Development of Improved Characteristic Equations for Lake Rukwa in Tanzania

Often Lake Rukwa characteristics have been misreported in literature giving different volumes and surface areas at similar water surface elevations. This study aimed at establishing reliable lake characteristics elevation-area-storage equations for Lake Rukwa by utilising all available data and information to define the bathymetry and derive characteristic equations. A procedure was developed that combines historical lake extents, spot heights from topographical maps and surveyed lake bathymetry to define refined bathymetry to levels it has never reached. It combined spot heights around the lake and selected 13,934 surveyed points (from 107,938 available) within the lake confined by the 820 m land contour boundary and define topographical raster image, which was used to extract lake volumes and surface areas between the lowest point (778 m) and 820 m boundary. Change-point analysis was used to detect segmentation of the elevation-area and elevation-volume relationships, which were fitted to a shifted power model. Contours generated from a refined bathymetry raster indicated Lake Rukwa to comprise two north and south lake basins, which are separated by a ridge lying at an altitude of 794.3 m. The north and south lakes consist respectively of five (5) and three (3) deeper depressions (pools) paralleling the northwest- southeast Konongo Scarp, which are disconnected below altitudes 792 m (north) and 789.4 m (south). Characteristic elevation-area and elevation-volume equations are segmented for lake below ridge altitude (794.3 m) whereas single relationships prevail for a single Lake Rukwa. Comparison of lake volumes estimated by refined and old equations indicated underestimation of lake stored volumes between 782.2 m and 805.65 m altitudes and overestimation thereafter by the old equations although the under/over-estimation remained within 10% between 801 m and 812 m. Old elevation-area equations underestimate lake surface area of up to 796.8 m, thereafter overestimate the lake area up to an altitude of 804.85 m and above this altitude underestimation re-appear. The old equations under/over-estimation, however, remains within 11% for altitudes between 794.3 m and 810 m. The refined equations indicate surface areas of north and south lakes at ridge altitude to be 2,554.4 and 837.1 km2 , respectively forming a 3,391.5 km2 lake while at its highest recorded historical elevation of 804.69 m, Lake Rukwa is 183 km long and 17-51 km wide occupying an area of 5,614.7 km2 (north: 4,409.8 km2; south: 1,204.9 km2) and containing 58.243 km3 of water (north: 44.318 km3; south: 13.925 km3). The developed characteristic equations can be used for water management studies of Lake Rukwa

Estimating Flood Magnitudes of Ungauged Urban Msimbazi River Catchment in Dar es Salaam, Tanzania

More often daily climate data have been used in hydrological models to estimate flood flows in small data scarce urban and rural catchments where flow peaking and recession are quick within short periods of few hours. This study assesses magnitudes of error of using daily climate data to simulate flood flow hydrographs of a small urban river catchment, Msimbazi River. Spot discharge measurements were available for Apr-May 2014 and MarMay 2015 periods.10-minutes climate records were available at one station, daily temperatures at two stations while daily rainfall records at several stations within the catchment. Visual analysis characterise rainfall events during the 2011, 2014 and 2015 floods, Thiessen-polygon was used for catchment rainfall, Hargreave-Samani model for catchment potential evapotranspiration and flow hydrographs were estimated by calibrated HBV model. Length weighted channel slope was estimated from segment slopes established from available topographical maps and used in estimating time of concentration for the catchment using Kirpich method. Results indicated that simulated flood hydrographs using 10-minutes climate inputs produced higher flood peaks for both December 2011 (peak: 471.6 m3 /s) and April 2014 (peak: 393.5 m3 /s) and expected hydrograph recession behaviour reproducing the Kirpich estimate of time of concentration of 7 hours. Simulated flood peaks using daily climate inputs were 252.3 m3 /s for December 2011 and 205 m3 /s for the April 2014 event being 53% and 52% of those simulated from 10-minutes climate inputs. Despite usefulness of 10-minute climate data, these data are required at more stations within the catchment for reliable simulation of fast receding urban floods and therefore more automatic weather stations are needed in Dar es Salaam

Rainfall variability in Southern Africa, its influences on streamflow variations and its relationships with climatic variations

Hydrological variability involving rainfall and streamflows in southern Africa have been often studied separately or have used cumulative rainfall and streamflow indices. The main objective of this study was to investigate spatio-temporal variations of rainfall, their influences on streamflows and their relationships with climatic variations with emphasis on indices that characterise the hydrological extremes, floods and droughts. It was found that 60-70% of the time when it rains, daily rainfalls are below their long-term averages and daily amounts below 10 mm are the most frequent in southern Africa. Spatially, climatologies of rainfall sub-divided the southern African subcontinent into the dry western/southwestern part and the “humid” eastern and northern part. The daily amounts below 20 mm contribute significantly to annual rainfall amounts in the dry part while all types of daily rainfall exceeding 1 mm have comparable contributions in the humid part. The climatologies indicated the highest likelihood of experiencing intense daily events during the core of the wet seasons with the highest frequencies in central Mozambique and the southern highlands of Tanzania. Interannual variations of rainfall indicated that significant changes had occurred between the late-1940s and early-1980s, particularly in the 1970s. The changes in rainfall were more evident in the number of daily rainfall events than in rainfall amounts, led generally to increasing early summer and decreased late summer rainfall. It was also found that intra-seasonal dry day sequences were an important parameter in the definition of a rainy season’s onset and end in southern Africa apart from rainfall amounts. Interannual variations of the rainy season characteristics (onset, end, duration) followed the variations of rainfall amounts and number of events. The duration of the rainy season was affected by the onset (Tanzania), onset or end (tropical southern Africa - southwestern highlands of Tanzania, Zambia, northern Zimbabwe and central Mozambique) and end (the remaing part of southern Africa). Flow duration curves (FDCs) identified three types of rivers (ephemeral, seasonal and perennial) in southern Africa with ephemeral rivers found mainly in the dry western part of the region. Seasonal streamflow patterns followed those of rainfall while interannual streamflow variations indicated significant changes of mean flows with little evidences of high and low flow regime changes except in Namibia and some parts of northern Zimbabwe. It was, however, not possible to provide strong links between the identified changes in streamflows and those in rainfall. Regarding the influences of climate variability on hydrological variability in southern Africa, rainfall variations in southern Africa were found to be influenced strongly by ENSO and SST in the tropical Indian ocean and moderately by SST in the south Madagascar basin. The influence of ENSO was consistent for all types of daily rainfall and peaks for the light and moderate (< 20 mm) events in the southern part and for the intense events in the northern part. SST in the tropical Indian ocean influence the light and moderate events while SST close to the region influence the heavy events. However, the relationships experienced significant changes in the mid-1950s and in the 1970s. The former changes led to improved associations while the latter deteriorated or reversed the relationships. The influences of climatic variables on streamflows and rainy season characteristics were inferred from the rainfall-streamflow and rainfall-climatic variables relationships

Land Cover Change Detection in the Urban Catchments of Dar es Salaam, Tanzania using Remote Sensing and GIS Techniques

In this study, the Maximum Likelihood (ML) classification, Normalized Difference Vegetation Index (NDVI) and Artificial Neural Network (ANN) methods were applied to three (3) Landsat images collected over time (1979, 1998 and 2014), that contained historical land cover features for the urban catchments of Dar es Salaam. Five major land cover classes were identified, mapped, and the land cover changes investigated. The major land cover changes observed from post-classification comparisons of the classified images are: the forest land losing 17.09% of its area in the period 1979-1998 to other land covers, mainly turning to grassland, and from 1998 to 2014, 17.55% of the total study area turned to high and medium/low-density built-up areas. Growth in urban settlement and infrastructure was observed to be continuously increasing and the high and medium/low-density built-up areas are projected to cover 66.09% of the total area by 2030; this is an increment of 29.01% from 37.08% coverage in 2014. This shift in land cover was further validated by the results of the Normalized Difference Vegetation Index (NDVI) analysis which showed a similar trend (shift from thick vegetation towards barren land) from 1998 to 2014, with median NDVI values changing from 0.52 to 0.36 respectively. These land cover changes are most likely the results of activities related to the increase in total population, the influx of urban population and the growth of the economy.Keywords: Maximum Likelihood, NDVI, Artificial Neural Network, Landsat, QGIS

Suitability of Flood Hazard Assessment Methods for Tanzania: A Case of Little Ruaha and Upper Ngerengere Catchment

Understanding the applicability of flood quantile estimation methods in flood hazard assessment is fundamental for planning, prevention, and management of flood risks. Therefore, this study evaluates and compares three hydrological methods, namely Hydrologiska Byråns Vattenbalansavdelning (HBV), Soil Conservation Service-Curve Number (SCS-CN), and regional regression equation (RRE), to estimate flood quantiles embedded in the existing flood damage assessment framework by applying them to two different river catchments, Little Ruaha (LR) and Upper Ngerengere (UN), Tanzania. The evaluation of method performance was carried out using three standard statistical measures for data from 1954 to 2010 and the 1971–1988 period in LR and UN catchments (LRC and UNC). The findings indicated that no single approach could fit all catchments and return periods for these case studies. Overall performance indicated that the RRE method provides more accurate and consistent quantile estimates than other approaches. These findings indicate that spatial scale, model structure, parameters, and hydro-climatic data condition are the most important elements influencing the suitability of the supplied methods for flood risk assessments, which serve as the foundation for developing an improved flood damage assessment framework. Keywords:&nbsp;Flood Quantiles; Estimate Methods; Flood Risk Management; Little Ruaha; Ngerenger

Spatial and Temporal Variation of Rainfall and Streamflow in the Kikuletwa Catchment of Upper Pangani Basin, Tanzania

Streamflow and rainfall records from 1980 to 2015 as a (common period for the analyzed stations) were used to analyze the variations of rainfall and streamflow in the Kikuletwa catchment. Also, the analysis of the longest time series available at each station up to 2015 (referred to as the whole series in this study) was conducted to relate past rainfall and streamflow changes, at the tributaries of Kikuletwa River located above the Rundugai natural springs as recommended from previous studies. Various methods such as simple statistics of the mean, standard deviation, coefficient of variance, and graphs were used to analyze intra-annual variations. Multi-year variability was analyzed by trends and change point tests using MannKendall and Pettitt tests respectively. The results of the study revealed the spatial variation of rainfall which was related to elevation differences. The streamflow amounts were found to vary from upstream to downstream. The whole time series analysis of annual rainfall and streamflow amounts revealed a decrease in rainfall and streamflow amounts for almost all stations though a significant decrease was only observed at two stations located on the upstream (for rainfall) and two stations located above the Rundugai natural springs (for streamflow). During 1980 – 2015, trends analysis indicated significant decreasing trends only in annual rainfall amounts at the two stations located on the upstream of the catchment with Z values of -3.20 and -2.68. In contrast, average annual flow trends analysis indicated significant decreasing trends at four stations out of five with Z values of -2.52, -2.28, -1.99 and -3.4 and, at one station insignificant decreasing trend was observed. The findings revealed the existence of other catchment influences to the streamflow changes other than rainfall during 1980-2015. The study provides very useful information that decides the necessity for separating the climate and human influences to the streamflow changes to find the most influencing factor

Future Trade-Off for Water Resource Allocation: The Role of Land Cover/Land Use Change

This research article was published by MDPIGlobal croplands, pastures, and human settlements Have expanded in recent decades. This is accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. In sub-Saharan Africa, policies are implemented without critical consideration; e.g., agricultural expansions impair ecosystem services. We studied land use/cover and the associated rate of change for four time epochs, i.e., 1991, 2001, 2011, and 2021. This employed remote sensing and GIS techniques for analysis, while future projections were modeled using cellular automata and the Markov chain. The kappa coefficient statistics were used to assess the accuracy of the final classified image, while reference images for accuracy assessment were developed based on ground truthing. Overall change between 1991 and 2021 showed that major percentage losses were experienced by water, forest, woodland, and wetland, which decreased by 8222 Ha (44.11%), 426,161 Ha (35.72%), 399,584 Ha (35.01%), and 105,186 Ha (34.82%), respectively. On the other Hand, a percentage increase during the same period was experienced in cultivated land, built-up areas, and grasslands, which increased by 659,346 Ha (205.28%), 11,894 Ha (159.93%), and 33,547 Ha (98.47%), respectively. However, this expansion of thirsty sectors Has not reversed the increasing amount of water discharged out of the Kilombero River catchment. We recommend the promotion of agroforests along with participatory law enforcement and capacity building of local communities’ institutions

Climate adjustments over Africa accompanying the Indian monsoon onset

International audienc

Analysis of spatial and temporal trend of hydro‑climatic parameters in the Kilombero River Catchment, Tanzania

This research article was published by Springer Nature Limited in 2023Inadequate knowledge on actual water availability, have raised social-economic conficts that necessitate proper water management. This requires a better understanding of spatial–temporal trends of hydro-climatic variables as the main contributor to available water for use by sectors of economy. The study has analysed the trend of hydro-climatic variables viz. precipitation, temperature, evapotranspiration and river discharge. One downstream river gauge station was used for discharge data whereas a total of 9 daily observed and 29 grided satellite stations were used for climate data. Climate Hazards Group InfraRed Precipitation was used for precipitation data and Observational Reanalysis Hybrid was used for Temperature data. Mann–Kendall Statistical test, Sen’s slope estimator and ArcMap Inverse Distance Weighted Interpolation functionality were employed for temporal, magnitude and spatial trend analysis respectively. Results confrmed that, spatially, there are three main climatic zones in the study area viz. Udzungwa escarpment, Kilombero valley and Mahenge escarpment. On temporal analysis, with exception of the declining potential evapotranspiration trend, all other variables are on increase. This is with catchment rates of 2.08 mm/year, 0.05 °C/year, 0.02 °C/year, 498.6 ­m3 /s/year and − 2.27 mm/year for precipitation, Tmax, Tmin, river discharge and PET respectively. Furthermore, rainfalls start late by a month (November) while temperatures picks earlier by September and October for Tmax and Tmin respectively. Water availability matches farming season. However, it is recommended to improve water resources management practices to limit fow impairment as expansions in sectors of economy are expected. Furthermore, landuse change analysis is recommended to ascertain actual trend and hence future water uptake