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. 2026 Jan 6;16:4499. doi: 10.1038/s41598-025-34683-z

Impact of extreme weather events on faecal sludge management based on standardized precipitation index in Dar es Salaam

Anna Mremi 1,2,✉, Richard Kimwaga 1, Deogratias M M Mulungu 1, Fides Izdori 1
PMCID: PMC12864831  PMID: 41495438

Abstract

In developing countries, climate variability poses significant challenges to urban water resource management and faecal sludge management, particularly in flood- and drought-prone areas. Inadequate consideration of climate variability in faecal sludge management planning has led to service disruptions in flood- and drought-prone urban areas. This study integrates multi-timescale climate trend analysis with faecal sludge management challenges, providing novel insights into climate-resilient sanitation strategies. The extreme climate events and their implications on faecal sludge management were assessed by applying the Standardized Precipitation Index (SPI) at multiple timescales (SPI-12, SPI-6, and SPI-3). The Mann–Kendall test and Sen’s slope estimator were applied to analyse long-term trends and the magnitude of change at annual, seasonal, and monthly levels. Climate Hazards Group InfraRed Precipitation with Station rainfall data (1981–2021) from four stations were used, with validation conducted through Pearson correlation against observed data from Dar es Salaam International Airport. Results indicate increasing precipitation variability, with statistically significant positive trends in some seasons, particularly during the long rains. Extreme wet periods (2019–2020, SPI: 2.48–2.92) have increased flood risks, while severe droughts (2002–2003, SPI: -1.57 to -2.08) have exacerbated water scarcity, thereby impacting the reliability of faecal sludge management services. The Sinza River Catchment, a lowland urban area where over 99% of residents rely on onsite sanitation, remains highly vulnerable to climate extremes, causing pit latrine failures due to flooding and limited desludging options during droughts. Addressing these issues requires climate-resilient faecal sludge management strategies, including flood-resistant latrines, water-efficient sanitation technologies, and the integration of policy into urban resilience planning.

Keywords: Climate variability, Extreme events, Faecal sludge management, Standardized precipitation index, Sinza river catchment

Subject terms: Health occupations, Risk factors

Introduction

Climate variability intensifies current urban sanitation challenges, particularly in low-income and unplanned settlements1. Climate-induced drought intensification has resulted in prolonged periods of water scarcity, critically impacting water resources and severely affecting sanitation systems and community health in water-stressed regions2. For example, flooding in Dar es Salaam’s informal settlements has caused pit latrine overflows, contaminating water sources and disrupting faecal sludge management (FSM) services [3;4]. Climate variability also increases the frequency and intensity of floods, overwhelming water management infrastructures, contaminating water sources, and damaging sanitation facilities, thus posing significant risks to public health and exacerbating challenges in FSM, especially in low-lying urban areas3,4. The challenges posed by climate variability result in significant obstacles to urban water management, particularly in developing countries where resources for monitoring and adaptation are often limited5,6. Furthermore, extreme weather events, particularly floods and droughts, significantly impact FSM systems by causing latrine overflows9, sludge accumulation10, and environmental contamination11. For instance, the heavy rains of 2018 and 2019 in Dar es Salaam caused overflowing of pit latrines in flood-prone areas such as Tandale, contaminating drainage systems and increasing the risk of cholera outbreaks10,11. Drought has led to water shortages, especially during the dry season, hindering sludge removal since water is essential for dilution. As a result, residents have abandoned water-dependent systems like septic tanks and rely on dry facilities, particularly in schools and public areas12,13. To address such challenges, the Standardized Precipitation Index (SPI) has emerged as a crucial tool for characterizing meteorological drought and monitoring unusually wet or dry conditions2. The SPI was selected for its ability to assess precipitation variability across different timescales, making it suitable for both short- and long-term FSM planning12,13. Promoted by the World Meteorological Organization (WMO), the SPI is widely used in research and operational activities globally, offering valuable insights for National Meteorological and Hydrological Services (NMHSs)18. Its standardized nature allows for easier regional comparisons, whereas indices like the Rainfall Anomaly Index (RAI) focus on short-term deviations, and the Palmer Drought Severity Index (PDSI) is more suited for soil moisture assessment rather than for direct rainfall variability analysis2,15. The SPI is more commonly used in large-scale hydrological and drought studies. In smaller areas like the Sinza River Catchment, rainfall variability can be highly localized, making the use of high-resolution gridded rainfall data, such as Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), crucial. Studies have indicated that the SPI is more sensitive in detecting drought conditions compared to the RAI16.

The Sustainable Development Goal (SDG) 6.2 aims to achieve universal access to adequate sanitation and hygiene by 2030; therefore, understanding climate variability is very crucial1. This study addressed the question: “How do extreme precipitation events, as measured by SPI, impact FSM infrastructure and services in the Sinza River Catchment? It is being hypothesized that extreme wet and dry periods significantly disrupt FSM, with spatial variations across sub-catchments. Extreme weather events, such as droughts and floods, can severely impact water availability and sanitation infrastructure, potentially hindering progress towards SDG 6.22. In low-lying urban areas, concern about climate-induced challenges and their effects on FSM can exacerbate existing sanitation challenges, as shown in Fig. 1. This study aimed to analyse climate variability (1981–2021) through Standardized Precipitation Index (SPI) analysis of CHIRPS rainfall data, examining how extreme weather events affect FSM infrastructure and services in the Sinza River Catchment. Using GIS-based spatial analysis, the research identified vulnerability patterns across the catchment, hence recommend climate-resilient FSM strategies to enhance sustainability in both flood-prone and water-scarce areas. Other studies such as Sakijege et al. (2012) and Ahumuza (2014) addressed FSM infrastructure but did not quantitatively link failures to extreme rainfall variability, a gap this study sought to fill using SPI analysis.

Fig. 1.

Fig. 1

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A pit latrine submerged in floodwater, illustrating the susceptibility of onsite sanitation systems to flooding3.

Methodology

Study area

The Sinza River Catchment, as indicated in Fig. 2, is located in Ubungo District, Dar es Salaam, Tanzania. It covers an area of 21.93 km² and extends across 34 wards, with elevations ranging from sea level to 156 m high4. This catchment runs for approximately 18.28 km, flowing into the Msimbazi River and eventually reaches the Indian Ocean. The catchment has 83 sub-catchments and is fed by over 20 tributaries, including the Kiboko and Mzinga (Mtunduge) Rivers. The Sinza River Catchment was selected due to its high flood vulnerability, exacerbated by poor drainage and dense informal settlements, making it a representative case for studying climate impacts on FSM in rapidly urbanizing African cities5. The catchment is characterized by informal settlements, poor drainage, and reliance on onsite sanitation systems, making it a relevant case for studying FSM under climate variability. Previous studies have highlighted the recurrent flooding6 and water scarcity7 issues in this region, and how they disrupt FSM operations20,21 and increase public health risks8. According to RamaniHuria10, Kinondoni contains many wards affected by flooding; these include Magomeni, Mzimuni, Tandale, Ubungo, and wards on the coast, such as Msasani and Kawe. The area experiences a bimodal rainfall pattern, with long rains (Masika) from mid-March to May and short rains (Vuli) from October to December. It also has a dry season (Kiangazi) from June to September. The region maintains high temperatures year-round, with daily averages ranging from 26 °C to 35 °C. The annual rainfall varies, with a maximum of 2069.3 mm and a minimum of 31.8 mm11.

Fig. 2.

Fig. 2

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Sinza River Catchment, sub-catchments, and rain gauge stations base map © esri and its contributors.

Data collection

Rainfall data

This study used CHIRPS satellite daily rainfall data due to the scarcity of reliable ground-based data12. CHIRPS was selected for its long-term availability, high spatial resolution (4.8 km), and free access, covering the period 1981–2021 for four stations (Ubungo, Msimbazi, Kimara, and Mbezi), as indicated in Table 1. To validate CHIRPS data, observed rainfall data from the Dar es Salaam International Airport (DIA) station were used. The airport station, located approximately 10 km from the study area, experiences different rainfall patterns due to topographical differences and urban heat island effects13. However, CHIRPS may underestimate localized extreme rainfall events due to satellite-based estimates, a limitation mitigated by validation with DIA station data14. Therefore, this spatial disparity introduces potential uncertainties, particularly in analysing extreme events. CHIRPS data combines satellite imagery with ground station data and performs well in seasonal and monthly precipitation patterns4 but may not fully capture the intensity and spatial distribution of a short-duration, for example, high-intensity rainfall events that often trigger flash flooding in urban environments like the Sinza River Catchment.

Table 1.

Geographic coordinates of meteorological stations within the Sinza river catchment providing spatial reference points for the study.

Station code Name of the station Latitude Longitude
9,639,001 Msimbazi Mission 6°47’60.00"S 39°15’0.00"E
96,390,482 Ubungo Maji 6°46’48.00"S 39°12’0.00"E
CHIRPS Kimara (New) 6°46’51.84"S 39°10’26.54"E
CHIRPS Mbezi (New) 6°47’8.34"S 39° 8’49.79"E
-6.8 39.25
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Spatial data analysis

The Sinza River Catchment was delineated into 83 sub-catchments using a Digital Elevation Model (DEM) in ArcGIS 10.4, obtained from Open Topography (https://opentopography.org/). CHIRPS satellite rainfall data were utilized to analyse spatial precipitation variability, as it has been widely applied in studies across East Africa, including the Usangu, Msimbazi, and Ruvu catchments28,29. Spatial analysis employed Inverse Distance Weighting (IDW) interpolation in ArcGIS 10.4 to visualize SPI values across the study area, allowing for a continuous spatial distribution of rainfall variability. However, no spatial clustering test, such as Moran’s I, was conducted, as the study primarily aimed to identify general spatial rainfall trends rather than measure spatial autocorrelation. The interpolated SPI values were assessed across sub-catchments to explore spatial patterns and their implications for FSM vulnerability.

Data validation

A linear correlation analysis was conducted to assess the relationship between CHIRPS data and the observed data from the DIA station. This statistical method was employed to quantify the strength and direction of the linear relationship between the two datasets, providing confidence in the use of CHIRPS data for subsequent analyses. The data exhibited a strong positive correlation, demonstrating a high level of similarity between the datasets, as shown in Table 2. According to the analysis, a correlation coefficient (r) of 0.7 or higher signifies a strong correlation and acceptable reliability, and values between 0.5 and 0.7 indicate moderate reliability1,28. A Pearson correlation coefficient of 0.8876 (p < 0.05) between CHIRPS and DIA station data confirmed reliability, with r > 0.8 considered acceptable. Therefore, the correlation was significant (p < 0.05), with no adjustments needed due to high agreement between the datasets.

Table 2.

The pearson correlation of re-analysis data for CHIRPS at DIA14.

Pearson correlation coefficient(r)
Variables Annually Monthly Seasonally
Uncorrected CHIRPS & Observed 0.81 0.86 0.99
Corrected CHIRPS & Observed 0.83 0.86 0.99
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Standardized precipitation index (SPI) calculation

This study employed the Standardized Precipitation Index (SPI) at multiple timescales (3, 6, and 12 months) to comprehensively analyse precipitation variability in the Sinza River Catchment (23 km²). The SPI is a probability index developed to represent abnormal wetness and dryness better than the Palmer indices. SPI-3 and SPI-6 are prioritized to capture short- and medium-term precipitation anomalies that trigger flash floods in lowland areas like Tandale, where rapid inundation directly impacts sanitation infrastructure18. These shorter timescales effectively reveal seasonal patterns that influence immediate operational challenges for FSM services, including accessibility issues and groundwater fluctuations. SPI was calculated at 3-, 6-, and 12-month timescales to capture short-term (e.g., seasonal flooding) and long-term (e.g., multi-year drought) impacts on FSM, with the 12-month SPI prioritized for annual planning cycles12, affecting infrastructure planning and groundwater resources. The catchment’s small size and quick hydrological response make shorter-term indices more diagnostically valuable for understanding the temporal relationship between precipitation extremes and sanitation system failures in this urban setting19. The values were interpreted as positive (above-average precipitation) and negative (below-average precipitation), ranging from − 2 to + 2, with extremes beyond these indicating severe drought or exceptionally wet conditions.

Analysis of extreme events

Using the calculated SPI values, this study identified periods of extreme drought (SPI ≤ −1.5) and extreme wetness (SPI ≥ 1.5) for each station29,30. The frequency and intensity of extreme climate events were assessed to capture variability patterns within the catchment, using the threshold values outlined in Table 3. Event intensity was determined based on SPI classifications, where larger absolute SPI values denoted higher severity. Specifically, SPI values ≥ 2.0 were considered extremely wet, while SPI ≤ −2.0 indicated extreme drought, following the criteria by McKee 199318,20. The frequency of extreme events was calculated by counting the number of months and years that exceeded these thresholds within the 1981–2021 period. This allowed identification of recurring droughts and flood-prone periods and provided insight into the temporal clustering and persistence of extreme events within the Sinza River Catchment. Globally, the recognized thresholds standardize precipitation anomalies across diverse climatic regions, enabling consistent comparison of wet and dry periods through the statistical distribution of long-term precipitation records. While adhering to these standardized thresholds for comparative consistency, the study acknowledged that the impacts of SPI-defined precipitation extremes varied based on local environmental, hydrological, and socioeconomic contexts13,16. The study also applied the Mann-Kendall Test and Sen’s Slope Estimator to analyse rainfall trends due to their robustness in detecting monotonic trends in climatic data22.

Table 3.

The standard precipitation index (SPI) categories and values26.

Index category Value(s)
Extremely wet ≥ 2.0
Severely wet 1.5 to 1.99
Moderately wet 1.0 to 1.49
Near Normally 0.99 to − 0.99
Moderate dry −1.0 to − 1.49
Severely dry −1.5 to − 1.49
External dry ≤−2.0
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The Mann-Kendall Test is a non-parametric method, suitable for non-normally distributed hydrometeorological data, while Sen’s Slope Estimator provides a quantitative measure of trend magnitude22. These methods were preferred over parametric approaches like linear regression, which assume normally-distributed residuals and are more sensitive to outliers. In Dar es Salaam’s Sinza River Catchment, flood impacts are intensified by rapid urbanization, inadequate drainage, and informal settlements in the flood-prone areas. Research has established that these settlements experience earlier FSM failures due to poor waste management, high population density, and non-resilient sanitation infrastructure6,32. Similarly, drought conditions affect local FSM systems at less severe SPI values than globally-defined thresholds suggest, as residents depend primarily on the rain-fed Ruvu River and intermittent DAWASA water supply systems25. This study calculated SPI at 3, 6, and 12-month timescales while maintaining global SPI categories to ensure methodological consistency and facilitate cross-regional comparison. Observed discrepancies between global SPI-defined conditions and actual FSM system responses are examined in the results and analysis section.

Assessment of FSM impacts

Originally, SPI was developed for drought assessment, but it can be used to analyse both water deficits and surpluses27. This was applied to evaluate the impacts of extreme weather events on FSM in the Sinza River Catchment of Dar es Salaam, Tanzania, over 40 years (1981–2021). To evaluate the potential impacts of climate variability on FSM in lowland areas, the study correlated the identified extreme events with known challenges in FSM systems. The study used household surveys (308 respondents), focus group discussions (36 local leaders), field observation, and literature review to identify issues like pit latrine overflows during floods. This included assessing the impacts of drought periods on water availability for sanitation, the effects of extreme wet periods on sanitation infrastructure, and potential flooding and system overflows. By combining the SPI analysis with FSM considerations, the study aimed to provide a comprehensive assessment of how climate variability affects sanitation management in the Sinza River Catchment, informing future resilience strategies and urban planning decisions.

Results and discussion

Findings

The results section presents the findings from the SPI analysis and explores their implications for FSM in the Sinza River Catchment.

SPI trends (1981–2021)

The SPI for four rainfall stations Kimara, Mbezi, Ubungo, and Msimbazi was calculated covering a period of over 40 years (1981–2021). The SPI analysis provided insight into the variability in rainfall patterns, highlighting extreme wet and dry periods that impact water resources and FSM. The SPI values varied significantly over the studied period, showing both prolonged dry periods and extreme wet conditions. SPI values below − 1 represented droughts, while values above 1 signified wet periods29,30. This variation highlights the influence of climate variability on FSM services, as both excessive rainfall and droughts affect the operation and sustainability of these systems. Similar studies across Sub-Saharan Africa have found that fluctuating rainfall patterns lead to challenges in managing FSM services in low-income urban settlements28. Studies in Dar es Salaam have shown that fluctuating rainfall patterns significantly impact FSM services in low-income urban settlements23. Intense rainfall events overwhelm existing drainage systems in informal areas, leading to flooding and the spread of faecal contamination29. Additionally, seasonal variations in rainfall affect the accessibility of areas for faecal sludge collection, with heavy rains rendering many roads impassable for collection vehicles. These challenges are particularly pronounced in low-lying, flood-prone informal settlements, where unpredictable rainfall patterns exacerbate the difficulties in maintaining consistent FSM services6,34.

Analysis of SPI-12 data for Sinza river catchment

Dry periods

The analysis of SPI-12 reveals distinct patterns of prolonged droughts and wet periods over the study period. Severe drought conditions were observed across all stations, between 1987 and 1989, with SPI values dropping below − 1.5, as presented in Fig. 3. These findings correspond with regional droughts recorded at Kilwa during 1980–1989 and particularly the dry period from 2000 to 2009, in Tanzania. Another extended dry period occurred from 2002 to 2004, reaching its peak severity in April 2003 when SPI values dropped significantly. Similarly, the 2009–2011 period exhibited notable drought conditions affecting all stations.

Fig. 3.

Fig. 3

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Spatial distribution of SPI in 1987–1988 and 2002–2003 in the Sinza River Catchment. Base map © Esri and its contributors.

In contrast, the most pronounced wet periods included 1994–1996, which was characterized by moderate to severe wetness, and 2014–2015, during which consistently wet conditions were recorded. The most extreme wet period was observed from 2017 to 2020, with SPI values exceeding 2.0, indicating extremely wet conditions across all stations. Severe drought, characterized by an SPI value of −2.08at Msimbazi, aligns with other studies in East Africa that observed decreased precipitation, impacting both agricultural production and urban water supply28,31. It has been noted that dry periods reduce groundwater recharge and water availability for flushing and cleaning onsite sanitation systems, such as pit latrines, commonly used in the Sinza River Catchment. The spatial variability of dryness observed in the Sinza River Catchment is consistent with findings from urban hydrological studies in Dar es Salaam. Spatial patterns of water scarcity are influenced by factors such as urbanization, land cover changes, and local topography24. Kjellen (2007) found that water access and availability in Dar es Salaam vary significantly across different areas of the city, with some neighbourhoods experiencing more severe water scarcity than others32. Similarly Mtoni(2012),observed that groundwater depletion varies spatially across the city, contributing to localized drought conditions33. These events affect the operation of wastewater treatment facilities, drainage and sewerage infrastructure, and water delivery systems, posing significant risks to public health34.

Kimara station recorded an SPI of approximately − 0.08, indicating near-normal conditions over the long term. However, extreme values were observed, with the lowest SPI (−2.17) occurring in December 2003 and the highest (3.84) in April 2020, as presented in Fig. 4. The most severe drought was recorded in 2003–2004, while the most significant extremely wet period (SPI ≥ 2.0) occurred between 2019 and 2020. Overall, extremely wet events accounted for 7.4% of the observations, while extremely dry events (SPI ≤ − 2.0) constituted 5.3%. Mbezi station exhibited a similar trend, with an average SPI of −0.10. The most extreme values were recorded in December 2003 (−2.10) and April 2020 (3.75). The station’s pattern closely followed that of 12-Month K, though with slight variations in magnitude. The frequency of extreme wet events reached 7.8%, while extreme dry conditions were recorded in 4.9% of observations.

Fig. 4.

Fig. 4

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SPI (12) for the years 1981–2020 in the Sinza River Catchment.

Ubungo station showed an average SPI of −0.08, suggesting near-normal long-term conditions. However, this station recorded the lowest SPI (−3.00) in February 2002, indicating a more intense localized drought. The highest SPI (3.48) was observed in April 2020, as shown in Fig. 4. Compared to other stations, Ubungo displayed more variability in drought conditions. Extreme wet events accounted for 6.5% of observations, while extreme dry conditions made up 4.7%. Msimbazi station recorded the lowest average SPI among all stations (−0.12), yet it remained within the near-normal range. The lowest SPI (−3.03) was observed in April 2003, coinciding with the severe drought period, while the highest (3.36) occurred in April 2020, as shown in Fig. 4. The 2002–2004 drought was the most severe for this station, exhibiting distinct patterns from other stations in certain periods. The frequency of extreme wet conditions was recorded at 7.1%, while extreme dryness occurred in 5.1% of observations.

A comparative analysis of the four stations indicated a strong positive correlation (r > 0.85) in SPI trends, as shown in Table 4, reflecting similar climatic influences. However, Misimbazi occasionally showed deviations in the timing and intensity of extreme events. Spatially, Ubungo experienced more intense localized droughts, while Misimbazi recorded more extreme values during certain dry periods. Mbezi’s SPI pattern closely mirrored that of Kimara.

Table 4.

Correlation metrics between the stations.

Kimara Mbezi Ubungo Msimbazi
Kimara 1.0000 0.8798 0.7600 0.6696
Mbezi 0.8798 1.0000 0.8650 0.8498
Ubungo 0.7600 0.8650 1.0000 0.9096
Msimbazi 0.6696 0.8498 0.9096 1.0000
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Wet period

Several synchronized extreme events were observed across stations. April 2020 emerged as the wettest period on record, with SPI values exceeding 3.0 across multiple stations. Similarly, December 2003 and April 2003 represented the most severe drought periods, with SPI values below − 2.0 in most stations. The extended wet period from 2017 to 2020 was consistently extreme, impacting all locations (see Fig. 4). The findings suggest a recurrence of major droughts approximately every 10–12 years, typically lasting between 12 and 24 months. Notably, the severity of recent drought events, particularly in 2002–2004, appears to have intensified compared to earlier occurrences. Conversely, extremely wet conditions (SPI ≥ 1.8) have become more frequent and intense, particularly between 2017 and 2020, with April 2020 standing out as the wettest period recorded. A clear multi-year cycle is evident in precipitation patterns, with recent decades showing increased fluctuations between extreme wet and dry conditions. While all stations generally experience similar climatic trends, localized variations indicate that certain areas, such as Ubungo and Misimbazi, are more prone to extreme events.

As shown in Fig. 5, extreme wet conditions were recorded in 1997–1998, with SPI values reaching their peak at Kimara (1.54) and Ubungo (1.61), followed by Mbezi (1.47) and Msimbazi (1.30), indicating a period of excessive rainfall. This aligns with the severe flooding experienced across East Africa, which has been attributed to the 1997–1998 El Niño event Mahongo et al., (2013). Another significant wet period occurred in 2019–2020, with SPI values of 2.85 at Kimara, 2.92 at Mbezi, 2.48 at Ubungo, and 2.61 at Msimbazi. These periods posed serious challenges for FSM systems, as intense rainfall caused widespread flooding of pit latrines. Several studies have documented the impact of heavy rains on sanitation infrastructure in low-lying urban areas35,36. Wet conditions are particularly problematic for FSM services in densely populated areas like Tandale, where faecal sludge from flooded pit latrines can mix with floodwaters, spreading contaminants. Similar issues have been reported in Dar es Salaam during extreme rainfall events, where inundated pit latrines have led to public health crises10. Hambati et al. (2015) identified significant spatial variations in flood risk across different wards, with low-lying areas near rivers being particularly vulnerable. Abebe(2019) utilised high-resolution digital elevation models to demonstrate how micro-topography within urban catchments creates localized pockets of high flood risk37. These findings collectively support the observed spatial variability in wetness within the Sinza River Catchment.

Fig. 5.

Fig. 5

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Spatial distribution of SPI for 1997–1998 and 2019–2020 in the Sinza River Catchment, based on CHIRPS data. Base map © Esri and its contributors.

Analysis of SPI-6 data for Sinza river catchment

The SPI-6 data for the Sinza River Catchment reveals a dynamic precipitation regime with clear bimodal seasonality and strong connections to large-scale climate drivers like ENSO. The catchment experiences significant variability in precipitation, with extreme events becoming potentially more frequent in recent decades. This variability has important implications for groundwater recharge, water availability, and overall water resources management in the region. Understanding these patterns can help in developing more resilient water management strategies that account for both extreme wet and dry conditions. The analysis of SPI-6 data in Fig. 6 highlights the region’s characteristic bimodal rainfall pattern, with two distinct wet seasons occurring from March to May (MAM, long rains) and from October to December (OND, short rains). This cyclical pattern is evident in the fluctuations of SPI values over time, reflecting seasonal variability in precipitation. The MAM season exhibits higher variability compared to the OND season. Notably, strong positive anomalies were recorded in 1998, when SPI values exceeded + 2.0 across all stations, as well as during 2018–2020, which showed consistently high positive values. The year 2020, in particular, experienced peak SPI values exceeding + 3.0 at some stations. In contrast, significant droughts during the MAM season were observed in 1991–1992, 2003–2004, and 2010–2011, with negative SPI values prolonged dry conditions. The OND season displays a different pattern of variability, with notable wet seasons recorded in 1997–1998, 2006–2007, and 2015–2016. Conversely, significant dry spells occurred during 1987–1988, 2005–2006, and 2016–2017, with negative SPI values indicating reduced rainfall and potential water shortages.

Fig. 6.

Fig. 6

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SPI (6) for the years 1981–2020 in the Sinza River Catchment.

Extreme wet periods

The dataset identifies several extreme wet periods with SPI values exceeding + 2.0. The most prominent occurred in 1998, with mid-year SPI values surpassing + 2.5 across multiple stations, coinciding with the strong 1997–1998 El Niño event31. El Niño events, such as those in 1997–1998 and 2015–201638, were associated with extreme wet conditions in the catchment, as shown in Fig. 7, producing the highest SPI values in the dataset. Another prolonged wet period from 2017 to 2020 exhibited consistently high SPI values, peaking in March 2020 with some stations recording values above + 3.0, marking the wettest period in the dataset. The 2019–2020 wet period may relate to Indian Ocean Dipole (IOD) events, as noted in regional studies39. Additionally, 1995–1996 experienced significant rainfall, with SPI values exceeding + 1.5 at all stations. Prolonged wet periods, particularly in 1998, 2018, and 2020, likely contributed to substantial groundwater replenishment, presenting opportunities for sustainable water storage.

Fig. 7.

Fig. 7

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SPI 3-month for the Years 1981–2020 in the Sinza River Catchment.

Extreme dry periods

Severe drought conditions, characterised by SPI values dropping below − 2.0, were most notable in 2002–2003, affecting all stations with significant water shortages. Similarly, 1988–1989 was marked by SPI values approaching or exceeding − 1.5 across stations. The year 2010 experienced pronounced dry conditions, with most stations recording SPI values below − 1.5. Another severe drought was observed in 2003–2004, affecting particularly the Msimbazi area, where SPI values fell below − 2.0. However, recurring droughts, such as those in 2002–2003 and 2010, highlight the vulnerability of the catchment to extended dry conditions, which may have led to groundwater depletion and reduced baseflow in Sinza River. Given the bimodal rainfall pattern, water management strategies should be designed to optimize recharge during the wet seasons while preparing for drier periods.

Comparative analysis

Extreme events in Sinza river catchment (1981–2021)

This analysis examines the Standardised Precipitation Index (SPI) data for four stations in the Sinza River Basin from 1981 to 2021: Kimara, Mbezi, Ubungo, and Msimbazi. Negative SPI values indicate drought conditions, while positive values suggest higher rainfall or flood conditions, as shown in Table 3. The Sinza River Catchment has experienced significant fluctuations in precipitation over the four decades studied, as shown in Fig. 4. The most extreme wet period occurred in 2019–2020, with SPI values ranging from 2.48 in Ubungo to 2.92 in Mbezi. Another notable wet period was 2017–2018, with SPI values between 2.34 (Ubungo) and 2.68 (Kimara). These extreme wet years indicate increased flood risks and potential strain on FSM infrastructure. Conversely, severe drought periods were observed, particularly in 2002–2003, when SPI values ranged from − 1.57 in Ubungo to −2.08 in Msimbazi. Another significant drought occurred in 1987–1988, with SPI values between − 1.43 (Mbezi) and − 1.68 (Ubungo) as shown in Fig. 3. These dry spells suggest prolonged water scarcity issues that can impact FSM services and increase the risk of groundwater contamination. During droughts, faecal sludge tends to accumulate in pits, particularly in water closet systems, increasing hygiene risks due to flies, foul odour, inadequate emptying, and potential environmental contamination35,40–45. This aligns with findings from studies carried out in other unplanned settlements, where drought conditions have similarly disrupted FSM services41. FSM challenges vary across sub-catchments, with low-lying areas like Tandale facing frequent pit latrine flooding, while elevated sub-catchments experience drought-related sludge accumulation. Furthermore, the frequency and intensity of extreme weather events, including droughts and floods, are increasing32,46,47, exacerbating challenges in FSM service provision.

SPI-12, SPI-6, and SPI-3 in the Sinza river catchment

The analysis of SPI-12, SPI-6, and SPI-3 provides insight into long-term, seasonal, and short-term rainfall variability in the Sinza River Catchment, with direct implications for sanitation infrastructure and FSM. SPI-12, which captures long-term trends, highlights extended wet and dry periods that shape the region’s water availability. Prolonged wet conditions, such as those recorded in 1998, 2018, and 2020, caused significant flooding, increased groundwater levels, and overwhelmed sanitation systems, particularly pit latrines and septic tanks. These conditions increase the likelihood of direct faecal waste exposure and contamination, particularly in densely populated, low-lying areas.

Conversely, SPI-12 also reveals prolonged drought periods, such as 1987–1988, 2002–2003, and 2010–2011, where significantly negative values indicate extended water shortages. The reliance of most residents on rain-fed water supply systems exacerbates FSM challenges during these dry spells, making desludging services difficult and leading to sludge accumulation in pits. The lack of water for flushing further contributes to unhygienic conditions, increasing the risk of faecal contamination and disease outbreaks. SPI-6, which focuses on seasonal variability, is particularly useful in understanding FSM challenges since it captures the impact of the long rains (MAM) and short rains (OND), on sanitation infrastructure. Unlike SPI-12, which identifies broader trends, SPI-6 highlights how seasonal flooding events rapidly fill pit latrines and increase contamination risks, while seasonal droughts contribute to FSM service disruptions. The analysis shows that extreme wet periods, particularly in 2019–2020 and 2017–2018, resulted in overflowing latrines, structural failures, and groundwater contamination, affecting both public health and environmental quality.

Although SPI-3 is primarily used for detecting short-term rainfall anomalies, it remains valuable for FSM planning, especially in urban settings where sudden rainfall deficits or extreme precipitation events can disrupt sanitation services. SPI-3 can identify short-term droughts that may cause groundwater drawdown, affecting pit latrines and septic systems reliant on shallow water tables. It can also highlight intense short-term rainfall that may cause flash flooding, overwhelm drainage infrastructure and exacerbate pit latrine overflow. Short-term rainfall fluctuations may significantly impact desludging services, particularly for households dependent on manual emptying methods, which are often hindered by water shortages or excessive flooding. Incorporating SPI-3 into FSM planning enables real-time adaptation strategies, such as pre-emptive desludging before expected extreme rainfall events or implementing temporary containment measures during short-term dry periods. While SPI-12 and SPI-6 remain the primary tools for long-term and seasonal planning, SPI-3 can serve as an early warning system for FSM operators, enabling them to mitigate sanitation-related risks associated with rapid shifts in precipitation patterns.

The spatial analysis of SPI values across the four stations (Kimara, Mbezi, Ubungo, and Msimbazi) reveals distinct differences in FSM challenges across sub-catchments. For example, as Msimbazi is in a low-lying floodplain, it consistently records more severe SPI-negative values during drought periods (e.g., −2.08 in 2002–2003), indicating higher vulnerability to groundwater depletion and FSM service disruptions. This makes pit latrines in Msimbazi particularly susceptible to collapse during dry spells due to soil instability. Conversely, Kimara and Mbezi, located in higher-elevation areas, record higher SPI values during wet periods (e.g., 2.85 in 2019–2020), suggesting that these areas experience more intense rainfall events, which can contribute to runoff and flooding downstream. This highlights the disproportionate impact of upstream runoff on low-lying settlements like Tandale, where high groundwater levels exacerbate latrine overflow risks. These spatial differences indicate that FSM planning should adopt a sub-catchment-specific approach, where upstream flood management interventions (such as improved drainage in Kimara and Mbezi) can help mitigate sanitation risks in downstream areas like Msimbazi and Tandale.

Discussion

Exposure, risk, and vulnerability of sanitation systems in lowland areas

The findings from SPI-12 and SPI-6 data have been integrated with the qualitative interview responses to assess the exposure, risk, and vulnerability of sanitation systems in lowland areas of Tandale. Given the area’s high population density, proximity to water sources, and the dominance of onsite sanitation (primarily pit latrines), the impacts of extreme climatic conditions, including floods and droughts, are significant. Both SPI-12 and SPI-6 analyses indicate frequent and intense rainfall events contributing to flooding in Tandale. The survey data reveals that 57.8% of respondents have experienced flooding, with 52.9% reporting damage to property and houses and others citing health risks and displacement. Additionally, 63% observed an increase in groundwater levels, exacerbating the risk of pit latrine overflow and contamination of water sources, as indicated in Fig. 8.

Fig. 8.

Fig. 8

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Impacts of flooding in tandale.

Given that 70.8% of residents rely on pit latrines, they are likely to be affected by heavy rainfall as the saturation of soil during extended wet periods compromises the structural integrity of pits, leading to direct faecal waste exposure. Furthermore, 26.6% of latrines are constructed with semi-permeable walls and open bottoms, increasing the risk of groundwater contamination. For instance, in 1998, the SPI-6 values exceeded + 2.0 across all stations, peaking above + 3.0 in March 2020, indicating extreme wet conditions. These heavy rainfall events may have caused widespread pit latrine overflow and increased groundwater contamination. Similarly, the prolonged wet period from 2018 to 2020, with consistently high SPI values, suggests recurring flood risks that exacerbate sanitation vulnerabilities in low-lying areas. Conversely, drought conditions also pose risks to FSM services. During 2002–2003, SPI-12 values dropped below − 2.0, indicating a severe drought that likely led to reduced water availability for desludging services. As a result, 63.6% of residents reported to have experienced water shortages for sanitation. This challenge is particularly critical in Tandale, where 98.1% of residents rely on water from the Dar es Salaam Water Supply and Sanitation Authority (DAWASA), as illustrated in Fig. 9. The primary source of this supply, the Ruvu River, is a rain-fed system that experiences a significant drop in water levels during drought periods. With limited water access, pit maintenance became increasingly difficult, exacerbating sanitation risks in the community. Additionally, the 2010–2011 drought, characterized by negative SPI trends, would have further limited FSM operations, increasing the frequency of improper faecal sludge disposal and environmental pollution. This limits the availability of water for toilet flushing, handwashing, and desludging, leading to improper waste disposal, as evidenced by 21.8% of respondents reporting faecal discharge into the environment.

Fig. 9.

Fig. 9

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Sanitation services and water availability.

The recurrence of extreme events, both floods and droughts, highlights the unsuitability of current sanitation infrastructure, particularly in unplanned settlements where 80.5% of residents live. Without interventions such as improved drainage and climate-resilient FSM systems, these challenges are expected to worsen under increasing climate variability. Despite the climatic pressures, 64.6% of respondents believe their sanitation infrastructure is designed to withstand extreme weather events. However, only 33.4% have elevated pit latrines and 18.1% have elevated septic tanks, indicating that a significant portion remains vulnerable. Moreover, 28.6% of respondents stated that their systems are not designed for extreme weather, highlighting a gap in climate-resilient sanitation infrastructure. The reliability FSM services in Tandale faces significant operational challenges during extreme events. While 73.4% consider FSM services reliable, a notable 19.8% believe services become unsafe during heavy rainfall and floods, as shown in Fig. 10. Floods frequently disrupt access to sanitation facilities, and latrine overflows increase the spread of waterborne diseases, particularly in areas where desludging services are infrequent.

Fig. 10.

Fig. 10

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Availability and reliability of sanitation services during extreme events.

Vulnerability of the community and FSM management infrastructure

The economic situation of the population significantly influences people’s ability to invest in resilient sanitation solutions. The study indicates that 61.1% of residents earn less than Tshs. 200,000 per month, with 25.3% having no income, as shown in Fig. 11a. Limited financial capacity prevents many households from upgrading sanitation infrastructure, making them more susceptible to faecal contamination and disease outbreaks. A critical concern is the frequency of pit emptying. The data reveals that 16.2% of respondents desludge in less than a year, while 15.9% never desludge, as shown in Fig. 11b. FSM services become unreliable during extreme events, and prolonged desludging intervals heighten public health risks, particularly when pits overflow due to rising groundwater levels. Despite these vulnerabilities, 62.3% of respondents advocate for additional climate-resilient FSM services. The most commonly suggested improvements include sewer system expansion (31.2%), improved drainage systems (25.5%), waste management strategies (13.5%), and river reconstruction to mitigate runoff impacts (9%). These adaptation strategies align with the urgent need to enhance sanitation infrastructure resilience, particularly in low-lying flood-prone areas. Without targeted interventions, extreme climatic events will continue to exacerbate FSM challenges, increasing the risk of disease outbreaks, environmental pollution, and infrastructure failure.

Fig. 11.

Fig. 11

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(a) Emptying frequencies, (b) Economic strength of the community.

The FSM challenges observed in the Sinza River Catchment, such as increased pit latrine filling rates, frequent overflows, and difficulties in emptying services, are not unique to this area the same problem has been reported in Keko, Manzese, Dar es Salaam20,48. Studies from Nairobi, Kenya, and Kampala, Uganda, report similar issues, where extreme rainfall events lead to pit latrine overflows, contamination of water sources, and disruptions in sludge emptying services49,50. Similar FSM challenges occur in Nairobi and Kampala, where flooding disrupts pit latrines, though Sinza’s dense informal settlements amplify such risks. Comparisons beyond East Africa reveal similar FSM challenges in rapidly urbanizing cities in South Asia and West Africa, where climate variability exacerbates sanitation infrastructure vulnerabilities49,51. The increasing frequency of extreme precipitation events, as highlighted in our study, aligns with global trends in FSM-related climate risks. These comparisons underscore the need for region-specific yet adaptable FSM strategies that integrate climate resilience into urban sanitation planning.

Rainfall trends and their implications for FSM

The Mann-Kendall trend test was applied to assess long-term precipitation trends in the Sinza River Catchment. The Mann-Kendall test confirmed increasing SPI variability (p < 0.05), consistent with regional trends47 which indicate an increasing trend in annual rainfall across Ubungo, Mbezi, and Kimara stations, with Kimara showing a statistically significant trend (Z = 1.97, p < 0.05). Although Msimbazi showed a positive trend, it was not statistically significant. These trends suggest increasing rainfall variability, aligning with SPI-based seasonal and inter-annual fluctuations.

The observed rainfall trends highlight the need for adaptive strategies for FSM planning. Increasing precipitation may lead to frequent pit latrine overflows, structural damage, and increased desludging frequency, particularly in high populationdensity areas. The SPI-06 analysis provides a broader view of cumulative seasonal rainfall patterns relevant to FSM planning, while SPI-3 helps identify short-term anomalies that could temporarily impact desludging operations and groundwater levels. By integrating both SPI analysis and Mann-Kendall trend results, FSM resilience strategies can be better aligned with long-term and short-term climate variability in the study area. These trends align with TMA reports, which note increasing extreme weather in East Africa48. Additionally, the IPCC’s Sixth Assessment Report projects an increase in the frequency and intensity of heavy precipitation events across Sub-Saharan Africa with additional global warming49. While annual rainfall trends indicate a general increase across the Sinza River Catchment, seasonal variability plays a crucial role in shaping FSM challenges. Seasonal trends reveal significant increases in Masika (long rains) at Mbezi (Z = 2.2), Kimara (Z = 1.9), Ubungo (Z = 1.8), and Msimbazi (Z = 1.8), suggesting that peak rainfall months may exacerbate FSM-related issues, particularly pit latrine overflow and structural failures. These findings align with the SPI-6 analysis, which captures the cumulative seasonal rainfall and its impact on sanitation infrastructure. The increasing Masika trends are particularly relevant for FSM planning, as heavy rainfall events intensify flood risks in low-lying areas like Tandale. Previous studies, for example50, have highlighted the link between increased seasonal rainfall and latrine collapse in informal settlements, emphasizing the need for flood-resilient sanitation designs.

Conversely, Vuli (short rains) show moderate positive trends, while Kiangazi (dry season) trends remain weak or negative (Z = 1.4 at Msimbazi). These dry season trends align with SPI-3 findings, which help identify short-term rainfall deficits that may disrupt desludging services due to reduced water availability. Previous studies have noted that prolonged dry spells can lower groundwater levels12, affecting desludging practices51 and increasing latrine abandonment in water-scarce urban areas52.

Generally, the Vuli season has shorter, more sporadic rainfall events which exhibit more variability in rainfall amounts compared to the Masika season. The Masika season is characterized by severe and prolonged rains due to higher cumulative rainfall, leading to saturated soils, river overflows, and widespread inundation. Vuli is more erratic, influenced by climate drivers such as the Indian Ocean Dipole (IOD) and El Niño-Southern Oscillation (ENSO). In low-lying areas like Tandale, this increases the risk of pit latrine overflow, faecal sludge contamination, and infrastructure failure. Vuli floods, on the other hand, are more likely to result from intense but short-duration storms, causing flash floods. These can overwhelm local drainage systems and rapidly transport runoff from upstream, exacerbating localized flooding. While Vuli events may not cause as much prolonged inundation as Masika, they can still lead to infrastructure damage, latrine collapse, and faecal sludge exposure due to sudden water surges53.

Conclusion and recommendations

In conclusion, the analysis of SPI data for the Sinza River Catchment from 1981 to 2021 reveals significant climate variability, with extreme wet periods (such as 2019–2020 with SPI values up to 2.92) and severe dry spells (like 2002–2003 with SPI values as low as −2.08). These fluctuations pose substantial challenges for FSM in lowland urban areas, affecting infrastructure vulnerability, water availability, and public health. Nonetheless, the study faced limitations, including the use of SPI derived from fixed meteorological stations, which may not have fully captured the spatial variability of floods and droughts across the catchment. In addition, limited access to FSM data, such as pit latrine damage, service routes, and failure records, may have constrained the depth of analysis on service disruptions during extreme events.

To address these challenges, the study recommends that a mix of climate-resilient FSM technologies capable of withstanding both flood and drought conditions should be used. This includes raised latrines for flood-prone areas and water-efficient systems for dry periods. Additionally, enhancing urban drainage infrastructure, developing early warning systems for extreme weather events, and promoting water conservation and reuse practices are crucial. Community engagement in FSM planning and implementation is essential to ensure strategies are culturally appropriate and address the needs of vulnerable populations. Furthermore, integrating FSM considerations into broader urban resilience plans and investing in capacity building for local authorities and service providers will be key to developing adaptive, long-term solutions that can respond effectively to the observed cyclical patterns of wet and dry periods in the Sinza River Catchment. Policy makers should integrate climate data into FSM planning to ensure infrastructure and service delivery are adaptive to changing climatic conditions, particularly in informal settlements.

These findings inform resilience strategies across East Africa, where cities like Nairobi and Kampala face similar climate-driven FSM challenges [9;62]. This study further informs FSM strategies in other flood- or drought-prone urban areas by emphasizing the need for adaptive sanitation planning, investment in resilient infrastructure, and community-driven solutions. Sinza’s flood-resistant latrine designs, like elevated pit latrines and septic tanks, which are also applied in unplanned settlements, can inform FSM strategies in other flood-prone urban areas like Kampala. Therefore, future research should explore climate impacts on wastewater treatment efficiency and FSM delivery in other urban catchments.

Acknowledgements

Acknowledge institutional support from University of Dar es salaam and Financial support from Water Institute Tanzania.

Author contributions

A.M conceptualized, investigated, did data curation, developed the methodology, did the formal analysis, visualized, and wrote the original draft. R.K conceptualized, validated, supervised, reviewed, and edited the article. D.M.M.M conceptualized, validated, supervised, reviewed, and edited the article. Fides Izdori supervised, reviewed, and edited the article.

Funding

The study was funded by the Water Institute-Dar es Salaam.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author upon request.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

All work complies with ethical standards.

Consent for publication

The authors give their permission to publish.

Consent to participate

Authors consent to their participation in the entire review process.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and/or analysed during the current study are available from the corresponding author upon request.

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