Articles | Volume 11, issue 1
https://doi.org/10.5194/ascmo-11-59-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-11-59-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
14 Mar 2025
| 14 Mar 2025
Analysis of meteorological drought using satellite-based rainfall products over southern Ethiopia
Tesfay Mekonnen Weldegerima
CORRESPONDING AUTHOR
Faculty of Meteorology and Hydrology, Institute of Water Technology, Arba Minch University, Arba Minch, Ethiopia
Tewelde Berihu Gebresilassie
Faculty of Meteorology and Hydrology, Institute of Water Technology, Arba Minch University, Arba Minch, Ethiopia
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Related subject area
Climate research
Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales
Formally combining different lines of evidence in extreme-event attribution
Environmental sensitivity of the Caribbean economic growth rate
Spatial patterns and indices for heat waves and droughts over Europe using a decomposition of extremal dependency
Changes in the distribution of annual maximum temperatures in Europe
Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios
Comparing climate time series – Part 4: Annual cycles
Statistical reconstruction of European winter snowfall in reanalysis and climate models based on air temperature and total precipitation
A multi-method framework for global real-time climate attribution
Analysis of the evolution of parametric drivers of high-end sea-level hazards
Comparing climate time series – Part 3: Discriminant analysis
Spatial heterogeneity in rain-bearing winds, seasonality and rainfall variability in southern Africa's winter rainfall zone
Spatial heterogeneity of 2015–2017 drought intensity in South Africa's winter rainfall zone
A statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea level
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
A protocol for probabilistic extreme event attribution analyses
The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models
A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series
Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany
Possible impacts of climate change on fog in the Arctic and subpolar North Atlantic
Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models
Comparison and assessment of large-scale surface temperature in climate model simulations
Future climate emulations using quantile regressions on large ensembles
Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures
Estimates of climate system properties incorporating recent climate change
The joint influence of break and noise variance on the break detection capability in time series homogenization
A space–time statistical climate model for hurricane intensification in the North Atlantic basin
Building a traceable climate model hierarchy with multi-level emulators
Chalachew Muluken Liyew, Elvira Di Nardo, Rosa Meo, and Stefano Ferraris
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 173–194, https://doi.org/10.5194/ascmo-10-173-2024, https://doi.org/10.5194/ascmo-10-173-2024, 2024
Short summary
Short summary
Global warming is a big issue: it is necessary to know more details to make a forecast model and plan adaptation measures. Warming varies in space and time and models often average it over large areas. However, it shows great variations between months of the year. It also varies between regions of the world and between lowland and highland regions. This paper uses statistical and machine learning techniques to quantify such differences between Italy and the UK at different altitudes.
Friederike E. L. Otto, Clair Barnes, Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, and Robert Vautard
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 159–171, https://doi.org/10.5194/ascmo-10-159-2024, https://doi.org/10.5194/ascmo-10-159-2024, 2024
Short summary
Short summary
To assess the role of climate change in individual weather events, different lines of evidence need to be combined in order to draw robust conclusions about whether observed changes can be attributed to anthropogenic climate change. Here we present a transparent method, developed over 8 years, to combine such lines of evidence in a single framework and draw conclusions about the overarching role of human-induced climate change in individual weather events.
Mark R. Jury
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 95–104, https://doi.org/10.5194/ascmo-10-95-2024, https://doi.org/10.5194/ascmo-10-95-2024, 2024
Short summary
Short summary
A unique link is found between the Caribbean GDP growth rate and the tropical climate system. Although the Pacific El Niño–Southern Oscillation governs some aspects of this link, the Walker circulation and associated humidity over the equatorial Atlantic emerge as leading predictors of economic prosperity in the central Antilles islands.
Svenja Szemkus and Petra Friederichs
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 29–49, https://doi.org/10.5194/ascmo-10-29-2024, https://doi.org/10.5194/ascmo-10-29-2024, 2024
Short summary
Short summary
This paper uses the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019), which we extend to the description of common extremes in two variables. We develop an extreme pattern index (EPI), a pattern-based aggregation to describe spatially extended weather extremes. Our results show that the EPI is suitable for describing heat waves. We extend the EPI to describe extremes in two variables and obtain an index to describe compound precipitation deficits and heat waves.
Graeme Auld, Gabriele C. Hegerl, and Ioannis Papastathopoulos
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 45–66, https://doi.org/10.5194/ascmo-9-45-2023, https://doi.org/10.5194/ascmo-9-45-2023, 2023
Short summary
Short summary
In this paper we consider the problem of detecting changes in the distribution of the annual maximum temperature, during the years 1950–2018, across Europe.
We find that, on average, the temperature that would be expected to be exceeded
approximately once every 100 years in the 1950 climate is expected to be exceeded once every 6 years in the 2018 climate. This is of concern due to the devastating effects on humans and natural systems that are caused by extreme temperatures.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 29–44, https://doi.org/10.5194/ascmo-9-29-2023, https://doi.org/10.5194/ascmo-9-29-2023, 2023
Short summary
Short summary
Climate model output has systematic errors which can be reduced with statistical methods. We review existing bias-adjustment methods for climate data and discuss their skills and issues. We define three demands for the method and then evaluate them using real and artificially created daily temperature and precipitation data for Austria to show how biases can also be introduced with bias-adjustment methods themselves.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 187–203, https://doi.org/10.5194/ascmo-8-187-2022, https://doi.org/10.5194/ascmo-8-187-2022, 2022
Short summary
Short summary
Most climate time series contain annual and diurnal cycles. However, an objective criterion for deciding whether two time series have statistically equivalent annual and diurnal cycles is lacking, particularly if the residual variability is serially correlated. Such a criterion would be helpful in deciding whether a new version of a climate model better simulates such cycles. This paper derives an objective rule for such decisions based on a rigorous statistical framework.
Flavio Maria Emanuele Pons and Davide Faranda
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 155–186, https://doi.org/10.5194/ascmo-8-155-2022, https://doi.org/10.5194/ascmo-8-155-2022, 2022
Short summary
Short summary
The objective motivating this study is the assessment of the impacts of winter climate extremes, which requires accurate simulation of snowfall. However, climate simulation models contain physical approximations, which result in biases that must be corrected using past data as a reference. We show how to exploit simulated temperature and precipitation to estimate snowfall from already bias-corrected variables, without requiring the elaboration of complex, multivariate bias adjustment techniques.
Daniel M. Gilford, Andrew Pershing, Benjamin H. Strauss, Karsten Haustein, and Friederike E. L. Otto
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 135–154, https://doi.org/10.5194/ascmo-8-135-2022, https://doi.org/10.5194/ascmo-8-135-2022, 2022
Short summary
Short summary
We developed a framework to produce global real-time estimates of how human-caused climate change affects the likelihood of daily weather events. A multi-method approach provides ensemble attribution estimates accompanied by confidence intervals, creating new opportunities for climate change communication. Methodological efficiency permits daily analysis using forecasts or observations. Applications with daily maximum temperature highlight the framework's capacity on daily and global scales.
Alana Hough and Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 117–134, https://doi.org/10.5194/ascmo-8-117-2022, https://doi.org/10.5194/ascmo-8-117-2022, 2022
Short summary
Short summary
We use machine learning to assess how different geophysical uncertainties relate to the severity of future sea-level rise. We show how the contributions to coastal hazard from different sea-level processes evolve over time and find that near-term sea-level hazards are driven by thermal expansion and the melting of glaciers and ice caps, while long-term hazards are driven by ice loss from the major ice sheets.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 97–115, https://doi.org/10.5194/ascmo-8-97-2022, https://doi.org/10.5194/ascmo-8-97-2022, 2022
Short summary
Short summary
A common problem in climate studies is to decide whether a climate model is realistic. Such decisions are not straightforward because the time series are serially correlated and multivariate. Part II derived a test for deciding wether a simulation is statistically distinguishable from observations. However, the test itself provides no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between stochastic processes.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 31–62, https://doi.org/10.5194/ascmo-8-31-2022, https://doi.org/10.5194/ascmo-8-31-2022, 2022
Short summary
Short summary
Cape Town is situated in a small but ecologically and climatically highly diverse and vulnerable pocket of South Africa uniquely receiving its rain mostly in winter. We show complex structures in the spatial patterns of rainfall seasonality and year-to-year changes in rainfall within this domain, tied to spatial differences in the rain-bearing winds. This allows us to develop a new spatial subdivision of the region to help future studies distinguish spatially distinct climate change responses.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 63–81, https://doi.org/10.5194/ascmo-8-63-2022, https://doi.org/10.5194/ascmo-8-63-2022, 2022
Short summary
Short summary
The
Day Zerowater crisis affecting Cape Town after the severe 2015–2017 drought motivated renewed research interest into causes and projections of rainfall variability and change in this water-stressed region. Unusually few wet months and very wet days characterised the Day Zero Drought. Its extent expanded as it shifted gradually north-eastward, concurrent with changes in the weather systems driving drought. Our results emphasise the need to consider the interplay between drought drivers.
Erica L. Ashe, Nicole S. Khan, Lauren T. Toth, Andrea Dutton, and Robert E. Kopp
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 1–29, https://doi.org/10.5194/ascmo-8-1-2022, https://doi.org/10.5194/ascmo-8-1-2022, 2022
Short summary
Short summary
We develop a new technique to integrate realistic uncertainties in probabilistic models of past sea-level change. The new framework performs better than past methods (in precision, accuracy, bias, and model fit) because it enables the incorporation of previously unused data and exploits correlations in the data. This method has the potential to assess the validity of past estimates of extreme sea-level rise and highstands providing better context in which to place current sea-level change.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
Short summary
Short summary
Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, https://doi.org/10.5194/ascmo-6-177-2020, 2020
Short summary
Short summary
Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
Mark D. Risser and Michael F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 115–139, https://doi.org/10.5194/ascmo-6-115-2020, https://doi.org/10.5194/ascmo-6-115-2020, 2020
Short summary
Short summary
Evaluation of modern high-resolution global climate models often does not account for the geographic location of the underlying weather station data. In this paper, we quantify the impact of geographic sampling on the relative performance of climate model representations of precipitation extremes over the United States. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance.
Donald P. Cummins, David B. Stephenson, and Peter A. Stott
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 91–102, https://doi.org/10.5194/ascmo-6-91-2020, https://doi.org/10.5194/ascmo-6-91-2020, 2020
Short summary
Short summary
We have developed a novel and fast statistical method for diagnosing effective radiative forcing (ERF), a measure of the net effect of greenhouse gas emissions on Earth's energy budget. Our method works by inverting a recursive digital filter energy balance representation of global climate models and has been successfully validated using simulated data from UK Met Office climate models. We have estimated time series of historical ERF by applying our method to the global temperature record.
Meagan Carney and Holger Kantz
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 61–77, https://doi.org/10.5194/ascmo-6-61-2020, https://doi.org/10.5194/ascmo-6-61-2020, 2020
Short summary
Short summary
Extremes in weather can have lasting effects on human health and resource consumption. Studying the recurrence of these events on a regional scale can improve response times and provide insight into a changing climate. We introduce a set of clustering tools that allow for regional clustering of weather recordings from stations across Germany. We use these clusters to form regional models of summer temperature extremes and find an increase in the mean from 1960 to 2018.
Richard E. Danielson, Minghong Zhang, and William A. Perrie
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 31–43, https://doi.org/10.5194/ascmo-6-31-2020, https://doi.org/10.5194/ascmo-6-31-2020, 2020
Short summary
Short summary
Visibility is estimated for the 21st century using global and regional climate model output. A baseline decrease in visibility in the Arctic (10 %) is more notable than in the North Atlantic (< 5 %). We develop an adjustment that yields greater consistency among models and explore the justification of our ad hoc adjustment toward ship observations during the historical period. Baseline estimates are found to be sensitive to the representation of temperature and humidity.
Sophie C. Lewis, Sarah E. Perkins-Kirkpatrick, and Andrew D. King
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 133–146, https://doi.org/10.5194/ascmo-5-133-2019, https://doi.org/10.5194/ascmo-5-133-2019, 2019
Short summary
Short summary
Extreme temperature and precipitation events in Australia have caused significant socio-economic and environmental impacts. Determining the factors contributing to these extremes is an active area of research. This paper describes a set of studies that have examined the causes of extreme climate events in recent years in Australia. Ideally, this review will be useful for the application of these extreme event attribution approaches to climate and weather extremes occurring elsewhere.
Raquel Barata, Raquel Prado, and Bruno Sansó
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 67–85, https://doi.org/10.5194/ascmo-5-67-2019, https://doi.org/10.5194/ascmo-5-67-2019, 2019
Matz A. Haugen, Michael L. Stein, Ryan L. Sriver, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 37–55, https://doi.org/10.5194/ascmo-5-37-2019, https://doi.org/10.5194/ascmo-5-37-2019, 2019
Short summary
Short summary
This work uses current temperature observations combined with climate models to project future temperature estimates, e.g., 100 years into the future. We accomplish this by modeling temperature as a smooth function of time both in the seasonal variation as well as in the annual trend. These smooth functions are estimated at multiple quantiles that are all projected into the future. We hope that this work can be used as a template for how other climate variables can be projected into the future.
Rasmus E. Benestad, Bob van Oort, Flavio Justino, Frode Stordal, Kajsa M. Parding, Abdelkader Mezghani, Helene B. Erlandsen, Jana Sillmann, and Milton E. Pereira-Flores
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 37–52, https://doi.org/10.5194/ascmo-4-37-2018, https://doi.org/10.5194/ascmo-4-37-2018, 2018
Short summary
Short summary
A new study indicates that heatwaves in India will become more frequent and last longer with global warming. Its results were derived from a large number of global climate models, and the calculations differed from previous studies in the way they included advanced statistical theory. The projected changes in the Indian heatwaves will have a negative consequence for wheat crops in India.
Alex G. Libardoni, Chris E. Forest, Andrei P. Sokolov, and Erwan Monier
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 19–36, https://doi.org/10.5194/ascmo-4-19-2018, https://doi.org/10.5194/ascmo-4-19-2018, 2018
Short summary
Short summary
We present new probabilistic estimates of model parameters in the MIT Earth System Model using more recent data and an updated method. Model output is compared to observed climate change to determine which sets of model parameters best simulate the past. In response to increasing surface temperatures and accelerated heat storage in the ocean, our estimates of climate sensitivity and ocean diffusivity are higher. Using a new interpolation algorithm results in smoother probability distributions.
Ralf Lindau and Victor Karel Christiaan Venema
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 1–18, https://doi.org/10.5194/ascmo-4-1-2018, https://doi.org/10.5194/ascmo-4-1-2018, 2018
Short summary
Short summary
Climate data contain spurious breaks, e.g., by relocation of stations, which makes it difficult to infer the secular temperature trend. Homogenization algorithms use the difference time series of neighboring stations to detect and eliminate this spurious break signal. For low signal-to-noise ratios, i.e., large distances between stations, the correct break positions may not only remain undetected, but segmentations explaining mainly the noise can be erroneously assessed as significant and true.
Erik Fraza, James B. Elsner, and Thomas H. Jagger
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 105–114, https://doi.org/10.5194/ascmo-2-105-2016, https://doi.org/10.5194/ascmo-2-105-2016, 2016
Short summary
Short summary
Climate influences on hurricane intensification are investigated by averaging hourly intensification rates over the period 1975–2014 in 8° by 8° latitude–longitude grid cells. The statistical effects of hurricane intensity, sea-surface temperature (SST), El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Madden–Julian Oscillation (MJO) are quantified. Intensity, SST, and NAO had a positive effect on intensification rates. The NAO effect should be further studied.
Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 17–37, https://doi.org/10.5194/ascmo-2-17-2016, https://doi.org/10.5194/ascmo-2-17-2016, 2016
Short summary
Short summary
In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
Cited articles
Aleminew, A. and Alemayehu, M.: Soil fertility depletion and its management options under crop production perspectives in Ethiopia: A review, Agricultural Reviews, 41, 91–105, 2020.
Amenu, B. T. and Mamo, G. S.: Impacts of Population Pressure on Agricultural Production and Farming Practices of Households: The Case of Essera Woreda in Dawro Zone, SNNPR, Ethiopia, Earth Sci., 10, 298–314, 2021.
Asefa, M., Cao, M., He, Y., Mekonnen, E., Song, X., and Yang, J.: Ethiopian vegetation types, climate and topography, Plant Divers., 42, 302–311, 2020.
Atiah, W. A., Amekudzi, L. K., Aryee, J. N. A., Preko, K., and Danuor, S. K.: Validation of satellite and merged rainfall data over Ghana, West Africa, Atmosphere, 11, 859, https://doi.org/10.3390/atmos11080859, 2020.
Ayehu, G. T., Tadesse, T., Gessesse, B., and Dinku, T.: Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia, Atmos. Meas. Tech., 11, 1921–1936, https://doi.org/10.5194/amt-11-1921-2018, 2018.
Ayugi, B., Eresanya, E. O., Onyango, A. O., Ogou, F. K., Okoro, E. C., and Okoye, C. O.: Review of meteorological drought in Africa: historical trends, impacts, mitigation measures, and prospects, Pure Appl. Geophys., 179, 1365–1386, 2022.
Ayugi, B., Tan, G., Rouyun, N., Zeyao, D., Ojara, M., and Mumo, L.: Characterization of historical trends of droughts and pluvial scenarios over Kenya, East Africa, Preprints.org [preprint], https://doi.org/10.20944/preprints201912.0343.v1, 25 December 2019.
Bayissa, Y., Tadesse T., and Demisse G.: Building a high-resolution vegetation outlook model to monitor agricultural drought for the Upper Blue Nile Basin, Ethiopia, Remote Sens., 11, 371, https://doi.org/10.3390/rs11040371, 2019.
Bitew, M. M., Gebremichael, M., Ghebremichael, L. T., and Bayissa, Y. A.: Evaluation of high-resolution satellite rainfall products through streamflow simulation in hydrological modeling of a small mountainous watershed in Ethiopia, J. Hydrometeorol., 13, 338–350, 2012.
Boken, V. K., Cracknell, A. P., and Heathcote, R. L.: Monitoring and predicting agricultural drought: a global study, Oxford University Press, p. 496, https://doi.org/10.1093/oso/9780195162349.001.0001, 2005.
Brito, S. S. B., Cunha, A. P. M. A., Cunningham, C. C., Alvalá, R. C., Marengo, J. A., and Carvalho, M. A.: Frequency, duration, and severity of drought in the Semiarid Northeast Brazil region, Int. J. Climatol., 38, 517–529, 2018.
Brocca, L., Melone, F., Moramarco, T., and Morbidelli, R.: Spatial-temporal variability of soil moisture and its estimation across scales, Water Resour. Res., 46, W02516, https://doi.org/10.1029/2009WR008016, 2010.
Caloiero, T., Caroletti, G. N., and Coscarelli, R.: IMERG-based meteorological drought analysis over Italy, Climate, 9, 65, https://doi.org/10.3390/cli9040065, 2021.
Degefu, M. A. and Bewket, W.: Trends and spatial patterns of drought incidence in the omo-ghibe river basin, Ethiopia, Geogr. Ann. A, 97, 395–414, 2015.
Dembélé, M. and Zwart, S. J.: Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa, Int. J. Remote Sens., 37, 3995–4014, 2016.
Diem, J. E., Hartter, J., Ryan, S. J., and Palace, M. W.: Validation of satellite rainfall products for western Uganda, J. Hydrometeorol., 15, 2030–2038, 2014.
Dinku, T., Chidzambwa, S., Ceccato, P., Connor, S. J., and Ropelewski, C. F.: Validation of high-resolution satellite rainfall products over complex terrain, Int. J. Remote Sens., 29, 4097–4110, 2008.
Dinku, T., Faniriantsoa, R., Islam, S., Nsengiyumva, G., and Grossi, A.: The Climate Data Tool: Enhancing Climate Services across Africa, Front. Climate, 3, 787519, https://doi.org/10.3389/fclim.2021.787519, 2021.
Esayas, B., Simane, B., Teferi, E., Ongoma, V., and Tefera, N.: Trends in extreme climate events over three agroecological zones of southern Ethiopia, Adv. Meteorol., 1, 1–17, 2018.
Estébanez-Camarena, M., Curzi, F., Taormina, R., van de Giesen, N., and ten Veldhuis, M. C.: The Role of Water Vapor Observations in Satellite Rainfall Detection Highlighted by a Deep Learning Approach, Atmosphere, 14, 974, https://doi.org/10.3390/atmos14060974, 2023.
Feidas, H.: Validation of satellite rainfall products over Greece, Theor. Appl. Climatol., 99, 193–216, 2010.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., and Shukla, S.: The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes, Sci Data, 2, 1–21, 2015.
Gezie, M.: Farmer's response to climate change and variability in Ethiopia: A review, Cogent Food Agric., 5, 1613770, https://doi.org/10.1080/23311932.2019.1613770, 2019.
Han, Z., Huang, S., Zhao, J., Leng, G., Huang, Q., and Zhang, H.: Long-chain propagation pathways from meteorological to hydrological, agricultural and groundwater drought and their dynamics in China, J. Hydrol., 625, 130131, https://doi.org/10.1016/j.jhydrol.2023.130131, 2023.
Hordofa, T., Menkir, M., Awulachew, S. B., and Erkossa, T.: Irrigation and rain-fed crop production system in Ethiopia, impact of irrigation on poverty and environment in Ethiopia, International Water Management Institute(IWMI) Conference Papers (No. h044065), 27–36, 2008.
Kalimeris, A. and Kolios, S.: TRMM-based rainfall variability over the Central Mediterranean and its relationships with atmospheric and oceanic climatic modes, Atmos. Res., 230, 104649, https://doi.org/10.1016/j.atmosres.2019.104649, 2019.
Khalili, D., Farnoud, T., Jamshidi, H., Kamgar-Haghighi, A. A., and Zand-Parsa, S.: Comparability analyses of the SPI and RDI meteorological drought indices in different climatic zones, Water Resour. Manage., 25, 1737–1757, 2011.
Kimani, M. W., Hoedjes, J. C. B., and Su, Z.: An assessment of satellite-derived rainfall products relative to ground observations over East Africa, Remote Sens., 9, 430, https://doi.org/10.3390/rs9050430, 2017.
Legesse, S. A.: The outlook of Ethiopian long rain season from the global circulation model, Environmental Systems Research., 5, 1–16, 2016.
Levizzani, V., Amorati, R., and Meneguzzo, F.: A review of satellite-based rainfall estimation methods, European Commission Project MUSIC Report (EVK1-CT-2000-00058), 66, 2002.
Li, L., She, D., Zheng, H., Lin, P., and Yang, Z. L.: Elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China, J. Hydrometeorol., 21, 1513–1530, 2020.
Liou, Y. A. and Mulualem, G. M.: Spatio–temporal assessment of drought in Ethiopia and the impact of recent intense droughts, Remote Sens., 11, 1828, https://doi.org/10.3390/rs11151828, 2019.
Liu, X., Zhu, X., Pan, Y., Li, S., Liu, Y., and Ma, Y.: Agricultural drought monitoring: Progress, challenges, and prospects, J. Geogr. Sci., 26, 750–767, 2016.
Maidment, R., Black, E., Greatrex, H., and Young, M.: TAMSAT. Satellite Precipitation Measurement, Volume 1, 393–407, 2020.
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, California, 1993, Vol. 17, 22, 179–183, 1993.
Mekonen, A. A., Berlie, A. B., and Ferede, M. B.: Spatial and temporal drought incidence analysis in the northeastern highlands of Ethiopia, Geoenvironmental Disasters, 7, 1–17, 2020.
Mera, G. A.: Drought and its impacts in Ethiopia, Weather Clim Extrem., 22, 24–35, 2018.
Moges, D. M. and Bhat, H. G.: Climate change and its implications for rainfed agriculture in Ethiopia, J. Water Clim. Change, 12, 1229–1244, 2021.
Novella, N. S. and Thiaw, W. M.: African rainfall climatology version 2 for famine early warning systems, J. Appl. Meteorol. Clim., 52, 588–606, 2013.
Ntale, H. K. and Gan, T. Y.: Drought indices and their application to East Africa, Int. J. Climatol., 23, 1335–1357, 2003.
Olagunju, T. E.: Drought, desertification and the Nigerian environment: A review, Journal of Ecology and the Natural Environment, 7, 196–209, 2015.
Paulo, A. A. and Pereira, L. S.: Drought concepts and characterization: comparing drought indices applied at local and regional scales, Water Int., 31, 37–49, 2006.
Quiring, S. M.: Monitoring drought: an evaluation of meteorological drought indices, Geogr Compass., 31, 64–88, 2009.
Salimi, H., Asadi, E., and Darbandi, S.: Meteorological and hydrological drought monitoring using several drought indices, Appl. Water Sci., 11, 1–10, 2021.
Santos, L. O. F. dos, Querino, C. A. S., Querino, J. K. A. da, S., Pedreira Junior, A. L., Moura, A. R. de, M., and Machado, N. G.: Validation of rainfall data estimated by GPM satellite on Southern Amazon region, Revista Ambiente & Água., 14, https://doi.org/10.4136/ambi-agua.2249, 2018.
Shalishe, A., Bhowmick, A., and Elias, K.: Meteorological Drought Monitoring Based on Satellite CHIRPS Product over Gamo Zone, Southern Ethiopia, Adv. Meteorol., 1, 9323263, https://doi.org/10.1155/2022/9323263, 2022.
Shawky, A. I. and Bakoban, R. A.: Exponentiated gamma distribution: Different methods of estimations, J. Appl. Math., 1, 284296, https://doi.org/10.1155/2012/284296, 2012.
Stampoulis, D. and Anagnostou, E. N.: Evaluation of global satellite rainfall products over continental Europe, J. Hydrometeorol., 13, 588–603, 2012.
Tadesse, K. E., Melesse, A. M., Abebe, A., Lakew, H. B., and Paron, P.: Evaluation of global precipitation products over wabi shebelle river basin, Ethiopia, Hydrology, 9, 66, https://doi.org/10.3390/hydrology9050066, 2022.
Tan, M. L., Gassman, P. W., and Cracknell, A. P.: Assessment of three long-term gridded climate products for hydro-climatic simulations in tropical river basins, Water, 9, 229, https://doi.org/10.3390/w9030229, 2017.
Tarnavsky, E., Grimes, D., Maidment, R., Black, E., Allan, R. P., and Stringer, M.: Extension of the TAMSAT satellite-based rainfall monitoring over Africa and from 1983 to present, J. Appl. Meteorol. Clim., 53, 2805–2822, 2014.
Tobin, K. J. and Bennett, M. E.: Satellite precipitation products and hydrologic applications, Water Int., 39, 360–380, 2014.
Van Loon, A. F.: Hydrological drought explained, Wiley Interdisciplinary Reviews: Water, 2, 359–392, 2015.
Vicente-Serrano, S. M, Quiring, S. M., Pena-Gallardo, M., Yuan, S., and Dominguez-Castro, F.: A review of environmental droughts: Increased risk under global warming?, Earth Sci. Rev., 201, 102953, https://doi.org/10.1016/j.earscirev.2019.102953, 2020.
Viste, E., Korecha, D., and Sorteberg, A.: Recent drought and precipitation tendencies in Ethiopia, Theor. Appl. Climatol., 112, 535–551, 2013.
Wakjira, M. T., Peleg, N., Anghileri, D., Molnar, D., Alamirew, T., and Six, J.: Rainfall seasonality and timing: implications for cereal crop production in Ethiopia, Agr. Forest Meteorol., 310, 108633, https://doi.org/10.1016/j.agrformet.2021.108633, 2021.
Wilhelmi, O. V. and Wilhite, D. A.: Assessing vulnerability to agricultural drought: a Nebraska case study, Nat. Hazards, 25, 37–58, 2002.
Wilhite, D. A. and Buchanan-Smith M.: Drought as hazard understanding the natural and social context, Drought and water crises: Science, technology, and management issues, 3–29, CRC Press, Boca Raton, FL, 3–29, 2005.
Wilson, D. J., Western, A. W., and Grayson, R. B.: Identifying and quantifying sources of variability in temporal and spatial soil moisture observations, Water Resour Res., 40, W02507, https://doi.org/10.1029/2003WR002306, 2004.
Wiwoho, B. S., Astuti, I. S., Alfarizi, I. A. G., and Sucahyo, H. R.: Validation of three daily satellite rainfall products in a humid tropic watershed, Brantas, Indonesia: implications to land characteristics and hydrological modelling, Hydrology, 8, 154, https://doi.org/10.3390/hydrology8040154, 2021.
Worqlul, A. W., Yen, H., Collick, A. S., Tilahun, S. A., Langan, S., and Steenhuis, T. S.: Evaluation of CFSR, TMPA 3B42 and ground-based rainfall data as input for hydrological models, in data-scarce regions: The upper Blue Nile Basin, Ethiopia, Catena, 152, 242–251, 2017.
Yigezu, W. G.: The challenges and prospects of Ethiopian agriculture, Cogent Food Agric., 7, 1923619, https://doi.org/10.1080/23311932.2021.1923619, 2021.
Yihdego, Y., Vaheddoost, B., and Al-Weshah, R. A.: Drought indices and indicators revisited, Arab. J. Geosci., 12, 1–12, 2019.
Young, M. P., Williams, C. J. R., Chiu, J. C., Maidment, R. I., and Chen, S. H.: Investigation of discrepancies in satellite rainfall estimates over Ethiopia, J. Hydrometeorol., 15, 2347–2369, 2014.
Zambrano-Bigiarini, M., Nauditt, A., Birkel, C., Verbist, K., and Ribbe, L.: Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile, Hydrol. Earth Syst. Sci., 21, 1295–1320, https://doi.org/10.5194/hess-21-1295-2017, 2017.
Zargar, A., Sadiq, R., Naser, B., and Khan, F. I.: A review of drought indices, Environ. Rev., 19, 333–349, 2011.
Short summary
Drought frequently affects southern Ethiopia, and inadequate rainfall data complicate monitoring. Satellite data from three sources were evaluated for accuracy, showing the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) to be the most reliable. We assessed drought severity using the standardized precipitation index, aligning results with historical drought patterns. The study emphasizes that the CHIRPS enhances drought monitoring and early-warning systems in the region.
Drought frequently affects southern Ethiopia, and inadequate rainfall data complicate...
