Impact of land-use/cover change on water quality in the Mindu Dam drainage, Tanzania

Land use/cover change is rapidly increasing and has adverse effects and implications at local, regional and global environmental scales (Mzuza et al. 2019). In developing countries, the rapid land-use/cover change affects resources such as forests, water quality and quantity, land, soil and vegetation (Twisa & Buchroithner 2019). Increased anthropogenic activities, urbanization, land demand and changing technologies cause land-use/cover change worldwide (Panwar & Malik 2017). Population growth and land development trigger land-use/cover changes, mainly through converting forests into built-up and agricultural land (Dube et al. 2014). Further, the struggles to feed the earth's population increase the demand for farmland and fertilizer applications (Vitousek et al. 2009). This results in severe deterioration of water quality in most freshwater bodies and presents a risk to human health, biodiversity and food productivity (Ramadas & Samantaray 2018).

Several studies have shown a significant correlation between land-use/cover change and parameters of water quality at a catchment scale (Du Plessis et al. 2014; Kibena et al. 2014; Teixeira et al. 2014; Namugize et al. 2018). Land use/cover change is of primary concern in understanding the interactions of human activities with global environmental change (Cheruto et al. 2016). It is a fundamental component in modern strategies for managing and monitoring natural resources (Kumari et al. 2014) while assessing environmental change at various spatial-temporal scales (Lambin 1997). Water pollution caused by land-use/cover is one of the most pressing concerns facing the world in developed and developing countries (Chaudhry & Malik 2017). Deterioration of freshwater quality suggests the collective outcomes of natural courses and the changes of land-use/cover (Kazi et al. 2009).

Land use/cover change results in soil erosion and sedimentation, seriously affecting water quality of small freshwater reservoirs, such as Mindu Dam (Natkhin et al. 2015). Understanding the correlation between land-use/cover and water quality parameters is vital for future water quality management (Rajib et al. 2016). Viewing the earth from space allows one to study the impact of land-use/cover change on water quality (Cheruto et al. 2016; Yang et al. 2022).

Several researchers have used techniques such as geospatial modelling, regression modelling and time series analysis to identify the spatial–temporal relationships between water quality changes and different anthropogenic activities (Panwar & Malik 2017; Sagan et al. 2020; Tahiru et al. 2020). Furthermore, various models have been established to forecast and simulate LULC change, including artificial neural networks, statistical analysis, cellular automata (CA) and Markov chain (Subedi et al. 2013). Several studies indicate that combining CA and the Markov model has advantages in studying land use changes (Palinkas et al. 2015). When combined with Remote Sensing (RS) and Geographical Information System (GIS), the CA–Markov model becomes a powerful tool for simulating spatial LULC change (Li & Reynolds 1997; Myint & Wang 2006; Guan et al. 2011; Riccioli et al. 2013; Roose & Hietala 2018). Hence, this study used RS and GIS techniques to analyse and predict land-use/cover patterns around the Mindu Dam catchment area and their effects on water quality. The findings will help water resource managers and decision-makers to take adaptive measures to ensure sustainable water quality development. Furthermore, they will provide the basic scientific knowledge to aid decision-making and future environmental protection in the area.

The Mindu Dam is the main source of freshwater provisions in the municipal and peri-urban communities of Morogoro (Mdegela et al. 2009). The main socioeconomic activities in the catchment include crop cultivation, fishing and small-scale mining and settlement development. These anthropogenic pressures result in nutrient enrichment from agricultural runoff and industrial wastewater discharge, affecting water quality (Mdegela et al. 2013). Analysing and predicting land-use/cover change around the Mindu Dam is important for natural and socioeconomic development on spatial and temporal scales (Kamusoko et al. 2011; Dube et al. 2014).

Clouds-free Landsat satellite images between 1990 and 2020 were used to evaluate land-use/cover change in the study area (Table 1). The satellite images were accessed from the United States Geological Survey – USGS website (https://glovis.usgs.gov/). The images were classified using the hybrid classification method, which combines both commonly used supervised (Maximum Likelihood Classification – MLC) and unsupervised (Iterative Self-Organizing Data Analysis – ISODATA) methods (Sun et al. 2013; Li et al. 2014; Kumar et al. 2020; Rozario & Gomes 2021). The classification was initially carried out using the ISODATA method. In this case, a maximum of 32 land-use/cover classes were formulated. The formulated classes were visually interpreted based on ground truthing data, base map and Google Earth images. Similar classes were combined and recorded into major land-use/cover classes established during ground truthing. Then, the ISODATA output was included in MLC. In this case, a classification scheme major from CCI Global was adopted. The major land cover classes included in the MLC were; forest, woodland, bushland, grassland, waterbodies, cultivated land, built-up areas and bare land. The accuracy was evaluated with both visual judgement and confusion matrix. Change detection was conducted through overlay (intersection) techniques according to Kashaigili & Majaliwa (2010).

To better simulate temporal and spatial patterns of land-use/cover changes in quantity and space, the combination of cellular automata and Markov Chain (CA–Markov) was developed using IDRISI Selva 17.0 software. It involved two main stages: calculating conversion probability using Markov chain analysis and spatial specification of land-use/cover coverage simulated based on CA spatial operator and multi-criteria evaluation (MCE).

For model validation, the simulated land-use/cover map for 2020 was compared with the actual satellite-derived land-use/cover map based on the Kappa statistics. Then, the standard Kappa index was used to check whether the model is valid (usually, the Kappa index for an accurate model is >70%). Using the VALIDATE tool, IDRISI gave the Kstandard of 0.80, Kno of 0.84, Klocation of 0.83 and Kstratum of 0.83, all above 0.7.

The samples for water quality analysis were collected in triplicate (at surface, 1 and 2 m depth) from well-distributed 30 sampling points within the dam (APHA 2012). The location was recorded using handheld GPS. The water samples for measuring Chl-a were collected in amber bottles, stored in the dark (to prevent photodegradation), frozen at −4 °C and transported to a laboratory until further processing. The integrated sample of 500 mL was prepared, and 200 mL was filtered through glass microfibre filters grade C GF/C 47 mm (Whatman™) filter paper and stored at 4 °C in the dark for Chl-a extraction. Furthermore, water samples were collected for the analysis of temperature, pH, nitrate and phosphorus as per the Standard Methods for the Examination of Water and Wastewater (APHA 2012). Following collection, the samples for nutrient analysis were preserved as per APHA (2012) and transported to the laboratory for analysis. The cadmium reduction method and ascorbic acid methods were used for the analysis of nitrates (⁠⁠) and phosphates (⁠⁠), respectively, with a DR6000 (Hach Spectrophotometer) (APHA 2012). Physicochemical parameters (temperature and pH) were analysed in triplicates onsite by using a portable meter (YSI 556 MPS, USA). The chlorophyll-a concentrations were determined with a spectrophotometer, and pigment extraction was conducted in subdued light to avoid degradation. Pigment extraction from the GF filters was carried out using 90% analytical grade acetone whereby filters were placed in a test tube for extraction using 10 ml acetone overnight. The mixture was macerated at 500 rpm for 1 min at 4 °C. After centrifugation, the supernatant was immediately used for pigment quantification. Absorbance was measured at 400–750 nm using spectrophotometer (UV-1800PC, China).

Water quality variables for the past year (1990–2010) were accessed from the Water Institute (Dar es Salaam).

The Pearson correlation analyses were performed using XLSTAT. Pearson correlation coefficient ‘r’ was considered significant at p < 0.05.

Furthermore, the pattern of land-use/cover changes during the studied period (1990–2020) indicates a general decrease in woodland, forest, bushland, while an increase was observed on cultivated land, grassland, bare land and built-up area (Table 3). This is probably due to the intensification of human activities on natural resources. The negative annual rate increase change between 1990 and 2020 was detected in woodland, forest and bushland, while positive annual increase change was detected in cultivated land, grassland, built-up area and bare land (Table 4). This shows the transition of natural resources to agricultural activities and human settlements.

Further land-use/cover change analysis was performed by observing the areas changed with their corresponding percentages based on the transition matrix cross-tabulation from 1990 to 2020 (Table 5). This shows a detailed image of the plots of land that were transformed from one class to another. The highest conversion (Table 5) was observed in grassland, as almost 43.15% of the total area was converted to woodland. The rest was converted to bushland (30.83%), forest (12.16%), cultivated land (10.57%) and a total of (0.24%) to water, built-up area and bare land. Most of the forest was converted to woodland (10%), while more significant portion of cultivated land was converted to bushland (39.45%). During the study duration (1990–2020), 91.40% of water remained intact, followed by forest, woodland, bushland, cultivated land, built-up area, bare land and grassland. The remaining forest area decreased to 67.06% in the period 2000–2010. Forest loss during this period was likely due to illegal charcoal production and subsistence farming. The efforts to recover the lost forest were observed in 2010–2020, where 75.17% of the forest area remained intact. Although the forest did not change much, almost 24.99% was gained from woodland, followed by bushland 20.18%, grassland (12.16), water (6.05%), bare land (3.48%) and cultivated land (3.24%). The change speed of one land-use/cover between 1990 and 2020 was relatively slow, but the highest conversion was experienced in the grassland, with 10% converted to cultivated land. The transformation was probably due to the higher population. People tend to search for cheaper land for accommodation and farming. The results for the study area from 1990 to 2020 on different classes of land-use/cover shows that woodland and bushland experienced the highest conversions to grassland and cultivated land. Similar trends have been reported by Hassan et al. (2016), whereby agricultural increase converted bushland and woodland into cultivated land, and the abandoned former farms became grasslands.

Each pixel conditional probability matrix to belong to a specified class in 2040 from 2020 is expressed in Table 6 and Figure 3. Thus, the transitional probability matrix is presented cartographically in these maps. During 2020 and projected 2040, 13% of bare land remained unchanged, followed by 16% grassland, 27% for both woodland and bushland, 49% for the forest, 50% of built-up area, 51% of cultivated land and 78% of the water. These results indicate that bare land and grassland will be most affected, with the probability that 43% of bare land will be converted to bushland while 59% will be converted to cultivated land. The projection results showed that the built-up area, cultivated land and water would all remain above 50% of their current land-use/cover, while the most significant share for cultivated land will be gained from grassland

The extent of land-use/cover types projected in 2030 and 2040 is shown in Table 7 and predicted maps in Figure 4. Projection of land cover for the study area for 2030 and 2040. Land use/cover of the year 2030 indicated that the area would be covered by cultivated land (46.20%), followed by bushland, grassland, woodland and forest and built-up area, bare land and water. Additionally, the area will be covered by cultivated land (47.80%), grassland, bushland, woodland, forest, built-up area, bare land and water by 2040. Forest, woodland, bushland and water are predicted to experience net losses by 2040, whereas grassland, cultivated land, built-up area and bare land are predicted to experience net gains. The land cover and use are geared at expanding the amount of cultivated land. The increase of cultivation land is expected to be due to the pressure from the growing population that leads to the expansion of land for agricultural activities.

The mean chlorophyll-a concentration of 30 samples was (83.277 μg/L) and a relatively high standard deviation (46.30582 μg/L) was recorded, which indicated the spatial variability of chlorophyll-a concentration (Table 8). The highest concentration (140.181 μg/L) was recorded at point S11. The mean concentrations of phosphate, nitrate, pH and temperature were 0.82 ± 1.35 mg/L, 2.63 ± 1.28 mg/L, 7.87 ± 0.35 and 27.96 ± 0.95, respectively. The high concentration of 7.851 mg/L was measured at sampling point S24 for phosphate and 7.618 mg/L for nitrate at sampling point S30.

Table 9 presents the results of correlation analysis between land-use/cover change and the average values of the water quality parameters. The patterns revealed that chlorophyll-a (Chl-a) was significantly and negatively correlated to change in forest, woodland and bushland but positively correlated to change in cultivated land, built-up and bare land. Phosphate (P) significantly and negatively correlated to bushland but positively correlated with change in cultivated land, built-up and bare land. Nitrate (N) was significantly and negatively correlated to change in forest, woodland and bushland but positively correlated with change in grassland, cultivated land, built-up and bare land. pH significantly and negatively correlated to change in forest and woodland but positively correlated with change in cultivated land, built-up and bare land. Similarly, water temperature significantly and negatively correlated to change in forest and woodland but positively correlated with change in cultivated land, built-up and bare land.

The strong correlation between cultivated land and nitrate, phosphate and chlorophyll-a concentration might be due to an increase in agricultural land which intensify the use of chemical fertilizers and pesticides that results in water quality deterioration. These findings are supported by Ranjan & Kumari (2018), that intensive agricultural practices negatively affect water quality. The quality of water is also affected by human interference related to urbanization and land development. The findings of this study reveal that the areas for cultivated land and the built-up area increase all the time, including urbanization and industries, which might have been the leading cause of water quality degradation in the Mindu Dam. The findings agree with that of Azyana et al. (2012) that developed lands were the best indicator for predicting water quality degradation. Water resource planning essentially resolves into three issues: the extent of available water resources in terms of quantity and quality, the future requirements of water for various purposes, and how these can be met. This calls for action on what causes land-use/cover change, especially the contribution by human activities and provides a legal framework to guide the activities to ensure sustainable use of available land.

This study aimed to analyse and predict land-use/cover change and its effects on water quality parameters in Mindu Dam. The study revealed that cultivated land and built-up areas activities are the primary sources of water quality deterioration in the study area. An increase in population and land development creates demand for water resources, increasing stress on the aquatic ecosystem. The buffer of natural vegetation surrounding Mindu Dam is insufficient to moderate water quality degradation from agricultural runoff in the long term. Land use/cover change is an environmental problem that threatens the dam and social and economic crisis. Increasing population growth and land development contribute to land-use/cover changes. This results in severe deterioration of water quality in most freshwater bodies due to pollution and consequent eutrophication. This critical problem alarms water managers in terms of implementing a water quality system that considers other sectors. For example, data on the water quality can be developed if a well-defined link can be established between land use management and the water quality, both at the catchment and hydrological intensities.

Furthermore, for the sustainability of Mindu Dam, we suggest numerous policy effects to optimize the management. There is a need to develop a nexus of land-use/cover and water quality for monitoring, including preventing pollution. However, the success of such efforts will rely on coordinated actions by all stakeholders from different sectors. Therefore, mitigation and adaptation should be considered for water quality management of the dam, while land use management practices must be considered for sustainable resource management and water quality monitoring.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.