Impact of Land-Use / Land-Cover Change on Drinking Water Ecosystem Services in Wami River Basin, Tanzania

: Land-use / land-cover changes are considered the dominant form of anthropogenic pressure on the environment, causing changes in ecosystem service patterns and a ﬀ ecting water supply services. Using the spatial econometric technique, we analysed the impact of land-use / land-cover change on water ecosystem services for domestic use upstream and downstream of the Wami River Basin. The results in terms of land-use / land-cover classes during the study period (2011–2016) indicate that cultivated land showed maximum positive changes in both sub-catchments, while bushland and woodland showed maximum negative changes upstream and downstream. The results showed that bushland, woodland, cultivated land, and grassland were signiﬁcantly correlated with water point characteristics in both sub-catchments. For functionality characteristics, a signiﬁcant e ﬀ ect was observed in bushland and grassland upstream and downstream, respectively, while su ﬃ cient water was found in woodland upstream and grassland downstream. Moreover, bushland was observed to have a signiﬁcant number of water points with poor quality of water upstream, and a substantial number of water points with good quality of water were found in grassland downstream. We found that all measured land-use / land-cover changes and water point characteristic correlations were statistically signiﬁcant; therefore, we concluded that land-use / land-cover change a ﬀ ects the water ecosystem in the basin. These results could facilitate decision-making and development of related policies and might support ﬁnding sustainable strategies for water ecosystem services for domestic use.


Introduction
In recent years, substantial changes in land-use/land-cover (LU/LC) have taken place due to human activities [1,2]. LU/LC change linked to human factors, such as agricultural loss, overexploitation of forests, and urbanisation, has caused natural resource shortages, including widespread and permanent losses of biodiversity across the sphere [3,4]. Population growth continues to modify the landscape and natural lands through socio-ecological and socio-economic phenomena at extremely high rates, causing effects of LU/LC on ecosystem services [5][6][7]. However, despite the increasing awareness that human well-being strongly depends on natural ecosystems [8], many environments continue to

Study Area
The study location of the Wami River Basin (Figure 1) ranges between 5 • -7 • S and 36 • -39 • E, covering the semi-arid areas of central Tanzania. It covers 41,167 km 2 and is characterised by humid inland swamps linked to the Indian Ocean. The basin topographically ranges from 2-2370 m above sea level. Kinyasungwe, Mkondoa, and Wami are the three major sub-catchments forming the Wami River Catchment, receiving an average rainfall of 550-1000 mm per annum [43]. The lowlands are warm, whereas the highlands are cold. The Wami River Catchment is comprised of five basin draining zones-(i) upland plains, (ii) mountain torrents, (iii) inland plains, (iv) rejuvenated cascades, and (v) coastal plains-which characterise the topography of the study area. The river's path varies: a relatively straight and confined pattern is observed in elevated and mountainous areas, but a meandering system dominates the coastal and inland plains, interfered with in few areas by minor cascades at certain gradient levels [44]. On the other hand, a cascade zone can be seen near Wami at Mandera, upstream from the bridge crossing on the Chalinze-Segera highway [45].
Resources 2020, 9, x FOR PEER REVIEW 3 of 17 warm, whereas the highlands are cold. The Wami River Catchment is comprised of five basin draining zones-(i) upland plains, (ii) mountain torrents, (iii) inland plains, (iv) rejuvenated cascades, and (v) coastal plains-which characterise the topography of the study area. The river's path varies: a relatively straight and confined pattern is observed in elevated and mountainous areas, but a meandering system dominates the coastal and inland plains, interfered with in few areas by minor cascades at certain gradient levels [44]. On the other hand, a cascade zone can be seen near Wami at Mandera, upstream from the bridge crossing on the Chalinze-Segera highway [45].

Spatial Regression Analysis
In this study, the spatial regression method was used to examine the impact of LU/LC change on the water supply service accessibility at Kinyasungwe sub-catchment (upstream) and Wami subcatchment (downstream). LU/LC change results were adopted from Twisa and Buchroithner, 2019 ( Table 1). The water points data were extracted from the Water Point Mapping System (WPMS), which is managed by the Ministry of Water, Tanzania, on August 2016, and only water points from surface water sources were considered and information regarding three water point typologies (functionality, water quantity, and water quality) selected, including population data ( Table 2). The software packages Quantum Geographic Information System (QGIS 2.18) and GeoDa were employed at different stages of analysis.
Ordinary least squares (OLS) regression was used to examine the correlation between water points and LU/LC types surrounding the water points. However, the OLS regression model output can be biased if heterogeneity and autocorrelation effects are present [46]. Statistical tests, including the Breusch-Pagan (BP) test and Moran I test, were executed to detect the heterogeneity and autocorrelation effects of the spatial data. Based on the test results, the decision was made as to whether it was necessary to run the spatial lag and the spatial error regression models. The test results were statistically significant, indicating spatial autocorrelation in residues. Therefore, there was a need to run the spatial error and spatial lag regression models.
In this analysis, the water point data, including population information, were integrated with a LU/LC map, followed by spatial correlation analysis. The water point shapefile obtained was overlaid

Spatial Regression Analysis
In this study, the spatial regression method was used to examine the impact of LU/LC change on the water supply service accessibility at Kinyasungwe sub-catchment (upstream) and Wami sub-catchment (downstream). LU/LC change results were adopted from Twisa and Buchroithner, 2019 ( Table 1). The water points data were extracted from the Water Point Mapping System (WPMS), which is managed by the Ministry of Water, Tanzania, on August 2016, and only water points from surface water sources were considered and information regarding three water point typologies (functionality, water quantity, and water quality) selected, including population data ( Table 2). The software packages Quantum Geographic Information System (QGIS 2.18) and GeoDa were employed at different stages of analysis.
Ordinary least squares (OLS) regression was used to examine the correlation between water points and LU/LC types surrounding the water points. However, the OLS regression model output can be biased if heterogeneity and autocorrelation effects are present [46]. Statistical tests, including the Breusch-Pagan (BP) test and Moran I test, were executed to detect the heterogeneity and autocorrelation effects of the spatial data. Based on the test results, the decision was made as to whether it was necessary to run the spatial lag and the spatial error regression models. The test results were statistically significant, indicating spatial autocorrelation in residues. Therefore, there was a need to run the spatial error and spatial lag regression models.

Bushland
Mainly comprised of plants that are multi-stemmed from a single root base.

Woodland
An assemblage of trees with canopy ranging from 20% to 80% but which may, on rare occasions, be closed entirely.

Swamp
The low-lying, uncultivated ground where water collects; a bog or marsh.

Cultivated land
Crop fields and fallow lands. Settlement area Residential, commercial, industry, transportation, roads, mixed urban. Grassland Mainly composed of grass.

Forest
The continuous stand of trees, many of which may attain a height of 50 m including natural forest, mangrove and plantation forest.

Water
River, open water, lakes, ponds and reservoirs.

Open land
The land area of exposed soil and barren area influenced by a human. Airfield Area of the plot set aside for the take-off, landing and maintenance of aircraft. Table 2. Description of typologies in the water point mapping system.

Functional
Reference to the functionality of a water point indicating that it yields water regularly and is used daily Non-functional Reference to the functionality of a water point indicating that it does not yield water for any reason (hardware problem, dry source, or poor management) Sufficient A dimensionless quantity to which no physical dimension is applicable and where the amount of water satisfies human needs (based on user perspective) Insufficient A dimensionless quantity to which no physical dimension is applicable and where the amount of water does not satisfy human needs Dry Waterpoint characterized by an absence of water Good water quality A qualitative statement on the condition of water relative to the desires of human needs based on user perspective, referred to as clear water which does not taste salty Poor water quality A qualitative statement on the condition of water relative to the desires of human needs based on user perspective, referred to as salty, coloured, and/or fluoride water In this analysis, the water point data, including population information, were integrated with a LU/LC map, followed by spatial correlation analysis. The water point shapefile obtained was overlaid with the 2011 and 2016 LU/LC cover maps. The 400 m (according to Ministry of Water, Tanzania) buffers were generated using the buffer(s) options, followed by intersection analysis between LU/LC and water points at the 400 m buffer zone using the intersection option. This process integrated the water points and LU/LC in a single shapefile, and later imported it for spatial regression analysis between the populations served by the water points and LULC.

Accuracy Assessment
Accuracy assessment classifications were undertaken for both sub-catchments, and the producer accuracy (PA), user accuracy (UA), overall accuracy (OA), and the Kappa coefficients (KC) for 2011 and 2016 are displayed in Table 3. The overall accuracy ranged from 91% to 98%, with Kappa indices ranging from 0.76 to 0.95. According to Mango [47], this overall accuracy is acceptable; therefore, this suggests that the LU/LC category accuracy is within the satisfactory threshold. These results provide a significant verification for the subsequent analysis of LU/LC changes.  Table 4 and Figure 2. Ten major LU/LC classes were identified: bushland, swamp, woodland, cultivated land, water, settlement area, forest, grassland, airfield, and open land. As revealed in Table 4   Significant increases in cultivated land were observed during the study period (2011-2016), as shown in Figure 3. Cultivated land showed the highest increases in area coverage in the 2011-2016 change period, of about 552.91 km 2 (3.29%). Additionally, growth in settlement area was observed, at about 60.77 km 2 (0.36%) in the 2011-2016 change period. A small increase in water (11.08 km 2 /0.07%) was observed in the study period. On the other hand, many other LU/LC categories revealed a decreasing trend. Bushland and grassland significantly decreased by 326.7 km 2 (1.95%) and 203.43 km 2 (1.21%), respectively. Swamp and forest showed a slight decrease during the study period of 21.72 km 2 (0.13%) and 7.69 km 2 (0.05%), respectively. Woodland decreased by 65.23 km 2 (0.39%) during the study period. Significant increases in cultivated land were observed during the study period (2011-2016), as shown in Figure 3. Cultivated land showed the highest increases in area coverage in the 2011-2016 change period, of about 552.91 km 2 (3.29%). Additionally, growth in settlement area was observed, at about 60.77 km 2 (0.36%) in the 2011-2016 change period. A small increase in water (11.08 km 2 /0.07%) was observed in the study period. On the other hand, many other LU/LC categories revealed a decreasing trend. Bushland and grassland significantly decreased by 326.7 km 2 (1.95%) and 203.43 km 2 (1.21%), respectively. Swamp and forest showed a slight decrease during the study period of 21.72 km 2 (0.13%) and 7.69 km 2 (0.05%), respectively. Woodland decreased by 65.23 km 2 (0.39%) during the study period. Significant increases in cultivated land were observed during the study period (2011-2016), as shown in Figure 3. Cultivated land showed the highest increases in area coverage in the 2011-2016 change period, of about 552.91 km 2 (3.29%). Additionally, growth in settlement area was observed, at about 60.77 km 2 (0.36%) in the 2011-2016 change period. A small increase in water (11.08 km 2 /0.07%) was observed in the study period. On the other hand, many other LU/LC categories revealed a decreasing trend. Bushland and grassland significantly decreased by 326.7 km 2 (1.95%) and 203.43 km 2 (1.21%), respectively. Swamp and forest showed a slight decrease during the study period of 21.72 km 2 (0.13%) and 7.69 km 2 (0.05%), respectively. Woodland decreased by 65.23 km 2 (0.39%) during the study period.

Spatial Regression Analysis
Statistical tests, including the Breusch-Pagan (BP) test and Moran I test, were performed to identify the spatial autocorrelation effect and the heterogeneity of the spatial data at the Kinyasungwe sub-catchment. The test results were statistically significant, indicating spatial autocorrelation in residues, and hence a need to run the spatial lag and the spatial error regression models (Table 5). For the estimates of the spatial lag model at the Kinyasungwe sub-catchment, the number of populations served by each water point was the dependent variable, and the six LU/LC types surrounding the water points were the independent variables. At the Kinyasungwe sub-catchment, the number of populations served by water points had a mean value of approximately 219 and a standard deviation of 491.841, while the adjusted R-Squared value was 0.8599, which indicated that these variables explained the model. Table 6 shows the result of multiple linear regression using ordinary least squares (OLS) at the Kinyasungwe sub-catchment. The p-value is less than 0.05 in the OLS test for bushland, cultivated land, and grassland, which proves that the model is significant. Woodland, swamp, and settlement have a p-value of more than 0.05, which means they are not significant predictors for the model.

Impacts of Individual LU/LC Changes on Water Point Characteristics
The result of the performance of water point characteristics on individual LU/LCs is presented in Table 7 and Figure 4, while the spatial index analysis is shown in Figure 5. The spatial index is defined as follows: 1-highest coverage, 2-high coverage, 3-average coverage, 4-below coverage and 5-lowest coverage. The results showed that bushland, woodland, cultivated land, and grassland were significantly correlated with water point characteristics. For functionality characteristics, a significant effect was observed in bushland, whereby 58.62% of the water points were found to be in the non-functional category, followed by cultivated land (57.22%) and grassland (50.12%). However, fewer non-functionality water points were observed for woodland (16.67%), and this implies that 83.33% of the water points found were functional. For water point quantity, sufficient water was found in the points for woodland, with 76.67% of all water points have enough water. However, the rest of the LU/LCs were found to have many water points with insufficient water, including grassland (76, or 54%), cultivated land (63.92%), and bushland (62.07%). Furthermore, the results showed that bushland had a significant amount of water points of poor quality (58.62%), followed by cultivated land (46.52%) and grassland (37.04), while woodland had fewer water points of poor quality (13.33%).        Table 8 and Figure 6. Nine LU/LC class were identified: bushland, swamp, woodland, cultivated land, water, settlement area, forest, grassland, and open land. As shown in Table 8 Table 8 and Figure 6. Nine LU/LC class were identified: bushland, swamp, woodland, cultivated land, water, settlement area, forest, grassland, and open land. As shown in Table 8   Substantial increases in cultivated land were observed during the study period (2011-2016), as shown in Figure 7. Cultivated land showed the highest increase in area coverage in the 2011-2016 change period, of about 977.09 km 2 (6.77%). Growth in settlement area was also observed, at about 46.13 km 2 (0.33%) in the 2011-2016 change period. A slight increase in water (1.31 km 2 /0.01%) was observed during the study period. On the other hand, the other LU/LC categories showed a decreasing trend. Woodland and bushland showed significant decreases, by 697.90 km 2 (4.84%) and 184.92 km 2 (1.21%) respectively. Grassland, forest, and swamp showed a decreasing trend during the study period, by 100 km 2 (0.70%), 32.26 km 2 (0.22%), and 9.01 km 2 (0.06%), respectively.

Spatial Regression Analysis
Statistical tests, including the Breusch-Pagan (BP) test and Moran I test, were performed to identify the spatial autocorrelation effect and the heterogeneity of spatial data at the Wami subcatchment. The test results were statistically significant, indicating spatial autocorrelation in residues, and therefore a need to run the spatial lag and the spatial error regression models (Table 9). For the estimates of the spatial lag model at the Wami sub-catchment, the populations served by water points were the dependent variable and the six LU/LC types surrounding the water point were independent variables. At the Wami sub-catchmnet, the number of populations served by each water point had a mean value of approximately 4879 and standard deviation of 2764, while the adjusted Rsquared value was 0.02336, which indicated that these variables explained the model. Table 10 shows the result of multiple linear regression using ordinary least squares (OLS) at the Wami subcatchment. The p-value is less than 0.05 in the OLS test for bushland, woodland, cultivated land, and grassland, which proves that the model is significant. This table shows only those variables with pvalues lower than 0.05, which can be considered significant predictors for the OLS model. Swamp

Spatial Regression Analysis
Statistical tests, including the Breusch-Pagan (BP) test and Moran I test, were performed to identify the spatial autocorrelation effect and the heterogeneity of spatial data at the Wami sub-catchment. The test results were statistically significant, indicating spatial autocorrelation in residues, and therefore a need to run the spatial lag and the spatial error regression models (Table 9). For the estimates of the spatial lag model at the Wami sub-catchment, the populations served by water points were the dependent variable and the six LU/LC types surrounding the water point were independent variables. At the Wami sub-catchmnet, the number of populations served by each water point had a mean value of approximately 4879 and standard deviation of 2764, while the adjusted R-squared value was 0.02336, which indicated that these variables explained the model. Table 10 shows the result of multiple linear regression using ordinary least squares (OLS) at the Wami sub-catchment. The p-value is less than 0.05 in the OLS test for bushland, woodland, cultivated land, and grassland, which proves that the model is significant. This table shows only those variables with p-values lower than 0.05, which can be considered significant predictors for the OLS model. Swamp and settlement have p-values of more than 0.05; this means they are not significant predictors for the model.

Impacts of Individual LU/LC Changes on Water Point Characteristics
The results of the impact of water point characteristics on individual LU/LCs are presented in Table 11 and Figure 8, while the spatial index analysis is shown in Figure 9. The results showed that bushland, woodland, cultivated land, and grassland were statistically significantly correlated with water point characteristics. For functionality characteristics, a significant effect was observed for grassland, whereby 40.00% of the water points were found to be in the category of non-functionality, followed by cultivated land (37.00%) and woodland (25.00%). However, fewer non-functionality water points were observed for woodland (18.60%); this implies that 81.40% of water points were functional. For water point quantity, sufficient water was found in grassland, with 100% of all water points having enough water, followed by bushland (98.80%), woodland (84.40%), and cultivated land (75.40%). Moreover, the results showed that all LU/LCs were observed to have a significant number of water points with good quality (grassland (100%), bushland (98.80%), and woodland (90.60%)), except for cultivated land (46.52%).

Discussion
The evidence shows that community water supplies in rural areas in developing countries, such as in Africa, are mostly insufficient and unreliable given the rate of population growth [48][49][50]. Safe and clean drinking water is one of the most vital needs of life [51]. Presently, water shortages affect approximately 40% of people across the world, and by 2030, the overall demand for water is estimated to increase by 50% [52]. Challenges in the water sector are linked with population increase, climate change, and LU/LC change, resulting in shortages of various societal needs, including the supply of safe drinking water [53]. Global trends such as LU/LC change pose severe challenges to resource management [54], including of water. Such changes are disturbing in the light of growing insecurities and pressures around managing resources [55]. Increasing human activities across the world are causing significant modification of the land surface, which has a severe effect on the functioning of global systems [1]. The effects include land degradation [56], impacts on hydrological cycles [57][58][59], and a decline in ecosystem services [60], including water for domestic use. LU/LC change is a worldwide phenomenon, which will continue to cause increased demand for water resources and their associated ecosystem services, particularly for drinking water [60]. While several

Discussion
The evidence shows that community water supplies in rural areas in developing countries, such as in Africa, are mostly insufficient and unreliable given the rate of population growth [48][49][50]. Safe and clean drinking water is one of the most vital needs of life [51]. Presently, water shortages affect approximately 40% of people across the world, and by 2030, the overall demand for water is estimated to increase by 50% [52]. Challenges in the water sector are linked with population increase, climate change, and LU/LC change, resulting in shortages of various societal needs, including the supply of safe drinking water [53]. Global trends such as LU/LC change pose severe challenges to resource management [54], including of water. Such changes are disturbing in the light of growing insecurities and pressures around managing resources [55]. Increasing human activities across the world are causing significant modification of the land surface, which has a severe effect on the functioning of global systems [1]. The effects include land degradation [56], impacts on hydrological cycles [57][58][59], and a decline in ecosystem services [60], including water for domestic use. LU/LC change is a worldwide phenomenon, which will continue to cause increased demand for water resources and their associated ecosystem services, particularly for drinking water [60]. While several

Discussion
The evidence shows that community water supplies in rural areas in developing countries, such as in Africa, are mostly insufficient and unreliable given the rate of population growth [48][49][50]. Safe and clean drinking water is one of the most vital needs of life [51]. Presently, water shortages affect approximately 40% of people across the world, and by 2030, the overall demand for water is estimated to increase by 50% [52]. Challenges in the water sector are linked with population increase, climate change, and LU/LC change, resulting in shortages of various societal needs, including the supply of safe drinking water [53]. Global trends such as LU/LC change pose severe challenges to resource management [54], including of water. Such changes are disturbing in the light of growing insecurities and pressures around managing resources [55]. Increasing human activities across the world are causing significant modification of the land surface, which has a severe effect on the functioning of global systems [1]. The effects include land degradation [56], impacts on hydrological cycles [57][58][59], and a decline in ecosystem services [60], including water for domestic use. LU/LC change is a worldwide phenomenon, which will continue to cause increased demand for water resources and their associated ecosystem services, particularly for drinking water [60]. While several researchers have observed how water resources are becoming strained due to LU/LC change, none have researched how water ecosystem services are affected.
Using the spatial econometric technique, we analysed the impact of LU/LC changes on water ecosystem services for domestic use in the Wami River Basin. The LU/LC changes both upstream and downstream of the basin have already been presented. The findings during the study period (2011-2016) on different classes revealed that cultivated land showed maximum positive changes in both sub-catchments, while bushland and woodland showed maximum negative changes in the Kinyasungwe sub-catchment and Wami sub-catchment, respectively. The results showed that bushland, woodland, cultivated land, and grassland were significantly correlated with water point characteristics in both sub-catchments. For functionality characteristics, a significant effect was observed for bushland and grassland in the Kinyasungwe sub-catchment and Wami sub-catchment, respectively, while sufficient water was found at the water points for woodland (Kinyasungwe sub-catchment) and grassland (Wami sub-catchment). Furthermore, the results showed that bushland had a significant amount of water points of poor quality at the Kinyasungwe sub-catchment, while a significant number of water points of good quality were found in grassland of the Wami sub-catchment.
Population growth and economic development have led to changes in LU/LC worldwide, while ecosystems services are continuously threatened by human activities and stand to be further affected [2,61,62]. Resulting changes can have positive consequences such as increased vulnerability, resulting in a decreased supply of ecosystem services including water availability and quality, especially in arid and semiarid regions [61]. With the rapid expansion of LU/LC, ecosystem services for drinking water will continue to be affected [63,64]. It is critical to research the typologies of water points and role of LU/LC changes in influencing water supply services [65,66]. Numerous concerns exist today in areas where demands on land and water ecosystem services can be high due to natural degradation from agriculture and settlement expansion [67]. The loss of ecosystem services such as drinking water due to natural resource reduction are not native phenomena, but rather a collective universal issue [68]. A number of studies have been performed worldwide showing that natural resources are decreasing, leading to losses of ecosystem services; namely, the supporting, regulating, and provisioning of services [57,69]. In future, the loss of ecosystem services due to unforeseen LU/LC change is expected to continue, especially in poor countries, and this will affect land and water resources, which provide a lot of ecosystem services [70].
Water shortages and pollution are all signs of stress, especially in rural areas which have difficult interactions with the natural water cycle [71]. To reduce the damage to ecosystem services for drinking water around the basin, it is critical to identify land uses which are highly vulnerable to human activities [72]. The relative influence of the different LU/LC changes on water provisioning services and water supply is based on the LU/LC patterns affecting annual water available for drinking [63,73]. The impacts of cumulative pressures on ecosystems may not be observed for many years, until threshold point is reached, which activates rapid or permanent changes [74]. This will impact the supply of ecosystem services that are essential for human well-beings including provision of drinking water [75]. In order to more effectively manage ecosystem services, there is a need to understand how incremental impacts on biodiversity and ecosystems affect the goods and services they provide [76]. In this context, efficient and protection-oriented plans and policies should be adopted for sustainable land and water resource development in the basin.

Conclusions
Our aim in this study was to analyse the impacts of LU/LC change on water ecosystem services for domestic use in the Wami River Basin. We found that the LU/LC changes and water point characteristic correlations are all statistically significant. We have also shown that different LU/LCs can affect drinking water services, with a likely drop in service coverage. Our study confirms the influence of LU/LC change on water point functionality, water quantity and water quality, and, therefore, we conclude that LU/LC change affects water ecosystems for drinking water in the basin. These findings point to an inherent reduction of natural resources, resulting from increasing agriculture and urbanization, and explain natural resource decreases in the Wami River Basin. Hence, they support initiation of dialogue on policies at the national level that could emphasise either land-use and water resource choices, or motivations that could potentially be used by the community regarding natural resource protection. A very strong argument exists in these cases to develop a nexus of land and water resource systems for environmental protection, including decreasing or preventing impacts. However, such an act may be challenged if the relationships between land, water and other sectors have not been clearly determined due to a lack of data. This critical problem alarms stakeholders in land and water resources in terms of the implementation of a resource nexus system that takes other sectors into account. The demand for sustainability, accountability, and incentives for rural water supply services may evidently arise. For example, compensation schemes and data on the water supply can be developed if a well-defined link can be established between land use management and the water supply, both at economic and hydrological intensities. Furthermore, for the sustainability of rural water supply services, we suggest numerous policy effects to optimize resource management in the basin. First, the authority should make use of the Water Point Mapping System to establish inter-sectorial cooperation mechanisms, reinforce temporal and spatial data availability in the basin, and build specific standard bases for services. This will help to increase water ecosystem service benefits, while reducing the spatial and temporal effects of LU/LC change and other factors. Second, other factors such as operation and maintenance, technology, financing, and management of water points determine the standards of the services. Thus, construction of new water points must be based on a researched evaluation of suitability, as well as the management capacity of an area. At the same time, proper land management in the basin could prevent impacts on the environmental ecosystem and reduce challenges to water ecosystem services.
Author Contributions: The four authors contributed in a substantial way to the manuscript. All authors discussed the structure of the manuscript and read and approved the submitted manuscript.
Funding: This research was funded by the Ministry of Water and the United Republic of Tanzania, and the APC was funded by Dresden University of Technology (TU Dresden).