This page has only limited features, please log in for full access.

Dr. Solomon Hailu Gebrechorkos
School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK

Basic Info


Research Keywords & Expertise

0 hydrology modelling
0 Climate modeling and downscaling
0 Climate change adaptation and mitigation
0 Hydrology and Water Resources Management
0 water resource

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Preprint content
Published: 04 March 2021
Reads 0
Downloads 0

Hydrological extreme events such as droughts and floods have a wide range of impacts on society and sectors such as agriculture and energy production. The impact of these extremes are projected to increase with future climate change and there is an urgent need to develop adaptation measures to reduce and manage the impacts. Long-term analysis of hydrological extremes, using a combination of models and climate data, helps better plan and manage water resources under global change. In this study, we modelled and analyzed hydrological extremes of the Volta river basin at very high-resolution (>10000 river reaches) using the Variable Infiltration Capacity (VIC) hydrological model, the vector-based river network routing model (RAPID), and high-resolution meteorological forcing datasets. The output from the VIC model is evaluated at multiple time scales (daily to annual) and for extreme events (droughts and floods) using observed streamflow data during the period 1979-2013.  The model performed very well in areas less affected by dams, with performance increasing from daily to annual time scale. The modelled streamflow data is used to assess changes and variability in droughts (duration days and severity) and floods (annual daily maximum). The results show a decreasing and increasing trend in moderate and severe droughts in northern-eastern and southern parts of the basin, respectively. An increasing trend in floods is observed in the upper part of the basin (Black and White Volta) and the main river of the Lower Volta and we found a strong correlation with changes in precipitation and soil moisture.

ACS Style

Solomon Hailu Gebrechorkos; Ming Pan; Peirong Lin; David Pritchard; Nathan Forsythe; Hayley Fowler; Justin Sheffield. Modelling hydrological droughts and floods in the Volta Basin, West Africa . 2021, 1 .

AMA Style

Solomon Hailu Gebrechorkos, Ming Pan, Peirong Lin, David Pritchard, Nathan Forsythe, Hayley Fowler, Justin Sheffield. Modelling hydrological droughts and floods in the Volta Basin, West Africa . . 2021; ():1.

Chicago/Turabian Style

Solomon Hailu Gebrechorkos; Ming Pan; Peirong Lin; David Pritchard; Nathan Forsythe; Hayley Fowler; Justin Sheffield. 2021. "Modelling hydrological droughts and floods in the Volta Basin, West Africa ." , no. : 1.

Preprint content
Published: 04 March 2021
Reads 0
Downloads 0

Agriculture is a key sector in fighting hunger in Sub Saharan Africa. Almost 95% of the agriculture in Africa is rain-fed and smallholder farmers play a crucial role as they produce most of the food consumed by local populations. These characteristics make the SSA agricultural landscape very diverse and particularly vulnerable to weather extremes. The ability of forecasting hydrological variability has increased in recent years due to advancements in the understanding of hydro-climatic processes, growing availability of high-resolution remote sensing datasets, and the increase of computational power, which has promoted the development of high-quality computer-based hydrological models. When adopted in data scarce regions, these models provide new insight into the hydrological budget and in characterizing the hydrological variability of these areas. In this work, we combine the hyper-resolution hydrological model HydroBlocks and the river routing model RAPID to simulate the spatial and temporal heterogeneity of the land surface processes in Malawi at 30 m resolution. The model simulations show high variability of the hydrological variables, particularly soil moisture, across the country. We use these results to further analyse water and food security indicators in the transboundary catchment of Lake Chilwa shared between Malawi and Mozambique. The start and duration of the maize cropping season and the lake level show a large interannual variability which allow us to quantify the weather-related vulnerability of the local smallholder farming system. This work is part of the research activities of the UKRI-GCRF funded project “Building research capacity for sustainable water and food security in drylands of sub-Saharan Africa” (BRECcIA - http://www.gcrf-breccia.com/).

ACS Style

Daniela Anghileri; Noemi Vergopolan; Solomon Gebrechorkos; Justin Sheffield. Hyper-resolution hydrological modelling to assess water and food security in Malawi. 2021, 1 .

AMA Style

Daniela Anghileri, Noemi Vergopolan, Solomon Gebrechorkos, Justin Sheffield. Hyper-resolution hydrological modelling to assess water and food security in Malawi. . 2021; ():1.

Chicago/Turabian Style

Daniela Anghileri; Noemi Vergopolan; Solomon Gebrechorkos; Justin Sheffield. 2021. "Hyper-resolution hydrological modelling to assess water and food security in Malawi." , no. : 1.

Journal article
Published: 14 September 2020 in Remote Sensing
Reads 0
Downloads 0

Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.

ACS Style

Ali Hamoud Al-Falahi; Naeem Saddique; Uwe Spank; Solomon H. Gebrechorkos; Christian Bernhofer. Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management. Remote Sensing 2020, 12, 2984 .

AMA Style

Ali Hamoud Al-Falahi, Naeem Saddique, Uwe Spank, Solomon H. Gebrechorkos, Christian Bernhofer. Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management. Remote Sensing. 2020; 12 (18):2984.

Chicago/Turabian Style

Ali Hamoud Al-Falahi; Naeem Saddique; Uwe Spank; Solomon H. Gebrechorkos; Christian Bernhofer. 2020. "Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management." Remote Sensing 12, no. 18: 2984.

Journal article
Published: 28 June 2020 in Science of The Total Environment
Reads 0
Downloads 0

Local-scale climate change adaptation is receiving more attention to reduce the adverse effects of climate change. The process of developing adaptation measures at local-scale (e.g., river basins) requires high-quality climate information with higher resolution. Climate projections are available at a coarser spatial resolution from Global Climate Models (GCMs) and require spatial downscaling and bias correction to drive hydrological models. We used the hybrid multiple linear regression and stochastic weather generator model (Statistical Down-Scaling Model, SDSM) to develop a location-based climate projection, equivalent to future station data, from GCMs. Meteorological data from 24 ground stations and the most accurate satellite and reanalysis products identified for the region, such as Climate Hazards Group InfraRed Precipitation with Station Data were used. The Soil Water Assessment Tool (SWAT) was used to assess the impacts of the projected climate on hydrology. Both SDSM and SWAT were calibrated and validated using the observed climate and streamflow data, respectively. Climate projection based on SDSM, in one of the large and agricultural intensive basins in Ethiopia (i.e., Awash), show high variability in precipitation but an increase in maximum (Tmax) and minimum (Tmin) temperature, which agrees with global warming. On average, the projection shows an increase in annual precipitation (>10%), Tmax (>0.4 °C), Tmin (>0.2 °C) and streamflow (>34%) in the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100) under RCP2.6-RCP8.5. Although no significant trend in precipitation is found, streamflow during March–May and June–September is projected to increase throughout the 21 century by an average of more than 1.1% and 24%, respectively. However, streamflow is projected to decrease during January–February and October–November by more than 6%. Overall, considering the projected warming and changes in seasonal flow, local-scale adaptation measures to limit the impact on agriculture, water and energy sectors are required.

ACS Style

Solomon H. Gebrechorkos; Christian Bernhofer; Stephan Hülsmann. Climate change impact assessment on the hydrology of a large river basin in Ethiopia using a local-scale climate modelling approach. Science of The Total Environment 2020, 742, 140504 .

AMA Style

Solomon H. Gebrechorkos, Christian Bernhofer, Stephan Hülsmann. Climate change impact assessment on the hydrology of a large river basin in Ethiopia using a local-scale climate modelling approach. Science of The Total Environment. 2020; 742 ():140504.

Chicago/Turabian Style

Solomon H. Gebrechorkos; Christian Bernhofer; Stephan Hülsmann. 2020. "Climate change impact assessment on the hydrology of a large river basin in Ethiopia using a local-scale climate modelling approach." Science of The Total Environment 742, no. : 140504.

Journal article
Published: 23 January 2020 in Global and Planetary Change
Reads 0
Downloads 0

Analysis of climate variability and change as a basis for adaptation and mitigation strategies requires long-term observations. However, the limited availability of ground station data constrains studies focusing on detecting variability and changes in climate and drought monitoring, particularly in developing countries of East Africa. Here, we use high-resolution precipitation (1981–2016) and maximum and minimum temperature (T-max and T-min) (1979–2012) datasets from international databases like the Climate Hazard Group (CHG), representing the most accurate data sources for the region. We assessed seasonal, annual, and decadal variability in rainfall, T-max and T-min and drought conditions using the Standardized Precipitation Index (SPI). The impact of changes in Sea Surface Temperature on rainfall variability and droughts is assessed using the Nino3.4 and Indian Ocean Dipole (IOD) indices. The results show maximum variability in rainfall during October–December (OND, short rainy season) followed by March–May (MAM, long rainy season). Rainfall variability during OND showed a significant correlation with IOD in Ethiopia (69%), Kenya (80%), and Tanzania (63%). In Ethiopia, the period June–September (JJAS) showed a significant negative correlation (−56%) with the Nino3.4. Based on the 12-month SPI, the eastern and western parts of the region are getting drier and wetter, respectively with an average of mild, moderate, and severe droughts of more than 37%, 6%, and 2% of the study period, respectively. The observed severe droughts (e.g., 1999/2000) and extreme floods (e.g., 1997/1998) were found to be linked to respective negative and positive anomalies of the Nino3.4. In general, climate data products with high spatial resolution and accuracy help detect changes and variability in climate at local scale where adaptation is required.

ACS Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. Analysis of climate variability and droughts in East Africa using high-resolution climate data products. Global and Planetary Change 2020, 186, 103130 .

AMA Style

Solomon H. Gebrechorkos, Stephan Hülsmann, Christian Bernhofer. Analysis of climate variability and droughts in East Africa using high-resolution climate data products. Global and Planetary Change. 2020; 186 ():103130.

Chicago/Turabian Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. 2020. "Analysis of climate variability and droughts in East Africa using high-resolution climate data products." Global and Planetary Change 186, no. : 103130.

Journal article
Published: 06 August 2019 in Scientific Reports
Reads 0
Downloads 0

Detecting changes in climate is a prerequisite for a better understanding of the climate and developing adaptation and mitigation measures at a regional and local scale. In this study long-term trends in rainfall and maximum and minimum temperature (T-max and T-min) were analysed on seasonal and annual time scales for East Africa. High resolution gridded rainfall (1981–2016) and temperature (1979–2010) data from international databases like the Climate Hazards Group are used. Long-term seasonal trend analysis shows a non-significant (except for small areas), decreasing (increasing) trend in rainfall in eastern (western) parts of Ethiopia and Kenya and a decreasing trend in large parts of Tanzania during the long rainy season. On the other hand, a non-significant increasing trend in large parts of the region is observed during the short rain season. With regard to annual trends, results largely confirm seasonal analyses: only a few significant trends in rainfall, but significant increasing trends in T-max (up to 1.9 °C) and T-min (up to 1.2 °C) for virtually the whole region. Our results demonstrate the need and added value of analysing climate trends based on data with high spatial resolution allowing sustainable adaptation measures at local scales.

ACS Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. Long-term trends in rainfall and temperature using high-resolution climate datasets in East Africa. Scientific Reports 2019, 9, 1 -9.

AMA Style

Solomon H. Gebrechorkos, Stephan Hülsmann, Christian Bernhofer. Long-term trends in rainfall and temperature using high-resolution climate datasets in East Africa. Scientific Reports. 2019; 9 (1):1-9.

Chicago/Turabian Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. 2019. "Long-term trends in rainfall and temperature using high-resolution climate datasets in East Africa." Scientific Reports 9, no. 1: 1-9.

Journal article
Published: 06 May 2019 in Science of The Total Environment
Reads 0
Downloads 0

In East Africa, climate change and variability have shown a strong impact on sectors such as agriculture, energy, and water. To allow mitigation and adaptation of the possible impacts of the projected change in climate, this study applies a Statistical Downscaling Model (SDSM) to generate a high-resolution climate projection, equivalent to future station data, to drive impact assessment models in selected, agricultural intensive, basins of Ethiopia (EthShed), Kenya (KenShed), and Tanzania (TanShed). Observed and large-scale climate variables (predictors) are obtained from the national meteorological agency of Ethiopia and international databases. BROOK90, a physical-based hydrological model, is used to assess the impacts of the projected change in precipitation and maximum and minimum temperature (T-max, and T-min) on the water balance. Based on SDSM, the results show an increase in precipitation, relative to the baseline period (1961–1990), in EthShed (14% - 50%) and KenShed (15% - 86%) and a decrease in TanShed (1.3% - 6.3%) in the 20s (2011–2040), 50s (2041–2070), and 80s (2071–2100) under the three Representative Concentration Pathways (RCPs; RCP2.6, RCP4.5, and RCP8.5). T-max (anomalies up to 3.7 °C) and T-min (anomalies up to 2.76 °C) will be warmer than the baseline period throughout the 21 century in all three basins. In line with the projected change in precipitation and temperature, an increase (decrease) in seasonal and annual streamflow, soil-water, and evaporation in EthShed and KenShed (TanShed) is projected in the 20s, 50s, and 80s. In general, sustainable adaptation measures are required to be developed in a site-specific manner, considering the projected increase in temperature and evaporation in all three basins and a decrease in soil-water and streamflow in TanShed.

ACS Style

Solomon H. Gebrechorkos; Christian Bernhofer; Stephan Hülsmann. Impacts of projected change in climate on water balance in basins of East Africa. Science of The Total Environment 2019, 682, 160 -170.

AMA Style

Solomon H. Gebrechorkos, Christian Bernhofer, Stephan Hülsmann. Impacts of projected change in climate on water balance in basins of East Africa. Science of The Total Environment. 2019; 682 ():160-170.

Chicago/Turabian Style

Solomon H. Gebrechorkos; Christian Bernhofer; Stephan Hülsmann. 2019. "Impacts of projected change in climate on water balance in basins of East Africa." Science of The Total Environment 682, no. : 160-170.

Data descriptor
Published: 15 April 2019 in Scientific Data
Reads 0
Downloads 0

For many regions of the world, current climate change projections are only available at coarser spatial resolution from Global Climate Models (GCMs) that cannot directly be used in impact assessment and adaptation studies at regional and local scale. Impact assessment studies require high-resolution climate data to drive impact assessment models. To overcome this data challenge, we produced a station based climate projection (precipitation and maximum and minimum temperature) for Ethiopia, Kenya, and Tanzania using observed daily data from 211 stations obtained from the National Meteorological Agency of Ethiopia and international databases. Moreover, 26 large-scale climate variables derived from the National Centers for Environmental Prediction reanalysis data (1961–2005) and second generation Canadian Earth System Model (CanESM2, 1961–2100) are used. Statistical Down-Scaling Model (SDSM) is used to produce the required high-resolution climate projection by developing a statistical relationship between the large- and local-scale climate variables. The predictors are analysed more than 16458 times and we provided 20 ensembles for the current (1961–2005) and future (2006–2100, under RCP2.6, RCP4.5, and RCP8.5) climate. Machine-accessible metadata file describing the reported data (ISA-Tab format)

ACS Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. Statistically downscaled climate dataset for East Africa. Scientific Data 2019, 6, 1 -8.

AMA Style

Solomon H. Gebrechorkos, Stephan Hülsmann, Christian Bernhofer. Statistically downscaled climate dataset for East Africa. Scientific Data. 2019; 6 (1):1-8.

Chicago/Turabian Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. 2019. "Statistically downscaled climate dataset for East Africa." Scientific Data 6, no. 1: 1-8.

Accepted manuscript
Published: 08 February 2019 in Environmental Research Letters
Reads 0
Downloads 0

In order to overcome limitations of climate projections from Global Climate Models, such as coarse spatial resolution and biases, in this study, the Statistical Down-Scaling Model (SDSM) is used to downscale daily precipitation and maximum and minimum temperature (T-max and T-min) required by impact assessment models. We focus on East Africa, a region known to be highly vulnerable to climate change and at the same time facing challenges concerning availability and accessibility of climate data. SDSM is first calibrated and validated using observed daily precipitation, T-max, and T-min from 214 stations and predictors derived from the reanalysis data of the National Centers for Environmental Prediction (NCEP). For projection (2006-2100), the same predictors derived from the second generation Canadian Earth System Model (CanESM2) are used. SDSM projections show an increase in precipitation during the short-rain season (October-December) in large parts of the region in the 2020s (2011-2040), 2050s (2041-2070), and 2080s (2071-2100). During the long-rain season (March-May) precipitation is expected to increase (up to 680 mm) in Ethiopia, mainly in the western part, and Kenya and decrease (up to -500 mm) in Tanzania in the 2020s, 2050s, and 2080s. However, the western part of Ethiopia will be much drier than the baseline period (1961-1990) during June-September (JJAS) in the 2020s, 2050s, and 2080s, which indicates a shift in precipitation from JJAS to MAM. Annually, precipitation, T-max, and T-min will be higher than during the baseline period throughout the 21 century in large parts of the region. The projection based on SDSM is in line with the direction of CMIP5 GCMs but differs in magnitude, particularly for T-max and T-min. Overall, we conclude that the downscaled data allow for much more fine-scaled adaptation plans and ultimately better management of the impacts of projected climate in basins of Ethiopia, Kenya, and Tanzania.

ACS Style

Solomon Hailu Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. Regional climate projections for impact assessment studies in East Africa. Environmental Research Letters 2019, 14, 044031 .

AMA Style

Solomon Hailu Gebrechorkos, Stephan Hülsmann, Christian Bernhofer. Regional climate projections for impact assessment studies in East Africa. Environmental Research Letters. 2019; 14 (4):044031.

Chicago/Turabian Style

Solomon Hailu Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. 2019. "Regional climate projections for impact assessment studies in East Africa." Environmental Research Letters 14, no. 4: 044031.

Journal article
Published: 28 August 2018 in Hydrology and Earth System Sciences
Reads 0
Downloads 0

Managing environmental resources under conditions of climate change and extreme climate events remains among the most challenging research tasks in the field of sustainable development. A particular challenge in many regions such as East Africa is often the lack of sufficiently long-term and spatially representative observed climate data. To overcome this data challenge we used a combination of accessible data sources based on station data, earth observations by remote sensing, and regional climate models. The accuracy of the Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation (CHIRP), CHIRP with Station data (CHIRPS), Observational-Reanalysis Hybrid (ORH), and regional climate models (RCMs) are evaluated against station data obtained from the respective national weather services and international databases. We did so by performing a comparison in three ways: point to pixel, point to area grid cell average, and stations' average to area grid cell average over 21 regions of East Africa: 17 in Ethiopia, 2 in Kenya, and 2 in Tanzania. We found that the latter method provides better correlation and significantly reduces biases and errors. The correlations were analysed at daily, dekadal (10 days), and monthly resolution for rainfall and maximum and minimum temperature (Tmax and Tmin) covering the period of 1983–2005. At a daily timescale, CHIRPS, followed by ARC2 and CHIRP, is the best performing rainfall product compared to ORH, individual RCMs (I-RCM), and RCMs' mean (RCMs). CHIRPS captures the daily rainfall characteristics well, such as average daily rainfall, amount of wet periods, and total rainfall. Compared to CHIRPS, ARC2 showed higher underestimation of the total (−30 %) and daily (−14 %) rainfall. CHIRP, on the other hand, showed higher underestimation of the average daily rainfall (−53 %) and duration of dry periods (−29 %). Overall, the evaluation revealed that in terms of multiple statistical measures used on daily, dekadal, and monthly timescales, CHIRPS, CHIRP, and ARC2 are the best performing rainfall products, while ORH, I-RCM, and RCMs are the worst performing products. For Tmax and Tmin, ORH was identified as the most suitable product compared to I-RCM and RCMs. Our results indicate that CHIRPS (rainfall) and ORH (Tmax and Tmin), with higher spatial resolution, should be the preferential data sources to be used for climate change and hydrological studies in areas of East Africa where station data are not accessible.

ACS Style

Solomon Hailu Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. Evaluation of multiple climate data sources for managing environmental resources in East Africa. Hydrology and Earth System Sciences 2018, 22, 4547 -4564.

AMA Style

Solomon Hailu Gebrechorkos, Stephan Hülsmann, Christian Bernhofer. Evaluation of multiple climate data sources for managing environmental resources in East Africa. Hydrology and Earth System Sciences. 2018; 22 (8):4547-4564.

Chicago/Turabian Style

Solomon Hailu Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. 2018. "Evaluation of multiple climate data sources for managing environmental resources in East Africa." Hydrology and Earth System Sciences 22, no. 8: 4547-4564.

Research article
Published: 14 August 2018 in International Journal of Climatology
Reads 0
Downloads 0

East Africa is one of the most vulnerable regions of Africa to extreme weather and climate events. Regional and local information on climate extremes is critical for monitoring and managing the impacts and developing sustainable adaptation measures. However, this type of information is not readily available at the necessary spatial resolution. Therefore, here we test trends and variability of temperature (1979–2010) and precipitation (1981–2016) extremes in East Africa, particularly Ethiopia, Kenya, and Tanzania, at a spatial resolution of 0.1 and 0.05°, respectively, using the indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We use gridded data sets with high accuracy and resolution from the Terrestrial Hydrology Research Group, University of Princeton and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Trends of 19 indices are computed by fitting a linear model and using the nonparametric Mann–Kendall test and the magnitude of change is computed using the Sen's slope method. The results show an increasing trend in monthly maximum and minimum values of daily maximum and minimum temperature in large parts of the region. This is accompanied by significant increasing trends in warm nights (TN90p), warm days (TX90p), warm spell duration index (WSDI), and summer days index (SU). In addition, cold days (TX10p) and cold nights (TN10p) showed a significant decreasing trend. In general, the results show an increasing tendency in temperatures extremes, which is in line with rising global mean temperature. In addition, most of the temperature extremes observed after 2000 are warmer than the long‐term mean (1979–2010). Precipitation indices, on the other hand, showed increasing and decreasing trends in Ethiopia, Kenya, and Tanzania, but no general pattern. The outcomes enable identifying hot spot areas and planning of adaptation and mitigation measures at much finer spatial scale than previously possible.

ACS Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. Changes in temperature and precipitation extremes in Ethiopia, Kenya, and Tanzania. International Journal of Climatology 2018, 39, 18 -30.

AMA Style

Solomon H. Gebrechorkos, Stephan Hülsmann, Christian Bernhofer. Changes in temperature and precipitation extremes in Ethiopia, Kenya, and Tanzania. International Journal of Climatology. 2018; 39 (1):18-30.

Chicago/Turabian Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. 2018. "Changes in temperature and precipitation extremes in Ethiopia, Kenya, and Tanzania." International Journal of Climatology 39, no. 1: 18-30.

Chapter
Published: 14 April 2018 in Managing Water, Soil and Waste Resources to Achieve Sustainable Development Goals
Reads 0
Downloads 0

Research seeks to treat each resource embedded in the nexus as connected to the other resources. This approach is unique from other natural resource research agendas where the primary focus is on system efficiencies or examinations of a single resource. The nexus by emphasizing trade-offs places a premium on coordination. From a governance perspective coordination is not limited to decisions involving finances and allocation of trained human resources among different agencies organized both vertically and horizontally within a multi-level governance framework. Coordination could also be extended to include uses of data between public agencies, private sector and individuals. Due to nexus interconnectivity, we suggest here that social network analysis (SNA) is an appropriate tool that can divulge and highlight the relational complexities that exist within the nexus and among stakeholders that work with the singular elements of the nexus. We suggest that in the cases of organisations with a high institutional capacity by means of expertise, resources, and other assets, the Water-Energy-Food (WEF) network will be highly connected between resource areas in the overall network. Two network tie characteristics—density and centrality—are particularly important to understand a critical mass of interests within a multi-level governance framework. The paper concludes by arguing for the organisation of data covering different dimensions of the Water-Energy-Food nexus through the mechanism of an observatory that could potentially improve our understanding of thresholds of environmental resource use and the incentives required for public agencies to act in support of sustainable development.

ACS Style

Mathew Kurian; Kent E. Portney; Gerhard Rappold; Bryce Hannibal; Solomon Hailu Gebrechorkos. Governance of Water-Energy-Food Nexus: A Social Network Analysis Approach to Understanding Agency Behaviour. Managing Water, Soil and Waste Resources to Achieve Sustainable Development Goals 2018, 125 -147.

AMA Style

Mathew Kurian, Kent E. Portney, Gerhard Rappold, Bryce Hannibal, Solomon Hailu Gebrechorkos. Governance of Water-Energy-Food Nexus: A Social Network Analysis Approach to Understanding Agency Behaviour. Managing Water, Soil and Waste Resources to Achieve Sustainable Development Goals. 2018; ():125-147.

Chicago/Turabian Style

Mathew Kurian; Kent E. Portney; Gerhard Rappold; Bryce Hannibal; Solomon Hailu Gebrechorkos. 2018. "Governance of Water-Energy-Food Nexus: A Social Network Analysis Approach to Understanding Agency Behaviour." Managing Water, Soil and Waste Resources to Achieve Sustainable Development Goals , no. : 125-147.

Preprint content
Published: 20 January 2018
Reads 0
Downloads 0
ACS Style

Solomon Hailu Gebrechorkos. Reply to Anonymous Referee #2. 2018, 1 .

AMA Style

Solomon Hailu Gebrechorkos. Reply to Anonymous Referee #2. . 2018; ():1.

Chicago/Turabian Style

Solomon Hailu Gebrechorkos. 2018. "Reply to Anonymous Referee #2." , no. : 1.

Preprint content
Published: 20 January 2018
Reads 0
Downloads 0
ACS Style

Solomon Hailu Gebrechorkos. Reply to Anonymous Referee #1. 2018, 1 .

AMA Style

Solomon Hailu Gebrechorkos. Reply to Anonymous Referee #1. . 2018; ():1.

Chicago/Turabian Style

Solomon Hailu Gebrechorkos. 2018. "Reply to Anonymous Referee #1." , no. : 1.

Preprint content
Published: 29 September 2017
Reads 0
Downloads 0

Managing environmental resources under conditions of climate change and extreme climate events remains among the most challenging research tasks in the field of sustainable development. A particular challenge in many regions such as East Africa is often the lack of sufficiently long-term and spatially representative observed climate data. To overcome this data challenge we used a combination of accessible data sources based on station data, earth observation by remote sensing, and regional climate models. The accuracy of the Africa Rainfall Climatology version 2 (ARC2), Climate Hazards Group InfraRed Precipitation (CHIRP), CHIRP with Station data (CHIRPS), Observational-Reanalysis hybrid (ORH), and Regional Climate Models (RCMs) are evaluated against station data obtained from the respective national weather services and international databases. We did so by relating point to pixel, point to area grid cell average, and stations average to area grid cell average over 21 regions of East Africa: 17 in Ethiopia, two in Kenya and two in Tanzania. We found that the latter method provides better correlation and significantly reduces biases and errors. The correlations were analyzed at daily, dekadal (10 days), and monthly resolution for rainfall and maximum and minimum temperature (T-max and T-min) covering the period of 1983–2005. At daily time scale, CHIRPS, followed by ARC2 and CHIRP are the best performing rainfall products compared to ORH, RCM, and RCMS. CHIRPS captures well the daily rainfall characteristics such as rainfall intensity, amount of wet days, and total rainfall. Compared to CHIRPS, ARC2 showed higher underestimation of the total rainfall (−30 %) and daily intensity (−14 %). CHIRP on the other hand, showed higher underestimation of the daily intensity (−53 %) and duration of dry days (−29 %). Overall, the evaluation revealed that in terms of multiple statistical measures used on daily, dekadal, and monthly time scale, CHIRPS, CHIRP, and ARC2 are the best performing rainfall products while ORH, individual RCM, and RCMs are the least performing products. For T-max and T-min, ORH was identified as the most suitable product compared to RCM and RCMs. Our results indicate that CHIRPS (rainfall) and ORH (T-max and T-min), with higher spatial resolution, should be the preferential data sources to be used for climate change and hydrological studies in areas where station data are not accessible.

ACS Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa. 2017, 2017, 1 -43.

AMA Style

Solomon H. Gebrechorkos, Stephan Hülsmann, Christian Bernhofer. Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa. . 2017; 2017 ():1-43.

Chicago/Turabian Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. 2017. "Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa." 2017, no. : 1-43.

Preprint content
Published: 29 September 2017
Reads 0
Downloads 0
ACS Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. Supplementary material to "Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa". 2017, 1 .

AMA Style

Solomon H. Gebrechorkos, Stephan Hülsmann, Christian Bernhofer. Supplementary material to "Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa". . 2017; ():1.

Chicago/Turabian Style

Solomon H. Gebrechorkos; Stephan Hülsmann; Christian Bernhofer. 2017. "Supplementary material to "Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa"." , no. : 1.