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Comparing Remotely-Sensed Surface Energy Balance Evapotranspiration Estimates in Heterogeneous and Data-Limited Regions: A Case Study of Tanzania's Kilombero Valley
Stockholm University, Faculty of Science, Department of Physical Geography. University of Dar es Salaam, Tanzania; Water Institute, Dar es Salaam, Tanzania.
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Number of Authors: 52019 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 11, no 11, article id 1289Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. Vol. 11, no 11, article id 1289
Keywords [en]
SEBAL, SSEBop, S-SEBI, MODIS, remote sensing, ensemble mean, model, Kilombero valley
National Category
Earth and Related Environmental Sciences
Research subject
Physical Geography
Identifiers
URN: urn:nbn:se:su:diva-170884DOI: 10.3390/rs11111289ISI: 000472648000032OAI: oai:DiVA.org:su-170884DiVA, id: diva2:1338520
Available from: 2019-07-23 Created: 2019-07-23 Last updated: 2020-03-13Bibliographically approved
In thesis
1. Modelling water resources despite data limitations in Tanzania’s Kilombero Valley
Open this publication in new window or tab >>Modelling water resources despite data limitations in Tanzania’s Kilombero Valley
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Water is a vital resource for survival on the Earth. Sustainable management of water resources is therefore required for the wellbeing of present and future generations. A cornerstone of water resources management is scientific guidance supported by relevant data (in terms of quantity and quality). Most developing regions, where such guidance is crucial due to the intimate connection between natural resources and livelihoods, unfortunately face data limitations. This thesis aims to develop systematic approaches for informing water resources management in data limited regions. Specifically, this work targets Tanzania’s Kilombero Valley (KV) basin as an exemplar of a data limited region undergoing social-economic development through expansion and intensification of agriculture and other water-related interventions. Through a synthesis of lessons learned from the ongoing evolution of hydrological modelling development for water resources management in the Eastern Africa, several promising approaches were identified that could potentially be robust despite data limitations across the region. Putting these approaches into practice, recession analysis based on non-continuous discharge data in conjunction with estimations of the actual evapotranspiration (ET) using remote sensing techniques provided a basis to improve process understanding and help characterize the hydrological systems in the KV basin. This understanding translated into more-informed parameter estimation and improved accuracy when integrated into the development of a hydrological modelling framework using the Soil and Water Assessment Tool (SWAT) model. The modeling framework established for KV has potential to be used as tool for estimating impacts of water resources management strategies relative to future anthropogenic pressures and climatic changes. What is even more promising, is the possibility to derive scientific guidance to assist water resources management in a data limited region through implementation of an integrated workflow which employs state-of-the-science approaches. The methodological framework for model development adopted in this thesis could be applied in any data limited region facing similar challenges as those of the KV basin.

Place, publisher, year, edition, pages
Stockholm: Department of Physical Geography, Stockholm University, 2020. p. 43
Series
Dissertations in Physical Geography, ISSN 2003-2358 ; 5
Keywords
Hydrological modelling, Recession analysis, Remote sensing, Water resources, Evapotranspiration, Transmissivity, SWAT model, Kilombero Valley
National Category
Oceanography, Hydrology and Water Resources
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-179139 (URN)978-91-7911-068-0 (ISBN)978-91-7911-069-7 (ISBN)
Public defence
2020-04-29, digitally via conference (Zoom), public link https://stockholmuniversity.zoom.us/j/941366896, Stockholm, 13:00 (English)
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Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Submitted.

Available from: 2020-04-06 Created: 2020-02-20 Last updated: 2020-05-25Bibliographically approved

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