Climatic impact assessment in upperBlueNilebasin has been studied. The most relevant are presented as follows;
The study of Tazebe et al. (2009) on potentail impact of climatechange on the hydrology and water resource of the Nile River basin using macroscale hydrological model driven by 21 century simulation of temeprature and precipitation downscaled from 11 Global Circulation Model(GCMs) and two emisssion of senarios A2 and B2. The result from average of multimodels showed the entire Nilebasin experience increasing in precipitation in early century (2010-2039) and decrease in (2040-2069) and (2070-2099) also minimum and maximum tempearture increases in all periods. In addition, the Nile River basin stream flow increased in near (2010-2039) and decreases in (2040-2069) and (2070-2099) following precipitation. Shimelis et al. (2011) has carried out climatic changeimpact on the agricultural water resouce variability in the northen highlands of Ethiopia in their study using 15 GCM downscaled result on SWAT model for B1, A1b and A2 emission senarios. They conclued GCM don’t project significant change in rainfall in the region because of half of the models indicating increasing and half suggest decreasing and all models show regional warming in all periods the smallest change indicated in lower emission B1 and the largest change are in higher emission A2. Their output of SWAT model from the downscaled result of GCMs the stream flow shows reduction in all future periods only one from GCM result indicates increasing of stream flow in future periods for all emission senarios.
Many studies have used statistical downscaling techniques for basin scale hydrology analysis. For example,  used four statistical downscaling methods to get 12-km grid resolution data from the original NCEP/NCAR Reanalysis grid size, to simulate wet-day fraction, extreme events, and weather patterns in the United States. Reference  used three statistical downscaling methods for simulating trends of wet spell, dry spell, and rainfall inter-annual variability in the Yellow River Region. Reference  used fifteen different RCP models and several statistical downscaling methods to simulate extreme river flows in 11 catchments in the European region. Reference  used statistically downscaled GCM output to simulate river discharge in the Upper Hanjiang Basin, China. Reference  used a statistical bias correction method for extreme rainfall, normal rainfall and frequency of dry days. Bias correction of heavy rainfall was conducted by using generalized Pareto distribution (GPD), while bias correction of normal rainfall was conducted by using monthly correction and frequency based on gamma distribution.
to assess the future climatechangeimpact on hydrology of the sub-basin after calibration and validation done. The climate scenario for future period was developed from statistical downscaling using the HadCM GCM predictor variables for the two SRES emission scenarios (A2 and B2) for 90 years based on the mean of 20 ensembles and the analysis was done based on three 30-year periods centred on the 2020s (2011-2040), 2050s (2041-2070) and 2080s (2071-2099). The average annual precipitation in the watershed might reduce up to 9.84 %, 23.29 % and 41.51 % and 9.27 %, 20.71 % and 35.37 % in 2020s, 2050s, and 2080s for A2a and B2a emission scenarios, respectively as shown Figure 4 (a) and (b). This finding is not unique to this study, (Girma, 2012) found out that the CCLM downscaling resulted in the upperBlueNile were 1.8, -6.6 and -6.4% in 2020s, 2050s and 2080s respectively. The result of this analysis confirmed also with the (IPCC, 2007) mid-range emission scenario show that compared to the (1961-1990) annual precipitation show a change of between 0.6 to 4.9% and 1.1 to 18.2% for 2030 and 2050 respectively.
4.4.2. Climatechange and LUCC impacts on Missisquoi river watershed nitrogen export
Using projected climate scenarios and LUCC simulations generated by an agent- based land transition model (Y. Tsai et al., 2015), we found that LUCC was the dominant factor rather than climatechange for NO3-N and NH4-N export. In this study, the median annual NO3-N load (2021 – 2050) under proAg was two times larger than the medians under BAU and proForest scenarios. Similarly, the median annual NH4-N load under proAg (2021 – 2050) was 1.16 times larger than the median under the BAU and 1.20 times larger than the median under proForest scenario. The large impact of LUCC is likely because agricultural land is a large non-point nitrogen source due to fertilizer and manure applications (Fan & Shibata, 2015). Thus, more agricultural land means more nitrogen inputs to the watershed. However, NO3-N and NH4-N export did not increase at the same rate under the proAg scenario. NO3-N export initially increased more quickly than NH4-N export. This is likely because plants have uptake preference. And current land use change transition can change plants to unmatured states. The unmatured states need several years to grow and then its uptake ability grows along the time.
Climate and land cover change are very important issues in terms of global context and their responses to environmental and socio-economic drivers. The dynamic of these two factors is currently affecting the environment in unbalanced way including watershedhydrology. The change of observed steam flow is the effect of the combined change of climate and land use land cover of the catchment. In this paper the impact of climatechange on stream flow specifically on extreme flow events were evaluated through application of Soil and Water Assessment Tool (SWAT) model in Gumara watershed, UpperBlueNilebasin Ethiopia. The trend of regional climate, like temperature, rainfall and evapotranspiration of the past 40 years in the study area were tested and then the extent of changes has also been evaluated in terms of monthly bases by using two decadal time periods. The period between1973-1982 was taken as baseline and 2004-2013 was used as changestudy. The efficiency of the model was determined by Nash-Sutcliffe (NS) and Relative Volume error (RVe) and their values were 0.66 and 0.72% for calibration and 0.64 and 1.23% for validation respectively. Both the high and low flows of the catchment have been taken from the simulated stream flow. The high flow has been identified using Annual Maximum (AM) method and the low flow was also identified by using Seven Day Sustained (SDS) minimum annual flow of the river. The impact of climatechange was more significant on high stream flow than low flow of the catchment. Due to climatechange, when the high flow was increasing by 17.08%, the low flow was decreasing by 6%. The overall results of the study indicated that Climatechange is more responsible for stream flow during wet season than dry season.
of projected climatechange on the hydrology of the UpperBlueNile catchment using two model ensembles consisting of five global CMIP5 Earth System Models and ten Regional Climate Models (CORDEX Africa). WATCH forcing data were used to calibrate an eco-hydrological model and to bias-correct both model ensembles using slightly differing approaches. On the one hand it was found that the bias-correction methods considerably improved the performance of average rainfall characteristics in the reference period (1970–1999) in most of the cases. This also holds true for non-extreme discharge conditions between
As hypothesized, better access to early warning about drought and flood before it happened has a significant and positive impact on the likelihood of using agronomic practices and SWC measures on their farmland at 1 and 5% significance level, respectively. The results reveal that getting access to climate warning about drought and/or flood increases the likelihood of using agronomic prac- tices (9%), and SWC measures (8.4%). This implies that farmers who get early warning about drought and/or flood will try to construct SWC measures such as stone bunds, soil bunds, check dams, and hillside terrace either to preserve the moisture content of the soil not to loss of water associated with an increased evapotranspira- tion due to increased drought or to reduce soil erosion to be happened due to the flood. Moreover, early warn- ing mechanism helps farmers to use drought-tolerant varieties to cope with increased temperature. This result supports the findings of earlier researchers on technol- ogy adoption. Phillipo et al.  noted that information on climate warning empowered smallholder farmers to adapt to climate variability and change. Alike to this, a study conducted by Deressa et al.  in assessing cli- mate change adaptations of smallholder farmers in South Eastern Ethiopia revealed that better access to informa- tion on climatechange has a significant and positive impact on the likelihood of using different crop varie- ties. Although it is noted that climate warning system helped increase the uptake of adaptation options, the effective early warning system remains an important concern voiced by survey respondents. This leads to the
CP 19 Project Workshop Proceedings 8 for supplementary irrigation. Canal and reservoir siltation is a major problem exacerbating socio-economic burdens on poor riparian farmers together with the seasonality of the river flow. Solutions lie in improving agricultural practices and conserving water at all levels by all stakeholders: both within Ethiopia and downstream communities. Particularly, there is a paramount need for integrated agricultural water management to overcome the effects of water shortage in small scale agriculture, alleviate poverty and food insecurity; and avert the negative impacts of climatechange in this part of the basin through improving rainfed system. As a component of the water demand assessment, identification of appropriate agricultural water management interventions, analysis of impacts on productivity and poverty alleviation as well as hydrology need to be carried out. This study therefore focuses on various indigenous and research based agricultural water management technological interventions suitable for small holder farmers in the Abbay basin, and attempts to quantify their impacts on productivity of the small holders agricultural as well as the overall livelihood of the farming communities. Therefore the overall objectives are to:
We used the intensity analysis approach of Aldwaik and Pontius (2012) to analyze land use/cover changes in the Mara River basin in East Africa. The land use changes, especially deforestation in the Mau forest at the headwaters, have been blamed for change in the flow regime of the Mara River as well as deterioration of river water quality (Gereta at al., 2009; Kiragu, 2009). Mwangi et al. (2016b) estimated that about 97% of change in the streamflow of Nyangores tributary of the Mara River was caused by land use change, particularly deforestation and expansion of agriculture. A previous land use study by Mati et al. (2008) reported that large scale deforestation occurred in the upper Mara and conversion of rangelands to agriculture in the mid regions of the basin between 1973 and 2000. The Mati et al. (2008) study focused on the net changes of land use and did not investigate the dynamics of land use in details to determine dominant signals of land use change. Analyses based on net changes may fail to reveal the total change on the landscape (Yuan et al., 2016; Fuchs et al., 2015). This is because a zero net change does not necessarily imply a lack of change. There is a possibility that a change occurs in a such a way that the location of a land category changes between time 1 and time 2 while the quantity (size) remains the same (Pontius et al., 2004). For example, analysis of net changes may indicate that a particular land use (e.g. forest) did not change from time point 1 to a subsequent time point 2. However, whereas the size of forest may have remained constant, there could have been deforestation in some parts of the study area which was accompanied by regrowth or afforestation (of equal size) in other parts. This suggests that although the size of forest was constant, the forest was not stable. Revealing this kind of information is important for conservation and management because the changes may have caused a change of hydrological catchment properties (e.g. infiltration properties) which subsequently affects catchment water yield. Our study focused on analysis of transitions among various land use categories at different time intervals. We particularly pay special attention to changes in forest and agriculture with an aim of revealing underlying processes, trends and possible driving forces.
Having empirically derived learning measures and personality traits, we first see how the information acquisition process is related with one’s personality traits. Future adaptation to climatechange starts with learning and accumulation of knowledge through information acquisition. A correlation analysis of selected information acquisition process: participation in farmer’s training, and experience sharing; extension contact to trail and demonstrate HYV, SWC and environmental protection with the Big Five personality traits shows that there is a significant correlation with personality traits. Secondly, the trivariate probit regression results show that there is a strong relationship between learning and personality trait variables and adaptive response. The stocks of adopters of SWC and irrigation technologies have positive influence on decisions related to adoption of SWC and irrigation technologies respectively. The stocks of homophilc adopters have no significant relationship with adoption decisions, indicating that the presence of such social groups have no influence on individuals decisions or there is an overlap between stock of adopter and homophilic adopters. This opens an opportunity to design further behavioural underpinnings like, household economic and social status, membership in development groups and other income sources (Suresh et al. 2012) that would affect information seeking behaviour of farmers. As part of the main findings of this chapter, we learned that adoption of irrigation technologies and SWC have a positive and significant effect on the adoption of high yield crops and livestock varieties. This indicates that there is a strong recursive relationship between adoptions of irrigation technologies and SWC, and adoption of high yield crops and livestock varieties. However, we find that correlation coefficients in the case of adoption of irrigation technologies and SWC (RHO23) is significantly different from zero. This shows that the two decisions are made jointly. This would result in high correlation between disturbance terms in the trivariate probit model. A further study using a heckit approximation (Holm and Arednt 2013) is recommended provided that the sample size is sufficiently large.
soil map and a land use map from the Amhara Design & Supervision Works Enterprise (ADSWE 2017). Daily weather data including rainfall and maximum and minimum temperature were available from the National Meteorological Service Agency (NMA 2016). The SWAT model with its sub-basins, hydrologic response units (HRUs), and river network shapefiles were inputs to the coupled SWATMOD-Prep model (Table 1). Moreover for the parameterization of MODFLOW, maps of initial GW head were prepared by inverse distance weighted (IDW) interpolation of water level data from hand-dug wells and boreholes that were collected from Amhara Water Works Construction (AWWCE 2016) and Tana Basin Development Authority (TBDA 2016). Saha et al. (2017) have applied the same approach in their modelling study of temporal dynamics of groundwater-surface water interactions for a watershed in Canada. Hydraulic conductivities, specific storage, and specific yield values were assigned to each soil unit based on previously published values (Morris and Jonson 1967), and river bed material K (default value of MODFLOW) were used as input data for the MODFLOW model. The horizontal and vertical anisotropy factors for all materials were set to 1, assuming that these anisotropies were not changing both horizontally and vertically. The streamflow data from gauging stations Gilgelabay near Merawi, Gumara near Bahirdar, and Ribb near Addis Zemen for the years 1980 to 2014 were provided by the Ministry of Water, Irrigation and Electricity of the Ethiopian Government (MoWIE 2016) to validate the coupled model.
The authors of studies using complex model ensembles in the UBN, cited above, applied different approaches to gen- erate climate input time series for hydrological modelling. Elshamy et al. (2009) used a distribution mapping approach to simultaneously downscale and bias-correct 17 CMIP3 2 GCMs (SRES A1B) and applied the corrected climate data to run the Nile Forecasting System in the UBN. The delta- change method was used by Mengistu and Sorteberg (2012) and Kim et al. (2008) to generate time series of tempera- ture and precipitation used as input for hydrological mod- elling. Mengistu and Sorteberg (2012) used 19 GCMs of the CMIP3 model ensemble (SRES scenarios A2, A1B, and B1) to generate climate inputs for the SWAT model and Kim et al. (2008) used six GCMs (SRES A2) to run a monthly water balance model. Setegn et al. (2011) applied a downscaling approach for daily temperature and precipitation data to 15 CMIP3 GCMs (SRES scenarios A2, A1B, and B1) using a cumulative frequency distribution approach. They used the climate data to run the SWAT model in the Lake Tana basin. Beyene et al. (2010) performed a quantile mapping approach to bias-correct 11 CMIP3 GCMs (SRES A2 and B1) to run the VIC hydrological model for the entire Nilebasin. Re- cently, Teklesadik et al. (2017) published a study comparing climatechange impacts, particularly on actual evapotranspi- ration, using six hydrological models driven by the same four CMIP5 GCMs used in the study at hand. Liersch et al. (2017) used a climate model ensemble to analyse the impacts of the Grand Ethiopian Renaissance Dam on downstream dis- charges under current and future climate conditions based on the 10 “best” global and regional climate models identified in this study.
However, there is inadequate evidence to what extent that adoption of those adaptation options impacted household food security in Ethiopia in general and in Muger sub-basin in particular. The results of these pre- vious studies are highly fragmented and are of little help in addressing local conditions in relation to adaptations to climatechange. Moreover, the studies overlooked the likelihood household food security impact of adaptation options at the household level. Using household-level data collected from a random cross-sectional sample of 442 farmers in the study area, the aim of the current study is to provide a comprehensive analysis on the impact of adaptation options of climatechange and vari- ability in the Muger sub-basin of the upperBlue-Nile of Ethiopia. The specific objective of the paper is, therefore, to estimate the effect of adoption of soil and water con- servation measures, small-scale irrigation, agronomic practices, and livelihood diversification strategies as climatechange adaptation options on household food security status measured by household calorie intake/day per adult equivalent using propensity score matching techniques. Addressing this question provides empirical evidence on the importance of adaptation options in im- proving household food security. Furthermore, this study provides important insights and lessons on the import- ance of access to resources on the ability of the farm households to invest in climatechange adaptation op- tions that latter improve household’s capacity to adopt adaptation options to changing the climate. In addition, the finding of the study can be used in designing better adaptation interventions that can accommodate the existing resource potentials.
Jeans, K., Noordwijk, M. V., Joshi, L., Widayati, A., Farida and Leimona, B. 2006. Rapid hydrological appraisal in the context of environmental service rewards. World Agroforestry Center. [Online]. Available from: http://www.worldagroforestrycentre.org/sea [Date accessed: 21-09-10].
Legesse, D., Vallet-Coulomb, C. and Gasse, F. 2003. Hydrological response of a catchment to climate and land use change in Tropical Africa: casestudy South Central Ethiopia. Journal of Hydrology, 275: 67 – 85. Leonard, B. 2003. Land Degradation in Ethiopia: Its extent and Impact. Commissioned by the government with
Potential of climatechangeimpact assessment on hydrology and water resources of rivers is increasing from time to time due to its importance for water resources plan- ning and management in the future. In order to carry out climatechangeimpact stu- dies, using General Climate Models (GCM) is a common practice and before using any of these models, it is essential to validate the models for the selected study area. BlueNile River is one of the most sensitive rivers towards climatechange impacts. The main source of BlueNile River is Lake Tana where the two adjacent tributary rivers, Ribb & Gumera, are located and the main object of this paper is validation of current 15 GCM outputs (IPCC-AR5) over these two rivers using empirical quantile perturbation downscaling technique. The performance of the downscaled outputs of GCMs were evaluated using statistical indicators and graphical techniques for eva- potranspiration, rainfall and temperature variables using observed daily meteorolog- ical datasets collected from five stations (Addis Zemen, Bahirdar, Debretabor, Wore- ta and Yifag) for the control period 1971-2000. Analysis results showed that the cor- relation coefficient of all models for mean monthly (MM) rainfall are 12% - 45%; and the Bias and RMSE −46 mm to +169 mm and 62 mm to 241 mm, respectively. The Bias and RMSE for MM maximum temperature are −2.5˚C to +35˚C; and 1˚C to 35˚C whereas for MM minimum temperature −6˚C to +22˚C and 1.7˚C to 23˚C, re- spectively. For the case of MM evapotranspiration, which is estimated using FAO- Penman-Montheith equation, the Bias and RMSE values vary from −35 mm to +10 mm; and +11 mm to +36 mm, respectively. The variation in the performance level of these models indicates that there is high uncertainty in the GCM outputs. Therefore, to use these GCM models for any climatechange studies in the basin, careful selec- tion has to be made.
The BlueNile River basin is one of the most sensitive basins to changing climate and water resources variabi- lity in the region (Kim and Kaluarachchi 2009). Specific- ally, the sub-basin is expressed as one of the erosion hot spot and vulnerable areas of the BlueNile river basin (MoWIE 2014). Therefore, this study is conducted to assess the vulnerability levels of all agro-ecological sys- tems (AES) found in Fincha ’ a sub-basin by using AES as a unit of analysis. The sub-basin is selected for the re- search because no similar study is conducted in the area before. Thus, it is imperative to understand at the local level the nature of climate variability and change vulne- rability of smallholder farmers’ agriculture. The rele- vance of AES-based generalization to household-level analysis allows us to map vulnerability profiles across the sub-basin. It is also important for adaptation plan- ning and allows decision makers to understand patterns of vulnerability across relatively broad geographical loca- tions. Although the study is site-specific, the findings obtained from the study provide context-specific contri- bution to the agro-ecological system based understand- ing of the impact of climate variability and change and adaptation responses given as a nation.
A number of statistical tests were carried out to compare the skills of the two downscaling models categorized into two main classes. First, quantitative statistical tests using met- rics, such as mean absolute error (MAE), root mean square error and bias. These metrics are by far the most widely used and accepted of the many possible numerical metrics (Amirabadizadeh et al., 2016; Bennett et al., 2013) to eval- uate the comparative performance of the models to simulate the current climate variable of precipitation on the basis of long-term monthly averages defined by using Eqs. (7)–(9). In this study correlation and correlation-based measures such as coefficient of determination (R 2 ) and coefficient of efficiency (Nash–Sutcliffe efficiency, NSE) are not included due to the fact that these measures are oversensitive to extreme values and are insensitive to additive and proportional differences between model simulations and observations (Legates and McCabe, 1999). Evaluation was done in two steps as sug- gested by Goly et al. (2014): (i) equally weighing the met- rics and (ii) varying the weights of metrics. For the case of equally weighted the following steps were applied. (a) Com- parison of the values of the performance metrics among the models and ranking (obtaining individual model rankings for each performance metrics) at station level. Here, score 1 will be given to the model that has smaller metrics value and score 3 to the one having larger value and 2 for the model having the value in between. (b) Summing up the score per- taining to each model across all the stations. (c) Once the final scores are obtained for each evaluation metric, the mod- els are ranked again based on the totals by summing up the metrics score value for each models.
Eastern Nile Technical Regional Office, P.O. Box 27173-1000, Adis Ababa, Ethiopia
4 Nile Forecast Center, Ministry of Water Resources and Irrigation, Giza 12666, Egypt
Abstract: This study describes implementation of hydrological climatechangeimpact assessment tool utilising a combi- nation of statistical spatiotemporal downscaling and an operational hydrological model known as the Nile Forecasting System. A spatial rainfall generator was used to produce high-resolution (daily, 20km) gridded rainfall data required by the distributed hydrological model from monthly GCM outputs. The combined system was used to assess the sensitivity of upperBlueNile flows at Diem flow gauging station to changes in future rainfall during the June-September rainy sea- son based on output from three GCMs. The assessment also incorporated future evapotranspiration changes over the ba- sin. The climatechange scenarios derived in this study were broadly in line with other studies, with the majority of scenar- ios indicating wetter conditions in the future. Translating the impacts into runoff in the basin showed increased future mean flows, although these would be offset to some degree by rising evapotranspiration. Impacts on extreme runoff indi- cated the possibility of more severe floods in future. These are likely to be exacerbated by land-use changes including overgrazing, deforestation, and improper farming practices. BlueNilebasin flood managers therefore need to continue to prepare for the possibility of more frequent floods by adopting a range of measures to minimise loss of life and guard against other flood damage.
Factors that may be important for increasing SOC storage include (i) litter production (both above and below ground); (ii) litter quality; (iii) placing organic matter deeper in the soil either directly by increasing below-ground inputs or indirectly by enhancing surface mixing by soil organisms; (iv) increasing physical protec- tion through either intra-aggregate or organic mineral complexes; and (v) microclimate change (Lemma et al. 2006). On the other hand, elevation and temperature dif- ferences are identified as the dominant controls on moun- tain SOC at regional scales (Djukic et al. 2010; Van Miegroet et al. 2007, 2005; Leifeld et al. 2005), local topo- graphic changes (e.g., slope curvature and aspect, Egli et al. 2009; Tan et al. 2004), soil properties (e.g., soil type, soil moisture, pH and clay-content; Djukic et al. 2010; Leifeld et al. 2005), and vegetation (e.g., type and stand age; Luys- saert et al. 2008; Zhou et al. 2006) may introduce a large variability of mountain SOC at local scales. Small-scale variability may even impose strong scatter at large-scales and conceal relationships between SOC, topography, and climate. Small changes in the SOC pool therefore can have large implications for atmospheric CO 2 concentrations
Abstract. Climate simulations are the fuel to drive hydro- logical models that are used to assess the impacts of climatechange and variability on hydrological parameters, such as river discharges, soil moisture, and evapotranspiration. Un- like with cars, where we know which fuel the engine re- quires, we never know in advance what unexpected side ef- fects might be caused by the fuel we feed our models with. Sometimes we increase the fuel’s octane number (bias cor- rection) to achieve better performance and find out that the model behaves differently but not always as was expected or desired. This study investigates the impacts of projected cli- mate change on the hydrology of the UpperBlueNile catch- ment using two model ensembles consisting of five global CMIP5 Earth system models and 10 regional climate mod- els (CORDEX Africa). WATCH forcing data were used to calibrate an eco-hydrological model and to bias-correct both model ensembles using slightly differing approaches. On the one hand it was found that the bias correction methods con- siderably improved the performance of average rainfall char- acteristics in the reference period (1970–1999) in most of the cases. This also holds true for non-extreme discharge conditions between Q 20 and Q 80 . On the other hand, bias- corrected simulations tend to overemphasize magnitudes of projected change signals and extremes. A general weakness of both uncorrected and bias-corrected simulations is the rather poor representation of high and low flows and their extremes, which were often deteriorated by bias correction. This inaccuracy is a crucial deficiency for regional impact studies dealing with water management issues and it is there- fore important to analyse model performance and character-