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Hydroclimate is non-stationary and varies in often unpredictable ways on local, regional and global scales, which can lead to water insecurity. This editorial relates the advances and challenges in our understanding of the spatio-temporal relationship between climate variability and change and the components of the hydrologic cycle through the lens of six articles, which contributed to the Water Special Issue: Hydroclimatic Variability at Local, Regional and Global Scales. The relationship between the El Niño/Southern Oscillation and precipitation, temperature, and evapotranspiration is examined within the Indian Summer monsoon, gauge-based precipitation datasets are intercompared over Pakistan, trends in precipitation, temperature, and streamflow are investigated in Ethiopia and China, alternate configurations of hydroclimate modeling are assessed over Canada, and finally, future limitations in groundwater supply are presented for Italy. [ABSTRACT FROM AUTHOR]

The results of metrological, hydrological, and environmental data analyses are mainly dependent on the reliable estimation of missing data. In this study, 21 classical methods were evaluated to determine the best method for infilling the missing precipitation data in Ethiopia. The monthly data collected from 15 different stations over 34 years from 1980 to 2013 were considered. Homogeneity and trend tests were performed to check the data. The results of the different methods were compared using the mean absolute error (MAE), root-mean-square error (RMSE), coefficient of efficiency (CE), similarity index (S-index), skill score (SS), and Pearson correlation coefficient (rPearson). The results of this paper confirmed that the normal ratio (NR), multiple linear regression (MLR), inverse distance weighting (IDW), correlation coefficient weighting (CCW), and arithmetic average (AA) methods are the most reliable methods of those studied. The NR method provides the most accurate estimations with rPearson of 0.945, mean absolute error of 22.90 mm, RMSE of 33.695 mm, similarity index of 0.999, CE index of 0.998, and skill score of 0.998. When comparing the observed results and the estimated results from the NR, MLR, IDW, CCW, and AA methods, the MAE and RMSE were found to be low, and high values of CE, S-index, SS, and rPearson were achieved. On the other hand, using the closet station (CS), UK traditional, linear regression (LR), expectation maximization (EM), and multiple imputations (MI) methods gave the lowest accuracy, with MAE and RMSE values varying from 30.424 to 47.641 mm and from 49.564 to 58.765 mm, respectively. The results of this study suggest that the recommended methods are applicable for different types of climatic data in Ethiopia and arid regions in other countries around the world. [ABSTRACT FROM AUTHOR]

Abstract: With the development of global changes, researchers from all over the world increasingly pay attention to drought detection, and severe droughts that may have resulted from climate change. In this paper, spatial and temporal variability of drought is evaluated based on precipitation data and remotely sensed images. The standard precipitation index (SPI) and vegetation condition index (VCI) are used to evaluate the spatial and temporal characteristics of meteorological and vegetative drought in Tigray, Northern Ethiopia. Based on the drought critical values of SPI and VCI defining drought, the spatial and temporal extent of droughts in the study area is established. We processed 396 decadal images in order to produce the multi-temporal VCI drought maps. The results of the SPI and VCI analysis reveal that the eastern and southern zones of the study region suffered a recurrent cycle of drought over the last decade. Results further show that there is a time lag between the period of the peak VCI and precipitation values obtained from the meteorological stations across the study area. A significant agreement was observed between VCI values with the current plus last two-months of precipitation. The study demonstrates the utility of the vegetation condition index in semi-arid and arid regions. [Copyright &y& Elsevier]

Upper Blue Nile basin (UBNB) is the water tower of Ethiopia and downstream countries. It contributes significant moisture to the surrounding atmosphere. However, the contribution of the moisture from the basin to the precipitation in the UBNB is not well documented. Therefore, this paper is aimed to estimate the role of UBNB moisture budget and recycling ratio for spatiotemporal precipitation distributions. To this end, we used the European Center for Medium-range Weather Forecast (ECMWF) data from 1979 to 2017. The derived ECMWF results are correlated with in-situ observations with the correlation coefficient of 0.82. During summer season most of the UBNB moisture is converted to precipitation around the central parts of the study area, while in spring it contributes to the southern parts of the study area. Furthermore, the northwest part of the study area is affected by the basins moisture during the autumn season. The calculated recycling ratios for four seasons (summer, autumn, spring, and winter) are 9.70%, 16.33%, 19.01%, and 35.30% respectively with the annual average value of 20.11%. It is evident that the maximum amount of precipitation is captured from the local moisture during the winter season. Generally, UBNB moisture budget had a lesser contribution of precipitation over the study area. It rather contributed significant precipitation to the neighboring countries. Hence, further studies on moisture budget are required to explain this phenomenon in the context of Ethiopia. Image 1 • The precipitation pattern over upper Blue Nile basin exhibits a decreasing trend from east to northwest (1979–2017). • Recycling ratio for four seasons (summer, autumn, spring and winter) were 9.70%, 16.33%, 19.01% and 35.30% respectively. • The maximum amount of precipitation is extracted from the locale moistures during the winter season. • Egypt and Sudan have got significance precipitation from the upper Blue Nile basin moisture budgets. [ABSTRACT FROM AUTHOR]

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. Significant changes in monthly maximum values of daily maximum (TXx) and minimum (TNx) and monthly minimum values of daily maximum (TXn) and minimum (TNn) temperature are observed in large parts of East Africa. This change is accompanied by significant increasing trends in warm indices (e.g., warm nights and warm days) and decrease in cold indices (e.g., cold nights and days). Observed temperature extremes after 2000 are warmer than the base period (1981–2010). [ABSTRACT FROM AUTHOR]

ABSTRACT This study examines the performance of multimodel numerical simulations and multiobservational databases focusing on seasonal cycles and spatial variations of precipitation over Ethiopia. Seven regional climate models ( RCMs) driven by the European Center for Medium Range Weather Forecasting ( ECMWF) Interim reanalysis ( ERA-Interim) and generated in the framework of COordinated Regional climate Downscaling EXperiment ( CORDEX) project, and four observational databases computed using different interpolation techniques and blending strategies were evaluated against typical observational database produced by Climate Research Unit ( CRU) over Ethiopia on monthly basis. All were produced at 48.8 km grid resolution for the period 1989-2008. The preliminary results showed that ensembles [multimodel ensemble ( MME) + multiobservational ensemble ( MOE)] were as good as CRU in reproducing the temporal variability and the geographical distribution of precipitation. Comparison of seasonal means and temporal correlation results revealed that there were good agreements between ensembles and CRU at each grid point and in close proximity to each other. Results of rotated principal components ( RPCs), rotated empirical orthogonal functions ( REOFs), and the associated power spectra showed that every ensemble's element was able to simulate the seasonal cycles and homogeneous precipitation zones of CRU reasonably well. Excessive and deficient rainfall periods, which were seen in every ensemble's RPCs, matched CRU historical records. [ABSTRACT FROM AUTHOR]

ABSTRACT Ethiopia has wide eco-environmental diversity ranging from extreme heat at one of the lowest places in the world to one of the coolest summits in Africa. Associated with this environmental diversity and climate change, climatic extremes are expected to change over time and also vary across eco-environments in the country. This study was conducted to examine the trends of past precipitation and temperature extremes over three eco-environments in Ethiopia. The study involved analysis of 20 extreme indices computed from daily temperature and precipitation data spanning over 42 years (1967-2008). The climate data were obtained from 11 stations selected from three major eco-environments (pastoral, agropastoral and highland). The results indicated positive trends for maximum value of the maximum temperature ( TXx), warm days ( TX90p), warm nights ( TN90p) and warm spell duration indicators ( WSDI) and negative trends for cool days ( TX10p), cool nights ( TN10p) and cold spell duration indicators ( CSDI) in more than 8 of the 11 stations studied. However, most of the trends were not significant at many of the stations and the significant trends were not uniquely differentiated by eco-environments. Unlike temperature extremes, precipitation extreme trends showed high variability among nearby stations within eco-environments and were not significant at many of the stations studied. It is concluded that trends of temperature and precipitation extremes vary considerably among stations located within a given eco-environment indicating that the response of local climate to global warming could be different in physiographically diverse regions. [ABSTRACT FROM AUTHOR]