Spatio Temporal Drought Characterization And Forecasting Using Indices And Artificial Neural Networks A Case Of The Upper Tana River Basin Kenya
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Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya
Doctoral Thesis / Dissertation from the year 2016 in the subject Engineering - Civil Engineering, grade: 80.0, Egerton University, course: AGRICULTURAL ENGINEERING, language: English, abstract: Drought is a critical stochastic natural disaster that adversely affects water resources, ecosystems and people. Drought is a condition characterized by scarcity of precipitation and/or water quantity that negatively affects the global, regional and local land-scales. At both global and regional scales, drought frequency and severity have been increasing leading to direct and indirect decline in water resources. Increase in drought severity and frequency in the upper Tana River basin, Kenya, water resources systems have been adversely affected. Timely detection and forecasting of drought is crucial in planning and management of water resources. The main objective of this research was to formulate the most appropriate models for assessment and forecasting of drought using Indices and Artificial Neural Networks (ANNs) for the basin. Hydro-meteorlogical data for the period 1970-2010 at sixteen hydrometric stations was used to test the performance of the indices in forecasting of the future drought at 1, 3, 6, 9, 12, 18 and 24-months lead times, by constructing ANN models with different time delays. Drought conditions at monthly temporal resolution were evaluated using selected drought indices. The occurrence of drought was investigated using non-parametric Man-kendall trend test. Spatial distribution of drought severity was determined using Kriging interpolation techinique. In addition, a standard Nonlinear-Integrated Drought Index (NDI), for drought forecasting in the basin was developed using hydro-meteoroogical data for the river basin. The results of spaial drought show that the south-eastern parts of the basin are more prone to drought risks than the north-western areas. The Mann-Kendall trend test indicates an increasing drought trend in the south-eastern and no trend in north-western areas of the basin. Development of Surface Water Supply Index (SWSI) function, NDI and characteristic curves defining the return period and the probability of different drought magnitudes based on Drought Indices (DIs) was achieved. Drought Severity-Duration-Frequency (SDF) curves were developed. The formulated NDI tool can be adopted for a synchronized assessment and forecasting of all the three operational drought types in the basin. The results can be used in assisting water resources managers for timely detection and forecasting of drought conditions in prioritized planning of drought preparedness and early warning systems.
Drought Forecasting Under Climate Change Scenarios Using Artificial Neural Networks for Sustainable Water Resources Management in Upper Tana River Basin, Kenya
Author: PhD Mutua (Rer. nat., Prof. Dr.-Ing. Benedict Mwavu)
language: en
Publisher:
Release Date: 2018
Climate change has continued to impact negatively on water resources globally. For instance, extreme weather conditions especially the drought phenomena have become frequent in Africa. This has prompted water engineers and hydrologists to formulate mitigation and adaptation measures to address these challenges. The frequency of drought event of a defined severity for a defined return period is fundamental in planning, designing, operating and managing water resources systems within a basin. This paper presents an analysis of the hydrological drought frequency for the upper Tana River basin in Kenya using the absolute Stream flow Drought Index (SDI) and modified Gumbel technique. The study used a 41-year (1970-2010) stream flow data and forecasted hydrological droughts for 2, 5, 10, 20, 50, 100, 200, 500 and 1000-year return periods in relation to the selected stream flows. The results provide an overview of drought trends within the river basin and therefore would be very useful in applying drought adaptation policies by water resource managers.
Spatio-temporal characterisation of drought: data analytics, modelling, tracking, impact and prediction
Studies of drought have increased in light of new data availability and advances in spatio-temporal analysis. However, the following gaps still need to be filled: 1) methods to characterise drought that explicitly consider its spatio-temporal features, such as spatial extent (area) and pathway; 2) methods to monitor and predict drought that include the above-mentioned characteristics and 3) approaches for visualising and analysing drought characteristics to facilitate interpretation of its variation. This research aims to explore, analyse and propose improvements to the spatio-temporal characterisation of drought. Outcomes provide new perspectives towards better prediction. The following objectives were proposed. 1) Improve the methodology for characterising drought based on the phenomenon’s spatial features. 2) Develop a visual approach to analysing drought variations. 3) Develop a methodology for spatial drought tracking. 4) Explore machine learning (ML) techniques to predict crop-yield responses to drought. The four objectives were addressed and results are presented. Finally, a scope was formulated for integrating ML and the spatio-temporal analysis of drought. Proposed scope opens a new area of potential for drought prediction (i.e. predicting spatial drought tracks and areas). It is expected that the drought tracking and prediction method will help populations cope with drought and its severe impacts.