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

Improving flood and drought management in transboundary Upper Jhelum Basin-South Asia

The two contrasting extremes of the hydrological spectrum have substantial and far-reaching impacts on a wide range of sectors including water resources, agriculture and food security, energy, infrastructure, and ecosystem. The compounding factors of climate change, burgeoning population, and rapid economic development create unprecedented challenges in devising effective and sustainable strategies to cope with these natural disasters and minimize their devastating impacts. This study identifies the geographical areas that are prone to meteorological wet-dry extreme events, as drivers of hydrologic floods and droughts, and their temporal compounding in the transboundary Upper Jhelum Basin-South Asia. Additionally, the study provides a comprehensive overview of the existing and proposed water development projects, their coping capacities, and potential impacts that may be positive or negative in hydrologic, social, economic and environment terms. Extensive review and data analysis revealed that the both Pakistan and India, along with state governments, have implemented a significant number of water-related projects across the basin, however significant progress towards achieving their stated goals remains elusive. Currently, Pakistan operates 15 runoff river type hydropower plants, and an additional 11 similar projects are under construction. In contrast, Indian administrative Kashmir has 10 such plants in operation and 4 under construction. The primary factors that impede the realization of expected benefits from these projects are geography, high flow variability across seasons, climate change, insufficient planning, geopolitical disputes, lack of transboundary cooperation, financial limitations, and reservoir operation. Based on these factors, the present study suggests some alternative water management measures that offer flexibility, cost-effectiveness, accessibility, and a low environmental impact. These solutions include implementation of sub-surface floodwater harvesting system in the southwest of the basin, where extreme wet and dry events occur in close succession, augmentation of existing hydropower reservoirs with floating photovoltaic technology, and non-structural measures, including early warning systems, ecosystems-based adaptation, and green infrastructure interventions such as restoring headwater forests, reclaiming floodplains, and wetlands. This will result in reduced flood and drought impacts at local and downstream areas and reduce the reliance of local communities on forest wood.

Environmental and social impacts of carbon sequestration

Climate change requires major mitigation efforts, mainly emission reduction. Carbon sequestration and avoided deforestation are complementary mitigation strategies that can promote nature conservation and local development but may also have undesirable impacts. We reviewed 246 articles citing impacts, risks, or concerns from carbon projects, and 78 others related to this topic. Most of the impacts cited focus on biodiversity, especially in afforestation projects, and on social effects related to avoided deforestation projects. Concerns were raised about project effectiveness, the permanence of carbon stored, and leakage. Recommendations include accounting for uncertainty, assessing both mitigation and contribution to climate change, defining permanence, creating contingency plans, promoting local projects, proposing alternative livelihoods, ensuring a fair distribution of benefits, combining timber production and carbon sequestration, ensuring sustainable development and minimizing leakage. A holistic approach that combines carbon sequestration, nature conservation, and poverty alleviation must be applied. The potential occurrence of negative impacts does not invalidate carbon projects but makes it advisable to conduct proper environmental impact assessments, considering direct and indirect impacts, minimizing the negative effects while maximizing the positive ones, and weighing the trade-offs between them to guide decision-making. Public participation and transparency are essential. Integr Environ Assess Manag 2024;00:1-27. © 2024 SETAC.

Effect of land use land cover changes on hydrological response of Punpun River basin

The present study assessed the hydrological response of land use land cover (LULC) change on the Punpun River basin. High-resolution gridded rainfall and temperature data from the years 1995 to 2020 have been used in the Soil and Water Assessment Tool (SWAT) in the Geographic Information System (GIS) to analyze the hydrological response of the Punpun River basin and water balance components. Hydrological Response Units (HRUs) have been created for the basin. Each HRU is based on a distinct combination of soil, slope, and land use. Five SWAT models have been prepared based on the LULC of every 5-year interval to simulate the basin's hydrological response. The period selected for calibration is 1995-2015 and for validation is 2016-2020 for the modeling of daily streamflow data. The observed and simulated streamflow was checked for performance indices of coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), and percent bias (PBIAS) on daily time steps. The results were found to be good with R2 = 0.72, NSE = 0.68, and PBIAS = 23.2 for calibration and R2 = 0.93, NSE = 0.77, and PBIAS = 19.8 for validation. The study reveals that 7.01% of evapotranspiration (ET) was increased from 1995 to 2020 with increase in agricultural area of 21.86%. It was also found that built-up area, surface runoff, and water yield have been increased by 9.14, 14.43, and 17.40%, respectively. Further, the groundwater contribution of the basin was decreased.

Classification of land use/land cover using artificial intelligence (ANN-RF)

Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).

The application of SWAT+ model to quantify the impacts of sensitive LULC changes on water balance in Guder catchment, Oromia, Ethiopia

Global population growth and scarce resources increase the competition for land use. Despite the fact that the impacts of climate change have been recognized, the conversion of LULC is still often neglected and threatens catchment hydrology. This is mostly seen in the developing world, where agriculture is the crucial source of their food security. The conversion of LULC has been jeopardizing water balance components and damaging ecosystem. This study demonstrates the application of SWAT+ in quantifying the impacts of LULC changes on the Guder catchment water balance. The impacts were quantified between 2003 and 2021, and the watershed experienced an increase in agriculture and settlement while forest, shrubland, and wetlands declined. The time-series-based performance of the SWAT + model shows the model is more restructured and capable of simulating streamflow compared to observed during calibration and validation. In this long-term evaluation, the model simulates changes in runoff of 56.5%, water yield of 65.2%, lateral flow of 21.6%, percolation of 46.2%, return flow of 76.4%, and ET of 0.2% between 2003 and 2013. Moreover, some attributes of the water balance have increased from 2013 to 2021, with runoff of 34.3%, water yield of 2.3%, ET of 4.5%, and lateral flow of 72.6%. However, as a result of increasing settlement, which reduces infiltration through interceptions and converts rainfall to runoff, percolation and return flow were decreased by 45.6% and 86.7%, respectively. Water yield and runoff show a linear relationship with changes in LULC, and the most sensitive land use changes that affect them are agriculture, forest, and settlements. The simulation results show a water balance deficit under the impacts of LULC changes in the third simulation. Furthermore, the increased surface of runoff has been limiting the amount of groundwater recharge into the soil and reducing return flow and percolation in the second simulation.

Analysis of Machine Learning Techniques for Sentinel-2A Satellite Images

This article presents the comparative analysis of classification techniques to assign land use and land cover classes from different strategies (pixel-based, object-based, rule-based, distance-based, and neural-based) with a Sentinel-2A satellite image for 2016. The study area is the Sana’a city of Yemen which covers about 18,796.88 km2 land area. This research aims to present the fundamentals of supervised machine learning approaches, including their limitations and strengths and experimentation for twelve classifiers. The outcome of experimentation showed that the Random Forest could be a good choice as a classifier for object-based strategy. In contrast, DTC and SVM were efficient in rule-based and pixel-based strategies. Results also showed that the highest accuracy was with object-based strategy, followed by rule-based and then pixel-based and distance-based strategies.

Quantitatively Assessing the Future Land-Use/Land-Cover Changes and Their Driving Factors in the Upper Stream of the Awash River Based on the CA–Markov Model and Their Implications for Water Resources Management

Despite the rapid economic and population growth, the risks related to the current dynamics of land use and land cover (LULC) have attracted a lot of attention in Ethiopia. Therefore, a complete investigation of past and future LULC changes is essential for sustainable water resources and land-use planning and management. Since the 1980s, LULC change has been detected in the upper stream of the Awash River basin. The main purpose of this research was to investigate the current dynamics of LULC and use the combined application of the cellular automata and the Markov chain (CA–Markov) model to simulate the year 2038 LULC in the future; key informant interviews, household surveys, focus group discussions, and field observations were used to assess the consequences and drivers of LULC changes in the upstream Awash basin (USAB). This research highlighted the importance of remote sensing (RS) and geographic information system (GIS) techniques for analyzing the LULC changes in the USAB. Multi-temporal cloud-free Landsat images of three sequential data sets for the periods (1984, 2000, and 2019) were employed to classify based on supervised classification and map LULC changes. Satellite imagery enhancement techniques were performed to improve and visualize the image for interpretation. ArcGIS10.4 and IDRISI software was used for LULC classification, data processing, and analyses. Based on Landsat 5 TM-GLS 1984, Landsat 7 ETM-GLS 2000, and Landsat 8 2019 OLI-TIRS, the supervised maximum likelihood image classification method was used to map the LULC dynamics. Landsat images from 1984, 2000, and 2019 were classified to simulate possible LULC in 2019 and 2038. The result reveals that the maximum area is covered by agricultural land and shrubland. It showed, to the areal extent, a substantial increase in agricultural land and urbanization and a decrease in shrubland, forest, grassland, and water. The LULC dynamics showed that those larger change rates were observed from forest and shrubland to agricultural areas. The results of the study show the radical changes in LULC during 1984–2019; the main reasons for this were agricultural expansion and urbanization. From 1984 to 2019, agriculture increased by 62%, urban area increased by 570.5%, and forest decreased by 88.7%. In the same year, the area of shrubland decreased by 68.6%, the area of water decreased by 65.5%, and the area of grassland decreased by 57.7%. In view of the greater increase in agricultural land and urbanization, as well as the decrease in shrubland, it means that the LULC of the region has changed. This research provides valuable information for water resources managers and land-use planners to make changes in the improvement of future LULC policies and development of sub-basin management strategies in the context of sustainable water resources and land-use planning and management.

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

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.

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

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.

Assessment of the Impacts of Climate and LULC Changes on the Water Yield in the Citarum River Basin, West Java Province, Indonesia

Changes in climate and land use land cover (LULC) are important factors that affect water yield (WY). This study explores which factors have more significant impact on changes in WY, spatially and temporally, within the Citarum River Basin Unit (RBU), West Java Province, Indonesia with an area of ±11.317 km2. The climate in the area of Citarum RBU belongs to the Am climate type, which is characterized by the presence of one or more dry months. The objectives of the study were: (1) To estimate a water yield model using integrated valuation of ecosystem services and tradeoffs (InVEST), and (2) to test the sensitivity of water yield (WY) to changes in climate variables (rainfall and evapotranspiration) and in LULC. The integration of remote sensing (RS), geographic information system (GIS), and the integrated valuation of ecosystem services and tradeoffs (InVEST) approach were used in this study. InVEST is a suite of models used to map and value the goods and services from nature that sustain and fulfill human life. The parameters used for determining the WY are LULC, precipitation, average annual potential evapotranspiration, soil depth, and plant available water content (PAWC). The results showed that the WY within the territory of Citarum RBU was 12.17 billion m3/year, with mean WY (MWY) of 935.26 mm/year. The results also show that the magnitude of MWY in Citarum RBU is lower than the results obtained in Lake Rawa Pening Catchment Areas, Semarang Regency and Salatiga City, Central Java (1.137 mm/year) and in the Patuha Mountain region, Bandung Regency, West Java (2.163 mm/year), which have the same climatic conditions. The WY volume decreased from 2006, to 2012, and 2018. Based on the results of the simulation, climatic parameters played a major role affecting WY compared to changes in LULC in the Citarum RBU. This model also shows that the effect of changes in rainfall (14.06–27.53%) is more dominant followed by the effect of evapotranspiration (10.97–23.86%) and LULC (10.29–12.96%). The InVEST model is very effective and robust for estimating WY in Citarum RBU, which was indicated by high coefficient of determination (R2) 0.9942 and the RSME value of 0.70.

Spatio-Temporal Evolution and Driving Factors of Rural Settlements in Low Hilly Region-A Case Study of 17 Cities in Hubei Province, China

With the rapid development of the social economy, factors of social and economic development in China's rural areas have been continuously reorganized, and the pattern and distribution of rural residential areas have undergone significant changes. In rural areas, there have been many peculiar phenomena of "reducing people but not reducing land in rural areas, which has caused tremendous pressure on land resource protection. We used geographic detectors and a geographically temporally weighted regression model (GTWR) to explore the rural settlements' evolution and driving mechanism in Hubei Province from 1990 to 2015. The results show that the kernel density of rural settlements decreased from 1.62 villages/km² in 1990 to 1.60 villages/km² in 2015. The scale of rural residential patches has obvious regional differentiation characteristics. From southeast to northwest, there is a wave-like distribution structure of "high-low-high-low-high", and the clustering characteristics of "cold and hot spots" are strengthened with time. Based on GTWR analysis, the total rural population, total power of agricultural machinery, and rural electricity consumption have promoted the expansion of rural settlements, with the regression coefficients 0.096, 0.484, and 0.878, respectively. Cultivated land, agricultural output value, and rural labor force have negative impacts on the expansion, the regression coefficients of the village were -0.584, -0.510, and -0.109, respectively.

A Framework to Assess the Reliability of a Multipurpose Reservoir under Uncertainty in Land Use

Socioeconomic development in watersheds lead to land-use changes, which can alter water and sediment inflows into reservoirs, leading to uncertainty in water supply reliability. A modelling framework coupling the Soil and Water Assessments Tool (SWAT) and the @RISK genetic algorithm optimisation tool was developed to optimise water allocation and estimate water supply reliability under uncertainty in future land-use. The multi-purpose Nuicoc reservoir in Vietnam was used as a case study. Modelling results showed that an expansion of the urban areas by 10% and conversion of 5% of the forest to agricultural areas produced the highest water releases for downstream demands of all simulated scenarios, with 5 Mcm/year greater water releases than the baseline for the case where sedimentation was not considered. However, when sedimentation was considered, it generated the greatest decrease in water releases, with 6.25 Mcm/year less than the baseline. Additionally, it was determined that spatial distribution of land-use significantly affect sediment inflows into the reservoir, highlighting the importance of targeted sediment management. This demonstrates the usefulness of the proposed framework for decision-makers in assessing the impact of possible land-use changes on the reservoir operation.

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

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.