Water pollution caused by land-use/cover change is one of the most pressing problems facing both industrialized and developing countries. Freshwater quality deterioration suggests the collective outcomes of natural courses and alterations in land-use/cover. Understanding the correlation among land-use/cover and water quality parameters is vital for future water quality management. In this work, land-use/cover pattern and its effect on water quality in the Mindu Dam drainage area were analysed using Remote Sensing (RS) and Geographical Information System (GIS) techniques together with cellular automata–Markov model. The land-use/cover images between 1990 and 2020 were used to assess historical and spatial change of land-use/cover change, and projected for 2030 and 2040. We discovered that the dynamics of land use and land cover during the study period were significant. A strong correlation was revealed between changes in the land-use/cover and water quality parameters. Furthermore, a strong correlation exists between cultivated land and measured nutrient (nitrate and phosphate) and chlorophyll-a concentration. The natural vegetation buffer around Mindu Dam should be sufficient to prevent long-term water quality degradation from agricultural runoff in order to manage water quality sustainably. Therefore, land-use/cover management practices must be considered for sustainable resource management and water quality monitoring.

  • Land use land cover change.

  • Impact of land-use/cover change on water quality.

  • Projection of land-use/cover change.

  • Water quality sustainability.

  • Effect of anthropogenic activities on land cover change.

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.

Study area

The study was conducted in the Mindu Dam and its surrounds, which are situated at 6.82°S and 37.66°E in the Morogoro region (Figure 1). The Mindu Dam catchment is 303 km2. The dam collects water from the Mzinga, Lukulunge, Mugera and Mlali tributaries of the Ngerengere River and the Uluguru mountain ranges. Bimodal rainfall pattern characterizes the catchment, the long rains (March, April and May) and the short rains (September, October and November). The annual rainfall in the area is between 800 and 1,500 mm, with an average of 890 mm. In total, 80% of the freshwater used in Morogoro's urban and peri-urban areas comes from the Mindu Dam (Ngonyani & Nkotagu Hudson 2007; Mdegela et al. 2009).
Figure 1

The study area of Mindu Dam drainage.

Figure 1

The study area of Mindu Dam drainage.

Close modal

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).

Land use/cover change analysis

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).

Table 1

Detailed data on the Landsat images used in this study

YearSatelliteSensorPath/RowAcquisition dateCloud cover
1990 Landsat 5 TM(SAM) 167/65 15 July 1990 1% 
2000 Landsat 7 ETM(SAM) 167/65 07 July 2000 2% 
2010 Landsat 7 ETM(BUMPER) 167/65 10 July 2010 9% 
2020 Landsat 8 OLI 167/65 16 September 2017 2.42% 
YearSatelliteSensorPath/RowAcquisition dateCloud cover
1990 Landsat 5 TM(SAM) 167/65 15 July 1990 1% 
2000 Landsat 7 ETM(SAM) 167/65 07 July 2000 2% 
2010 Landsat 7 ETM(BUMPER) 167/65 10 July 2010 9% 
2020 Landsat 8 OLI 167/65 16 September 2017 2.42% 

Land use/cover prediction using CA–Markov

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).

The mathematical expression of the Markov model is presented in Equation (1):
formula
(1)
where S(t + 1) represents the status of LULC at a time (t + 1), Pij represents a transitional matrix in Equation (2):
formula
(2)
and . Where . are the land uses and represents the transition probability between any pair of land uses. From the matrix, the rows and columns represent historical and current LULC classes, respectively. Furthermore, the CA's mathematical expression is presented in Equation (3):
formula
(3)

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.

Determination of water quality

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 linear relationship between land-use/cover variables and water quality parameters

Pearson correlation analysis was performed to determine the correlation between water quality parameters and land-use/cover change for Mindu Dam. In case, the mean change percentage of land use area in the basin between 1990 through 2020 was correlated with corresponding mean water quality values to establish the significance of the relationship between land use and water quality parameters (Palinkas et al. 2015; Tahiru et al. 2020) using the following equation:
formula
(4)
where r is the correlation coefficient; is the values of the x-variables in a sample; is the mean of the values of the x-variables; is the values of the y-variables in a sample; and is the mean of the values of the y-variables.

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

Land use/cover change pattern

The trend of land-use/cover change from 1990 to 2020 based on eight classes extracted from Mindu Dam drainage is presented in Tables 2 and 3. Additionally, the spatial representation of land-use/cover types from 1990 to 2020 is shown in Figures 24. In the year 1990, the pattern of land-use/cover calculated as the percentage of the total area studied was dominated by woodland, covering 40.25% of the total studied area, followed by bushland, forest, cultivated land, grassland, water, built-up area and bare land (Table 2). Moreover, trend changes were observed for all land-use/cover in 2000, 2010 and 2020, except for water, with the smallest changes. In 2020, the observed land-use/cover pattern was dominated by cultivated land (44.58%) followed by bushland, grassland, woodland, forest, water, built-up area and bare land (Table 2). This might be the result of population growth in the area. An increase in population increases food requirements to sustain livelihoods, resulting in land demand for cultivation. The findings agree with that of Mzuza et al. (2019); high population pressure led to increased conversion of forested land to cultivation land.
Table 2

Results of land-use/cover classification for 1990, 2000, 2010 and 2020 images

Year1990
2000
2010
2020
Land use/coverHa%Ha%Ha%Ha%
Forest 4,452 14.93 2,978 9.98 2,046 6.82 1,568 5.25 
Woodland 12,011 40.25 8,000 26.81 4,219 14.14 2,614 8.76 
Bushland 8,744 29.30 11,646 39.02 9,751 32.68 6,790 22.75 
Grassland 597 2.00 1,465 4.91 5,336 17.89 4,944 16.57 
Water 233 0.78 224 0.75 226 0.76 226 0.76 
Cultivated land 3,700 12.40 5,408 18.12 8,074 27.06 13,303 44.58 
Built-up area 79 0.26 93 0.31 124 0.42 204 0.68 
Bare land 26 0.09 30 0.10 67 0.22 194 0.65 
Total 29,843 100 29,843 100 29,843 100 29,843 100 
Year1990
2000
2010
2020
Land use/coverHa%Ha%Ha%Ha%
Forest 4,452 14.93 2,978 9.98 2,046 6.82 1,568 5.25 
Woodland 12,011 40.25 8,000 26.81 4,219 14.14 2,614 8.76 
Bushland 8,744 29.30 11,646 39.02 9,751 32.68 6,790 22.75 
Grassland 597 2.00 1,465 4.91 5,336 17.89 4,944 16.57 
Water 233 0.78 224 0.75 226 0.76 226 0.76 
Cultivated land 3,700 12.40 5,408 18.12 8,074 27.06 13,303 44.58 
Built-up area 79 0.26 93 0.31 124 0.42 204 0.68 
Bare land 26 0.09 30 0.10 67 0.22 194 0.65 
Total 29,843 100 29,843 100 29,843 100 29,843 100 
Table 3

Results of the land-use/cover change from 1990 to 2020

Year (change)1990–2000
2000–2010
2010–2020
1990–2020
Land use/coverHa%Ha%Ha%Ha%
Forest −1,474 −4.95 −932 −3.16 −478 −1.57 −2,884 −9.68 
Woodland −4,011 −13.44 −3,781 −12.67 −1,605 −5.38 −9,397 −31.49 
Bushland 2,902 9.72 −1,895 −6.34 −2,961 −9.93 −1,954 −6.55 
Grassland 868 2.91 3,871 12.98 −392 −1.32 4,347 14.57 
Water −9 −0.03 0.01 −7 0.02 
Cultivated land 1,708 5.72 2,666 8.94 5,229 17.52 9,603 32.18 
Built-up area 14 0.05 31 0.11 80 0.26 125 0.42 
Bare land 0.01 37 0.12 127 0.43 168 0.56 
Year (change)1990–2000
2000–2010
2010–2020
1990–2020
Land use/coverHa%Ha%Ha%Ha%
Forest −1,474 −4.95 −932 −3.16 −478 −1.57 −2,884 −9.68 
Woodland −4,011 −13.44 −3,781 −12.67 −1,605 −5.38 −9,397 −31.49 
Bushland 2,902 9.72 −1,895 −6.34 −2,961 −9.93 −1,954 −6.55 
Grassland 868 2.91 3,871 12.98 −392 −1.32 4,347 14.57 
Water −9 −0.03 0.01 −7 0.02 
Cultivated land 1,708 5.72 2,666 8.94 5,229 17.52 9,603 32.18 
Built-up area 14 0.05 31 0.11 80 0.26 125 0.42 
Bare land 0.01 37 0.12 127 0.43 168 0.56 
Figure 2

Land use/cover maps for 1990, 2000, 2010 and 2020. (Data source: USGS, map prepared by authors of this study.)

Figure 2

Land use/cover maps for 1990, 2000, 2010 and 2020. (Data source: USGS, map prepared by authors of this study.)

Close modal
Figure 3

Markovian conditional probability of individual land-use/cover. (Data source: USGS, map prepared by authors of this study.)

Figure 3

Markovian conditional probability of individual land-use/cover. (Data source: USGS, map prepared by authors of this study.)

Close modal
Figure 4

Projected land-use/cover for the year 2030 and 2040. (Data source: USGS, map prepared by authors of this study.)

Figure 4

Projected land-use/cover for the year 2030 and 2040. (Data source: USGS, map prepared by authors of this study.)

Close modal

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.

Table 4

Results of land-use/cover annual rate change

Year1990–20002000–20102010–20201990–2020
Land use/cover(ha/year)(ha/year)(ha/year)(ha/year)
Forest −211 −93 −68 −125 
Woodland −573 −378 −229 −409 
Bushland 414 −189 −423 −85 
Grassland 124 387 −56 189 
Water −1 
Cultivated land 244 267 747 418 
Built-up area 11 
Bare land 18 
Year1990–20002000–20102010–20201990–2020
Land use/cover(ha/year)(ha/year)(ha/year)(ha/year)
Forest −211 −93 −68 −125 
Woodland −573 −378 −229 −409 
Bushland 414 −189 −423 −85 
Grassland 124 387 −56 189 
Water −1 
Cultivated land 244 267 747 418 
Built-up area 11 
Bare land 18 

Land use/cover transition matrix

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.

Table 5

Transition matrix showing land-use/cover change between 1990 and 2020

2020
Area (ha)FRWLBLGLWTCLBLTBL
1990 FR 1,378 156 16 
WL 653 1,483 353 53 61 
BL 1,370 3,296 1,412 120 577 
GL 601 2,133 1,524 151 522 
WT 14 207 
CL 431 4,849 5,249 266 2,462 36 
BLT 48 90 36 30 
BL 42 99 38 
Percentage (%) FR WL BL GL WT CL BLT BL 
1990 FR 88 10 
WL 24.99 56.74 13.49 2.02 0.12 2.34 0.14 0.16 
BL 20.18 48.54 20.80 1.77 0.04 8.49 0.08 0.09 
GL 12.16 43.15 30.83 3.05 0.14 10.57 0.06 0.04 
WT 6.05 1.51 0.72 0.00 91.40 0.32 0.00 0.00 
CL 3.24 36.45 39.45 2.00 0.03 18.51 0.27 0.05 
BLT 0.09 23.48 44.22 0.00 0.00 17.52 14.70 0.00 
BL 3.48 21.51 50.76 1.48 0.00 19.61 0.09 3.06 
2020
Area (ha)FRWLBLGLWTCLBLTBL
1990 FR 1,378 156 16 
WL 653 1,483 353 53 61 
BL 1,370 3,296 1,412 120 577 
GL 601 2,133 1,524 151 522 
WT 14 207 
CL 431 4,849 5,249 266 2,462 36 
BLT 48 90 36 30 
BL 42 99 38 
Percentage (%) FR WL BL GL WT CL BLT BL 
1990 FR 88 10 
WL 24.99 56.74 13.49 2.02 0.12 2.34 0.14 0.16 
BL 20.18 48.54 20.80 1.77 0.04 8.49 0.08 0.09 
GL 12.16 43.15 30.83 3.05 0.14 10.57 0.06 0.04 
WT 6.05 1.51 0.72 0.00 91.40 0.32 0.00 0.00 
CL 3.24 36.45 39.45 2.00 0.03 18.51 0.27 0.05 
BLT 0.09 23.48 44.22 0.00 0.00 17.52 14.70 0.00 
BL 3.48 21.51 50.76 1.48 0.00 19.61 0.09 3.06 

FR, Forest; WL, Woodland; BSL, Bushland; GL, Grassland; WT, Water; CL, Cultivated land; BLT, Built-up land; BL, Bare land.

Note: The bold numbers on the diagonal show the percentage land use/cover that remained unchanged from 1990 to 2020, while the other numbers show the percentage that are converted from one class to another.

Conditional probability matrix and land-use/cover pattern for predicted land-use/cover

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

Table 6

Transitional probability matrix for land use/cover change 2020 to 2040

Percentage Land-use/cover2040
FRWLBSLGLWTCLBLTBL
2020 FR 49 10 19 13 
WL 27 29 14 25 
BSL 27 18 46 
GL 19 16 59 
WT 78 
CL 18 24 51 
BLT 38 50 
BA 43 19 19 13 
Percentage Land-use/cover2040
FRWLBSLGLWTCLBLTBL
2020 FR 49 10 19 13 
WL 27 29 14 25 
BSL 27 18 46 
GL 19 16 59 
WT 78 
CL 18 24 51 
BLT 38 50 
BA 43 19 19 13 

FR, Forest; WL, Woodland; BSL, Bushland; GL, Grassland; WT, Water; CL, Cultivated land; BLT, Built-up land; BL, Bare land.Note: Bold numbers on the diagonal represent percentages of Land use/cover that remained unchanged from 2020 to 2040, whereas other numbers represent percentage that had changed from one class to another.

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.

Table 7

Areas of individual land-use/cover change in the projected years 2030 and 2040

Year2030
2040
2020–2030
2030–2040
2020–2040
Land use/coverHa%Ha%Ha%Ha%Ha%
Forest 1,102 3.69 640 2.15 −466 −1.56 −462 −1.55 −928 −3.11 
Woodland 2,162 7.25 1,709 5.73 −453 −1.51 −453 −1.52 −906 −3.03 
Bushland 6,393 21.37 5,982 20.00 −397 −1.38 −411 −1.38 −808 −2.75 
Grassland 5,718 19.17 6,495 21.78 774 2.60 777 2.61 1,551 5.21 
Water 189 0.63 153 0.51 −37 −0.12 −35 −0.12 −73 −0.24 
Cultivated land 13,778 46.20 14,257 47.80 475 1.62 479 1.61 954 3.23 
Built-up area 266 0.89 329 1.10 62 0.21 62 0.21 124 0.42 
Bare land 236 0.79 278 0.93 42 0.14 42 0.14 84 0.28 
Total 29,843 100 29,843 100       
Year2030
2040
2020–2030
2030–2040
2020–2040
Land use/coverHa%Ha%Ha%Ha%Ha%
Forest 1,102 3.69 640 2.15 −466 −1.56 −462 −1.55 −928 −3.11 
Woodland 2,162 7.25 1,709 5.73 −453 −1.51 −453 −1.52 −906 −3.03 
Bushland 6,393 21.37 5,982 20.00 −397 −1.38 −411 −1.38 −808 −2.75 
Grassland 5,718 19.17 6,495 21.78 774 2.60 777 2.61 1,551 5.21 
Water 189 0.63 153 0.51 −37 −0.12 −35 −0.12 −73 −0.24 
Cultivated land 13,778 46.20 14,257 47.80 475 1.62 479 1.61 954 3.23 
Built-up area 266 0.89 329 1.10 62 0.21 62 0.21 124 0.42 
Bare land 236 0.79 278 0.93 42 0.14 42 0.14 84 0.28 
Total 29,843 100 29,843 100       

Water quality parameters results

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 8

Results and statistics for pH, temperature, chlorophyll-a, phosphate and nitrate as of 2020

YearChlorophyll-a (mg/m3)Phosphate (mg/L)Nitrate (mg/L)pHTemp (°C)
1990 34.65 ± 1.45a 0.09 ± 0.01a 0.07 ± 0.01a 7.01 ± 0.05a 27.01 ± 0.11a 
2000 46.77 ± 7.82b 0.12 ± 0.02a 0.91 ± 0.11a 7.17 ± 0.13a 26.90 ± 0.25a 
2010 59.20 ± 15.67c 0.62 ± 0.01b 1.05 ± 0.17c 7.27 ± 0.22b 27.06 ± 0.24a 
2020 83.28 ± 46.31e 0.82 ± 1.35c 2.63 ± 1.28d 7.87 ± 0.35c 27.96 ± 0.95b 
YearChlorophyll-a (mg/m3)Phosphate (mg/L)Nitrate (mg/L)pHTemp (°C)
1990 34.65 ± 1.45a 0.09 ± 0.01a 0.07 ± 0.01a 7.01 ± 0.05a 27.01 ± 0.11a 
2000 46.77 ± 7.82b 0.12 ± 0.02a 0.91 ± 0.11a 7.17 ± 0.13a 26.90 ± 0.25a 
2010 59.20 ± 15.67c 0.62 ± 0.01b 1.05 ± 0.17c 7.27 ± 0.22b 27.06 ± 0.24a 
2020 83.28 ± 46.31e 0.82 ± 1.35c 2.63 ± 1.28d 7.87 ± 0.35c 27.96 ± 0.95b 

Values that do not share the same superscript in the same column are statistically different at p < 0.05.

Pearson correlation analysis

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.

Table 9

Pearson correlation coefficients between land-use/cover change and water quality parameters

VariableForestWoodlandBushlandGrasslandCultivated landBuilt-up areaBare land
Chl-a −0.62* −0.68* −0.65* 0.44 0.99** 0.99** 0.96* 
−0.46 −0.49 −0.62* 0.11 0.95* 0.98** 0.99* 
−0.69* −0.77* −0.62* 0.61* 0.97* 0.95* 0.88* 
pH −0.77* −0.83* −0.51 0.57 0.98* 0.95* 0.88* 
Temp −0.90* −0.91* −0.24 0.52 0.93* 0.88* 0.77* 
VariableForestWoodlandBushlandGrasslandCultivated landBuilt-up areaBare land
Chl-a −0.62* −0.68* −0.65* 0.44 0.99** 0.99** 0.96* 
−0.46 −0.49 −0.62* 0.11 0.95* 0.98** 0.99* 
−0.69* −0.77* −0.62* 0.61* 0.97* 0.95* 0.88* 
pH −0.77* −0.83* −0.51 0.57 0.98* 0.95* 0.88* 
Temp −0.90* −0.91* −0.24 0.52 0.93* 0.88* 0.77* 

*p < 0.05; **p < 0.01.

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.

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