Contested governance of drinking water provisioning services in Nepal’s transboundary river basins

Bhattarai, Kiran Kumari; Pant, Laxmi Prasad; FitzGibbon, John

doi:10.1016/j.ecoser.2020.101184

Cited by 11 publications

(4 citation statements)

References 67 publications

(97 reference statements)

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“…Unsustainable utilization of natural resources across drainage basins globally threatens livelihoods and biodiversity. Changes in land use (defined as the function of surface cover) and land cover (defined as the natural and artificial material covering the earth surface) due to anthropogenic activities and climate change affect the supply and distribution of ecosystem services (ES) across basins [1,2]. The situation is more complex for transboundary basins (TDBs) because they provide ES to people across different nations.…”

Section: Introductionmentioning

confidence: 99%

Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning

2021

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Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural resources within the Okavango Basin is undoubtedly a complex issue. It is often difficult to accurately map land use and cover using remote sensing in heterogeneous landscapes. This study investigates the combined value of climate-based regionalization and integration of spectral bands with spectral indices to enhance the accuracy of multi-temporal land use/cover classification using deep learning and machine learning approaches. Two experiments were set up, the first entailing the integration of spectral bands with spectral indices and the second involving the combined integration of spectral indices and climate-based regionalization based on Koppen–Geiger climate zones. Landsat 5 TM and Landsat 8 OLI images, machine learning classifiers (random forest and extreme gradient boosting), and deep learning (neural network and deep neural network) classifiers were used in this study. Supervised classification using a total of 5140 samples was conducted for the years 1996, 2004, 2013, and 2020. Average overall accuracy and Kappa coefficients were used to validate the results. The study found that the integration of spectral bands with indices improves the accuracy of land use/cover classification using machine learning and deep learning. Post-feature selection combinations yield higher accuracies in comparison to combinations of bands and indices. A combined integration of spectral indices with bands and climate-based regionalization did not significantly improve the accuracy of land use/cover classification consistently for all the classifiers (p < 0.05). However, post-feature selection combinations and climate-based regionalization significantly improved the accuracy for all classifiers investigated in this study. Findings of this study will improve the reliability of land use/cover monitoring in complex heterogeneous TDBs.

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Section: Introductionmentioning

confidence: 99%

Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning

2021

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“…For example, the water supply in sub-basin 22 is significantly smaller than that in sub-basins 11 and 27, which have similar catchment areas and precipitation, mainly because sub-basin 22 comprises mostly agricultural land and only 0.1% of urban construction land. In contrast, the grassland and farmland areas in sub-basins 11 and 27 are smaller than those in sub-basin 22, whereas the area of urban construction land is substantially larger than that in sub-basin 22, which indicates that the flow production capacity of agricultural land is lower than that of urban construction land [51]. Although the water yield on urban construction land is higher because its large impervious area prevents water infiltration, most of the precipitation that falls to the ground flows into underground drainage pipes, making it difficult for water resources to be used [52].…”

Section: Ecosystem Service Flows and Determination Of Unit Costsmentioning

confidence: 87%

Integrating Water Quality Restoration Cost with Ecosystem Service Flow to Quantify an Ecological Compensation Standard: A Case Study of the Taoxi Creek Watershed

Chen

et al. 2022

Water

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Watershed ecological compensation is an important economic tool for solving the protection–development conflict. However, establishing a sound ecological compensation plan for rational water resource use at the watershed scale remains challenging. Monthly water samples were collected between April 2019 and July 2020 at 28 points to analyze the spatiotemporal variation in water quality in the Taoxi Creek watershed, which is important for local water security. The Soil and Water Assessment Tool was used to simulate water supply, demand, and spatial flow at the watershed scale. Water quality restoration cost was integrated with ecosystem service flow to quantify the watershed ecological compensation. The ecological compensation using water quality restoration cost based on pollutant treatment cost and water quality target level was CNY 11.9 million (USD 188 million). Taoxi Creek was identified as the major supplier of water ecosystem services for downstream residents, and the ecological compensation based on ecosystem service flow was CNY 18.9–47.2 million (USD 3.0–7.5 million). Combining both calculations, the watershed should annually receive CNY 30.8–59.1 million (USD 4.9–9.3 million) of economic compensation from downstream ecosystem beneficiaries. This study provides a scientific basis for improving the ecological compensation scheme in the Taoxi Creek watershed and a reference for ecological compensation formulation in other watersheds.

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“…Future studies must explore the complex cause-and-effect-feedback mechanism between the supply and demand of ecosystem services in the coupled system of humans and nature (Xiao et al, 2016; Xue et al, 2021). This should be based on the analysis of the integrated role of various elements in the process of ecosystem service flow transmission and their driving mechanisms, and incorporate ecosystem management and regulation into the framework of ecosystem service flow research, with a focus on providing a basis for the formulation of scientific and rational management policies through the analysis of interaction mechanisms of the various elements involved in the process of ecosystem service flow (Bhattarai et al, 2020; Cid et al, 2022). Furthermore, future studies must analyze the interaction mechanism among the elements involved in the flow of ecosystem service functions, to determine a basis for formulating scientific and reasonable management policies and realizing cooperative development between humans and nature (Figure 2).…”

Section: Perspectivementioning

confidence: 99%

Ecosystem services assessment from capacity to flow: A review

Wang

2022

Transactions in Earth, Environment, and Sustainability

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Ecosystem services are the basis for human survival and development and act as a bridge between nature and society. In recent years, ecosystem service flows have become the focus and primary challenge of ecosystem service research, with development in research efforts toward quantification and spatialization. This review explains the necessity of ecosystem service flows, discusses the progress of research in this area over the last decade, and compares the theoretical and representative research approaches from two conceptual perspectives on ecosystem service flows. One of these approaches focuses on processes, treating ecosystem service flows as the spatiotemporal association between ecological service provision areas and service benefit areas, and the other emphasizes final utility, considering ecosystem service flows as the actual supply of ecosystem services to beneficiaries in different areas. On this basis, the research gaps in theoretical principles, assessment methods, and applications are summarized, and future research perspectives on ecosystem service flows are proposed, with a focus on optimizing ecosystem service benefits and promoting cooperative development of systemic human–nature relationships.

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Contested governance of drinking water provisioning services in Nepal’s transboundary river basins

Cited by 11 publications

References 67 publications

Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning

Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning

Integrating Water Quality Restoration Cost with Ecosystem Service Flow to Quantify an Ecological Compensation Standard: A Case Study of the Taoxi Creek Watershed

Ecosystem services assessment from capacity to flow: A review

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