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Understanding extreme precipitation is very important for Ethiopia, which is heavily dependent on low-productivity rainfed agriculture but lacks structural and non-structural water regulating and storage mechanisms. There has been an increasing concern about whether there is an increasing trend in extreme precipitation as the climate changes. Existing analysis of this region has been descriptive, without taking advantage of the advances in extreme value modeling. After reviewing the statistical methodology on extremes, this paper presents an analysis based on the generalized extreme value modeling with daily time series of precipitation records at Debre Markos in the Northwestern Highlands of Ethiopia. We found no strong evidence to reject the null hypothesis that there is no increasing trend in extreme precipitation at this location. [ABSTRACT FROM AUTHOR]

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]

Summary In water resources modeling, meteorological data scarcity can be compensated by various global data sets, but those data sets can differ tremendously. In the literature, hydrological models of differing complexity are proposed for estimating the water resources of semi-arid catchments, and also to evaluate rainfall data sets. The goal of this paper is to provide a joint analysis of modeling uncertainty due to different input data and increasing model complexity. Impacts of mutually concealed uncertainties on model performance and model outputs are exemplified in two data sparse semi-arid catchments in Ethiopia. We applied a semi-distributed and a fully distributed hydrological model, having different levels of complexity. Three different satellite-based rainfall data sets and two temperature products were used as model inputs. The semi-distributed model demonstrated good validation performances, while the fully distributed model was more sensitive to data uncertainties. The application of TRMM version 6 completely failed and the high-resolution CMORPH precipitation estimate outperformed TRMM version 7. In contrast, the use of high-resolution temperature data did not improve the model results. The large differences in remotely sensed input data were buffered inside the hydrological models. Therefore, data set evaluations regarding only the simulated hydrographs were less meaningful. In contrast, the investigation of parameter evolution and distributed outputs’ variability appeared to be a valuable tool to uncover the interdependencies of data and model uncertainties. We suggest this procedure to be applied by default in water resources estimations that are affected by data scarcity, but especially when data sets are evaluated using hydrological models. Our case study demonstrates that estimations of groundwater recharge and actual evapotranspiration vary largely, depending on the applied data sets and models. The joint analysis reveals large interdependencies between data and model evaluations. It shows that traditional studies focusing only on one part of uncertainty, either the input uncertainty or the uncertainty arising from the choice of model structure are limited in their explanatory power of the modeling performance, particularly in poorly gauged regions. [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]

During boreal spring and fall, the low-level cross-equatorial flow along the East African coast weakens and reverses. These transition periods are embedded within the East African rainy seasons—the spring long rains and the fall short rains. ERAI, ERA5, JRA-55, and MERRA2 atmospheric reanalyses and CHIRPS2 and ARC2 rainfall observations are examined to improve our understanding of how the transition periods vary on interannual timescales, and how these variations are related to regional rainfall. Transition period length is positively correlated with spring and fall East African rainfall regionally. Variations in transition period length are associated with the seasonal development and variability of three low-level circulation features, namely, the South Indian Convergence Zone, the Mascarene High over the South Indian Ocean, and the Arabian High over the Arabian Sea. In addition to affecting the low-level moisture transport and convergence over East Africa, these circulation features interact to influence the start and end dates that define the transition period. Composites are constructed and analyzed for different transition period types based on length, start date, and end date to distinguish differences among event types as well as seasonal differences. Air–sea coupling over the western Indian Ocean is stronger for the spring transition than for the fall, modifying precipitation through variations in specific humidity. In the fall transition period, rainfall variations are primarily controlled by circulation variations. These results have implications for climate change prediction over East Africa, for example, as the Indian Ocean warms. [ABSTRACT FROM AUTHOR]

Water resources reanalysis (WRR) can be used as a numerical tool to advance our understanding of hydrological processes where in situ observations are limited. However, WRR products are associated with uncertainty that needs to be quantified to improve usability of such products in water resources applications. In this study, we evaluate estimates of water cycle components from 18 state-of-the-art WRR datasets derived from different land surface/hydrological models, meteorological forcing, and precipitation datasets. The evaluation was conducted at three spatial scales in the upper Blue Nile basin in Ethiopia. Precipitation, streamflow, evapotranspiration (ET), and terrestrial water storage (TWS) were evaluated against in situ daily precipitation and streamflow measurements, remote sensing–derived ET, and the NASA Gravity Recovery and Climate Experiment (GRACE) product, respectively. Our results highlight the current strengths and limitations of the available WRR datasets in analyzing the hydrological cycle and dynamics of the study basins, showing an overall underestimation of ET and TWS and overestimation of streamflow. While calibration improves streamflow simulation, it results in a relatively poorer performance in terms of ET. In addition, we show that the differences in the schemes used in the various land surface models resulted in significant differences in the estimation of the water cycle components from the respective WRR products, while we noted small differences among the products related to precipitation forcing. We did not identify a single product that consistently outperformed others; however, we found that there are specific WRR products that provided accurate representation of a single component of the water cycle (e.g., only runoff) in the area. [ABSTRACT FROM AUTHOR]

Precipitation during the Ethiopian Kiremt (June–September) season has exhibited significant interannual and multi‐decadal variability over the 20th and early 21st century. We investigate the temporal variability in the strength of the teleconnections between sea‐surface temperatures in key global oceanic regions, including the Tropical Pacific, Indian Ocean, and Tropical Atlantic, and Kiremt season precipitation at sub‐seasonal, interannual and multi‐decadal time scales. We also investigate the influence of the Madden‐Julian Oscillation (MJO). We perform a systematic analysis of 112‐year long (1901–2012) precipitation in the northern region of East Africa including the southern and central regions of Ethiopia and uncover interesting spatial, temporal and sub‐seasonal variability, and teleconnection patterns. Precipitation anomalies during wet and dry years extend throughout Northern Africa and also, during September extends over Indian subcontinent, suggesting large‐scale variability of wet/dry patterns. Wet (dry) years are accompanied by La Nina like (El Nino like) conditions in the tropical Pacific and extending into Atlantic and Indian Oceans. Through Bayesian dynamical linear modelling we find that temporal changes in seasonal precipitation correspond to changes in the strengths of sea surface temperature teleconnections, and that the relative strengths of these teleconnections rather than one dominant teleconnection influences precipitation variability. During three precipitation epochs in this region, the mid‐century pluvial, the late‐century drought, and the early 21st century, we find that changes in precipitation are related to changes in the main dynamical features of precipitation. These findings suggest that Kiremt season precipitation is in a new regime, and is of key interest to the agricultural and water resources communities who rely on accurate forecasts of precipitation to make operational decisions. [ABSTRACT FROM AUTHOR]

Monthly observed and future precipitation magnitudes were subjected to statistical trend analysis to examine possible time series behavior. Future precipitation was downscaled from large-scale output through statistical downscaling. The observed and downscaled future precipitation was analyzed for drought events using the Standardized Precipitation Index (SPI) method. In the Abaya Chamo sub-basin, Ethiopia precipitation is explained by below average magnitudes in most of the low land area, characterized by moderate to extreme drought episodes. Nine drought events were discerned during the period of 1988 to 2015, i.e. once in three years, resulting in harvest failure and subsequent food insecurity. The NCEP-NCAR and CanESM2 model predictors were used to statistically downscale the precipitation data. The monthly observed and downscaled precipitation magnitudes were in good agreement. The RCP-2.6, RCP-4.5 and RCP-8.5 long-term future scenarios were computed to evaluate future drought patterns. The mean annual precipitation scenario decreased by 0.2% to 13.7%, 0.5% to 6.4% and 0.1% to 1.3% for the period from 2016 to 2040, 2050s and 2080s respectively. The increase in mean precipitation was projected to be 0.7% to 12.2%, 0.2% to 11.7% and 0.1% to 17.8% for the period from 2016 to 2040, 2050s and 2080s respectively. The present analysis may provide useful information associated to drought events to decision makers and can be used as a basis for future research in this area. [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]

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning. [ABSTRACT FROM AUTHOR]

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Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills. [ABSTRACT FROM AUTHOR]

Despite large increases in national cereal production in recent decades, Ethiopia continues to experience regular acute food insecurity crises, often associated with drought events. However, the meteorology of these events is poorly defined and local populations frequently experience food insecurity crises in years when national rainfall and cereal production totals are high. Therefore, looking at national, or even to some extent sub-national, rainfall variability is a misappropriation of climate as a causal factor in food insecurity in Ethiopia. The distinction between 'drought' as catch-all driver of food insecurity and a more nuanced view of the relationship between rainfall variability and food security is necessary both for addressing food insecurity now and for interpreting long-term climate model projections. The on-going recurrence of acute food insecurity is a feature of the heterogeneity of climate and climate variability in Ethiopia, but only in the context of a food system dominated by smallholder farming and climate-sensitive livelihoods. Climate variability has the greatest adverse impact in the most marginal livelihood zones in the drier east of the country. Increasing the resilience of smallholder farmers and pastoralists to climate variability and improvements in early warning and disaster risk response could reduce the frequency and severity of food security crises. However, unless the food system in Ethiopia undergoes transformational adaptation, food insecurity crises will continue to occur, and the opportunity to achieve zero hunger by 2030 will be missed. [ABSTRACT FROM AUTHOR]

Impact of spatial data availability on the temperature and precipitation prediction characteristics of Weyib River basin in Ethiopia has been investigated using CMIP5-CanESM2 model for the RCP8.5, RCP4.5 and RCP2.6 scenarios. The objective of the present study is to characterize how future temperatures and precipitation prediction under CMIP5-CanESM2 model output varies against diverse averaged arbitrary spatial weather stations found in the basin. The statistical downscaling model tested and verified using the observed daily data for twelve, six and three averaged arbitrary spatial weather stations as well as for a single weather station was used to predict the future climate scenarios. The results revealed that the mean annual daily maximum and minimum temperature and precipitation for twelve, six and three arbitrary spatial stations have revealed an increasing trend in the upcoming periods until the end of the century. In single station analysis, the trend itself has changed from increasing trend to decreasing trend in case of maximum and minimum temperature. In case of precipitation, no visible trend has been observed in case of single station analysis. Therefore, the variation in amount and distribution of precipitation and temperature among the four averaged spatial stations in the same study area might affect the water resources and agriculture of the basin and also instead of using a single weather station to predict future climate variables for a particular study basin, it is more reliable using averages of numerous spatial weather stations data. [ABSTRACT FROM AUTHOR]

The hydrological response of catchments with different rainfall patterns was assessed to understand the availability of blue and green water and the impacts of changing precipitation and temperature in the Ethiopian Highlands. Monthly discharge of three small-scale catchments was simulated, calibrated, and validated with a dataset of more than 30 years. Different temperature and precipitation scenarios were used to compare the hydrological responses in all three catchments. Results indicate that runoff reacts disproportionately strongly to precipitation and temperature changes: a 24% increase in precipitation led to a 50% increase in average annual runoff, and an average annual rainfall–runoff ratio that was 20% higher. An increase in temperature led to an increase of evapotranspiration and resulted in a decrease in the rainfall–runoff ratio. But a comparison of combined results with different climate change scenarios shows that downstream stakeholders can expect a higher share of available blue water in the future. [ABSTRACT FROM PUBLISHER]

Kiremt-season (June-September) precipitation provides a significant water supply for Ethiopia, particularly in the central and northern regions. The response of Kiremt-season precipitation to climate change is thus of great concern to water resource managers. However, the complex processes that control Kiremt-season precipitation challenge the capability of general circulation models (GCMs) to accurately simulate precipitation amount and variability. This in turn raises questions about their utility for predicting future changes. This study assesses the impact of climate change on Kiremt-season precipitation using state-of-the-art GCMs participating in the Coupled Model Intercomparison Project Phase 5. Compared to models with a coarse resolution, high-resolution models (horizontal resolution <2°) can more accurately simulate precipitation, most likely due to their ability to capture precipitation induced by topography. Under the Representative Concentration Pathway (RCP) 4.5 scenario, these high-resolution models project an increase in precipitation over central Highlands and northern Great Rift Valley in Ethiopia, but a decrease in precipitation over the southern part of the country. Such a dipole pattern is attributable to the intensification of the North Atlantic subtropical high (NASH) in a warmer climate, which influences Ethiopian Kiremt-season precipitation mainly by modulating atmospheric vertical motion. Diagnosis of the omega equation demonstrates that an intensified NASH increases (decreases) the advection of warm air and positive vorticity into the central Highlands and northern Great Rift Valley (southern part of the country), enhancing upward motion over the northern Rift Valley but decreasing elsewhere. Under the RCP 4.5 scenario, the high-resolution models project an intensification of the NASH by 15 (3 × 10 m s) geopotential meters (stream function) at the 850-hPa level, contributing to the projected precipitation change over Ethiopia. The influence of the NASH on Kiremt-season precipitation becomes more evident in the future due to the offsetting effects of two other major circulation systems: the East African Low-level Jet (EALLJ) and the Tropical Easterly Jet (TEJ). The high-resolution models project a strengthening of the EALLJ, but a weakening of the TEJ. Future changes in the EALLJ and TEJ will drive this precipitation system in opposite directions, leading to small or no net changes in precipitation in Ethiopia. [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]

The article focuses on a study in Blue Nile River which examines the development of simple hydrology and erosion models through the use of saturation excess runoff principles and interflow processes. It states that a water balance approach is used by dividing the landscape into variable saturated areas, exposed rock and hillslopes. It notes that available precipitation and potential evaporation data and a minimum of calibration parameters are used to couple the model with erosion model.

Aridity is a risk with the threat of desertification because of erratic precipitation and prolonged droughts. Aridity indices are often used for the climate-based land classification and monitoring droughts. Hence, this study aimed at analysis of the spatial distribution of aridity indices in Raya valley, northern Ethiopia. Meteorological data from eight meteorological stations located throughout the study area were used to compute the three aridity indices employed: the De Martonne aridity index (IDM), the Pinna combinative index (IP), and the Food and Agriculture Organization aridity index (IFAO). Each aridity index value was computed at each station. Then, once the statistical properties of each aridity index at each station were assessed, inverse distance-weighted geographic information system interpolation technique was used for the spatial estimation of the aridity indices. The three indices indicated a high coefficient of determination which in common revealed the area prone to dry and semi-dry risk. However, IDM was found more appropriate for the considered area since it defines more the climate of each location using seven classes, and it can be computed at specific season and month. The annual, seasonal, and monthly aridity indices of IDM showed high spatial variability of aridity index. The spatial distribution of the IDM underlined irrigation requirements in the northern east part of the study area. This analysis may be helpful for the utilization of water resources and irrigation systems of the study area. [ABSTRACT FROM AUTHOR]