Quantifying the available and useable water is critical work in any water resource study and design project. However, it is challenging to provide a robust and accurate estimation of water use and distribution for better water resource management and planning. This study aims to estimate the water use by different sectors, including water supply and irrigation sectors, by adopting estimated demand and supply water quantity. The current total population of the subbasin has been estimated to be 1.21 million. Thus, in the subbasin, current water use is estimated as follows: domestic and nondomestic water use in the rural area is 3.5 Mm3/year and 0.174 Mm3/year, respectively. The domestic water use of the towns is 12.77 Mm3/year. The industrial water use of the urban areas is 21.2 Mm3/year, whereas the commercial, public, and institutional water use are 1.87 Mm3/year. The real loss for all the water supply uses is 7.8 Mm3/year. Thus, the total current water supply uses are about 47.225 Mm3/year. From the existing irrigation schemes, about 10,254.8 ha areas are irrigated by both smallholders and different investors, growing vegetables, cereals, and fruit trees. The annual irrigation water requirements of these schemes are computed to be 151.55 Mm3. Livestock water demand of the subbasin was assessed and estimated based on the population and consumption rates of the species. Currently, the subbasin has a total livestock population of 1,527,835, and the water demand of which is estimated at 5.3 Mm3 per annum. Hence, the total current water use estimate of the subbasin is 204.1 Mm3.
Understanding hydroclimatic variability and trend for the past four decades in the Upper Tekeze River basin is significant for future sustainable water resource management as it indicates regime shifts in hydrology. Despite its importance for improved and sustainable water allocation for water supply-demand and food security, varying patterns of streamflow and their association with climate change are not well understood in the basin. The main objective of this study was to characterize, quantify, and validate the variability and trends of hydroclimatic variables in the Upper Tekeze River basin at Ghba subbasin using graphical and statistical methods for homogeneous stations for the time period from 1953 to 2017, not uniform at all stations. The rainfall, temperature, and streamflow trends and their relationships were evaluated using the regression method, Mann–Kendall (MK) test, Spearman’s rho (SR) test, Sen’s slope, and correlation analysis. The analysis focused on rainfall, temperature, and streamflow collected from 11 climate and six hydrostations. For simplicity to discuss the interannual and temporal variability the stations were categorized into two clusters according to their record length, category 1 (1983–2017) and category 2 (1953–2017). About 73% and 27% of the rainfall stations exhibited normal to moderate annual rainfall variability. The MK and SR test showed that most of the significant trends in annual rainfall were no change except in one station decreasing and the test also showed no significant change in temperature except in three stations showed an increasing trend. Overall, streamflow trends and change point timings were found to be consistent among the stations and all have shown a decreasing trend. Changes in streamflow without significant change in rainfall suggest factors other than rainfall drive the change. Most likely the observed changes in streamflow regimes could be due to changes in catchment characteristics of the subbasin. These research results offer critical signals on the characteristics, variability and trend of rainfall, temperature, and streamflow necessary to design improved and sustainable water allocation strategies.
In most studies on hydroclimatic variability and trend, the notion of change point detection analysis of time series data has not been considered. Understanding the system is crucial for managing water resources sustainably in the future since it denotes a change in the status quo. If this happened, it is difficult to distinguish the time series data’s rising or falling tendencies in various areas when we look at the trend analysis alone. This study’s primary goal was to describe, quantify, and confirm the homogeneity and change point detection of hydroclimatic variables, including mean annual, seasonal, and monthly rainfall, air temperature, and streamflow. The method was employed using the four-homogeneity test, i.e., Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test at 5% significance level. In order to choose the homogenous stations, the test outputs were divided into three categories: “useful,” “doubtful,” and “suspect.” The results showed that most of the stations for annual rainfall and air temperature were homogenous. It is found that 68.8% and 56.2% of the air temperature and rainfall stations respectively, were classified as useful. Whereas, the streamflow stations were classified 100% as useful. Overall, the change point detection analyses timings were found at monthly, seasonal, and annual time scales. In the rainfall time series, no annual change points were detected. In the air temperature time series except at Edagahamus station, all stations experienced an increasing change point while the streamflow time series experienced a decreasing change point except at Agulai and Genfel hydro stations. While alterations in streamflow time series without a noticeable change in rainfall time series recommend the change is caused by variables besides rainfall. Most probably the observed abrupt alterations in streamflow could result from alterations in catchment characteristics like the subbasin’s land use and cover. These research findings offered important details on the homogeneity and change point detection of the research area’s air temperature, rainfall, and streamflow necessary for the planers, decision-makers, hydrologists, and engineers for a better water allocation strategy, impact assessment and trend analyses.
This paper proposes a methodology to model floodplain inundation patterns in data-scarce areas by using global remote sensing data. In particular, MODIS data are used for hydraulic model (HEC-RAS) calibration and validation purposes which is coupled with Geographic Information System (GIS) to map flood extent areas, while NASA's SRTM is used to describe floodplain topography. The Fogera floodplain (the upper Blue Nile in Ethiopia) is used as an example application to illustrate the methodology. To this end, parameter and input uncertainty is explicitly taken into account and visualized via probabilistic floodplain maps of the ensemble simulation. In view of that, model performance, reliability, and predictive uncertainties are critically discussed. This approach revealed that abetter characterization and visualization of the flood hazard. Also, the study investigates the impact of land-use changes on floodplain inundation patterns using a SWAT modeling system and the propagation of this land-use change in flood inundation patterns is seen again via probabilistic flood maps. This helps planners to use remote sensing data to model and monitor flooding.
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