Assessing and Landscape Mapping of the Regulatory Ecosystem Services of Lake Hawassa Basin in Ethiopia: Implications for Sustainable Natural Resource Management
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Regulating Services (RS) is one of the four Ecosystem Services (ES) derived from the diverse ecosystems in the Lake Hawassa Basin (LHB). These services are crucial for the local community, but ongoing anthropogenic activities are exerting negative pressure on these ecosystems, diminishing their capacity to provide RS. This study aimed to assess and map RS at the basin level and propose development options for decision-makers considering the study years of 2007, 2016 and 2024. The study utilized primary and secondary data collection and analysis, stakeholder consultations involving 60 participants, site visits, and tools such as land use land cover (LULC) classification using ArcGIS v10.1 and expert judgment matrix (EJM). The study prioritized 4 out of 11 RSs, created spatial pattern maps at the basin scale, and suggested development options to integrate into decision-making processes and sectoral policies. These recommendations aim to benefit current and future planning and management of development activities within the LHB. The study's methodology and results are vital for addressing biophysical and socioeconomic environmental problems, ensuring sustainable management of natural resources, and enhancing the well-being of the local community. The user-friendly methodology can be adopted globally, with future improvements suggested through additional methods like modeling and valuation of prioritized RS.
1. Introduction
Regulating Services (RS) is one of the four Ecosystem Services (ES) derived from the function and structure of diverse ecosystems in Lake Hawassa Basin (LHB), the study area. RS is crucial for the local community, but ongoing human activities are exerting negative pressure on these ecosystems and diminishing their capacity to provide RS (Burkhard et al., 2012b; Carpenter et al., 2009; Costanza et al., 1997; Foley et al., 2005; MEA, 2005; Perrings et al., 2011; TEEB, 2010). According to the Millennium Ecosystem Assessment Framework, the benefits of an ecosystem are primarily determined by the link between ES and human well-being (MEA, 2005).
Human well-being comprises five main components: health, basic material needs for a good life, security, good social relations, and freedom of choice and action. Ecosystems support human well-being through existing RSs. Well-being also depends on the supply and quality of human services, technology, and institutions. The strength of the linkages between categories of ES and components of human well-being varies and includes indications of the extent to which socioeconomic factors can mediate the linkage (MEA, 2005). As per this linkage, if a substitute for a degraded ecosystem service is available, there is a high potential for intervention. The strength of these linkages and the potential for intervention differ across ecosystems and regions. Besides the influence of RS on human well-being, other factors such as environmental, economic, social, technological, and cultural factors also play a vital role, and ecosystems are, in turn, affected by changes in human well-being (MEA, 2005).
LHB are known for its rich and diverse aquatic and terrestrial natural resources, which have the potential to provide RS. These services play a major role in securing the livelihood of local people, with biophysical conditions, human-induced temporal, and spatial changes in LULC, and climate change determining the provision of ES in the study area (Burkhard et al., 2012b; Riitters et al., 2000). The concept of ES is vital for the maintenance and restoration of basin ecosystems like those that existed in the LHB and adds value to the current and future natural resource conservation management practices. Overall, implementing sustainable management practices is essential to improve the use and importance of natural resources and ES in the study area, supporting residents and the surrounding community (EEA, 2013).
Population growth and economic expansion are the primary drivers of LULC change worldwide, especially in developing countries that prioritize economic prosperity Abdeta et al., 2024; Li et al., 2009; Mir et al., 2025; Tessema et al., 2024). Unsustainable management of natural resources has led to ecosystem conversion and degradation. There is a critical need to plan location-specific land use activities, including agricultural and fisheries activities that maintain ES (Egoh et al., 2007; Wade et al., 2010). Understanding the status of RSs and their dynamic patterns, including the relationship and interaction among ecosystem structures, functions, and landforms, is required for the effective management of RS. While previous studies have attempted to quantify and map ES’ functional values, basin-scale assessment is still necessary to understand how RS functions and benefits in specific locations at the basin scale (Kindu et al., 2016).
Currently, human activities are putting pressure on the study area’s ecosystem, disrupting and impacting the existing RSs within the LHB. Major threats include lack of coordination among sectors in controlling resource degradation, inadequate regulatory and enforcement mechanisms on unplanned resource abstraction and utilization, uncontrolled fishing practices, lake water abstraction for irrigation, improper land use management, deforestation, land degradation, lakeside farming which contribute increased erosion and sedimentation of the lake and other surrounding surface water sources, untreated effluent discharge of from industries, hospitals and other anthropogenic activities, low level of awareness, and knowledge of the importance and value of RS in the study area (Deng et al., 2016; IBC, 2005).
Previously available studies were lacked to assess the regulating ES using social data, geospatial information, and expert perspectives. For instance, Abate et al. (2015) and Lencha et al. (2021) assessed the water quality of the Lake Hawassa (Yilma, 2019) on economic value of the wetlands (Belete et al., 2024) on provisioning ES of the lake landscape (Degife et al., 2019) on LULC and its drivers in the Lake Hawassa watershed. The study also identified major gaps related to incomplete ES studies, often focusing on a single ecosystem service or problem, limited in number, and lacking basin-scale assessment. These gaps prompted the study to extend its scope to include a basin-scale assessment of the prioritized four types of RSs. This shortfall of previous ES studies challenges the author to conduct an in-depth analysis of a comparative assessment of RS in the study area. The study aims to address these gaps and provide alternative options to decision-makers, NGOs, Experts, and development partners to use in their decision-making processes for existing and future watershed and basin management plans within LHB and similar areas nationwide. The study’s output, coupled with efforts to integrate ecosystem service approaches into natural resource management plans, aims to safeguard the existing RS within the LHB. The ultimate goal is to suggest alternative development options to policymakers and development partners. Advancing this study can help to address gaps seen in ES assessment during planning for future Lake Hawassa Basin development activities, improving the use and management of RS in the study area, and can be input for future Lake Hawassa Basin planning and management.
2. Materials and Methods
This study was conducted in the same area as a previous study on provisioning services at Lake Hawassa Basin titled “Integrated Assessment and Mapping of Provisioning Services for Sustainable Management of Natural Resources: The Case of Lake Hawassa Basin, Ethiopia” by the same author. It used similar materials and methods (Bedilu & Teshome, 2024).
LHB is located 273 km from Addis Ababa, the capital city of Ethiopia, in the central northeast part of the Rift Valley Lake Basin (RVLB). The total Lake Hawassa catchment area is 436 km2, centered around Lake Hawassa, which covers 7% of the study area and includes Hawassa Town. The closed shallow Lake Hawassa has no surface water outlet, with water exiting through subsurface outflow and evapotranspiration. The LHB is located between latitudes 6°48’452 to 7°49’ 542 N and longitudes 38°16’ 34” to 38°43’262 E. The LHB spans two regional states, Sidama and Oromia, covering 71% and 21%, respectively (Figure 1) ((Bedilu & Teshome, 2024; MoWE, 2010b). The topography is flat to gently undulating, bounded by steep escarpments, with varied slopes: 56% flat to gentle (0–8%), 33% moderately sloping (8–30%), and 5% steep to very steep (> 30%). The altitude ranges from 1,680m at Lake Hawassa to 2,700 m on the eastern escarpment (Bedilu & Teshome, 2024; MoWE, 2010a). The local agroclimatic classification predominantly categorizes the area as a tepid, humid, sub-humid mountain, rift valley, and cold to humid agroecological zones. The mean annual rainfall at Hawassa is about 943.4 mm, with inter-annual variability but no significant trend. The mean monthly temperature varies from 18.6°C in November to 21°C in March. The mean annual temperature shows an increasing trend of about 0.05°C per year, attributed to climatic changes and increased deforestation (Bedilu & Teshome, 2024; MoWE 2007).
The study adopts the Millennium Ecosystem Assessment conceptual framework (MEA, 2005), which elucidates the dynamic interaction between ecosystems and their components, affecting human well-being due to direct and indirect shifts in human conditions that drive changes in ecosystem nature, structure, and function (Annex 1). Social, economic, and cultural factors associated with ecosystems modify human situations, and several natural forces influence the capacity of each ecosystem to provide RS in the study area (Bedilu & Teshome, 2024; MEA, 2005). The significant feature of the Millennium Ecosystem Service Assessment approach results from its interdisciplinary demand, underline the characterization of Lake Hawassa Basin goods and services and the respective basic ecological principles considered during the study. Moreover, the socioeconomic aspects that determine environmental evaluations and decision-making processes require in-depth analysis and understanding of the value and importance of RSs captured in this study for the successful implementation of this approach (Bedilu & Teshome, 2024; Muller et al., 2010).
The Millennium Ecosystem Assessment Approach is based on the linkages between two pillars: ES and Human Well-being (MEA, 2005) (Annex 2). The strength of these linkages between ecosystem service classes and components of human well-being indicates the extent to which the socioeconomic factors in the LHB intervene in the linkage. This linkage has significantly affected the existing RSs, defining the demand-supply chain within the study area (Annex 3) (Bedilu & Teshome, 2024; Burkhard et al, 2014; MEA, 2005).
Various methods and tools were employed in this study to identify, prioritize, and map the existing RS in the study area, as discussed in the following sections (Figure 2). Recognizing the relevance of this study for sustainable natural resource management, which is crucial for maintaining the existing RS, and the simplicity and user-friendliness of the study methodology; it is essential for interested parties conducting similar studies in the country and globally. This facilitates the straightforward application of these methods to support community livelihood, human well-being, and the sustainability of development projects at the Basin or watershed scale. However, the limited knowledge of this integrated ecosystem service assessment approach and the absence of similar studies in the area and country posed challenges for the author. Consequently, the author could not compare this study’s results with any similar papers conducted using this method in the country. Therefore, this study stands out among previous studies in the country, significantly contributing to the swift identification of spatial patterns of RS at the basin scale and supporting sound decision-making processes during the planning and implementation of watershed and related development projects.
The study emphasized collecting relevant information to understand the situation and trends of RS in the LHB. The Millennium ES Assessment Approach was adopted due to its reduced demand for complex and costly data gathering, collating, and analyzing. Primary and secondary data, along with community and stakeholder opinions, were collected during consultation sessions to describe RS features and trends at national, regional, and local levels (Kosmus et al., 2012). Major data sources included LULC, household population, climate, hydrology, soil, slope, vegetation, etc. collected from the Ministry of Water and Energy (MoWE) and other relevant institutions. Consultations with government and non-government organizations, universities, fishery associations, and others at local, regional, and national levels were conducted. Stakeholder consultations, site visits, and reviews of various study papers and working documents were carried out to gather key study data.
Stakeholder consultation was crucial for collecting, organizing, and analyzing the required data. Snowball sampling was used to identify stakeholders, ensuring full participation and avoid missing key informants (Goodman, 1961). Sixty (60) stakeholders from diverse community groups, which were purposively selected were including farmers, fishermen, tourists, academicians, officials, and experts, were identified, consulted, and trained before engaging in the selection and prioritization of RS. The consultations were aimed at gathering information on stakeholders’ perceptions and local knowledge regarding the importance and value of RS, informing and training them about the study methodologies, and seeking their consent to fully engage in the prioritization and selection process of the relevant four RS out of existing 11 RSs in the study area. Awareness and brief training on the Integrated Ecosystem Service Assessment Approach, including ecological, social, and economic factors, and the application of study checklists and the Expert Judgment Matrix (EJM) were provided to consulted stakeholders (LHB) (Fagerholm et al., 2012; Hein et al., 2006; Kandziora et al., 2013; Lamarque et al., 2011a).
The LHB comprises several ecosystems with varied functions, structures, and processes. LULC maps, coupled with knowledge of each LULC unit’s capacity to provide RSs, are used as tools for decision-makers to identify the potential of each ecosystem, possible conflicts, and their limits in environmental management (Burkhard et al., 2012a). Identifying and classifying LULC units played a crucial role in assessing and mapping the spatial patterns of the selected four relevant RSs. The identification exercise of each landscape unit was based on field survey findings and information from reviewed study documents and maps obtained from MoWE, regional bureaus, universities, and other institutions. The study used 10 LULC units classified based on the RVLB master plan study conducted by (MoWE, 2007) (Table 1).
The year 2007 was chosen as a baseline for this study and we adapted the data from the office of (Bedilu & Teshome, 2024; MoWE, 2007). The year 2016 and 2024 were deliberately selected to map the LULC following the standard land classification techniques. After the Landsat image was downloaded, the image was clipped by using the shape file of the Hawassa Lake Basin. Following that, each year LULC type was preprocessed and prepared using the ArcGIS software (Version 10.1). The images were classified following supervised classification using maximum likelihood classifier technique. The study tried to maintain the minimum level of image classification accuracy, which was 85% as recommended by many authors (Allam et al., 2019; Mohajane, et al., 2018).
No. | LULC | Code |
1 | Intensively cultivated farm | ICF |
2 | Forest including plantations | FP |
3 | Grassland | GL |
4 | Lake | L |
5 | Marshland | ML |
6 | Shrubland | SL |
7 | Urban/Settlements | U/S |
8 | Woodland | WL |
EJM is a popular ES assessment technique for prioritizing and selecting essential RSs and mapping practices (Table 2). This matrix is technically simple and swiftly provides information regarding prioritized and selected RS for mapping exercises, facilitating the involvement of 60 consulted stakeholders. The study adopted a procedure to evaluate spatial differences among the prioritized RSs based on the analysis of existing landscape data and the respective capacity of each LULC unit to provide these services (Burkhard et al., 2014; Burkhard et al., 2010a; Burkhard et al., 2010b; Burkhard et al., 2009).
No. | LULC | Water Purification | Flood Regulation | Erosion Regulation | Waste Regulation |
1 | Intensively cultivated farm | ||||
2 | Forest including plantation | ||||
3 | Grassland | ||||
4 | Lake | ||||
5 | Marshland | ||||
6 | Shrubland | ||||
7 | Settlement / Urban | ||||
8 | Woodland |
Following the conceptual framework adapted from (Burkhard et al., 2009; Mohajane, et al., 2018) the study used the 2007 LULC data to assess, identify, and illustrate the capacity of each landscape unit to provide RSs and to determine the overall existing potential capacities of the LHB ecosystems. The selection and prioritization of four RS from the existing 11 RSs were conducted through consultations and brief training of 60 stakeholders on the approach before starting the selection and prioritization process. Stakeholders used consecutive numerical values of 1 to 11 to assign their preferences, with 1 being the most important and 11 being the least significant. The remaining 9 RSs were sorted and ordered ascendingly using numbers between the two extremes (Table 3).
The output of stakeholders’ prioritization and selection exercise was used to evaluate the spatial patterns of the selected 4 RSs along with the corresponding capacity of each LULC unit through analysis of existing landscape data (Burkhard et al., 2010a; Burkhard et al., 2009). Stakeholders also placed values for each RS in the blank EJM across the corresponding LULC unit, with the y-axis designated for 10 LULC types and the x-axis for 4 prioritized RSs, as depicted in Table 2. The capacity of each LULC to provide the respective RS was assessed and presented on a 0-5 scale (Table 4).
RS | Rank |
Erosion regulation | 1 |
Flood (water flow) regulation | 2 |
water purification | 3 |
Waste Regulation | 4 |
Global climate regulation | 5 |
Air quality regulation | 6 |
Local climate regulation | 7 |
Nutrient regulation | 8 |
Natural hazard regulation | 9 |
Pest and disease control | 10 |
Pollination | 11 |
Score | Description |
0 | No relevant capacity |
1 | Very low relevant capacity |
2 | Low relevant capacity |
3 | Medium-high relevant capacity |
4 | High relevant capacity |
5 | Very high relevant capacity |
Mapping is essential to identify and demonstrate the potential capacity of each LULC unit and the spatial differences of the selected and prioritized four RS at the basin scale (Burkhard et al., 2010a). It helps to identify prevailing problems, especially in synergy and trade-offs among ES and between ES and biodiversity. Mapping plays a key role in communication to create awareness and initiate discussions on the importance and value of RSs among stakeholders, decision-makers, and development partners. The study presents the specific locations of top relevant RSs benefiting the community residing within and around the study area using spatial pattern RS mapping at the LHB scale. LULC data was used to map the selected and prioritized four RSs, following the mean value of each RS recorded in the EJM. All required data for mapping, including shapefiles, were obtained from MoWE. LULC maps were produced by extracting information from these shapefiles.
Relevant indicators for each selected RS were taken as input for analysis and mapping exercise (Kandziora et al., 2012) (Table 5). The selected 4 RS and the associated indicators were considered to collect information and used for further assessment to determine the spatial patterns of each RS within LHB (Kandziora et al., 2013). The spatial patterns of each RS generated from the LULC class were demonstrated on a basin scale map with varied colors attached to the 0–5 scale value assigned by stakeholders. Different colors were used to indicate the status of each LULC unit’s capacity to provide the respective RSs, denoting zero (0), no relevant capacity and deep green representing (5), very high relevant capacity (Table 4). LULC and RS spatial pattern maps were plotted using ArcGIS v10.1 to illustrate detailed information on the status and potential of LHB’s ecosystems to provide RS. These maps facilitate the identification and assessment of the spatial variation of existing annual potential RS in the study area. The detailed study findings are discussed in the result section below.
No. | RS | Service Definition | Potential Indicators |
1 | Global climate regulation | Long-term storage of greenhouse gases in ecosystems. | Source-sink of methane, carbon dioxide and water vapour (t C/ha*a) Amount of stored trace gases in marine systems, vegetation and soils (t C/ha) |
2 | Local climate regulation | Changes in local climate components like wind, precipitation, temperature, radiation due to ecosystem properties. | Temperature (°C), albedo (%), precipitation (mm), wind (Bft), temperature amplitudes (°C), evapotranspiration (mm), shaded areas (ha, %) |
3 | Air quality regulation | Capturing/filtering of dust, chemicals and gases. | Leaf area index Air quality amplitudes (ppb) Air quality standards deviation (ppb) Level of pollutants in the air Critical loads (kg/ha*a) Difference between open land and throughfall (kg/ha*a) |
4 | Water flow regulation | Maintaining water cycle features (e.g. water storage and buffer, natural drainage, irrigation and drought prevention). | Groundwater recharge rate (mm/ha*a) |
5 | Water purification | The capacity of an ecosystem to purify water, e.g. from sediments, pesticides, disease-causing microbes and pathogens. | Water quality indicators: Sediment load (g/l) Total dissolved solids (mg/l) 14 |
6 | Nutrient regulation | The capacity of an ecosystem to recycle nutrients, e.g. N, P. | Water quality indicators, e.g. N (mg/l), P (mg/l) Leakage of nutrients (kg/ha*a) Electrical conductivity (S/cm) Total dissolved solids (mg/l) Turnover rates of nutrients, e.g. N, P (y–1) |
7 | Erosion regulation | Soil retention and the capacity to prevent and mitigate soil erosion and landslides. | Vegetation cover (%) Loss of soil particles by water and wind (kg/ha*a) USLE factors for assessment of landslide frequency (n/ha*a) |
8 | Natural hazard protection | Protection and mitigation of floods, storms (hurricanes, typhoons…), fires and avalanches. | Number of prevented hazards (n/a) Natural barriers (dunes, mangroves, wetlands, coral reefs) (%, ha) |
9a | Pollination | Bees, birds, bats, moths, flies, wind, non-flying animals contribute to the dispersal of seeds and the reproduction of lots of plants. | Species numbers and amount of pollinators (n/ha) |
10a | Pest and disease control | The capacity of an ecosystem to control pests and diseases due to genetic variations of plants and animals making them less disease-prone and by actions of predators and parasites. | Populations of biological disease and pest control agents (n/ha) |
11a | Regulation of waste | The capacity of an ecosystem to filter and decompose organic material in water and soils. | Amount and number of decomposers (n/ha) Decomposition rate (kg/ha*a) |
3. Results
This section discusses the integrated assessment and spatial pattern mapping of four prioritized RS at the basin scale: Erosion Regulation, Flood Regulation, Water purification, and Waste Regulation. These services were prioritized by 60 stakeholders, including local community members, fishermen, tourists, officials, experts from government organizations, and university academicians. The prioritization process was based on stakeholders’ knowledge obtained through consultation and brief training, as well as their individual prior exposure and acquaintances with each RS. The selected RS were then mapped to depict their spatial variations at the basin scale.
LULC plays a key role in mapping the spatial patterns of the four selected RS. The study uses 10 LULC classes, which are demonstrated in a pie chart (Annex 4) and LULC map (Figure 3). The LULC map and pie chart show that intensively cultivated smallholder farms accounted for 60.2% of the study area, with perennial crops, like Enset, Sugar Cane, and coffee mainly in the eastern hills and cereals like Sorghum, Maize, and Barely in the western and southern parts. The remaining 39.8% of the LHB is covered by other LULC units, such as Grassland (9.5%), Shrubland (7.8%), Lake (6.5%), Marshland (5.1%), Urban/Settlements (3.1%), intensively cultivated mechanized farms (2.5%), Woodland (2.3%), Dense Forest (2%) and Plantation Forest (0.9%), which mainly located around the Wendo Genet and Wendo Koshe hills found in the eastern part of the LHB (Table 6 and Figure 3).
Recently, based on the LULC map of the study area, there was a shift in LULC from natural habitats to human modified ecosystem. The crop land (including both the smallholder and the mechanized farming) showed increment from nearly from 63.7 to 71.4%. This share of the total land size is significant. On the other hand, natural ecosystems like forest, grassland and shrub lands were declining (Table 6).
No | LULC | Area (KM2) (2007) | % (2007) | Area (KM2) (2016) | % (2016) | Area (KM2) (2024) | % (2024) |
1 | Intensively cultivated farm | 897.6 | 62.7 | 948.5 | 66.2 | 962.2 | 67.2 |
2 | Forest including plantation | 42 | 2.9 | 40.2 | 2.81 | 38.2 | 2.7 |
3 | Grassland | 136.6 | 9.5 | 101.1 | 7.06 | 92.6 | 6.5 |
4 | Woodland | 33.3 | 2.3 | 30.2 | 2.11 | 28.2 | 2.0 |
5 | Shrubland | 111.5 | 7.8 | 92.9 | 6.49 | 80.7 | 5.6 |
6 | Urban/Settlement | 44.9 | 3.1 | 53.7 | 3.75 | 65.2 | 4.6 |
7 | Marshland | 72.9 | 5.1 | 71.4 | 4.98 | 70.7 | 4.9 |
8 | Lake | 93.5 | 6.5 | 94.3 | 6.58 | 94.5 | 6.6 |
Total | 1432.3 | 100 | 1432.3 | 100 | 1432.3 | 100 |
These varied LULC data indicate that the study area encompasses diverse ecosystems with different capacities to provide RSs, benefiting the local community. Grassland, Marshland, Forest, and Lake ecosystems, covering about 22% of the area play key roles in providing water purification, and waste, erosion, and flood regulation services. This service benefits the community members in environmental protection by preventing the area from flood and erosion hazards, managing wastes, and purifying water resources (Annex 4). This information is crucial for decision-makers and development partners to identify areas for erosion and flood regulation projects. The mean values of the four prioritized RS across the 10 LULCs, affected by anthropogenic activities, are shown in the EJM (Table 7) and radar diagram (Figure 4).
No. | LULC | Water Purification | Flood Regulation | Erosion Regulation | Waste Regulation |
1 | Intensively cultivated farm | 0 | 3 | 2 | 1 |
2 | Forest including plantation | 5 | 5 | 5 | 5 |
3 | Grassland | 5 | 5 | 5 | 5 |
4 | Lake | 4 | 4 | 4 | 5 |
5 | Marshland | 5 | 5 | 5 | 5 |
6 | Shrubland | 3 | 3 | 3 | 3 |
7 | Settlement / Urban | 1 | 1 | 1 | 0 |
8 | Woodland | 3 | 4 | 3 | 3 |
Mean Value | 3 | 4 | 3 | 3 |
Several spatial pattern maps were plotted for the four selected RS, further elaborated in this section.
Water Purification: The spatial patterns of water purification services (Figure 5) show that grassland, marshland, and forest ecosystems have a mean value of very high relevant capacity (MV-5) to provide water purification RS. This benefits the local community through the self-treatment of surface water sources used for various purposes existing near their locality, without or with the limited intervention of development projects. Shrubland and woodland ecosystems have medium relevant capacity (MV-3) to provide water purification services to the local community, while intensive cultivation of mechanized and smallholder farm ecosystems have no relevant capacity (MV-0). The reason for the existence of low water purification capacity of cultivated land was due to the modification of the land. This caused change in soil physicochemical properties like soil compaction, loss of organic carbon soil and excess use of chemical fertilizers reduce the soil biota. As a result, it changes the hydrological cycle and increased the runoff and nutrient exports.
Flood Regulation: The spatial pattern of flood regulation services (Figure. 6) indicates major variations between forest and urban ecosystems, ranging from very high to very low capacity. Grassland, forest, and marshland ecosystems have a very high capacity for flood regulation. This information is vital for planning flood and stormwater regulation projects. The reason for higher flooding incidence in urban areas was due to the existence of impervious surface, which blocks the rainwater from infiltrating into the underground. In addition, the settlement areas are devoid of vegetation, occupied by large people, and exposed to flood water, which reduced the flood regulatory service of urban lands.
Erosion Regulation: The spatial pattern of erosion regulation services (Figure. 7) shows that urban and intensively cultivated areas have very low to low capacity (MV-1) for erosion regulation, covering about 65.8% of the study area. Lake, marshland, and forest ecosystems have a very high capacity (MV-5) for erosion regulation. The land size of 65.8% of the lake basin had low to very low erosion regulation services. These were mainly of the settlement and cultivation areas, which are known for lack of vegetation cover. Vegetation helps to absorb the rainwater, reduce energy from rainfall, and enhance its infiltration rate. In addition, cultivated lands are known for tillage practices, which exposed the soil to erosion.
Waste Regulation: The spatial pattern of waste regulation services (Figure 8) shows varied capacities across ecosystems. Urban areas have no relevant capacity (MV-0), while intensive cultivation areas have very low capacity (MV-1). Lake, marshland, grassland, and forest ecosystems have a very high capacity (MV-5) for waste regulation. The likely cause for the low waste regulation capacity in urban and cultivated lands is linked with the existence of high human population per square kilometer area, economic activities and land modification, even which increase waste generation. These results inform decision-makers about the specific locations where waste management and other environmental management practices are needed, aiding in the planning and implementation of sustainable development projects in the LHB.
4. Discussion and Conclusion
The study area encompasses about 10 ecosystems with varied potential to provide RS. The capacity of each ecosystem to provide these services is determined by the presence of natural resources, including biodiversity, and their structure and function, which contribute to human well-being (MEA, 2005; Burkhard et al., 2012b). One of the study’s outputs of this study is information on the capacity of different ecosystems in the LHB to provide RS, which is crucial for sustainable planning and implementation of development activities. The findings highlight the spatial variability of each RS, resulting from the structure and function of existing ecosystems and their respective supply capacities.
This information could be an essential inputs for planning and implementing natural resource conservation and management practices, as well as watershed management projects, ultimately benefiting the community (Bedilu & Teshome, 2024; García-Nieto et al., 2013; Martínez-Harms & Balvanera, 2012). The study results drawn from the integrated ES assessment approach (MEA, 2005) serve as a knowledge base for stakeholders, policymakers, and other interested parties in the nation or globally, illustrating the link between landscape type and RS distribution at the basin scale.
The study’s findings on the spatial patterns of RS and the capacity of each LULC simplify the planning process for policymakers and development partners. This facilitates valid development-oriented decisions that fit the environmental and social settings of the study area. For example, ecosystems with low capacity for erosion and flood regulation may require interventions such as stormwater and drainage management, soil and water conservation, etc. to prevent and minimize flood-related disasters. Adequate consideration of these findings can help integrate them into project design, ensuring sustainable development with minimal costs and time savings.
The study considered four relevant RS out of eleven existing in the study area, prioritized by stakeholders from various social groups (Bedilu & Teshome, 2024; Tëngo, et al., 2014). Their prioritization was based on the perceived value and importance of RS, relevant to human needs. The output of this prioritization exercise was used to fill the EJM, which served as a tool for mapping the spatial patterns of selected RS at the basin scale. Previous studies have highlighted the importance of including diverse stakeholder groups, particularly the local community, in ecosystem service assessment and prioritization exercises, as their exposure and knowledge to the landscape units vary (Bedilu & Teshome, 2024; Martín-López et al., 2012; Lamarque et al., 2011b).
Engaging local stakeholders in the decision-making process contributes to collective knowledge during development planning and familiarizes them with an integrated ecosystem assessment approach (Bedilu & Teshome, 2024; Tëngo et al., 2014; Swetnam et al., 2011). This encourages knowledge sharing and shared actions, enhancing sustainable management practices and understanding the multifunctional nature of ecosystems, ensuring adequate delivery of RS (Bedilu & Teshome, 2024; García-Llorente et al., 2012; Pilgrim et al., 2008 ; Pilgrim et al., 2007). Adopting this study’s approach can help address environmental and socioeconomic problems, such as land degradation, deforestation, pollution, and erosion, ensuring environmentally friendly, socially acceptable, and economically feasible development activities.
One of the major causes for the decline in the RS was the expansion of crop cultivation in the basin. The findings of this study suggest the need for practicing sustainable land management practices such as reforestation, using physical and biological soil-water conservation structures in cultivated land (Biratu et al., 2023; Mengist et al., 2023; Mitiku et al., 2006). This could help to enhance the RS from cultivated land. The study can therefore deliver qualitative information for stakeholders to design and implement strategies for the improvement of Rs especially such as erosion control, waste regulation, water purification, and flood control in the Hawassa Lake basin.
Overall, the implementation of adequate and proper land management approaches is essential to enhance the benefits of the basin in terms of its RS (Biratu et al., 2023). For instance, in China, regulating ES using protection scenario result sustainable socioeconomic development mainly by increasing the water resource (Yang, 2012). ES are essential for human existence and managing them in an efficient way is vital for ecosystem and socioeconomic development (Deng & Gibson, 2016).
In conclusion, the study’s assessment and mapping of RS provide valuable information for integrating into national sectoral policies, benefiting the planning and management of development activities within the LHB. The integrated assessment of RS is crucial for planning and implementing individual projects, collecting reliable data, and understanding the socio-cultural realities of communities and ecosystems (Bedilu & Teshome, 2024; MEA, 2005; Plieninger et al., 2013; Ryan, 2011). Including different stakeholder groups in ecosystem service assessment and mapping captures diverse knowledge sources, human-environment relations, and value systems. Appropriate consideration of the study methodology and results will play a key role in sustainable development project implementation, securing the livelihood of community members and human well-being in Lake Hawassa Bain. Future studies should consider additional methodologies, such as modeling and valuation of prioritized RS to enhance the study’s output.
5. Limitatio
The study had several limitations, including a lack of similar studies in the area to compare, a limited number of stakeholders, inadequate facilities or shapefiles specific to the study area, and limited awareness of the integrated ecosystem service assessment approach. The study prioritized the ES based on the expert judgement and partly biasness may occur due to inherent biases of the expert and difficulties in including large number of experts. However, the study applied available secondary data to keep the quality information which could reduce the high dependency on professional judgments. Carefully, each RS was explained to the experts to prioritize according to their benefits to human wellbeing that could help to reduce their biasness.
Conceptualization, B.A.R. and T.S.; methodology, B.A.R. and T.S.; formal analysis, B.A.R. and T.S.; investigation, B.A.R. and T.S.; resources, B.A.R. and T.S.; data curation, B.A.R. and T.S.; writing—original draft preparation, B.A.R. and T.S.; writing—review and editing, B.A.R. and T.S. All authors have read and agreed to the published version of the manuscript.
The data used to support the research findings are available from the corresponding author upon request.
Special thanks to Addis Ababa University and Kiel University for providing the necessary working environment, materials used for the fieldwork, and funding support.
The authors declare no conflict of interests.
Figure A1. Conceptual framework of interactions between biodiversity, ES, human well-being, and drivers of change
Sources: MEA, 2005
Figure A2. Linkages between ES and human well-being
Source: MEA, 2005
Figure A3. Conceptual model showing relations of ecosystem functions, services, and benefits
Sources: Burkhard et al., 2014
Figure A4. Pie chart for lake Hawassa basin LULC type and area coverage in percent (2007)
Source: MoWE, 2007; MoWE, 2010a; MoWE, 2010b
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