Heterogeneous Trends of Precipitation Extremes in Recent Two Decades Over East Africa

近20年东非极端降水变化趋势的空间异质性

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  • Corresponding author: Gensuo JIA, jiong@tea.ac.cn
  • Funds:

    Supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, CASEarth (XDA19030401); and National Key Research and Development Program of China (2016YFA0600303)

  • doi: 10.1007/s13351-021-1028-8
  • Note: This paper will appear in the forthcoming issue. It is not the finalized version yet. Please use with caution.

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  • East Africa is so vulnerable to the impacts of precipitation extremes which vary from frequent floods to prolonged droughts. However, systematic regional assessment of precipitation extremes across seasons has received little attention, and most previous studies of precipitation extremes have employed many indices and sparsely gauge observations giving marginalized details. In this study, we thus used three precipitation extreme indices to examine the intensity of the highest single-day rainfall record (rx1day), prevalence of very heavy rainfalls (r30mm), and persistence of successive wet days (cwd) at both the annual and seasonal scales over two decades (1998–2018) with the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis data. The results showed that the most intensive and frequent precipitation extremes are noticeable from January to May across areas extending from Madagascar to Tanzania coastal zone. These areas have also depicted patches of significant increasing trends in frequency, duration, and intensity of precipitation extremes annually and seasonally. Besides, significant declines in frequency and intensity of precipitation extremes are depicted along areas extending from western Ethiopia to Congo-Uganda, especially in June–September season. October–December season exhibits higher interannual variability amounting to overall weak and less significant trends. The subregional assessment showed overall declining intensity and frequency of precipitation extremes in northern part of the study areas. Mean wetness duration increased, with persistence of moderate wet days and slight reduction of severe events. The study unveils the higher susceptibility of the East Africa region to the widely observed hotspots of precipitation extremes posing threats to food security, water resource, and human well-being. The region should consider upscaling irrigation schemes while also planning resilient and supportive infrastructures to withstand the upsurging precipitation extremes, especially along the coastal zone.
    从频繁的洪水到长期的干旱,东非极易受到极端降水事件的影响。然而,对东非极端降水事件的已有研究大多采用了稀疏的地面观测资料,且缺乏对季节尺度极端降水事件的系统评估。本研究使用热带降雨测量任务(TRMM)的降水资料研究了近20年年际和季节尺度东非极端降水特征。结果表明,1–5月,马达加斯加到坦桑尼亚沿海地区极端降水频繁,且极端降水的频率、持续时间和强度呈增加趋势。6–9月,埃塞俄比亚西部到刚果–乌干达地区极端降水的频率和强度显著下降。 10–12月,极端降水的年际变率较高,总体趋势不显著。东非地区应考虑扩大灌溉计划,同时规划支持性的基础设施,以抵御频发的极端降水事件,尤其在对极端降水威胁更加敏感的沿海地区。
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  • Fig. 1.  The map of the study domain showing the topographical elevation (m) above the mean sea level, major rivers and lakes, and gauge stations used for data validation. Enclosed is the African continent map showing the location of the study domain (red-colored rectangle).

    Fig. 2.  The monthly rainfall climatology (left panel) and time series (right panel) from gauge stations (red line) and TRMM 3B42 product (blue line) during 1998–2009 over (a, b) Sudan, (c, d) Uganda, (e, f) Kenya, and (g, h) Tanzania averaged for all stations over a respective country shown in Fig. 1. The correlation coefficient (r) and its significance (p) between the two datasets are shown on the top right of each plot.

    Fig. 3.  Taylor diagrams showing the statistical comparison between monthly rain gauge observations (reference data; REF point) with TRMM-3B42 satellite-based precipitation product (blue marker) in reproducing the (a) annual rainfall cycle and (b) monthly temporal evolution over (1) Sudan, (2) Uganda, (3) Kenya, and (4) Tanzania during 1998–2009. The angular axis (dotted line) shows the correlation coefficient (r), the radial axis (dashed concentric arcs) shows the standardized deviations (SD), and the solid concentric semicircle denotes the centered root mean square error (RMSE) of TRMM against gauge observations. The gauge reference data point centered at REF is with r = 1, SD = 1, and RMSE = 0; and the closer the other point to it, the higher the agreement between the two datasets.

    Fig. 4.  (a–c) Annual mean, (d–f) trend, and (g–i) anomaly time series of the extreme rainfall intensity (rx1day; left panel), frequency (r30mm; middle panel), and duration of consecutive wet days (cwd; right panel). The anomaly time series is for the area weighted average over the Northern Subregion [NSR: 15°N–0°, 20°–41°E; red box in (d)] in red line color and Southern Subregion [SSR: 0°–15°S, 20°–41°E; blue box in (d)] in blue line color, and the overall trend of each line is shown by a dashed line of its corresponding color. The slopes or magnitudes of the trend lines are shown in Table 2.

    Fig. 5.  The extreme rainfall intensity (rx1day) (a−d) seasonal mean and (e−h) trend during the study period (1998−2018) for JF (first column), MAM (second column), JJAS (third column), and OND (fourth column). (i−l) show the anomaly time series of rx1day (mm day−1) over the Northern Subregion [NSR: 15°N−0°, 20°−41°E; red box in (e)] in red line color and Southern Subregion [SSR: 0−15°S, 20°−41°E; blue box in (e)] in blue line color for (i) JF, (j) MAM, (k) JJAS, and (l) OND, and the overall trend of each line is shown by a dashed line of its corresponding color. The slopes or magnitudes of the trend lines are shown in Table 2.

    Fig. 6.  As in Fig. 5, but for the frequency of extreme rainfall events (r30mm).

    Fig. 7.  As in As in Fig. 5, but for the extreme duration of consecutive wet days (cwd).

    Table 1.  The list of the used Precipitation Extreme Indices (PEIs) selected from ETCCDI

    IDNameDefinition/descriptionUnit
    rx1dayExtreme rainfall intensityHighest 1-day precipitation totalmm
    r30mmVery heavy rain dayCount of days when precipitation ≥ 30 mmday
    cwdConsecutive wet dayMaximum number of consecutive wet day (with precipitation ≥ 1 mm)day
    Download: Download as CSV

    Table 2.  Trends per year of PEIs averaged over Northern Subregion (NSR) and Southern Subregion (SSR) during 1998−2018 whose trend line and unit are shown in bottom panels of Figs. 47

    Index JF MAM JJAS OND Annual
    NSRSSRNSRSSRNSRSSRNSRSSRNSRSSR
    rx1day−0.192−0.138−0.2230.3880.5440.300−0.327−0.0220.6370.357
    r30mm−0.001 0.001−0.002−0.0040.010−0.002−0.004 0.0050.064 0.002
    cwd−0.010−0.019 0.023 0.018 0.027−0.014−0.005 0.024 0.025 0.022
    Note: Trends significant at the 5% level are shown in boldface.
    Download: Download as CSV
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Heterogeneous Trends of Precipitation Extremes in Recent Two Decades Over East Africa

    Corresponding author: Gensuo JIA, jiong@tea.ac.cn
  • 1. Key Laboratory of Regional Climate–Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. Physics Department, University of Dar es Salaam, Dar es Salaam P. O. Box 35063, Tanzania
Funds: Supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, CASEarth (XDA19030401); and National Key Research and Development Program of China (2016YFA0600303)

Abstract: East Africa is so vulnerable to the impacts of precipitation extremes which vary from frequent floods to prolonged droughts. However, systematic regional assessment of precipitation extremes across seasons has received little attention, and most previous studies of precipitation extremes have employed many indices and sparsely gauge observations giving marginalized details. In this study, we thus used three precipitation extreme indices to examine the intensity of the highest single-day rainfall record (rx1day), prevalence of very heavy rainfalls (r30mm), and persistence of successive wet days (cwd) at both the annual and seasonal scales over two decades (1998–2018) with the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis data. The results showed that the most intensive and frequent precipitation extremes are noticeable from January to May across areas extending from Madagascar to Tanzania coastal zone. These areas have also depicted patches of significant increasing trends in frequency, duration, and intensity of precipitation extremes annually and seasonally. Besides, significant declines in frequency and intensity of precipitation extremes are depicted along areas extending from western Ethiopia to Congo-Uganda, especially in June–September season. October–December season exhibits higher interannual variability amounting to overall weak and less significant trends. The subregional assessment showed overall declining intensity and frequency of precipitation extremes in northern part of the study areas. Mean wetness duration increased, with persistence of moderate wet days and slight reduction of severe events. The study unveils the higher susceptibility of the East Africa region to the widely observed hotspots of precipitation extremes posing threats to food security, water resource, and human well-being. The region should consider upscaling irrigation schemes while also planning resilient and supportive infrastructures to withstand the upsurging precipitation extremes, especially along the coastal zone.

近20年东非极端降水变化趋势的空间异质性

从频繁的洪水到长期的干旱,东非极易受到极端降水事件的影响。然而,对东非极端降水事件的已有研究大多采用了稀疏的地面观测资料,且缺乏对季节尺度极端降水事件的系统评估。本研究使用热带降雨测量任务(TRMM)的降水资料研究了近20年年际和季节尺度东非极端降水特征。结果表明,1–5月,马达加斯加到坦桑尼亚沿海地区极端降水频繁,且极端降水的频率、持续时间和强度呈增加趋势。6–9月,埃塞俄比亚西部到刚果–乌干达地区极端降水的频率和强度显著下降。 10–12月,极端降水的年际变率较高,总体趋势不显著。东非地区应考虑扩大灌溉计划,同时规划支持性的基础设施,以抵御频发的极端降水事件,尤其在对极端降水威胁更加敏感的沿海地区。
    • Africa is one of the most vulnerable continents to extreme weather and climate events, which is exacerbated by its higher exposure and low copying capacity (Niang et al., 2014). East African countries have often been facing precipitation extreme conditions alternating between droughts and floods (Hastenrath et al., 2010; Nicholson, 2016a). The drought conditions often persist for more than a single rainfall season or even years; meanwhile, they may be preceded and followed by abrupt severe floods (Lyon and Dewitt, 2012; Nicholson, 2016a). For instance, the 2010/2011 drought conditions that hit throughout much of the eastern Africa regions were followed by severe floods over the same areas (Nicholson, 2016a). Similarly, prolonged 2008 drought conditions were followed by flooding in early 2010 (Nicholson, 2016a), whereas the 1997/1998 severe floods during the El-Niño episode preceded 1998/1999 drought conditions. This is perhaps the heart of economic crisis within the region as the livelihoods and socio-economic activities of the vast majority rely on rain-fed agricultural systems, pastoralism, and fishery, which are very sensitive to extreme rainfall conditions. The alternating drought and flood episodes may thus keep on reversing the economic growth, and subsequently threatening the achievement of sustainable development among individuals, countries, and a region as a whole.

      Annual negative and positive trends of precipitation extreme indices have been pointed across East Africa although their significance was limited to only a few indices and stations. During 1981–2010, Djibouti experienced a significant decline in intensity and frequency of precipitation extremes (Ozer and Mahamoud, 2013). That was also the case for Eritrea during 1914–2015, which was further accompanied by shortening wetness persistence (Fessehaye et al., 2019). Insignificant decreasing trends in frequency, intensity, and duration of precipitation extremes were also pointed out for most stations over East Africa during the 1961–2010 period (Omondi et al., 2014). The reducing frequency of the very heavy rainy days was also observed across some stations in Kenya (1961–2010) and Uganda (1980–2010) (Ongoma et al., 2018b). Nonetheless, these notable trend patterns were incoherent for some neighbouring stations depicting opposite trends and rare statistical significance. Moreover, most of these studies have mainly focused on annual patterns of precipitation extremes leaving seasonal details little explored.

      Assessment of regional observed changes in precipitation extremes over the East Africa region during the fifth assessment cycle of the Intergovernmental Panel on Climate Change (IPCC) reports was constrained by the limited availability of literature (Seneviratne et al., 2012). This was also the case over most other African regions in addition to lack of data and inconsistency among reported patterns in some regions. The East Africa region has witnessed a growing number of studies afterwards exploring historical changes, variability, trends, and projections of precipitation and temperature related extremes from local to regional coverage (Omondi et al., 2014; Cattani et al., 2018; Ongoma et al., 2018a, b; Shiferaw et al., 2018). Precipitation extreme indices designated by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) have often been used in studying precipitation extremes across different parts of the world. Most of the available studies using precipitation extreme indices in East Africa have employed station gauge observations ranging from covering the whole East Africa region (Omondi et al., 2014) to subregional and local scales [e.g., Uganda and Kenya (Ongoma et al., 2018b), Djibouti city (Ozer and Mahamoud, 2013), subregions in Ethiopia (Mekasha et al., 2014; Mequaninta et al., 2016; Berhane et al., 2020), and Asmara in Eritrea (Fessehaye et al., 2019)] while Schmocker et al. (2016) used both station observations and reanalysis datasets on their study over the Mount Kenya region.

      However, it is noteworthy that, there are limited gauge observations in the East African countries like in most other developing countries (Dinku et al., 2007; Gebrechorkos et al., 2019, 2020) due to (among others) the limited funding allocated for the establishment, maintenances, and operational costs of such stations (Omondi et al., 2014). The poor spatial resolution of these in-situ observing stations within the region is a caveat that is always given when using the interpolated and other derived datasets from those inputs. This is pointed out by some of the available global observation station-related datasets like Climate Prediction Center (CPC) Global Unified Gauge-Based Analysis of Daily Precipitation (Chen et al., 2008). Moreover, restrictions or individual countries’ policies on data sharing across different countries have somewhat been hampering regional studies aiming to employ gauge station observations across several countries. As such, most of the studies within the region that employed field observations extended over local to national and subregional scales (Ozer and Mahamoud 2013; Mekasha et al., 2014; Mequaninta et al., 2016; Ongoma et al., 2018b; Schmocker et al., 2016; Fessehaye et al., 2019; Berhane et al., 2020). On the other hand, the daily field meteorological records were collected and compiled from all the 11 East African countries as a result of workshops that enabled the sharing of historical met data from each country (Omondi et al., 2014). Remote sensing data, in particular satellite datasets, have henceforth become of potential use in data-sparse regions, like East Africa in our case, as they provide both higher spatial and temporal resolutions and are freely available to the public (Michaelides et al., 2009; Cattani et al., 2016). These datasets enable even real-time monitoring and forecasting of precipitation extreme events and have become useful in climate analyses and application studies as they become of sufficient temporal length (Michaelides et al., 2009; Kucera et al., 2013).

      In examining precipitation extremes over the recent 1998–2018 period in East Africa, we thus employed high-resolution Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA, 3B42) data (Huffman et al., 2007). Our study provides a wide view of precipitation extremes across the whole East Africa region and neighbouring areas, unlike most previous studies which extended over local to national and subregional domains. The study gives a systematic assessment of precipitation extremes across seasons which had received little attention in addition to annual scale patterns. We chose three indices (cwd, rx1day, and r30mm) aiming at providing a detailed focus on duration, intensity, and frequency of precipitation extremes across the region.

    2.   Study domain
    • The East Africa region considered in this study extends to over 25°N–15°S, 20°–55°E across 11 countries including Burundi, Djibouti, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, South Sudan, Sudan, Tanzania, and Uganda, which are also often addressed as the Great Horn of Africa (Fig. 1). However, it is noteworthy that, based on regional political and economic integrations, the terms East Africa and Great Horn of Africa may be carrying several different definitions in terms of the number of member countries or areal coverage. Throughout this study, the term East Africa is used just to refer to the study domain as defined by the aforementioned latitudinal and longitudinal extents and not otherwise.

      Figure 1.  The map of the study domain showing the topographical elevation (m) above the mean sea level, major rivers and lakes, and gauge stations used for data validation. Enclosed is the African continent map showing the location of the study domain (red-colored rectangle).

      Eastern Africa is well known for its distinguished highlands, the famous Great African Rift Valley, and Great African Lakes. These diverse topographical features along with the rift valleys, lakes, and oceans play significant roles in modulating the East Africa regional climatic conditions through local atmospheric circulations like mountain and valley breezes as well as lake and sea breezes apart from large scale or global variations brought about by teleconnections.

      The annual rainfall cycle over much of the East Africa region peaks either once or twice a year (Liebmann et al., 2012; Yang et al., 2015; Cattani et al., 2016). The north-western (southern) areas’ rainfall regime peaks during boreal summer (winter), whereas equatorial to north–eastern areas is characterized by bimodal regime peaking in boreal spring and autumn (Figs. 2a, c, e, g). Based on the rainfall seasonality of the East Africa region, the selected seasons for this study are January–February (JF), March–April–May (MAM), June–July–August–September (JJAS), and October–November–December (OND). JF is taken as a transition season between OND and MAM, during which rainfall is mainly confined over the most southern areas of the study domain. These areas receive much of their rainfall from November to April (NDJFMA), thus accounting for both OND, JF, and MAM seasons. In the present study, JF was thus chosen to complement the rainfall regime in these further southern regions, whereas NDJFMA season was not used to avoid overlapping the same months in different seasons. Some studies with a higher spatial domain covering the whole or large part of East Africa have also used our similar selected seasons (Yang et al., 2015; Cattani et al., 2018; Wenhaji Ndomeni et al., 2018; Gebrechorkos et al., 2020).

      Figure 2.  The monthly rainfall climatology (left panel) and time series (right panel) from gauge stations (red line) and TRMM 3B42 product (blue line) during 1998–2009 over (a, b) Sudan, (c, d) Uganda, (e, f) Kenya, and (g, h) Tanzania averaged for all stations over a respective country shown in Fig. 1. The correlation coefficient (r) and its significance (p) between the two datasets are shown on the top right of each plot.

    3.   Data and methods
    • This study primarily employed the daily precipitation satellite product version 7 of TRMM Multi-Satellite Precipitation Analysis (TMPA 3B42;Huffman et al., 2007). TMPA 3B42 is derived from the space-borne observations and adjusted with monthly rain gauges to improve the understanding of the water cycle in the climate system (Huffman et al., 2007). TMPA datasets are the success of the international joint mission between the NASA of the United States and the Japan Aerospace Exploration Agency (JAXA) aimed at providing the best precipitation estimates over tropics where much of global precipitation occurs. The TMPA 3B42 data has high spatial (0.25° × 0.25°) and temporal (1-day) resolutions. Its spatial coverage extends from 50°S to 50°N and a temporal span from 1998 to near-present (2019, last accessed August 2019). For this study, we extracted the spatial domain of 15°S–25°N, 20°–55°E enclosing the 11 countries of the East Africa region for a period of 21 yr from 1 January 1998 to 31 December 31 2018.

      We first reviewed and assessed the applicability of the TRMM precipitation product within the study domain and its neighbouring regions. A validation study for monthly and seasonal mean rainfall fields from various TRMM products against high-density gauge datasets over West Africa showed that the final merged TRMM product is excellent agreeing with gauge observations within the region (Nicholson et al., 2003). Another validation study of 10-days and monthly rainfall totals from several satellite rainfall estimates against gauge observations over the Eastern Africa complex terrains showed that TRMM products also performed relatively well and showed the quality consistent with the western Africa study (Dinku et al., 2007). The evaluation of precipitation variability from several monthly satellite-derived products over the East Africa region against rain gauge products revealed advantages of TMPA 3B42 product over other products (Cattani et al., 2016). This dataset was also used to study the diurnal rainfall cycle over the Lake Victoria basin in East Africa following its high spatial and temporal resolution (Onyango et al., 2020). Moreover, several other studies across different parts of the world have employed this dataset in examining precipitation extremes, and pointed out that it relatively well agrees with gauge observations and even performs better than many other satellite data products (Jiang et al., 2017; Harrison et al., 2019; Mahbod et al., 2019; Tan, 2019). For example, across Sub-Saharan Africa, the performance of TRMM 3B42 ranked the highest of other satellite products in agreeing with gauge-based datasets to reproduce precipitation extreme indices, including the number of wet days (r1mm), precipitation total from wet days (prcptot), and maximum 1-day precipitation (rx1day) (Harrison et al., 2019). However, the performance of satellite-derived precipitation estimates within the East Africa region is partly challenged by its complex topographical features (Cattani et al., 2016).

      Furthermore, we validated the TRMM data with meteorological stations’ observations acquired from four countries within the East Africa region. These were monthly rain gauge observations for a total of 98 stations (Fig. 1) sourced from national hydrometeorological departments of Sudan (27), Uganda (12), Kenya (33), and Tanzania (26), covering the 1998–2009 period. Daily TRMM 3B42 precipitation product was first aggregated to monthly totals and then extracted for each corresponding gauge station. The monthly climatology of each station was evaluated for both datasets and then averaged for each country to reproduce annual rainfall cycles. Similarly, the monthly series of each country was obtained by averaging all stations for each dataset. The two datasets revealed strong agreement in reproducing the annual rainfall cycle and monthly temporal series (r > 0.97, p < 0.0005; Fig. 2). Moreover, the Taylor diagram was also used to graphically examine how closely the TRMM 3B42 product matches gauge observations (Taylor, 2001). The statistics show that the two datasets agree well on reproducing both the annual rainfall cycle and monthly temporal evolution. That is marked with the nearest positioning of the TRMM 3B42 data point for every country to the reference point of gauge observations data (Fig. 3). Almost similar spatial variability is detected with close normalized standard deviation (SD ≈ 1), low centered root mean square error (RMSE ≤ 0.25), and high correlation (r ≥ 0.97) between the two datasets.

      Figure 3.  Taylor diagrams showing the statistical comparison between monthly rain gauge observations (reference data; REF point) with TRMM-3B42 satellite-based precipitation product (blue marker) in reproducing the (a) annual rainfall cycle and (b) monthly temporal evolution over (1) Sudan, (2) Uganda, (3) Kenya, and (4) Tanzania during 1998–2009. The angular axis (dotted line) shows the correlation coefficient (r), the radial axis (dashed concentric arcs) shows the standardized deviations (SD), and the solid concentric semicircle denotes the centered root mean square error (RMSE) of TRMM against gauge observations. The gauge reference data point centered at REF is with r = 1, SD = 1, and RMSE = 0; and the closer the other point to it, the higher the agreement between the two datasets.

    • The assessment of precipitation extremes in this study employed three precipitation extreme indices (PEIs; Table 1), i.e., the highest single-day rainfall record (rx1day), prevalence of very heavy rainfalls of at least 30 mm in a day (r30mm), and persistence of successive wet days (cwd), to examine the temporal and spatial changes in the intensity, frequency, and duration of precipitation extremes, respectively. These indices are mathematically independent, but their cause and implication can be linked to each other. The selected indices are a subset of climate extreme indices designated by the WMO ETCCDI to enhance uniform monitoring, detection, and attribution of changes in climate extremes from daily datasets since the 1990s (Peterson and Manton, 2008). This allows the comparison of the results from different areas around the world as they conform to one another (Zhang X. B. et al., 2011). For instance, the IPCC reports evaluate available studies based on temperature and precipitation extreme indices across the globe to formulate several factual statements about the global and regional observed and projected changes in climate extremes (Alexander, 2016). The climate extreme indices, together with other additional sector-specific climate indices, are now operationally maintained by another re-established Expert Team on Sector-specific Climate Indices (ET-SCI) since 2018 (https://www.wcrp-climate.org/data-etccdi).

      IDNameDefinition/descriptionUnit
      rx1dayExtreme rainfall intensityHighest 1-day precipitation totalmm
      r30mmVery heavy rain dayCount of days when precipitation ≥ 30 mmday
      cwdConsecutive wet dayMaximum number of consecutive wet day (with precipitation ≥ 1 mm)day

      Table 1.  The list of the used Precipitation Extreme Indices (PEIs) selected from ETCCDI

    • The selected PEIs were computed from daily precipitation data (TRMM, 3B42) using ClimPACT2, an R software package designated by the ETCCDI and is now operationally maintained by ET-SCI. This package returns the computed PEIs at annual and monthly temporal scales. The annually computed PEIs are directly used for analyses of annual precipitation extremes patterns. Meanwhile, the monthly computed PEIs are evaluated to the selected seasonal time scales by using the Climate Data Operators (CDO; Schulzweida, 2019) for seasonal analyses of precipitation extremes. In performing the monthly-to-seasonal conversion in CDO, we used two major operators depending on the nature of the index. For rx1day and cwd, which are given as the maximum value each month, the seasonal maximum operator was used to select the maximum value for a season among the months of a particular season in every yearly time step. For r30mm, which counts the number of very heavy rainfall days in a month with at least 30 mm, the seasonal sum operator was supposed to obtain the total frequency in a season. However, the selected seasons (JF, MAM, JJAS, and OND) for our study vary in length, which would lead to significant variation in magnitudes among different seasons. Instead, we used a seasonal mean operator, which sums and then divides by the number of months in a season to obtain the seasonal mean values, enabling comparison among different seasons.

    • The overall tendency of precipitation extremes during the study period was assessed by computing the trends of the PEIs on each grid point by using the Theil–Sen estimator (Sen, 1968; Theil, 1992). This method estimates the trend by computing the median of slopes [Eq. (1)] among all data pairs in a time series at a particular grid point.

      $${M}_{i}=\frac{{x}_{j}-{x}_{k}}{j-k}, $$ (1)

      where $ {M}_{i} $ is the slope of the i data pair $ ({x}_{j},{x}_{k}) $ and $ j>k $.

      We used the Mann–Kendall (MK) test (Mann, 1945; Kendall, 1970) to statistically assess the significance of any downward or upward monotonic trends of the selected indices during the study period. These are non-parametric methods that are not affected by the type of dataset distribution, missing values and are less sensitive to outliers. We employed the Mann–Kendall test from the NCAR Command Language (NCL) built-in function (trend_manken), which returns both the Mann–Kendall trend significance and Theil–Sen estimate of the linear trend. The trend analysis was performed at both annual and seasonal timescaled PEIs. Spatial distribution of statistically significant trends at a 5% significance level are marked by stippled grid points.

      To assess the interannual evolution of each index, we selected the Northern Subregion (NSR: 15°N–0°, 20°–41°E) and Southern Subregion (SSR: 0°–15°S, 20°–41°E). The subregional analysis for temporal evolution was adopted rather than whole regional area averaging because the study domain is much characterized by the north–south seasonal migration of the rainfall cycle. Thus, the northern and southern subregions have been considered to represent the northern and southern hemispheric climatic dominancy during boreal summer and winter, respectively. Meanwhile, the climatic tendency during boreal spring and autumn is also embedded in both subregions along their transboundary region, providing the linkage between the two subregions. Temporal series was obtained by area weighting average of the index and then computing the anomaly series from its temporal mean. The trend of the anomaly series was also evaluated to assess the overall change during the study period. The significance of these trends was also assessed by using the Mann–Kendall test.

    4.   Results
    • According to the WMO Severe Weather Forecasting Demonstration Project-East Africa (SWFDP-EA), rainfall amounts exceeding 50 mm day−1 are deemed as extreme events of high risk across the region, occurring over the vast domain and along the Tanzanian and Kenyan coastal belt reaching up to 120 mm day−1 (Fig. 4a). For arid and semiarid areas over Kenya, Somalia, eastern Ethiopia, and Sudan, rx1day featured up to over 60 mm day−1 while annual rainfall total is on average less than 500 mm, implying that multiple occurrences of few such rainfall events may account for their annual precipitation total. These intense rainfall events over arid and semiarid areas play a significant role in recharging groundwater levels and episodically serving from water security recessions (Taylor et al., 2013; Cuthbert et al., 2019). Nevertheless, the very heavy rainfalls are usually less than six days over much of arid and semiarid areas (Fig. 4b). However, the most frequent very heavy rainfall events are widely seen over the Democratic Republic of Congo and Lake Victoria vicinity. Such rainfall events tend to affect people settling along the river streams and low-lying areas often hit with floods as exacerbated by their higher vulnerability and exposure conditions, including higher population density on unplanned settlements and poor drainage systems (Kiunsi, 2013; Bushesha and Mbura, 2015). cwd over the vast area of East Africa ranged within 5–10 days; however, mountainous areas depict the longest consecutive wet days especially Ethiopian highlands with up to 20 day yr−1 (Fig. 4c). These regions had also featured a higher average annual number of precipitating days (Cattani et al., 2018). This may be due to terrain influences on enhancing rainfall-producing systems over mountain ranges (e.g., Hession and Moore, 2011; Alex-Ogwang et al., 2014; Enyew and Steeneveld, 2014).

      Figure 4.  (a–c) Annual mean, (d–f) trend, and (g–i) anomaly time series of the extreme rainfall intensity (rx1day; left panel), frequency (r30mm; middle panel), and duration of consecutive wet days (cwd; right panel). The anomaly time series is for the area weighted average over the Northern Subregion [NSR: 15°N–0°, 20°–41°E; red box in (d)] in red line color and Southern Subregion [SSR: 0°–15°S, 20°–41°E; blue box in (d)] in blue line color, and the overall trend of each line is shown by a dashed line of its corresponding color. The slopes or magnitudes of the trend lines are shown in Table 2.

      The spatial trend patterns show that the extreme rainfall intensity exhibited a declining tendency over a larger area than frequency and duration indices. Both the northern and southern subregions were characterized by a reduction in extreme rainfall intensity (Fig. 4g; Table 2) because of the declines up to −2 mm day−1 yr−1 over much of the western areas extending from Eritrea to Congo (Fig. 4d). For r30mm, the average decrease in frequency is revealed over NSR (Fig. 4h) as marked by significant declining trends by up to −0.28 day yr−1 over western Ethiopia to Uganda−Congo transect region (Fig. 4e). Besides, both NSR and SSR regions feature an equivalent weak increase in wetness persistence (Fig. 4i) with noticeable patches of both positive and negative cwd trends (Fig. 4f). Some periods of prolonged decline in frequency, intensity, and duration of precipitation extremes are also identified, like in 2006−2010 (Figs. 4gi), which is in line with reported post-2005 prolonged drought episodes (Nicholson, 2016a). However, r30mm and cwd show recovery to positive trends towards the end of the study period. Spatial significant increasing trends in frequency, duration, and intensity of precipitation extremes are mainly revealed over a southeastern region emanating from Madagascar towards the Tanzanian coastal zone.

      Index JF MAM JJAS OND Annual
      NSRSSRNSRSSRNSRSSRNSRSSRNSRSSR
      rx1day−0.192−0.138−0.2230.3880.5440.300−0.327−0.0220.6370.357
      r30mm−0.001 0.001−0.002−0.0040.010−0.002−0.004 0.0050.064 0.002
      cwd−0.010−0.019 0.023 0.018 0.027−0.014−0.005 0.024 0.025 0.022
      Note: Trends significant at the 5% level are shown in boldface.

      Table 2.  Trends per year of PEIs averaged over Northern Subregion (NSR) and Southern Subregion (SSR) during 1998−2018 whose trend line and unit are shown in bottom panels of Figs. 47

    • The seasonal cycle of extreme rainfall intensity progresses from south to north following the regional annual rainfall cycle. The wettest conditions during JF are concentrated over the southern areas where intensive rainfall events exceeding 60 mm day−1 are revealed (Fig. 5a). The southeastern areas with further northward coastal extension also depict the highest rx1day of up to 100 mm day−1 during MAM (Fig. 5b). Over Kenya, Somalia, and eastern Ethiopia, where MAM is climatologically referred to as long rainy season, rx1day depicts as much as 60 mm day−1 intense rainfalls. During JJAS, intensive rainfalls of about 50 to 80 mm day−1 are observed throughout south–westward areas from western Ethiopia (Fig. 5c). Additionally, there is a notable narrow coastal belt which is more pronounced along Kenya−Somalia coastline with up to 60 mm day−1 leaving much of the remaining areas experiencing light or dry rainfall conditions. Bimodal rainfall regime areas across Somalia, Kenya, and Tanzania, where the second rainfall season during OND is climatologically referred to as short rains, depict the intensity of up to 60 mm day−1 mainly confined along eastern areas (Fig. 5d).

      Figure 5.  The extreme rainfall intensity (rx1day) (a−d) seasonal mean and (e−h) trend during the study period (1998−2018) for JF (first column), MAM (second column), JJAS (third column), and OND (fourth column). (i−l) show the anomaly time series of rx1day (mm day−1) over the Northern Subregion [NSR: 15°N−0°, 20°−41°E; red box in (e)] in red line color and Southern Subregion [SSR: 0−15°S, 20°−41°E; blue box in (e)] in blue line color for (i) JF, (j) MAM, (k) JJAS, and (l) OND, and the overall trend of each line is shown by a dashed line of its corresponding color. The slopes or magnitudes of the trend lines are shown in Table 2.

      MAM appears to have the highest intensity of extreme rainfall compared to other seasons revealing its more likely occurrence of extreme rainfall impacts, especially along the coast. In Tanzania, Mafuru and Guirong (2018) found that the highest MAM rainfall amounts and heavy rainfall events are concentrated along the coastal belt and western Lake Victoria basin. For instance, on 11 April 2014 (6 May 2015), extreme rainfall events of about 135 mm (110 mm) in 24 h were reported in Dar es Salaam (a Tanzanian coastal city), which led to heavy floods that left several people dead, injured, and displaced from their homes and infrastructures destructed (Mafuru and Guirong, 2018). The 2018 MAM rainfalls are also reported as one of the wettest on record, with prolonged heavy rainfall episodes culminating in severe floods across the region (Kilavi et al., 2018). Moreover, several intense record-breaking extreme rainfall events across East Africa and their associated impacts during MAM from 2017 to 2020 are reported (Chang’a et al., 2020).

      In JF, the highest increase in rx1day by up to 1.6 mm day−1 yr−1 was confined over southeastern areas from Madagascar towards the Tanzanian coast (Fig. 5e). However, northern and western of these areas were more characterized by declining tendency leading to an average decline over both NSR and SSR subregions (Fig. 5i). Precipitation extremes during MAM had been becoming less intense over much areas except along the northern Tanzanian coast, Kenya, Ethiopia and eastern areas, with SSR averaging a higher declining rate than NSR (Figs. 5f, j). Several studies have also pointed out the decline of long (MAM) rains from the 1980s and even more abruptly post 1998 (Williams and Funk, 2011; Lyon and Dewitt, 2012; Liebmann et al., 2014, 2017; Lyon, 2014). Nonetheless, Wainwright et al. (2019) found that the decline was due to the shortening of rainy season length (caused by later onset and earlier cessation) but not a decrease in rainy intensity. Besides, noticeable partial recovery of rx1day during the 2010s (Fig. 5j) is in line with the reported partial recovery of MAM rains from earlier cessation (Wainwright et al., 2019).

      Our analysis shows that wet areas during JJAS and OND were characterized by a reduction in rx1day with significance pronounced along the western Ethiopia–Congo transect region, giving a higher average NSR declining rate than SSR (Figs. 5g, 4h). On the other hand, eastern areas with rx1day intensifying tendency during MAM show opposite trends during OND except off the coast of Tanzania. An earlier study also detected a substantial reduction in summer (JJAS) rainfalls during the 1948−2009 period over many areas of East Africa (Williams et al., 2012). Their observed drying trend post-1980s was attributed to increasing warming trends over the southern tropical Indian Ocean, which was linked with an increase in low-level subsidence over East Africa. Besides, OND rainfalls (short rains) revealed a wetting tendency during the 1979−2012 period, though with higher interannual variability over the eastern horn areas (Liebmann et al., 2014). Its increase was attributed to the warming of the western Indian Ocean, whereas variability was associated with Indian Ocean Dipole and El Niño–Southern Oscillation interannual fluctuations. Over these eastern horn areas, particularly along the north-eastern Kenya−Somalia border and eastern Ethiopia, we also observe patches of increase in rx1day during OND, suggesting its wetting persistence over the recent decades.

    • The spatial distribution of mean seasonal frequency of precipitation extremes reveals north–south and west−east gradients. During JF, most frequent precipitation extremes appear over the most southeastern areas of East Africa reaching up to 3 day month−1 (Fig. 6a). On average, however, the vast area north of 10°S depicts less than 1.5 day month−1. Further analysis shows that frequent very heavy rainfalls during MAM season prevail along the Tanzania coastal zone and over Lake Victoria with an average of more than 2 day month−1 (Fig. 6b). Additionally, we observe an average of at least one r30mm event per month over southern areas of Tanzania and Ethiopia, central Kenya, and northern Congo.

      Figure 6.  As in Fig. 5, but for the frequency of extreme rainfall events (r30mm).

      A range of 1 to 2.5 day month−1 of extreme rainfalls demarcates well the climatological wet areas in the boreal summer (JJAS) with the highest frequencies spotted over western Ethiopia and western South Sudan (Fig. 6c). This is not surprising as throughout much of the north−western regions of the study domain are reported to experience bulk of their annual rainfall during the boreal summer that is even considered as the eastern reach of west African monsoons (Yang et al., 2015; Funk et al., 2016; Nicholson, 2017; Wenhaji Ndomeni et al., 2018). On the other hand, frequent r30mm events during OND are noticeable over Congo and Lake Victoria (Fig. 6d). Moreover, along the eastern horn areas, about 0.75 to 1.5 day month−1 of r30mm are noted during this remarkable short rainy season, particularly over southern areas of Somalia and Kenya.

      The seasonal spatial trend patterns in the frequency of precipitation extremes are localized over a few areas. The results show that areas from Madagascar to southern Tanzania featured a significant and the highest increase in the frequency of r30mm by up to 0.08 day month−1 yr−1 in JF (Fig. 6e). However, noticeable insignificant decreasing trends are revealed further inland and both NSR and SSR regions averaged weak overall trends with higher interannual fluctuations marked over SSR (Fig. 6i). Remarkable increasing frequency of r30mm incidences during MAM mainly prevails along Tanzania coastal zone whereas declining patterns are spotted over northeastern of Congo basin and southeastern of Tanzania (Fig. 6f). However, both NSR and SSR averaged a declining trend whose recovery has been notable since 2013 (Fig. 6j). During JJAS, r30mm had significantly been decreasing in frequency, particularly along the region from western Ethiopia to northeastern Congo basin, which determines the major decreasing trend in the NSR (Figs. 6g, k). JJAS notably depicts coherent and the highest decreasing trend patterns compared to other seasons. This declining tendency may reflect a prolonged reduction of the summer (JJAS) rainfalls since the 1980s, which was attributed to the warming of the southern tropical Indian Ocean (Williams et al., 2012). OND featured weak r30mm trends over both NSR and SSR with the locale around the Uganda–Congo border depicting decreasing patterns, whereas increasing signatures are spotted along western Congo and the southeastern part of the domain (Figs. 6h, l). About 10%−20% of MAM and JJAS rains were also reported to decline across Ethiopia, Sudan, and Uganda from the 1970s to the late 2000s leading to diminishing areas viable for agropastoral activities (Funk et al., 2011, 2012a, b). As both r30mm and rx1day revealed predominantly decreasing trends over these areas in JJAS, it suggests that the summer rainfalls have continued to decline for the recent two decades. This drying tendency was predicted to be exacerbated by the warming trend and further leading to food insecurity and social conflicts to a rapidly growing population within the region (Funk et al., 2011, 2012a, b).

    • The duration of extreme rainfalls depicts a remarkable higher length in wetness persistence across the highlands. In JF, the longest duration (up to 12 days) is concentrated over southern parts, especially over highlands; however, there is a notable southward increasing gradient across the domain (Fig. 7a). This is also the case for the mean spatial patterns of rx1day and r30mm indices during JF. In MAM, JJAS, and OND seasons, mean higher longer durations of cwd (above 7 days) are mainly spatially distributed over highlands as it was pointed out for its annual patterns (Figs. 7bd). The longest cwd is predominantly revealed over Ethiopian highlands during JJAS season reaching more than 15 days. Additionally, Kenya−Tanzania coastal belt also features up to 9 days of cwd during MAM.

      Figure 7.  As in As in Fig. 5, but for the extreme duration of consecutive wet days (cwd).

      The spatial pattern of cwd trends in JF shows patches of decreasing tendency over most parts except along a stretching region from Madagascar to southwestern Tanzania with positive trends (Fig. 7e). A significant increasing tendency of cwd during MAM is noticeable over some parts of Congo, Lake Victoria, and along Tanzania coastal zone towards Madagascar, which accounts for NSR and SSR increasing trends (Figs. 7f, j). On the other hand, its shortening tendency is mostly revealed over the Kenya−Tanzania border and southwestern highlands. For the JJAS season, we observe that a higher wetness tendency over NSR and SSR depicts a negative slope because an increase in cwd is seen across much of Sudan whereas a declining tendency prevails over southern to eastern coastal land areas (Figs. 7g, 6k). During OND, SSR featured weak increasing wetness duration due to notable cwd increasing trends across westward areas from western Tanzania, with a longer duration noted in 2006 (Figs. 7h, l). On the other hand, eastern land areas southward from southern Somalia and Ethiopian highlands were characterized by shortening of cwd. Downward (upward) trends of cwd shed light on the emerging risk of drought (flood) conditions (Zhang et al., 2012). However, that may further depend on the amount of precipitation accumulated during those successive wet days as well as their associated frequency and intensity (Zhang Q. et al., 2011). Moreover, the increasing tendency of cwd may also enrich soil moisture storage and groundwater recharge (Zolina et al., 2010).

    5.   Discussion and Conclusions
    • This study examined the intensity, frequency, and duration of precipitation extremes across the East Africa region over the recent two decades (1998−2018). On average, the most frequent and intensive precipitation extremes prevailed across three major regions: the Congo basin, Lake Victoria basin, and southeastern coastal region. This predominantly demonstrates the major moisture source regions for the East Africa domain, which favours precipitation extremes in conjunction with the prevailing atmospheric circulations. The Congo basin primarily benefits from the abundant moisture supply from its tropical rainforests apart from remote sources (Dyer et al., 2017). The westerlies from this region, usually referred to as the Congo air mass, which is characteristically moist, favour precipitation extremes over East Africa (Williams et al., 2012; Mafuru and Guirong, 2018). The Lake Victoria basin also derives its prevalent extreme wet conditions from the diurnal variations across the lake and offshores, which is also promoted by the ample spatial coverage of the lake (68,870 km2;Onyango et al., 2020). Meanwhile, the most prolonged duration of consecutive wet days is prevalent over the highlands, indicating the orographic influence on wet conditions (Hession and Moore, 2011; Alex-Ogwang et al., 2014). Turkana region depicts low cwd, r30mm, and rx1day promoting arid conditions and demarcating relatively higher values between Ethiopian and Uganda-Kenya highlands. The aridity conditions are associated with divergence circulation flow patterns along the entrance and core of the prevailing Turkana low-level jet (Nicholson et al., 2016b).

      The observed precipitation extremes over the Congo and Lake Victoria basins up to western Ethiopia (except cwd) tended to decrease, especially in JJAS with pronounced significant trends. This drying tendency has been associated with ecological changes, including the decline in Congo forest greenness and terrestrial water storage and an increase in land surface temperature (Chambers and Roberts, 2014; Zhou et al., 2014; Jiang et al., 2019). The drying trends have also been implicated by several drought episodes in spite of abrupt flood events (Nicholson, 2016a). However, the observed noticeable patches of increasing trends of consecutive wet days imply the prevailing light to moderate precipitation, lessening the severity of drying tendency. Besides, projections reveal a likely increase in the intensity of heavy rainfall events across the Congo basin towards the end of the 21st century (Blunden et al., 2011).

      On the other hand, precipitation extremes across the southeastern coastal region featured increasing tendency, especially in JF and MAM. The cities along this coastal zone have also been impacted by several flood events exacerbated by the low-lying coastal plains and weak or non-resilient infrastructures (Chang’a et al., 2020). This coastal region and much of the southwestern Indian Ocean are often vulnerable to tropical cyclones and storms that have tremendously resulted in loss of lives, properties, and damage to societal infrastructures and ecosystems, especially if they make landfall (Chikoore et al., 2015). Although much of the tropical cyclones and storms are tracked along the southwestern Indian Ocean and landfalling over Madagascar and Mozambique (Mavume et al., 2009; Fitchett and Grab, 2014), they also implicate substantial rainfall changes across some parts of East Africa (Wainwright et al., 2021). For instance, in March 2018, the tropical cyclones Eliakim and Dumazile over Madagascar were also linked to increased rainfall in East Africa (Wainwright et al., 2021). During 1994−2007, tropical cyclones had become more intense and frequent over the southwestern Indian ocean than 1980−1993, partly ascribed to an increase in sea surface temperature (SST) (Mavume et al., 2009).

      Previous studies have also reported warming of the southern tropical Indian Ocean since the 1960s, accompanied by a significant increase in evaporation, convection, and precipitation (Funk et al., 2008; Williams and Funk, 2011). These may have also implicated the increasing tendency of precipitation extremes over the southeastern coastal areas partly enhanced by the prevailing inflow of the south equatorial ocean current (Schott et al., 2009). Nevertheless, the warming of the Indian Ocean is attributed to an increase in subsidence over much of the inland areas of East Africa, significantly reducing moisture influx from the Congo airmasses (Funk et al., 2008; Williams and Funk, 2011; Williams et al., 2012). This largely accounts for the observed decreasing tendency in precipitation extremes across much of the study domain, including the Congo and Lake Victoria basins. Moreover, there was an increased zonal SST gradient over the tropical Pacific Ocean (Liebmann et al., 2014) triggered by the westward extension of the Indo-Pacific warm pool (Williams and Funk, 2011), which enhanced the Indian Ocean Walker circulation subsiding over East Africa associated with drying tendency.

      Some earlier studies have also reported the observed annual declining tendency in frequency and intensity of precipitation extremes across some parts of Congo to Lake Victoria basins though they were based on sparsely gauged observations (Aguilar et al., 2009; Omondi et al., 2014; Ongoma et al., 2018b). This has implied the prolonging of the drying tendency since the latter half of the 20th century. Besides, over the southeastern region, Vincent et al. (2011) found general annual decreasing trends of precipitation extremes during 1961−2008, implying the observed upward trends turned up over recent decades. Our study also shows that during the 1998−2018 study period, the MAM decreasing trends in intensity and frequency of precipitation extremes mainly occurred over the first decade and later turned to upward trends. This may be suggesting the decadal to multidecadal variability signal, which is linked to the natural variability of SST over the tropical Pacific Ocean (Lyon, 2014). On the other view, the swing from downward to upward trend may be unveiling the reported East Africa climate paradox with an observed MAM drying trend in the recent decades and projected future wetting tendency (Lyon and Vigaud, 2017; Walker et al., 2020). Besides, weak overall trends in precipitation extremes are observed in OND, indicating its higher interannual variability tendency, which has been chiefly linked to SST signatures associated with the Indian Ocean Dipole and El Niño–Southern Oscillation (Liebmann et al., 2014; Nicholson, 2015).

      Generally, observed seasonal patterns of precipitation extremes largely conform to their respective annual spatial and temporal patterns but further highlight the north–south and west–east seasonal progression of precipitation extremes within the region. A similar south−north gradient (but of precipitation amount) in JF season was revealed by Yang et al. (2015), attributed to the JF strong north−south SST gradient along the coast. Moreover, they found a general strong north–south conditional instability with northern (southern) areas of the domain being extremely stable (less stable), which favours dry (wet) conditions. These observations also reflect the positioning and migration of the ITCZ, which is often attributed to rainfall seasonality across the domain (Yang et al., 2015; Nicholson, 1996, 2018; Wenhaji Ndomeni et al., 2018).

      This study’s peculiarity includes integrating precipitation extremes analysis with the high-resolution TRMM satellite product validated against meteorological station observations. Also, it provides a broader view of precipitation extremes patterns across the East Africa region and bordering areas, unlike most previous studies which extended over local to subregional coverages. Moreover, analyses were extended across all seasons spanning within the region, which received little attention, particularly for precipitation extreme indices. Unlike other studies which have used several precipitation extreme indices, our study chose only three indices for the detailed focus on duration, intensity, and frequency of precipitation extremes across the region. The widely observed decreasing trends of precipitation extremes indicate higher susceptibility of the East Africa region to climate-induced changes in various sectors, including agriculture and ecosystems. The drying tendency has induced higher uncertainty on regional food security, which is over-reliant on rainfed subsistence farming, highlighting the need for upscaling irrigation schemes. Meanwhile, the observed increasing tendency of precipitation extremes along the coastal region indicates the significance for good planning of the coastal cities with more resilient and supportive infrastructures to precipitation extreme events.

      Acknowledgments. TRMM Multi-Satellite Precipitation Analysis (TMPA 3B42) is freely available online at https://doi.org/10.5067/TRMM/TMPA/DAY/7. The topographical elevation of the study domain is quality-controlled global Digital Elevation Model (DEM) data from the Global Land One-km Base Elevation (GLOBE) Project acquired from NOAA NGDC GLOBE data library at https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NGDC/.GLOBE/topo/datafiles.html. R software package for ClimPACT2 is available on GitHub at https://github.com/ARCCSS-extremes/climpact2. Data analyses and graphics visualizations throughout this study are mostly done by using NCAR Command Language (NCL) Version 6.6.2 (http://dx.doi.org/10.5065/D6WD3XH5) software (Meier-fleischer et al., 2017).

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