Evapotranspiration (ET) plays a crucial role in integrated water resources planning, development and management, especially in tropical and arid regions. Determining ET is not straightforward due to the heterogeneity and complexity found in real-world hydrological basins. This situation is often compounded in regions with limited hydro-meteorological data that are facing rapid development of irrigated agriculture. Remote sensing (RS) techniques have proven useful in this regard. In this study, we compared the daily actual ET estimates derived from 3 remotely-sensed surface energy balance (SEB) models, namely, the Surface Energy Balance Algorithm for Land (SEBAL) model, the Operational Simplified Surface Energy Balance (SSEBop) model, and the Simplified Surface Balance Index (S-SEBI) model. These products were generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery for a total of 44 satellite overpasses in 2005, 2010, and 2015 in the heterogeneous, highly-utilized, rapidly-developing and data-limited Kilombero Valley (KV) river basin in Tanzania, eastern Africa. Our results revealed that the SEBAL model had a relatively high ET compared to other models and the SSEBop model had relatively low ET compared to the other models. In addition, we found that the S-SEBI model had a statistically similar ET as the ensemble mean of all models. Further comparison of SEB models’ ET estimates across different land cover classes and different spatial scales revealed that almost all models’ ET estimates were statistically comparable (based on the Wilcoxon’s test and the Levene’s test at a 95% confidence level), which implies fidelity between and reliability of the ET estimates. Moreover, all SEB models managed to capture the two spatially-distinct ET regimes in KV: the stable/permanent ET regime on the mountainous parts of the KV and the seasonally varied ET over the floodplain which contains a Ramsar site (Kilombero Valley Floodplain). Our results have the potential to be used in hydrological modelling to explore and develop integrated water resources management in the valley. We believe that our approach can be applied elsewhere in the world especially where observed meteorological variables are limited.

Information on aquifer processes and characteristics across scales has long been a cornerstone for understanding water resources. However, point measurements are often limited in extent and representativeness. Techniques that increase the support scale (footprint) of measurements or leverage existing observations in novel ways can thus be useful. In this study, we used a recession-curve-displacement method to estimate regional-scale aquifer transmissivity (T) from streamflow records across the Kilombero Valley of Tanzania. We compare these estimates to local-scale estimates made from pumping tests across the Kilombero Valley. The median T from the pumping tests was 0.18 m 2 /min. This was quite similar to the median T estimated from the recession-curve-displacement method applied during the wet season for the entire basin (0.14 m 2 /min) and for one of the two sub-basins tested (0.16 m 2 /min). On the basis of our findings, there appears to be reasonable potential to inform water resource management and hydrologic model development through streamflow-derived transmissivity estimates, which is promising for data-limited environments facing rapid development, such as the Kilombero Valley.

This review provides an assessment of the evolution of hydrological modelling for Eastern Africa. We outline the historical development and perspectives considered as this region, like many regions around the world, sees increasing attention on how water resources can be sustainably developed. We emphasize the spatial scales and modelling approaches that typify the region and how these have changed with time. The review is done in two complementary approaches. The first approach is to explore a practical, real-world example providing context for the Eastern Africa region and the water resource development issues presently faced. We use Tanzania's 34 000 km2 Kilombero Valley (KV) river basin to explore implications of significant gaps in data and modelling scales. We hypothesize that these gaps limit the application of the current state-of-the-science to inform water management policy and practice under current and estimated future conditions. In our second approach, we investigate possible solutions to bridge these gaps through a review of case studies from other Eastern Africa's basins across a range of sizes. Our result highlight that some applications of the models considered under this review anticipated more recent international developments as indicated in Predictions in Ungauged Basins and Panta Rhei initiatives. Through this review, it is clear that there is a possibility to improve understanding of the hydrological processes relevant at scales such as the KV river basin through the use of (1) global precipitation datasets (e.g. satellite and/or homogenized observed data) as input data; (2) remote sensing datasets as model evaluation variable; (3) regionalization around the transferability of model parameters; (4) modification of model codes/structures to suit local conditions; and (5) understanding and application of uncertainty principles in hydrological modelling. Given that many regions of the world face similar water resource management challenges as Eastern Africa; it is likely that the findings of this review could help guide how we develop the next generation of modelling approaches to leverage information from various scales.