Hydrological responses to land degradation in the Northwest Benin Owena River Basin, Nigeria

Aladejana, Olabanji Odunayo; Salami, Ayobami T.; Adetoro, Olusola O.

doi:10.1016/j.jenvman.2018.07.095

Cited by 32 publications

(14 citation statements)

References 90 publications

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“…There is consensus regarding the responses of ET to ecological restoration, i.e., afforestation is generally effective in improving land surface greenness and simultaneously decreases water yields and increases ET at the watershed scale [16][17][18][19]. In contrast, vegetation degradation exerts the opposite effects and leads to decreased ET [20][21][22][23]; however, the consequences of the restoration on ET at regional scales and in large river basins are still contentious [18,24,25], because different land cover types have different biophysical effects due to different leaf area index (LAI), surface albedo, and root depth characteristics [16]. Moreover, stomatal responses to increased atmospheric CO 2 concentrations vary in vegetation species, which correspondingly have different evaporation rates [26,27].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Responses of Evapotranspiration and Its Components to Vegetation Restoration and Climate Change on the Loess Plateau of China

Qiu

Shi

et al. 2021

Remote Sensing

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Quantitatively identifying the influences of vegetation restoration (VR) on water resources is crucial to ecological planning. Although vegetation coverage has improved on the Loess Plateau (LP) of China since the implementation of VR policy, the way vegetation dynamics influences regional evapotranspiration (ET) remains controversial. In this study, we first investigate long-term spatiotemporal trends of total ET (TET) components, including ground evaporation (GE) and canopy ET (CET, sum of canopy interception and canopy transpiration) based on the GLEAM-ET dataset. The ET changes are attributed to VR on the LP from 2000 to 2015 and these results are quantitatively evaluated here using the Community Land Model (CLM). Finally, the relative contributions of VR and climate change to ET are identified by combining climate scenarios and VR scenarios. The results show that the positive effect of VR on CET is offset by the negative effect of VR on GE, which results in a weak variation in TET at an annual scale and an increased TET is only shown in summer. Regardless of the representative concentration pathway (RCP4.5 or RCP8.5), differences resulted from the responses of TET to different vegetation conditions ranging from −3.7 to −1.2 mm, while climate change from RCP4.5 to RCP8.5 caused an increase in TET ranging from 0.1 to 65.3 mm. These findings imply that climate change might play a dominant role in ET variability on the LP, and this work emphasizes the importance of comprehensively considering the interactions among climate factors to assess the relative contributions of VR and climate change to ET.

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Section: Introductionmentioning

confidence: 99%

Quantifying the Responses of Evapotranspiration and Its Components to Vegetation Restoration and Climate Change on the Loess Plateau of China

Qiu

Shi

et al. 2021

Remote Sensing

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“…At the same time, greening does not necessarily mean land improvement, as it can be caused by invasive plants encroachment and accompanying decreases in species richness and biodiversity (Admassie and Abebaw, 2020: Ethiopia case study). Major land use changes in the region since the 1970s included the rapid expansion of cropland areas and population settlements, and decreases in forests, grasslands, and water bodies (Hiernaux et al, 2009;Aladejana et al, 2018). Although there is considerable debate about the expansion of the Sahara Desert based on annual rainfall, Thomas and Nigam (2018) indicated that the Sahara expanded by 10% over the 20 th century (e.g., Osman and Mohamad, 2020: Sudan case study).…”

Section: Land Use and Land Degradationmentioning

confidence: 99%

Land, Climate, Energy, Agriculture and Development in the Sahel: Synthesis Paper of Case Studies Under the Sudano-Sahelian Initiative for Regional Development, Jobs, and Food Security

Mirzabaev

Sakketa²,

Sylla³

et al. 2021

SSRN Journal

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This paper synthesizes a set of national case studies conducted in the Sahelian countries during 2019-2020 as a collaboration between national universities and research institutes, and the Center for Development Research (ZEF), University of Bonn, with contributions from the Agrhymet Regional Centre, Permanent Interstate Committee for Drought Control in the Sahel (CILSS). These case studies provide up-to-date knowledge and critical insights on the nexus of land degradation, climate change and energy in the Sahel. The current synthesis paper highlights their major findings and provides crosscutting and cross-regional analytical conclusions. First, the synthesis paper explores current trends in the Sahel region on land use and land degradation, energy use and supply, climate change projections and impacts, as well as their interactions and links to agricultural growth, food security, poverty reduction, and peace in the region. Second, technological, socioeconomic and policy solutions at the nexus of land, water, energy and climate challenges that enable environmentally sustainable and socially inclusive rural development in the Sahel are discussed, including their interactions and implications for peace and stability in the region. The findings show that such socioeconomic solutions as improving access to markets, strengthening social safety nets, increasing investments to transport and energy infrastructures, promoting land tenure security, expanding off-farm employment opportunities can greatly contribute to rural development in the Sahel, particularly by aiding climate change resilience and sustainable land management. Key technological innovations highlighted across the case studies include expanding irrigation and adopting water use efficient irrigation techniques, crop diversification, expanding agricultural mechanization, investing into restoring and rehabilitating degraded lands through reforestation, afforestation and agroforestry practices. The key lessons learnt from ongoing national policy initiatives for sustainable development highlight the importance of active stakeholder consultation and participation in policy formulation, institution of effective policy monitoring and assessment mechanisms, and avoiding of excessive reliance on external sources of funding for the successful implementation of sustainable development policies and programs. Based on these findings, the synthesis paper proposes an agenda for applied research to provide guidance to and accompany promising development strategies in and for the region.

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“…As a result of human activities, such as land use change, LD can alter hydrological conditions that are crucial for water resources and sustainable river basin management (Aladejana et al 2018;Jiang et al 2019;Haghighi et al 2020). Therefore, efforts to prevent land degradation must be taken by agencies and governments worldwide (Keesstra et al 2016;Solomun et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Spatiotemporal patterns of land use change (anthropogenic factors) are the main factor in land degradation (Bewket and Sterk 2005;Gebremicael et al 2018). Other researchers have reported that direct anthropogenic disturbances in environments and ecosystems can increase land degradation (Ahiablame and Shakya 2016;Davudirad et al 2016;Aladejana et al 2018;Schwieger and Mbidzo 2020;Shao et al 2020). Jaquet et al (2015) found that outmigration has led to land degradation in a western Nepal watershed.…”

Section: Introductionmentioning

confidence: 99%

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

et al. 2020

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Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts.

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Hydrological responses to land degradation in the Northwest Benin Owena River Basin, Nigeria

Cited by 32 publications

References 90 publications

Quantifying the Responses of Evapotranspiration and Its Components to Vegetation Restoration and Climate Change on the Loess Plateau of China

Quantifying the Responses of Evapotranspiration and Its Components to Vegetation Restoration and Climate Change on the Loess Plateau of China

Land, Climate, Energy, Agriculture and Development in the Sahel: Synthesis Paper of Case Studies Under the Sudano-Sahelian Initiative for Regional Development, Jobs, and Food Security

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

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