Downscaling and Modeling the Effects of Climate Change on Hydrology and Water Resources in the Upper Blue Nile River Basin, Ethiopia

The Upper Blue Nile River Basin (UBNRB) located in the western part of Ethiopia, between 7° 45’ and 12° 45’N and 34° 05’ and 39° 45’E has a total area of 174962 km2 . More than 80% of the population in the basin is engaged in agricultural activities. Because of the particularly dry climate in the basin, likewise to most other regions of Ethiopia, the agricultural productivity depends to a very large extent on the occurrence of the seasonal rains. This situation makes agriculture highly vulnerable to the impact of potential climate hazards which are about to inflict Africa as a whole and Ethiopia in particular. To analyze these possible impacts of future climate change on the water resources in the UBNRB, in the first part of the thesis climate projection for precipitation, minimum and maximum temperatures in the basin, using downscaled predictors from three GCMs (ECHAM5, GFDL21 and CSIRO-MK3) under SRES scenarios A1B and A2 have been carried out. The two statistical downscaling models used are SDSM and LARS-WG, whereby SDSM is used to downscale ECHAM5-predictors alone and LARS-WG is applied in both mono-model mode with predictors from ECHAM5 and in multi-model mode with combined predictors from ECHAM5, GFDL21 and CSIRO-MK3. For the calibration/validation of the downscaled models, observed as well as NCEP climate data in the 1970 - 2000 reference period is used. The future projections are made for two time periods; 2046-2065 (2050s) and 2081-2100 (2090s). For the 2050s future time period the downscaled climate predictions indicate rise of 0.6°C to 2.7°C for the seasonal maximum temperatures Tmax, and of 0.5°C to 2.44°C for the minimum temperatures Tmin. Similarly, during the 2090s the seasonal Tmax increases by 0.9°C to 4.63°C and Tmin by 1°C to 4.6°C, whereby these increases are generally higher for the A2 than for the A1B scenario. For most sub-basins of the UBNRB, the predicted changes of Tmin are larger than those of Tmax. Meanwhile, for the precipitation, both downscaling tools predict large changes which, depending on the GCM employed, are such that the spring and summer seasons will be experiencing decreases between -36% to 1% and the autumn and winter seasons an increase of -8% to 126% for the two future time periods, regardless of the SRES scenario used. In the second part of the thesis the semi-distributed, physically based hydrologic model, SWAT (Soil Water Assessment Tool), is used to evaluate the impacts of the above-predicted future climate change on the hydrology and water resources of the UBNRB. Hereby the downscaled future predictors are used as input in the SWAT model to predict streamflow of the Upper Blue Nile as well as other relevant water resources parameter in the basin. Calibration and validation of the streamflow model is done again on 1970-2000 measured discharge at the outlet gage station Eldiem, whereby the most sensitive out the numerous “tuneable” calibration parameters in SWAT have been selected by means of a sophisticated sensitivity analysis. Consequently, a good calibration/validation model performance with a high NSE-coefficient of 0.89 is obtained. The results of the future simulations of streamflow in the basin, using both SDSM- and LARS-WG downscaled output in SWAT reveal a decline of -10% to -61% of the future Blue Nile streamflow, And, expectedly, these obviously adverse effects on the future UBNRB-water availibiliy are more exacerbated for the 2090’s than for the 2050’s, regardless of the SRES.

Pathogens occuring in the winter pea-maize-winter wheat rotation, their host specificity and the potential of compost in suppressing foot and root disease of peas

The overall aim of the work presented was to evaluate soil health management with a specific focus on soil borne diseases of peas. For that purpose field experiments were carried out from 2009 until 2013 to assess crop performance and pathogen occurrence in the rotation winter pea-maize-winter wheat and if the application of composts can improve system performance. The winter peas were left untreated or inoculated with Phoma medicaginis, in the presence or absence of yard waste compost at rate of 5 t dry matter ha-1. A second application of compost was made to the winter wheat. Fusarium ssp. were isolated and identified from the roots of all three crops and the Ascochyta complex pathogens on peas. Bioassays were conducted under controlled conditions to assess susceptibility of two peas to Fusarium avenaceum, F. solani, P. medicaginis and Didymella pinodes and of nine plant species to F. avenaceum. Also, effects of compost applications and temperature on pea diseases were assessed. Application of composts overall stabilized crop performance but it did not lead to significant yield increases nor did it affect pathogen composition and occurrence. Phoma medicaginis was dominating the pathogen complex on peas. F. graminearum, F. culmorum, F. proliferatum, Microdochium nivale, F. crookwellense, F. sambucinum, F. oxysporum, F. avenaceum and F. equiseti were frequently isolated species from maize and winter wheat with no obvious influence of the pre-crop on the Fusarium species composition. The spring pea Santana was considerably more susceptible to the pathogens tested than the winter pea EFB33 in both sterile sand and non-sterilized field soil. F. avenaceum was the most aggressive pathogen, followed by P. medicaginis, D. pinodes, and F. solani. Aggressiveness of all pathogens was greatly reduced in non-sterile field soil. F. avenaceum caused severe symptoms on roots of all nine plant species tested. Especially susceptible were Trifolium repens, T. subterraneum, Brassica juncea and Sinapis alba in addition to peas. Reduction of growing temperatures from 19/16°C day/night to 16/12°C and 13/10°C did not affect the efficacy of compost. It reduced plant growth and slightly increased disease on EFB33 whereas the highest disease severity on Santana was observed at the highest temperature, 19/16°C. Application of 20% v/v of compost reduced disease on peas due to all four pathogens depending on pea variety, pathogen and growing media used. Suppression was also achieved with lower application rate of 3.5% v/v. Tests with γ sterilized compost suggest that the suppression of disease caused by Fusarium spp. is biological in origin, whereas chemical and physical properties of compost are playing an additional role in the suppression of disease caused by D. pinodes and P. medicaginis.

The Effect of Climate Variability on Main Components of Cow Milk in Iran

The main purpose of this study is to assess the relationship between six bioclimatic indices for cattle (temperature humidity (THI), environmental stress (ESI), equivalent temperature (ESI), heat load (HLI), modified heat load (HLInew) and respiratory rate predictor(RRP)) and fundamental milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when cows use natural pasture, with possibility for cows to choose to stay in the barn or to graze on the pasture in the pasturing system. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty estimation through resampling in the confidence intervals. To find the relationships between climate indices (THI, ETI, HLI, HLInew, ESI and RRP) and main components of cow milk (fat, protein and yield), multiple liner regression is applied. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Cross validation is used to avoid over-fitting. Based on results of investigation the effect of heat stress indices on milk compounds separately, we suggest the use of ESI and RRP in the summer and ESI in the spring. THI and HLInew are suggested for fat content and HLInew also is suggested for protein content in the spring season. The best linear models are found in spring between milk yield as predictands and THI, ESI,HLI, ETI and RRP as predictors with p-value < 0.001 and R2 0.50, 0.49. In summer, milk yield with independent variables of THI, ETI and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. It is strongly suggested that new and significant indices are needed to control critical heat stress conditions that consider more predictors of the effect of climate variability on animal products, such as sunshine duration, quality of pasture, the number of days of stress (NDS), the color of skin with attention to large black spots, and categorical predictors such as breed, welfare facility, and management system. This methodology is suggested for studies investigating the impacts of climate variability/change on food quality/security, animal science and agriculture using short term data considering uncertainty or data collection is expensive, difficult, or data with gaps.