Modeling and assessing the effects of land use changes on runoff generation with the CLUE-s and WetSpa models

Mohammady, Majid; Moradi, Hamid Reza; Zeinivand, Hossein; Temme, A.J.A.M.; Yazdani, Mohammad; Pourghasemi, Hamid Reza

doi:10.1007/s00704-017-2190-x

Cited by 33 publications

(17 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LULC change is becoming a common problem for developing countries that have economies basically dependent on agriculture [6]. Conversion of the LULC type due to urbanization has affected sustainable management of water resources [7]. The urbanization process is transforming permeable land surfaces into impervious surfaces and ultimately changing regional hydrological characteristics [3].…”

Section: Introductionmentioning

confidence: 99%

Integrated Assessment of Climate Change and Land Use Change Impacts on Hydrology in the Kathmandu Valley Watershed, Central Nepal

Lamichhane

Shakya

2019

Water

View full text Add to dashboard Cite

The population growth and urbanization are rapidly increasing in both central and peripheral areas of the Kathmandu Valley (KV) watershed. Land use/cover (LULC) change and climate variability/change are exacerbating the hydrological cycle in the KV. This study aims to evaluate the extent of changes in hydrology due to changes in climate, LULC and integrated change considering both factors, with KV watershed in central Nepal as a case study. Historical LULC data were extracted from satellite image and future LULC are projected in decadal scale (2020 to 2050) using CLUE-S (the Conversion of Land Use and its Effects at Small regional contest) model. Future climate is projected based on three regional climate models (RCMs) and two representative concentration pathways (RCPs) scenarios, namely, RCP4.5 and RCP8.5. A hydrological model in soil and water assessment tool (SWAT) was developed to simulate hydrology and analyze impacts in hydrology under various scenarios. The modeling results show that the river runoff for RCP4.5 scenarios is projected to increase by 37%, 21%, and 12%, respectively, for climate change only, LULC only, and integrated changes of both. LULC change resulted in an increase in average annual flow, however, a decrease in base-flow. Furthermore, the impacts of integrated changes in both LULC and climate is not a simple superposition of individual changes.

show abstract

Section: Introductionmentioning

confidence: 99%

Integrated Assessment of Climate Change and Land Use Change Impacts on Hydrology in the Kathmandu Valley Watershed, Central Nepal

Lamichhane

Shakya

2019

Water

View full text Add to dashboard Cite

show abstract

“…Changes in land use can significantly impact and modify runoff [40]. Forest land has the most significant effect on runoff, followed by cultivated land, grassland, residential land, and industrial and mining land, with water areas and unutilized land exhibiting insignificant effects [35].…”

Section: Land-use Changementioning

confidence: 99%

Analysis of Runoff Trends and Drivers in the Haihe River Basin, China

Ren

Zheng

et al. 2020

IJERPH

View full text Add to dashboard Cite

During the past decades, runoff has been highly influenced by climate change and human activities in Haihe River basin, and it is important to analyze the runoff trends and the drivers of its change to guide water resources management. The Mann-Kendall method and Pettitt test were conducted to analyze the hydrological and climate trends. Data from six sub-basins were used, including runoff at six representative hydrological stations and precipitation and air temperature at 49 meteorological stations. We used multiple-regression analysis and policy review to explore the influence of climate change and human activities on the runoff change at six sub-basins. According to the results, annual runoff showed a significant downward trend at six hydrological stations (p < 0.05), and the most probable change points at all stations showed up during the period from the late 1970s to the early 1980s. Moreover, the middle and late 1990s could be another probable abrupt change point at Luan River and Chaobai River. The declining trend of the annual mean precipitation at the six sub-basins was insignificant (p > 0.05), and there were no significant abrupt change points except the Zhang River area (p < 0.05). Compared with the precipitation trend, the annual mean air temperature exhibited a significant increasing trend at all stations, and the period from the late 1980s to the early 1990s might be the most probable abrupt change points at all four sub-basins. The trend analysis and the abrupt change point analysis suggest that mean air temperature is the main climate factor that will lead to the decline in the runoff time-series, while the insignificant downward trend of the precipitation might accelerate the downward trend of the runoff data. Through elevant policy measures, including land-use reform and the construction of the Three-North (north, northeast, and northwest China) Shelter Forest, China started to implement a family-contract responsibility system and initiated the first stage of construction of the Three-North Shelter Forest Program in 1978. The land-use reform policies greatly stimulated the peasants' initiative for land management and significantly changed the land use pattern and water use quantity in the Haihe River basin in a short time. Besides, the precipitation decreased and the air temperature rose, so an abrupt change in runoff occurred from the late 1970s to the early 1980s. The abrupt change in the runoff in the middle and late 1990s highly tallied with the construction time of the Three-North Shelter Forest Program. After near 20 years of construction of the Three-North Shelter Forest Program, the forest area increased, the forest quality had been improved, and the vegetation coverage on the underlying surface had been changed significantly, so the construction of the Three-North Shelter Forest Program was an important cause of runoff change in the middle and late 1990s. Also, change in precipitation and air temperature enlarged the effect of change in the runoff.

show abstract

“…Then, the ordinary least squares (OLS) statistical approach was used to examine the regression coefficients of the various attributes of real estate prices and estimate housing prices. This approach overlooked the problem of spatial autocorrelation [19,60]. Spatial data contains spatial dependence and spatial heterogeneity stemming from spatial autocorrelation, which increases analysis difficulty.…”

Section: Spatial Regression Analysis Modelmentioning

confidence: 99%

An Application of the Spatial Autocorrelation Method on the Change of Real Estate Prices in Taitung City

Wang

Chang

Wang

2019

IJGI

View full text Add to dashboard Cite

The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 to 2017, including 3533 real estate transaction price records. The hedonic price method, spatial lag model and spatial error model were used to conduct global spatial self-correlation tests to explore the performance of house price variables and space price aggregation. We compare the three models by R² and Akaike Information Criterion (AIC) to determine the spatial self-correlation ability performance, and explore the spatial distribution of prices and the changes of price regions from the regional local indicators of spatial association spatial distribution map. Actual analysis results show an improvement in the ability to interpret real estate prices through the feature price mode from the R² value assessment, the spatial delay model and the spatial error model. Performance from the AIC values show that the difference of the spatial delay model is smaller than that of the feature price model and the spatial model, demonstrating a better performance from the space delay model and the spatial error model compared to the feature price model; improving upon the estimation bias caused by spatial self-correlation. For variables affecting house pricing, research results show that Moran’s I is more than 0 in real estate price analysis over the years, all of which show spatial positive correlation. From the LISA analysis of the spatial aggregation phenomenon, we see real estate prices rise in spaces surrounded by high-priced real estate contrast with the scope of space surrounded by low-cost real estate shifting in boundary over the years due to changes in the location and attributes of real estate trading transactions. Through the analysis of space price aggregation characteristics, we are able to observe the trajectory of urban development.

show abstract

Modeling and assessing the effects of land use changes on runoff generation with the CLUE-s and WetSpa models

Cited by 33 publications

References 69 publications

Integrated Assessment of Climate Change and Land Use Change Impacts on Hydrology in the Kathmandu Valley Watershed, Central Nepal

Integrated Assessment of Climate Change and Land Use Change Impacts on Hydrology in the Kathmandu Valley Watershed, Central Nepal

Analysis of Runoff Trends and Drivers in the Haihe River Basin, China

An Application of the Spatial Autocorrelation Method on the Change of Real Estate Prices in Taitung City

Contact Info

Product

Resources

About