Impact of Climate and Land Use/Land Cover Change on the Water Resources of a Tropical Inland Valley Catchment in Uganda, East Africa

Gabiri, Geofrey; Diekkrüger, Bernd; Näschen, Kristian; Leemhuis, Constanze; Linden, Roderick van der; Majaliwa, Jackson-Gilbert Mwanjalolo; Obando, Joy Apiyo

doi:10.3390/cli8070083

Cited by 23 publications

(19 citation statements)

References 66 publications

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“…The monitoring of land use and land cover (LULC) plays a vital role in the study of environmental change, management of natural resources (e.g., water and wastewater management and biomass monitoring) [1,2], urban planning, urban growth modelling [3,4] and creation of environmental policies for sustainable development [5][6][7]. Due to the expansion of artificial areas and an economic growth that is concomitant with increased human needs (which also causes extreme environmental stress), changes in LULC are steadily accelerating.…”

Section: Introductionmentioning

confidence: 99%

“…Improvements may The study area contains two landscape units in Hungary with different physical geographical and land cover properties: Gödöllői-hills and Marosszög (Figure 1). The Gödöllői-hills is a hilly region of Hungary comprising an area of 510 km 2 . The region is between 138 and 344 m above sea level, sloping slightly to the southeast.…”

Section: Introductionmentioning

confidence: 99%

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Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics

et al. 2020

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The use of an object-based image analysis (OBIA) method has recently become quite common for classifying high-resolution remote-sensed images. However, despite OBIA’s segmentation being equally useful for analysing medium-resolution images, it is not used for them as often. This study aims to analyse the effect of landscape metrics that have not yet been used in image classification to provide additional information for land cover mapping to improve the thematic accuracy of satellite image-based land cover mapping. To this end, multispectral satellite images taken by Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) during three different seasons in 2017 were analysed. The images were segmented, and based on these segments, four patch-level landscape metrics (mean patch size, total edge, mean shape index and fractal dimension) were calculated. A random forest classifier was applied for classification, and the Coordination of Information on the Environment Land Cover (CLC) 2018 database was used as reference data. According to the results, landscape metrics both with and without segmentation can significantly improve the overall accuracy of the classification over classification based on spectral values. The highest overall accuracy was achieved using all data (i.e., spectral values, segmentation, and metrics).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics

et al. 2020

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“…The topography in central Uganda is undulating, ranging from 900 to 1340 masl and is characterised by inland valleys occupied with narrow swampy bottomlands (Miyamoto et al, 2012). The studied inland valley covers 4.5 km 2 and is one of the headwater micro-catchments of the Lake Kyoga basin (Gabiri et al, 2020).…”

Section: Study Sitesmentioning

confidence: 99%

Assessing the effects of management and hydro-edaphic conditions on rice in contrasting East African wetlands using experimental and modelling approaches

Grotelüschen

Gaydon

Langensiepen

et al. 2021

Agricultural Water Management

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Lowland rice yields in East Africa remain low despite favourable hydro-edaphic conditions as benefits from improved cultural management vary between and within wetland types and interactions are poorly understood. Hence, multi-year agronomic field experiments were established to assess the differential responses of lowland rice to management (rainfed 0 and 60 kg N ha − 1 , and irrigated 120 kg N ha − 1 + 60 kg PK ha − 1 ) and field position within a floodplain in Tanzania (fringe and middle positions) and an inland valley in Uganda (valley-fringe, midvalley and valley-bottom positions). We then calibrated and validated the Agricultural Production System Simulator (APSIM), evaluated the importance of external water table data as model input and assessed the relative effects of water and N stress on yield as affected by wetland type and field position. Yields of 3.2-9.2 Mg ha − 1 were attained in the floodplain and of 1.9-6.3 Mg ha − 1 in the inland valley, highlighting the substantial scope to boost yields beyond current regional means of around 2 Mg ha − 1 . The model estimated grain yields in both wetlands well within the experimental uncertainty during model validation (n = 12, r 2 = 0.76, RMSEa= 0.92 Mg ha − 1 in the floodplain; n = 18, r 2 = 0.71, RMSEa= 0.72 Mg ha − 1 in the inland valley). Results further emphasised the importance of external water table data for sound model performance as they evidently alleviated seasonal droughts. Simulated abiotic stress patterns additionally highlighted hydro-edaphic differences from field positioning within and between both wetlands. While low soil N was generally the main yield constraint, water stress was comparably more pronounced in the inland valley and supplemental irrigation thus more beneficial on yield. Hydro-edaphic field conditions favoured rice production in the floodplain's fringe with comparably lower N stress, while large spatial-temporal variabilities prevented a distinct delineation based on toposequential field positions in the inland valley.

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“…The SWAT model is a spatially distributed hydrological model that was designed to assist water resource managers in assessing the impacts of climate change, land use, and management approaches on water resources [34,35]. This model has been widely employed by researchers for watershed modeling and water resource management in watersheds with different climatic and topographic characteristics, owing to its user-friendly interface (ArcSWAT) and flexible data needs [27,36]; it has already been validated for various watershed scales in different climatic situations around the world, and it has worked well even in complex and data-poor watersheds [37]. The SWAT model also has the advantage of allowing the simulation of a variety of physical processes (e.g., hydrological, sediment, and contaminants) in a watershed.…”

Section: Swat Modelmentioning

confidence: 99%

Future Climate Change Impact on the Nyabugogo Catchment Water Balance in Rwanda

Umugwaneza

Chen

Liu

et al. 2021

Water

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Droughts and floods are common in tropical regions, including Rwanda, and are likely to be aggravated by climate change. Consequently, assessing the effects of climate change on hydrological systems has become critical. The goal of this study is to analyze the impact of climate change on the water balance in the Nyabugogo catchment by downscaling 10 global climate models (GCMs) from CMIP6 using the inverse distance weighting (IDW) method. To apply climate change signals under the Shared Socioeconomic Pathways (SSPs) (low and high emission) scenarios, the Soil and Water Assessment Tool (SWAT) model was used. For the baseline scenario, the period 1950–2014 was employed, whereas the periods 2020–2050 and 2050–2100 were used for future scenario analysis. The streamflow was projected to decrease by 7.2 and 3.49% under SSP126 in the 2020–2050 and 2050–2100 periods, respectively; under SSP585, it showed a 3.26% increase in 2020–2050 and a 4.53% decrease in 2050–2100. The average annual surface runoff was projected to decrease by 11.66 (4.40)% under SSP126 in the 2020–2050 (2050–2100) period, while an increase of 3.25% in 2020–2050 and a decline of 5.42% in 2050–2100 were expected under SSP585. Climate change is expected to have an impact on the components of the hydrological cycle (such as streamflow and surface runoff). This situation may, therefore, lead to an increase in water stress, calling for the integrated management of available water resources in order to match the increasing water demand in the study area. This study’s findings could be useful for the establishment of adaptation plans to climate change, managing water resources, and water engineering.

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Impact of Climate and Land Use/Land Cover Change on the Water Resources of a Tropical Inland Valley Catchment in Uganda, East Africa

Cited by 23 publications

References 66 publications

Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics

Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics

Assessing the effects of management and hydro-edaphic conditions on rice in contrasting East African wetlands using experimental and modelling approaches

Future Climate Change Impact on the Nyabugogo Catchment Water Balance in Rwanda

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