Abstract
Several areas experience frequent floods due to anthropogenic activities. Among them, is the Dar es Salaam city, which experiences frequent floods along the Msimbazi River, whose flows originate from different tributaries including the Kinyerezi River. This study aims to evaluate the hydrological-sensitive areas of the Kinyerezi River sub-catchments using topographic index values (λ*) that enable the identification of areas with a higher probability of generating surface runoff. A digital elevation model was utilized to delineate the Kinyerezi River sub-catchment characteristics using ArcGIS 10.4. Soil infiltration rates (Ks) on selected open places were determined using a Guelph permeameter. Soil particle size distributions were analyzed and the λ* values were evaluated. The results showed the particle size distribution contains sand and silt-clay ranging from 46 to 84% and 16 to 53%, respectively. The Ks ranged from 0.6 to 7.8 mm/h while the sub-catchment KS3 scored the highest λ* value of about 10.7. Hence, there is a higher probability for generating surface runoff. Sub-catchment KS16 scored the smallest λ* value of 5.7, perceived to generate less surface runoff. Low-impact development practices capable of capturing runoff and enabling infiltration, evaporation, and detention should be employed in sub-catchments with higher λ* values.
HIGHLIGHTS
The study improves knowledge and awareness of the sensitive sub-catchments of the Kinyerezi River catchment.
The soil infiltration rate ranges are given for the Kinyerezi River sub-catchments and can be adopted by engineering practitioners in the design of stormwater management infrastructures in the catchment.
Gives the gravel, sand, and silt-clay contents for soils from the Kinyerezi River sub-catchments that may help engineering practitioners in deciding the type of stormwater management to adopt for the catchment.
INTRODUCTION
Urbanization contributes significantly to the rise in floods in cities because previous surfaces are often replaced with impervious ones, leading to increased stormwater runoff that can flood drainage streams and rivers (Kong et al. 2017). Catchment runoff is influenced mainly by land use and soil types, which in turn affect runoff volume and flow rates (Blair et al. 2014). Zakizadeh et al. (2022) reported that managing stormwater runoff involves monitoring the amount of runoff entering the system and taking preventative measures accordingly. According to the Tanzanian National Bureau of Statistics (2022), Dar es Salaam is one of the fastest growing cities in Africa region with an annual growth rate of 2.1%. This has attributed to the increase in the city's built-up area of 42% from 1989 to 2015 as reported by Igulu & Mshiu (2020). The Msimbazi Valley's built-up area from 1990 to 2019 expanded by 26.2% (Machiwa et al. 2021). These facts and many others, have increased flood disaster risks as reported by Sakijege (2013). Among the historical flood events experienced in Msimbazi River floods are in the years of 2007, 2011, 2014, 2015, 2018, 2019, and 2020 (Mzava et al. 2021). These events call for sustainable city development approaches that substantially promote natural hydrological processes.
Real estate development is surging in the Kinyerezi River catchment including the construction of villas, apartment complexes, office buildings, hotels, and shopping malls (Mkilima 2018). Rapid infrastructure development is reducing the infiltration capacity of pervious surfaces and creating impervious surfaces that favour more stormwater runoff generation. The Kinyerezi River discharges runoff into the Msimbazi River, but it is unsure about the sub-catchments within it that generate high volumes of runoff. In this study, these sub-catchments are known as the hydrological-sensitive area (HSA). The aim of this study is to identify the HSAs, by evaluating sub-catchment topographic index (λ*) values. The λ* value is proportional to the HSA and implies that there is a high probability of runoff generation from the respective sub-catchment (Martin-Mikle et al. 2015).
Several studies have employed λ* values to identify sub-catchments of HSA. Agnew et al. (2006) justified the method as being stronger in identifying sub-catchment HSAs. Martin-Mikle et al. (2015) used λ* values to determine the values of HSA for mixed-use watershed in central Oklahoma, USA. Xue et al. (2014) applied the method in establishing HSA values based on λ* values for the spatial–temporal variability of surface runoff probability in different periods for the Meishan watershed, China. Similarly, Aksoy et al. (2016) utilized the topographic Wetness Index and the SAGA (System for Automated Geoscientific Analyses) wetness index approach to map flood-prone areas at the regional scale, in Turkey.
MATERIALS AND METHODS
Study area
Determination of hydraulic conductivity (Ks)
Soil hydraulic conductivity (Ks) also known as infiltration rate is an essential parameter for evaluating the HSA of sub-catchments. Infiltration testing is conducted to a depth at the same depth as the bottom of the low-impact development (LID) practices that are expected to be installed (Cahill & Godwin 2018). However, in many cases, the test itself determines how deep the LID practice should extend. In general, the bio-retention facilities are recommended to have a total depth of 1.2 m that contains a surface layer depth of 0.3 m, a soil layer depth of 0.6 m and a storage layer depth of 0.3 m (Choi & Kim 2020) therefore, infiltration rate testing to a depth of 1.5 m gives suitable results for LID implementation. In addition, Armin & Associates Ltd (2013) reported on the LIDs such as infiltration trenches, detention ponds, and bio-retention ponds to require a total depth of 1.5 m that includes LID depth and depth from the bottom to the restrictive layers.
Soil hydraulic conductivity (infiltration rate) was determined by inputting used permeameter reservoir cross-sectional area (A) of 51 cm², constant water head height (H) measured for each borehole during the test, 5 cm used borehole radius (a), soil texture-structure category and steady rate of fall (R) into the ‘‘calculator spreadsheet’. The average infiltration rate was calculated for the sub-catchments to determine topographic index values. Soil samples were collected from each borehole at a depth of 1.5 m and their particle size distributions were analyzed in the lab.
Evaluating topographic index value for sub-catchments
Relatively higher λ* values (greater than standard deviations above the watershed mean) imply a greater likelihood of HSA – i.e., sub-catchments with a high probability of high runoff generation (Martin-Mikle et al. 2015; Giri et al. 2018) and the lower λ* value (less than 8) are considered to have very low probabilities of generating excess surface runoff (Agnew et al. 2006).
RESULTS AND DISCUSSION
Effect of clay, silt-clay, and gravel on hydraulic conductivity
Sub-catchment percentage silt-clay, sand, and gravel variation when compared to hydraulic conductivity
On the other hand, KS4 has the lowest infiltration rate (0.2 mm/h) with a proportional sand content of about 58%, this is likely due to the presence of a large pond, which saturates the surrounding soils and limits infiltration.
The infiltration rates obtained align with those adopted by Young et al. (2023) during the pilot phase of their water-sensitive decentralized stormwater management project for Mburahati Secondary School in Dar es Salaam. Similarly, the infiltration rates are close to that was reported by Winsemius et al. (2018) during the development of the hydrological and hydrodynamic model of the Msimbazi River basin-Dar es Salaam in which the soil infiltration rates adopted ranged from 10.4 to 33.33 mm/h.
The Hydrological Soil Group (HSG) from the Food and Agriculture Organization (FAO) website classifies the Kinyerezi River catchment as HSG group C, which has Ks between 1.3 and 3.8 mm/h with a clay content of about 20–40% (Mkilima 2018). The results obtained in this study agree well with the FAO soil classification, except for KS10. Similarly, the average hydraulic conductivity is within the range of the results reported by Lu et al. (2017) in the study on better-fitted probability of hydraulic conductivity for silty clay sites, in China.
Topographic index values
To identify HSA sub-catchments, the λ* values were determined using the expression proposed by Martin-Mikle et al. (2015) and Qiu et al. (2017) as represented in Equation (1). The sub-catchment's average Ks – i.e., 3.42 mm/h was utilized as recommended by Agnew et al. (2006) and Qiu et al. (2020). Depth (D) to the restrictive layer taken as 2 m, the contributing drainage area (A), impervious surfaces area (ISA) and average sub-catchment slope (S) were used to evaluate λ* value as illustrated in Table 1 using Equations (1) and (2).
Sub-catchment . | Ks . | A (ha) . | S (%) . | %impervious . | ISA (ha) . | DISA . | λ* . | Ranking . |
---|---|---|---|---|---|---|---|---|
KS2 | 3.42 | 93.3 | 9 | 35 | 32.6 | −63.3 | 9.6 | 8 |
KS3 | 3.42 | 120.1 | 13 | 35 | 42.0 | −82 | 10.7 | 1 |
KS5 | 3.42 | 142.2 | 10 | 29 | 41.2 | −80.5 | 10.2 | 3 |
KS7 | 3.42 | 58.6 | 10 | 24 | 14.0 | −26 | 9.2 | 10 |
KS8 | 3.42 | 173.6 | 12 | 12 | 19.9 | −37.9 | 10.4 | 2 |
KS10 | 3.42 | 108.2 | 9 | 8 | 8.8 | −15.7 | 10.1 | 4 |
KS11 | 3.42 | 131.6 | 10 | 10 | 12.6 | −23.2 | 9.68 | 7 |
KS13 | 3.42 | 61.6 | 13 | 11 | 6.7 | −11.4 | 8.5 | 12 |
KS14 | 3.42 | 54.5 | 10 | 32 | 17.4 | −32.9 | 8.9 | 11 |
KS15 | 3.42 | 58.2 | 10 | 9 | 5 | −8.1 | 7.8 | 13 |
KS16 | 3.42 | 35.4 | 11 | 38 | 13.4 | −24.9 | 5.7 | 15 |
KS17 | 3.42 | 67.7 | 10 | 33 | 22.3 | −42.7 | 9.74 | 5 |
KS18 | 3.42 | 44 | 11 | 18 | 7.9 | −13.8 | 7.0 | 14 |
KS20 | 3.42 | 75.1 | 10 | 17 | 13.0 | −24.1 | 9.3 | 9 |
KS21 | 3.42 | 179.2 | 11 | 15 | 26.3 | −50.7 | 9.72 | 6 |
Sub-catchment . | Ks . | A (ha) . | S (%) . | %impervious . | ISA (ha) . | DISA . | λ* . | Ranking . |
---|---|---|---|---|---|---|---|---|
KS2 | 3.42 | 93.3 | 9 | 35 | 32.6 | −63.3 | 9.6 | 8 |
KS3 | 3.42 | 120.1 | 13 | 35 | 42.0 | −82 | 10.7 | 1 |
KS5 | 3.42 | 142.2 | 10 | 29 | 41.2 | −80.5 | 10.2 | 3 |
KS7 | 3.42 | 58.6 | 10 | 24 | 14.0 | −26 | 9.2 | 10 |
KS8 | 3.42 | 173.6 | 12 | 12 | 19.9 | −37.9 | 10.4 | 2 |
KS10 | 3.42 | 108.2 | 9 | 8 | 8.8 | −15.7 | 10.1 | 4 |
KS11 | 3.42 | 131.6 | 10 | 10 | 12.6 | −23.2 | 9.68 | 7 |
KS13 | 3.42 | 61.6 | 13 | 11 | 6.7 | −11.4 | 8.5 | 12 |
KS14 | 3.42 | 54.5 | 10 | 32 | 17.4 | −32.9 | 8.9 | 11 |
KS15 | 3.42 | 58.2 | 10 | 9 | 5 | −8.1 | 7.8 | 13 |
KS16 | 3.42 | 35.4 | 11 | 38 | 13.4 | −24.9 | 5.7 | 15 |
KS17 | 3.42 | 67.7 | 10 | 33 | 22.3 | −42.7 | 9.74 | 5 |
KS18 | 3.42 | 44 | 11 | 18 | 7.9 | −13.8 | 7.0 | 14 |
KS20 | 3.42 | 75.1 | 10 | 17 | 13.0 | −24.1 | 9.3 | 9 |
KS21 | 3.42 | 179.2 | 11 | 15 | 26.3 | −50.7 | 9.72 | 6 |
Sub-catchments KS16, KS18, and KS15 are small, with the lowest λ* values about 5.7, 7.0, and 7.8, respectively, that are less than 8 therefore, are considered to have low probabilities of generating excess surface runoff (Agnew et al. 2006). The sub-catchments have small catchment areas of about 35.4, 44, and 58.2 ha, respectively, with high average slopes ranging from 10 to 11% hence, runoff generated within these sub-catchments runs into the Kinyerezi River with negligible impacts. Sub-catchments KS2, KS5, KS7, KS10, KS11, KS13, KS14, KS17, KS20, and KS21 have λ* values about 8–10.4 hence considered moderate probabilities of generating runoff within the Kinyerezi River catchment.
Agnew et al. (2006) evaluated the spatial monthly distribution of λ* values from January to December to capture seasonal variability in HSA, and found values between 7.7 and 10.3, noting that λ* was a consistent and reliable indicator of HSA. Similarly, Xue et al. (2014) used λ* on HSAs in the Meishan watershed to evaluate the spatiotemporal variability of surface runoff probabilities and the value ranged from about 13 to 17.
CONCLUSION AND RECOMMENDATIONS
The hydrologic sensitivity of sub-catchments is mainly influenced by their soil infiltration rates, slopes, catchment areas, and impervious areas. Those in the Kinyerezi River sub-catchment were tested and the soil samples were collected for particle size distribution analysis. The proportion of sand contents ranged from about 46 to 84% while silt-clay contents ranged from about 16 to 53%. Soil infiltration rates were between 0.6 and 7.8 mm/h. The highest λ* values about 10.7 and 10.4, were found in sub-catchments KS3 and KS8 respectively and imply that are the most hydrologically sensitive, with a high probability of runoff generation. Sub-catchments KS2, KS5, KS7, KS10, KS11, KS13, KS14, KS17, KS20, and KS21 have their λ* values ranging from 8 to 10.4; hence, implying moderate probability of runoff generations.
ACKNOWLEDGEMENTS
Authors would like to thank local government authorities in the Kinyerezi River catchment for their help and close support during hydraulic conductivity testing.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.