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Long-term trends of river flow, sediment yield and crop productivity of Andit tid watershed, central highland of Ethiopia
Long-term trends of river flow, sediment yield and crop productivity of Andit tid watershed,...
Woldemarim, Ayele; Getachew, Tilahun; Chanie, Tilashwork
ALL EARTH 2023, VOL. 35, NO. 1, 3–15 https://doi.org/10.1080/27669645.2022.2154461 Long-term trends of river flow, sediment yield and crop productivity of Andit tid watershed, central highland of Ethiopia a a b Ayele Woldemarim , Tilahun Getachew and Tilashwork Chanie a b Department of soil and water management, Debre Brihan Agricultural Research Center, Debre Brihan, Ethiopia; Department of soil and water management, Adet Agricultural Research Center, Adet, Ethiopia ABSTRACT ARTICLE HISTORY Received 27 July 2022 Andit tid watershed is part of Blue Nile basin located in the central highlands of Ethiopia. The Accepted 29 November 2022 lack of data and information at watershed level resulted in different conclusions from trend studies of river flow, sediment yield and crop productivity at a basin scale. There is an KEYWORDS opportunity to improve water and land if it can be underpinned by a better scientific under- River flow; sediment yield; standing of trends of flow, sediment yield and crop production at the basin level. This research crop production; trends; is carried out using descriptive statistics, Mann–Kendall (MK) and Pettit’s test to determine the Mann–Kendall test potential trends of river flow, sediment yield and crop productivity using Andit tid watershed case. The result showed that there was high variability of interannual river flow with CV >30%. The Pettitt test showed a significant abrupt change in monthly (March, July, August, September and October) and seasonal (summer and winter) river flow. The Pettitt test result of sediment yield and crop production showed no change. MK test showed a significant (P < 0.05) decreas- ing trend in March, August, September and October river flow. The other MK values showed no significant trends for all parameters. Researchers should consider representative watershed- based information and data for the analysis and interpretation of large basins. studies that were conducted in the same year resulted 1. Introduction in different trends and variability output. Nile (‘Abbay’ in its local name) is the longest river in the Studies that estimate annual sediment load from world and one of the most water-limited basins. Eighty- the Upper Blue Nile basin also reported different sedi- five percent of the total amount of water entering Lake ment yield results. For instance, as reviewed by Nasser at the Aswan dam originates from the Ethiopian Gebremicael et al. (2013) the annual sediment yield Highlands (Sutcliffe & Parks, 1999). The Upper Blue Nile of the Blue Nile basin ranged from 111 × 106 to River basin which contributes over 60% of the Nile’s 140 × 106 tons/year. To minimise this type of research water (Conway, 2000) is crucial for the socio-economic output difference, basin-representative, experimental development and environmental stability of Ethiopia, watersheds offer essential knowledge in recognising Sudan and Egypt. These countries have experienced the hydrological and erosive processes including serious problems in their storage reservoirs and irriga- trends of crop production. The only long-term river tion canals due to excessive sediment loads (Betrie et al., flow, sediment and crop yield data monitoring has 2011). Seleshi et al. (2011) did research at the border of been carried out in the small Soil Conservation Sudan and reported that sediment concentrations were Research Project (SCRP) watersheds which were estab- −1 as high as 12.3 g L . In this circumstance, studying the lished in 1981 and conserved with soil and water con- trend and variability of river flow and sediment yield is servation structures in the upper reaches of large crucial to exactly put measures to lengthen the lifespan basins (Steenhuis et al., 2014). For example, discharge of reservoirs and canals. assessment at the watershed outlet is the standard The long-term trend analysis of runoff in the Blue Nile analysis and forms the basis for the development of basin was studied by many scholars, for instance, many fundamental theories of runoff (Blume et al., Gebrehiwot et al. (2010), Kebede (2009), Legesse et al. 2007; Erturk, 2010). Andit tid watershed is one of the (2003) and Tesemma et al. (2010). However, the conclu- SCRP watersheds, which is representative of the upper sions of these studies have not shown a common con- Blue Nile basin. In this watershed, two rivers (Gudi sensus on the trends and variability of flow. Legesse et al. Bado and Wani Gedel) confluence 150 m above the (2003) reported an increasing trend; Tesemma et al. gauging station and create the river called ‘Hulet (2010) reported no change and Kebede (2009) and Wenz’. In the gauging station of this river, daily river Gebrehiwot et al. (2010) reported a decreasing trend of flow and event-based sediment samples data have the annual flow of the Blue Nile basin. Furthermore, been collected since July 1982. CONTACT Ayele Woldemarim email@example.com Debre Brihan Agricultural Research Center, Po Box 112, Debre Brihan, Ethiopia © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 4 A. WOLDEMARIAM ET AL. In Ethiopian highlands including Andit tid for further studies and for deciding other watershed- watershed in the upper Blue Nile basin, soil erosion based decisions rather they used simulated model and sedimentation become the major threat to crop output. production by washing the top fertile soil. The hillside and mountainous nature, and shape of this watershed 2. Methodology foster the formation of flooding and soil erosion. Numerous studies have been conducted in Andit tid 2.1. Description of the watershed over the years, including ones on suitability mapping Andit tid watershed is situated at 39°43’ E longitudes for major crops (Yohannes & Soromessa, 2018); land and 9°48’ N latitudes apart 180 km northeast of the capability classification (Yohannes & Soromessa, 2019); capital city of Ethiopia, Addis Ababa (Figure 1). The land use and land cover change impact on livelihoods altitude of the catchment ranges between 3040 (at and soil erosion (Abrham Tezera et al., 2016); soil ero- the outlet) to 3550 (at the elevated area of the sion estimation and mapping (Desalegn et al., 2018); watershed) m.a.s.l. The mean annual rainfall is 1585.2 comparison of CFSR and conventional weather data mm, and the minimum and maximum temperatures (Roth & Lemann, 2015) and rainfall-runoff modelling are 7°C and 17°C, respectively. The minimum and max- (Engda, 2009). While the very crucial watershed char- imum soil surface temperatures are 8°C and 20°C, acteristics of how are the monthly, seasonal and respectively. The agro-climatic zone of the watershed annual trends of the river flow and sediment yield is Wet Dega/Wet high Dega. The dominant soil types of were not addressed. Around 50 fixed plots in different the watershed are Humic and ochric Andosols, positions of bunds were set up in the watershed to Fluvisols, Regosols and Lithosols. It is characterised by assess the effects of soil and water conservation on severe soil degradation, especially in the lower part of agricultural productivity, and for the past 24 years, we the catchment. Soil fertility is limited through low pH have been gathering data on crop yield and agronomic and N- and P-deficiency. Smallholder mixed farming parameters from these plots. The effects of these soil systems including grain production (barley) and Ox- and water conservation structures particularly Fanya plough farming oriented with free grazing practices is juu and soil bunds on agricultural yield have not been the major practice of the watershed community. thoroughly studied. The productivity trends of the major crops grown in the watershed were not also studied. Studying these issues is therefore anticipated 2.2. River flow and sediment yield data collection to increase the watershed community’s understanding of the importance of soil and water conservation for The river gauging stage was monitored continuously increasing crop productivity, preparing for seasonal using a limnigraph accompanied by manual water- changes in peak river flow, and using various mulching level measurements during storm events. The stage techniques or green cover in the early rainy seasons to height (water level) which was monitored using limni- reduce bulk erosion. As an objective, this study was graph and the manual ruler was converted into dis- focused to (i) analyse the long-term trend and varia- charge using the following equation (Bosshart, 1997): bility of runoff, sediment yield and crop productivity of 2:749 QðH< ¼ 67Þ ¼ 0:03� H (1) Andit tid watershed using statistical methods and (ii) characterise the watershed based on river flow, sedi- 1:35 Qð67< H � 250Þ ¼ 10:846� H (2) ment yield and crop production using the data from 1994 to 2017. where Q is the runoff discharge in l/s and H is the true In this paper, we addressed the monthly, seasonal water level (height of the stage) in cm. and annual trends of runoff and sediment loss and Every 10 min during runoff events, one-litre grab the year-to-year changes in crop productivity in the samples were taken to measure the amount of sedi- watershed. Therefore, this study tells how the river ment immediately when the colour of the water turned flow, sediment yield and crop production have chan- brown. The sampling rate was reduced to 30 min and ged over time in the watershed; whether or not these hourly intervals once the water level dropped and the parameters are increasing or decreasing. From the colour returned to light brown. The overall stream flow result of this study, researchers can go further about and an estimate of the suspended material carried by the factors causing any abrupt happened in all studied the flow at that particular time interval were calculated parameters. It can be used as a ground truth reference using sediment samples and manual measurements of to evaluate the model simulation outputs of different the river’s water level. By oven-drying the one-litre studies that have been conducted in the Blue Nile samples and weighing the oven-dried soil, the quantity basin. Policymakers can use the result of this research of sediment load within the sample was determined. as the benchmark for making appropriate land man- The sum of the total water flow per time and the agement decisions, improving local land productivity sediment concentration as obtained from the and enhancing livelihood. The researchers can use it 1-L sample was then multiplied to get the total soil ALL EARTH 5 Figure 1. The location map of the study watershed. loss for that measurement period. Suspended sedi- tillage, predecessor crops and crop types was included. ment concentration was also determined by dividing Zone A (above the terrace or zone of deposition), Zone the weight of dry sediment by the volume of water B (between terraces) and Zone C (below terraces or (Miller et al., 2015; Womber et al., 2021). zone of transportation) were the locations from which For the seasonal analysis, the data have been samples were taken. The reason for different positions divided into four seasons based on the local situation, sampling was to identify the impact of soil conserva- which are winter (Bega) (December–February), Belg tion on crop production. The samples were taken from (March–May), summer (kiremt) (June–September) and 4 m of land for each plot position and we extrapolate spring (Tsedey) (October–November). The collected to kg/ha. The crop types, crop management and time time-series data have some missing values. The miss- of cropping are decided by the land owners (farmers). ing value is interpolated during data processing. We In this data, all the crop types may not be sown in the used the Auto-Regressive Integrated Moving Average fixed and non-fixed plots all over the study period (ARIMA) function to fill in the missing data. ARIMA (1994–2017). As a result, we only looked at the data model is considered a powerful and extensively used from a single crop’s growing years when analysing the statistical tool to analyse and predict time-series data. trend in crop production. This information was primar- The main advantages of the model are that it can ily used to assess how crop production in the detect seasonal changes and consider serial correlation watershed would be affected by soil and water con- within the time series (Yurekli et al., 2007). servation and other new agronomic practices. 2.3. Crop yield data collection 2.4. Homogeneity test of the data Crop yield samples were taken from 35 fixed and 50 The Pettitt test (Pettitt, 1979) was chosen to detect non-fixed plots located throughout the watershed dur- inhomogeneity in the time-series data. This test ing each cropping season. During samplings, informa- detects shifts in the average and calculates their sig- tion on crop management, inputs, soil depth, slope, nificance (Liu et al., 2012) in a hypothesis test. In the 6 A. WOLDEMARIAM ET AL. Pettitt test, the null hypothesis stated that the data are Xi is taken as a reference point which is compared with homogeneous, as against the alternative hypothesis the rest of the data point’s Xj so that tells that the data have abrupt change. The empirical If S > 0, then later observations in the time series significance level (p-value) was computed using tend to be larger than those that appear earlier in the XLSTAT 2020 v.3. In this study, this test was performed time series and it is an indicator of an increasing trend, at a significance level of 5%. while the reverse is true if S < 0 and this indicates a decreasing trend. Under the null hypothesis of no trend, the statistic S follows an approximately normal distribution with 2.5. Mann–Kendall (MK) test mean zero and variance (Kendall, 1975) statistic is Mann–Kendall trend test is an extremely important given as parameter for watershed modelling, and studying nðn 1Þð2nþ 5Þ