Quantifying the impacts of land use/land cover change on the water balance in the afforested River Basin, Pakistan

Saddique, Naeem; Mahmood, Talha; Bernhofer, Christian

doi:10.1007/s12665-020-09206-w

Cited by 58 publications

(33 citation statements)

References 57 publications

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“…In several studies, SWAT is distinctively utilized for investigating the impacts of LULC change on hydrological processes in small and large watersheds [21][22][23]. It is also commonly used for modelling and analyzing hydrological processes in the context of changing LULC and land management, with high efficiency [24].…”

Section: Introductionmentioning

confidence: 99%

Hydrological Responses of Watershed to Historical and Future Land Use Land Cover Change Dynamics of Nashe Watershed, Ethiopia

Leta

Demissie

Tränckner

2021

Water

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Land use land cover (LULC) change is the crucial driving force that affects the hydrological processes of a watershed. The changes of LULC have an important influence and are the main factor for monitoring the water balances. The assessment of LULC change is indispensable for sustainable development of land and water resources. Understanding the watershed responses to environmental changes and impacts of LULC classes on hydrological components is vigorous for planning water resources, land resource utilization, and hydrological balance sustaining. In this study, LULC effects on hydrological parameters of the Nashe watershed, Blue Nile River Basin are investigated. For this, historical and future LULC change scenarios in the Nashe watershed are implemented into a calibrated Soil and Water Assessment Tool (SWAT) model. Five LULC scenarios have been developed that represent baseline, current, and future periods corresponding to the map of 1990, 2005, 2019, 2035, and 2050. The predicted increase of agricultural and urban land by decreasing mainly forest land will lead till 2035 to an increase of 2.33% in surface runoff and a decline in ground water flow, lateral flow, and evapotranspiration. Between 2035 and 2050, a gradual increase of grass land and range land could mitigate the undesired tendency. The applied combination of LULC prognosis with process-based hydrologic modeling provide valuable data about the current and future understanding of variation in hydrological parameters and assist concerned bodies to improve land and water management in formulating approaches to minimize the conceivable increment of surface runoff.

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Section: Introductionmentioning

confidence: 99%

Hydrological Responses of Watershed to Historical and Future Land Use Land Cover Change Dynamics of Nashe Watershed, Ethiopia

Leta

Demissie

Tränckner

2021

Water

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“…The results of five GCMs under both RCPs 4.5 and RCP 8.5 scenarios, illustrates that the change in temperature and precipitation frequencies will affect the water resources and streamflow within the transboundary, directly or indirectly. Besides climate change, other anthropogenic changes can also have a significant impact on streamflow, such as future management practices and land-use change significantly influencing water availability [61,73].…”

Section: Discussionmentioning

confidence: 99%

Appraisal of Climate Change and Its Impact on Water Resources of Pakistan: A Case Study of Mangla Watershed

et al. 2020

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Water resources are highly dependent on climatic variations. The quantification of climate change impacts on surface water availability is critical for agriculture production and flood management. The current study focuses on the projected streamflow variations in the transboundary Mangla Dam watershed. Precipitation and temperature changes combined with future water assessment in the watershed are projected by applying multiple downscaling techniques for three periods (2021–2039, 2040–2069, and 2070–2099). Streamflows are simulated by using the Soil and Water Assessment Tool (SWAT) for the outputs of five global circulation models (GCMs) and their ensembles under two representative concentration pathways (RCPs). Spatial and temporal changes in defined future flow indexes, such as base streamflow, average flow, and high streamflow have been investigated in this study. Results depicted an overall increase in average annual flows under RCP 4.5 and RCP 8.5 up until 2099. The maximum values of low flow, median flow, and high flows under RCP 4.5 were found to be 55.96 m3/s, 856.94 m3/s, and 7506.2 m3/s and under RCP 8.5, 63.29 m3/s, 945.26 m3/s, 7569.8 m3/s, respectively, for these ensembles GCMs till 2099. Under RCP 4.5, the maximum increases in maximum temperature (Tmax), minimum temperature (Tmin), precipitation (Pr), and average annual streamflow were estimated as 5.3 °C, 2.0 °C, 128.4%, and 155.52%, respectively, up until 2099. In the case of RCP 8.5, the maximum increase in these hydro-metrological variables was up to 8.9 °C, 8.2 °C, 180.3%, and 181.56%, respectively, up until 2099. The increases in Tmax, Tmin, and Pr using ensemble GCMs under RCP 4.5 were found to be 1.95 °C, 1.68 °C and 93.28% (2021–2039), 1.84 °C, 1.34 °C, and 75.88%(2040–2069), 1.57 °C, 1.27 °C and 72.7% (2070–2099), respectively. Under RCP 8.5, the projected increases in Tmax, Tmin, and Pr using ensemble GCMs were found as 2.26 °C, 2.23 °C and 78.65% (2021–2039), 2.73 °C, 2.53 °C, and 83.79% (2040–2069), 2.80 °C, 2.63 °C and 67.89% (2070–2099), respectively. Three seasons (spring, winter, and autumn) showed a remarkable increase in streamflow, while the summer season showed a decrease in inflows. Based on modeling results, it is expected that the Mangla Watershed will experience more frequent extreme flow events in the future, due to climate change. These results indicate that the study of climate change’s impact on the water resources under a suitable downscaling technique is imperative for proper planning and management of the water resources.

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“…The total forest area in the entire province of KP was calculated as 10,123 km 2 using the GFW data set for 2019. The following study by Saddique et al [95] used Landsat TM, Landsat ETM+, and Landsat OLI to map the evergreen and deciduous forests in the river Jhelum basin (Mangal Dam watershed), AJK (33,397 km 2 area). Using a machine-learning algorithm (RF) in supervised image classification, they reported that the forest area showed a positive difference of 2806.87 km 2 between 2001-2009 and 2009-2018.…”

Section: Other Districtsmentioning

confidence: 99%

“…Saddique et al [95] published a study that used a machine learning algorithm (RF) to report overall accuracy of 90%, 92%, and 95%, and kappa coefficient of 89%, 90%, and 94% for 2001, 2009, and 2018. The producer's accuracy was calculated as >83%, >88%, and 93%, and the user's accuracy was calculated as >83%, >82%, and >94% for the same years, respectively.…”

Section: Accuracies Assessments and Validations For Forest Mappingmentioning

confidence: 99%

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Ahmad

Gilani

et al. 2021

Forests

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This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers' citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.

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Quantifying the impacts of land use/land cover change on the water balance in the afforested River Basin, Pakistan

Cited by 58 publications

References 57 publications

Hydrological Responses of Watershed to Historical and Future Land Use Land Cover Change Dynamics of Nashe Watershed, Ethiopia

Hydrological Responses of Watershed to Historical and Future Land Use Land Cover Change Dynamics of Nashe Watershed, Ethiopia

Appraisal of Climate Change and Its Impact on Water Resources of Pakistan: A Case Study of Mangla Watershed

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

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