correlation between land use (built-up areas) and water depicted an increasing population against decreasing dam water capacity. The study approach has the potential for deriving the catchment land–water nexus, which can aid in the formulation of sustainable catchment monitoring and development strategies.

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Section: Insights Into Lulc Change and Land-water Sustainabilitymentioning

confidence: 99%

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Ouma,

Nkwae,

Odirile

et al. 2024

Sustainability

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“…SWAT presented a greater potential in the management of the risks of extreme hydrological events [14,15]. The SWAT model comprehensively considers the hydrological process differences of the underlying surface of the basin and found that climate change and LUCC have impacts on hydrological processes such as evapotranspiration (ET), runoff, infiltration, and hydrological response [16]. Hence, the comparison of hydrological processes changes under different climate changes and LUCC scenarios using SWAT models is important for understanding the response of hydrological processes to changing environments.…”

Section: Introductionmentioning

confidence: 99%

Streamflow Response to Climate and Land-Use Changes in a Tropical Island Basin

Cao,

Sun,

et al. 2023

Sustainability

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The effects of climate change and of land use/cover change (LUCC) on streamflow as demonstrated by hydrological models are pressing issues on the frontiers of global environmental change research. The Nandu River Basin (NRB) as the largest of three river basins on the tropical Hainan Island, China, is subjected to an analysis of streamflow response to climate and to land-use change. It is based on the Soil and Water Assessment Tool (SWAT) coupled with climate change signals extracted from the global climate model data in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and with land-use change scenarios modeled by Cellular Automata (CA)—Markov. The results are summarized as follows: (1) Climate change contributed more to streamflow change than land-use change in the NRB, with contributions of 97.57% and 2.43%, respectively. Precipitation and temperature were the most important climate variables, contributing 92.66% and 4.91% to streamflow change. (2) In the tropical island basin from 1990 to 2015, LUCC regulated the hydrological processes in the NRB and affected hydrological processes by increasing evapotranspiration and decreasing surface runoff and subsurface flow, which resulted in decreasing streamflow. (3) Under the climate change and land-use change scenarios of the near-term period (2021–2040), the annual streamflow decreased as during the reference period (1995–2014); particularly, it decreased most (−6.16%) on the SSP126 path. These results present a case study for understanding the hydrological cycle of tropical island basins and to provide a theoretical basis for water resources management and regional sustainable development of tropical islands.

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Assessing the impact of land use and land cover (LULC) changes on the surface runoff downstream of an ungauged Bontanga watershed in Northern Ghana

Mwanga,

Shaibu,

Issaka

2024

Water Practice &Amp; Technology

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Anthropogenic activities exacerbated by population growth, demanding land for food production and settlements, have led degradation of the Bontanga watershed. The aim of this study was to assess the impact of land use and land cover (LULC) change on the surface runoff in the Bontanga watershed from 1997 to 2022. LULC change maps for the years 1997, 2002, 2013, and 2022 were generated from Landsat images using ArcGIS, achieving overall accuracies of 92, 91.89, 95.27, and 83.64%, respectively. Surface runoff was estimated using the SCS–CN model. Correlation analysis was employed to identify predominant LULC change classes that impact surface runoff. The agricultural land and built-up area increased by 20.81% and 27.20% causing an increase in the surface runoff by 13.92 and 14.49% from 1997 to 2022. Due to anthropogenic activities, dense forest, grassland, mixed forest and shrub land, mixed shrub and grassland decreased by 20.31, 29.97, 22.51, and 25.58% causing an increase in surface runoff by 14.98, 14.06, 15.78, and 15.05%, respectively. Correlation analysis showed that changes in surface runoff were positively correlated with agricultural land, and mixed forest and shrub and negatively correlated with mixed shrub and grassland, and grassland.

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Assessing Impacts of Land Use and Land Cover (LULC) Change on Stream Flow and Runoff in Rur Basin, Germany

Cited by 4 publications

References 71 publications

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Streamflow Response to Climate and Land-Use Changes in a Tropical Island Basin

Assessing the impact of land use and land cover (LULC) changes on the surface runoff downstream of an ungauged Bontanga watershed in Northern Ghana

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