Impact of population growth and land use and land cover (LULC) changes on water quality in tourism-dependent economies using a geographically weighted regression approach

Population growth and economic development are driving changes in land use/land cover (LULC) of the transboundary Lower Mekong River Basin (LMB), posing a serious threat to the integrity of the river system. Using data collected on a monthly basis over 30 years (1985–2015) at 14 stations located along the Lower Mekong river, this study explores whether spatiotemporal relationships exist between LULC changes and instream concentrations of total suspended solids (TSS) and nitrate—as proxies of water quality. The results show seasonal influences where temporal patterns of instream TSS and nitrate concentrations mirror patterns detected for discharge. Changes in LULC influenced instream TSS and nitrate levels differently over time and space. The seasonal Mann–Kendall (SMK) confirmed significant reduction of instream TSS concentrations at six stations (p < 0.05), while nitrate levels increased at five stations (p < 0.05), predominantly in stations located in the upper section of the basin where forest areas and mountainous topography dominate the landscape. Temporal correlation analyses point to the conversion of grassland (r = −0.61, p < 0.01) to paddy fields (r = 0.63, p < 0.01) and urban areas (r = 0.44, p < 0.05) as the changes in LULC that mostly impact instream nitrate contents. The reduction of TSS appears influenced by increased forest land cover (r = −0.72, p < 0.01) and by the development and operation of hydropower projects in the upper Mekong River. Spatial correlation analyses showed positive associations between forest land cover and instream concentrations of TSS (r = 0.64, p = 0.01) and nitrate (r = 0.54, p < 0.05), indicating that this type of LULC was heavily disturbed and harvested, resulting in soil erosion and runoff of nitrate rich sediment during the Wet season. Our results show that enhanced understanding of how LULC changes influence instream water quality at spatial and temporal scales is vital for assessing potential impacts of future land and water resource development on freshwater resources of the LMB.

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Population Growth–Land Use/Land Cover Transformations–Water Quality Nexus in Upper Ganga River Basin

10.5194/hess-2017-384 ◽

2017 ◽

Cited By ~ 3

Author(s):

Anoop Kumar Shukla ◽

Chandra Shekhar Prasad Ojha ◽

Ana Mijic ◽

Wouter Buytaert ◽

Shray Pathak ◽

...

Keyword(s):

Water Quality ◽

Land Use ◽

Land Cover ◽

Population Growth ◽

Urban Area ◽

River Basin ◽

Ganga River ◽

Quality Data ◽

Demographic Changes ◽

Land Use Land Cover

Abstract. For sustainable development in a river basin it is crucial to understand population growth–Land Use/Land Cover (LULC) transformations–water quality nexus. This study investigates effects of demographic changes and LULC transformations on surface water quality of Upper Ganga River basin. River gets polluted in both rural and urban area. In rural area, pollution is because of agricultural practices mainly fertilizers, whereas in urban area it is mainly because of domestic and industrial wastes. First, population data was analyzed statistically to study demographic changes in the river basin. LULC change detection was done over the period of February/March 2001 to 2012 [Landsat 7 Enhanced Thematic Mapper (ETM+) data] using remote sensing and Geographical Information System (GIS) techniques. Further, water quality parameters viz. Biological Oxygen Demand (BOD), Dissolve Oxygen (DO) %, Flouride (F), Hardness CaCO3, pH, Total Coliform bacteria and Turbidity were studied in basin for pre-monsoon (May), monsoon (July) and Post-monsoon (November) seasons. Non-parametric Mann–Kendall rank test was done on monthly water quality data to study existing trends. Further, Overall Index of Pollution (OIP) developed specifically for Upper Ganga River basin was used for spatio-temporal water quality assessment. From the results, it was observed that population has increased in the river basin. Therefore, significant and characteristic LULC changes are observed in the study area. Water quality degradation has occurred in the river basin consequently the health status of the rivers have also changed from range of acceptable to slightly polluted in urban areas.

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Land-Use and Land-Cover (LULC) Change Detection in Wami River Basin, Tanzania

Land ◽

10.3390/land8090136 ◽

2019 ◽

Vol 8 (9) ◽

pp. 136 ◽

Cited By ~ 6

Author(s):

Sekela Twisa ◽

Manfred F. Buchroithner

Keyword(s):

Land Use ◽

Land Cover ◽

Population Growth ◽

Change Detection ◽

River Basin ◽

Land Use Planning ◽

Management Practice ◽

Cultivated Land ◽

Lulc Change ◽

Lulc Changes

Anthropogenic activities have substantially changed natural landscapes, especially in regions which are extremely affected by population growth and climate change such as East African countries. Understanding the patterns of land-use and land-cover (LULC) change is important for efficient environmental management, including effective water management practice. Using remote sensing techniques and geographic information systems (GIS), this study focused on changes in LULC patterns of the upstream and downstream Wami River Basin over 16 years. Multitemporal satellite imagery of the Landsat series was used to map LULC changes and was divided into three stages (2000–2006, 2006–2011, and 2011–2016). The results for the change-detection analysis and the change matrix table from 2000 to 2016 show the extent of LULC changes occurring in different LULC classes, while most of the grassland, bushland, and woodland were intensively changed to cultivated land both upstream and downstream. These changes indicate that the increase of cultivated land was the result of population growth, especially downstream, while the primary socioeconomic activity remains agriculture both upstream and downstream. In general, net gain and net loss were observed downstream, which indicate that it was more affected compared to upstream. Hence, proper management of the basin, including land use planning, is required to avoid resources-use conflict between upstream and downstream users.

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Investigating land use and land cover changes in Dublin, Ireland using Satellite Imagery: A comparative analysis

10.5194/egusphere-egu2020-12132 ◽

2020 ◽

Author(s):

Bidroha Basu ◽

Arunima Sarkar Basu ◽

Srikanta Sannigrahi ◽

Francesco Pilla

Keyword(s):

Land Use ◽

Land Cover ◽

Population Growth ◽

Satellite Imagery ◽

Rainfall Runoff ◽

Lulc Changes ◽

The Past ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Over Time

<p>Over the past few decades, there has been over increasing pressure on land due to population growth, urbanization, agriculture expansion and industrialization. The change in land use and land cover (LULC) pattern are highly dependent on human intervention. Deforestation pattern has started due to growth of suburbs, cities, and industrial land. The alarming rate in change of LULC pattern was on a rising trend since 1990s and has been increasing over time. This study focuses on analyzing the changes in LULC pattern in Dublin, Ireland over the past two decades using remotely sensed LANDSAT satellite imagery data, and quantify the effect of LULC change in streamflow simulation in watershed at Dublin by using rainfall-runoff model. Benefit of using remotely sensed image to investigate LULC changes include availability of high-resolution spatial data at free of cost, images captured at high temporal resolution to monitor the changes in LULC during both seasonal and yearly timescale and readily availability of data. The potential classification of landforms has been done by performing both supervised as well as unsupervised classification. The results obtained from the classified images have been compared to google earth images to understand the accuracy of the image classification. The change in LULC can be characterized by changes in building density and urban/artificial area (build up areas increase due to population growth), changes in vegetation area as well as vegetation health, changes in waterbodies and barren land. Furthermore, a set of indices such as vegetation index, building index, water index and drought index were estimated, and their changes were monitored over time. Results of this analysis can be used to understand the driving factors affecting the changes in LULC and to develop mathematical models to predict future changes in landforms. Soil Water Assessment Tool (SWAT) based rainfall-runoff model were used to simulate the changes in runoff due to the LULC changes in watershed over two decades. The developed framework is highly replicable because of the used LANDSAT data and can be applied to generate essential information for conservation and management of green/forest lands, as well as changes in water availability and water stress in the assessed area.</p>

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Extracting the spatio-temporal linkages between land use/land cover and water quality parameters using spatio-temporal weighted regression

Water Quality Research Journal ◽

10.2166/wqrj.2018.004 ◽

2018 ◽

Vol 53 (4) ◽

pp. 205-218

Author(s):

Farid Karimipour ◽

Arash Madadi ◽

Mohammad Hosein Bashough

Keyword(s):

Water Quality ◽

Land Use ◽

Land Cover ◽

Temporal Variations ◽

Quality Parameters ◽

Water Quality Parameters ◽

Spatial And Temporal Variations ◽

Weighted Regression ◽

Land Use Land Cover ◽

Spatio Temporal

Abstract Studies in water quality management have indicated significant relationships between land use/land cover (LULC) variables and water quality parameters. Thus, understanding this linkage is essential in protecting and developing water resources. This article extends the conventional geographical weighted regression (GWR) to a temporal version in order to take both spatial and temporal variations of such linkages into account, which has been ignored by many of the previous efforts. The approach has been evaluated for total nitrates and nitrites' concentration as the case study. For this, observations of 45 water quality sampling stations were examined in a time interval of 20 years (1992–2011), and the linkages between LULC variables and NO2 + NO3 concentration were extracted through Pearson correlation coefficient as a global regression model, the conventional geographic weighted regression, and the proposed spatio-temporal weighted regression (STWR). Comparing the results based on two global criteria of goodness-of-fitness (R2) and residual sum of squares (RSS) verifies that the simultaneous consideration of spatial and temporal variations by STWR substantially improves the results.

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Exploring the Spatial Variation of the Relationship between Land Use and Water Quality in a Drainage Basin Using Geographically Weighted Regression

Ghana Journal of Geography ◽

10.4314/gjg.v12i2.7 ◽

2020 ◽

Vol 12 (2) ◽

pp. 147-168

Author(s):

Samuel Azua ◽

Taiye Oluwafemi Adewuyi ◽

Lazarus Mustapha Ojigi ◽

Omafuvwe Joseph Mudiare

Keyword(s):

Water Quality ◽

Land Use ◽

Geographically Weighted Regression ◽

Drainage Basin ◽

Water Quality Management ◽

Mixed Forest ◽

Weighted Regression ◽

Landsat 8 ◽

Sample Points ◽

The Relationship

The focus of this study is to determine the relationship between land use and water quality in the River Mu drainage basin for effective water quality management. Various land uses in the study area were identified and mapped using Landsat 8 OLI of 2016. Water samples were also collected from 112 sample sites using Stratified Random Sampling methods. The samples were analysed in terms of physicochemical parameters using standard methods. The results of land use and water quality parameters were regressed using Geographically Weighted Regression (GWR) to determine whether there exist spatially varying relationships. The results revealed that the local R2 values varied between 0.0 and 0.5, indicating a weak relationship between land use and water pollution, except for mixed forest and pH which recorded local R2 values of 0.7 towards the western region of the study area. This shows that the relationship between the two variables varied spatially across the drainage basin. The one-sample Kolmogorov Smirmov test-p<0.05 revealed that there were significant differences in pH (0.00), EC (0.00), turbidity (0.001), TDS (0.048), DO (0.003), NH4+ (0.002), Ca2+ (0.00), Cl- (0.036), Fe3+ (0.00) and Cr2+ (0.039) across the different sample points, whereas K+ (0.134), PO43- (0.715) and NO3- (0.501) were not significantly different across the different sample points. The study recommended that the procedure for water management be localized to sub-catchment and basin levels, to provide adequate attention to each sub-catchment depending on the level and nature of pollution identified.

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