Power Data Access Viewer-Based Meteorological Drought Analysis and Rainfall Variability in the Nile River Basin

. Meteorological drought poses a frequent challenge in the Nile River basin, yet its comprehensive evaluation across the basin has been hindered by insufcient recorded rainfall data. Common indices like the standard precipitation index, coefcients of variation, and precipitation concentration index serve as pivotal tools in gauging drought severity. Tis research aimed to assess the meteorological drought status in the Nile River basin by using the Power Data Access Viewer product rainfall data. Bias correction procedures were implemented to refne the monthly rainfall data for Bahirdar, Markos, Nekemt, and Muger stations, resulting in notable improvements in the coefcient of determination ( R 2 ) that were increased from 0.74 to 0.93, 0.72 to 0.89, 0.71 to 0.96, and 0.69 to 0.84, respectively. Te average spatial distribution of drought in the Nile basin was classifed as extremely wet (3.81%), severely wet (9.01%), moderately wet (7.36%), near normal (9.97%), moderately drought (21.20%), severely drought (17.11%), and extremely drought (31.54%). Approximately 10.33% of the Nile River basin was situated in regions characterized by high rainfall variability, while around 21.17% was located in areas with a notably irregular precipitation concentration index. Overall, this study sheds light on the prevailing meteorological drought patterns in the Nile River basin, emphasizing the signifcance of understanding and managing these phenomena for the sustainable development of the region.


Introduction
Drought is a recurring and severe phenomenon with signifcant impacts on the economy, society, and ecosystems of the world [1,2].Drought indices, which combine various variables like precipitation and evapotranspiration into a single numerical value, are essential for quantifying drought severity, detecting its onset and end, and planning water resources value [3].Drought stands out as a prominent natural hazard, exerting signifcant consequences on agricultural output, water reserves, ecosystem dynamics, the environment, as well as both global and local economies [4].
Meteorological droughts, characterized by prolonged rainfall defciency, can lead to agricultural droughts with soil water defciency and reduced crop yields [5].Analyzing rainfall variability is crucial for efective hydrological planning and management [6].
Riparian countries of the Nile River basin like Sudan, Egypt, and Ethiopia are highly vulnerable to drought due to climate change and reduced rainfall [6].However, accessing accurate precipitation data for drought monitoring systems is challenging.Te lack of climate data records in the many River basins, including the Nile River basin, hinders the interpretation of drought indices and the recommendation of drought-resilient agricultural practices.Te Prediction of Worldwide Energy Resources (POWER) data access viewer (DAV) provided by NASA/POWER ofers a solution by providing global weather data at a 1 °latitude-longitude resolution [7], and NASA satellite production rainfall data have good accuracy [8].
While earlier research has assessed drought indices at the watershed level, the accurate evaluation of meteorological and hydrological drought in the Nile River basin has been hindered by insufcient recorded rainfall and fow data across the basin's stations [9].Drought indices such as the standardized precipitation index, rainfall variability, and precipitation concentration index are evaluated using the POWER data access viewer product rainfall data [10].Te POWER Data Access Viewer (DAV) furnishes gridded rainfall data in long-term, consistent time series, covering expansive geographic areas, and this includes remote or ungauged regions where on-site station data may be lacking or limited [8].Tis feature is particularly benefcial for large river basins, where the precise depiction of spatial variations in rainfall is crucial [11].Te standard precipitation index (SPI) has advantages such as simplicity, standardization, and variable time scales [12].Precipitation data are often readily available from meteorological stations, satellites, or climate models.SPI's reliance on precipitation data enhances its applicability in data-scarce regions where obtaining comprehensive hydrological data may be challenging [13].SPI relies solely on precipitation data and does not incorporate other climatic variables such as temperature or evapotranspiration.Tis limitation means that SPI may not fully capture the complexities of drought conditions, especially in regions where temperature plays a signifcant role in water availability [14].Te standardized precipitation index (SPI) does not take into account soil moisture, and neglecting these factors may result in an incomplete grasp of the comprehensive meteorological drought conditions [15].Te standard precipitation index (SPI) is used in large river basins despite these limitations because it provides a standardized measure of precipitation anomalies [16], making it easier to compare drought conditions across diferent regions and periods [17].
Tis study assesses meteorological drought indices, rainfall variability, and concentration index in the Nile River basin, utilizing POWER data for Ethiopia, Sudan, and Egypt.Hydrological drought analysis is excluded due to fow data limitations.Te signifcance lies in enhancing understanding of the Nile River basin drought, ofering valuable insights for assessment, and guiding climate change adaptation and mitigation strategies.

Description of the Study Area.
Te study area spans three riparian countries: Ethiopia, Sudan, and Egypt.Riparian areas are crucial as they are situated along the banks of rivers and play a signifcant role in ecosystems, agriculture, and water resource management.Te specifed coordinates (between 20 °0′ and 40 °0′ East and 10 °0′ and 30 °0′ North) indicate a substantial region, covering a wide longitudinal and latitudinal range (Figure 1).Te distribution of the study area shows varying percentages in each country: 66.7% in Sudan, 18.3% in Ethiopia, and 15.0% in Egypt, and this suggests that the bulk of the study area is in Sudan, followed by Ethiopia and Egypt.

Data Analysis.
Te accuracy of the DAV rainfall product was evaluated using gauge rainfall as a reference.First, a comparison of satellite and gauge rainfall amounts through visual inspection of scatter plots was performed.Ten, performance indicators of relative percentage of bias (BIAS) were estimated at a monthly average time scale based on [18].Bias represents the systematic error of the satellitebased rainfall estimate as a percentage of the observed rainfall [19].A percentage of bias value closer to 0 indicates that the monthly satellite rainfall estimate is closer to the monthly observed rainfall [20].A positive bias indicates an overestimation, whereas a negative value indicates an underestimation by the satellite [21], and the bias was expressed by the following equation: where G is gauged monthly rainfall (mm), S is satellite products monthly rainfall (mm), and PBIAS is the percentage of bias (%).Te power data access viewer web mapping application contains geospatially enabled solar, meteorological, and cloud-related parameters formulated for assessing and designing renewable energy systems [22].Rainfall data for each month were acquired using the Power Data Access Viewer (DAV) from 30 station points situated along the Nile River basin.Te satellite rainfall data from DAV were then correlated with recorded data from stations in Bahirdar, Markos, Muger, and Nekemt.Based on [23], the bias of DAV satellite rainfall data was corrected by using the average monthly correction factor that developed from the above four meteorological stations (equation).
where p * is the bias-corrected rainfall, P is the uncorrected rainfall amount, and a and b are the average monthly transformation coefcients of Bahirdar, Markos, Muger, and Nekemt station.Te determination of the "b" parameter was done iteratively for each month until the corrected power monthly precipitation matched that of the observed precipitation time series.Ten the parameter "a" was determined such that the mean of the transformed monthly values corresponds with the observed mean.Finally, monthly constants a and b are applied to each uncorrected monthly DAV rainfall data to generate the corrected monthly rainfall in the Nile River basin across the riparian countries.

Meteorological Drought Index and Rainfall Variability.
Te standardized precipitation index (SPI) is a statistical tool used to assess and monitor meteorological drought to quantify and characterize precipitation defcits over various time scales of the region [24].A standardized precipitation index (SPI) is developed to monitor drought for several time scales by gathering the precipitation time series over the period [25].Positive SPI values indicate a wetter than typical period (accumulated precipitation is greater than the median), and negative SPI values represent a drier period with less precipitation than normal [26].Tis is an important metric for the water sector regarding quantity and quality of supply for human consumption and agricultural use [27].

Advances in Meteorology
Te spatial classifcation of the standard precipitation index was reclassifed using ArcGIS software according to the recommended classifcation (Table 1) and that was estimated using the following equation: where X i , X m , and SD x stand for annual rainfall of a particular year, long-term means annual rainfall through observation and standard deviation, respectively.Te variability of rainfall across watersheds or river basins is conveyed by the coefcient of variation [30].Te coefcient of variation (CV) of monthly or annual rainfall is a statistical measure that expresses the relative variability of monthly precipitation in a specifc location [31].It is calculated by dividing the standard deviation of monthly rainfall values by the mean (average) annual rainfall and then multiplying by 100 to express the result as a percentage as described in equation ( 4).Te coefcient of variation of annual rainfall has diferent classifcations as presented in Table 2, and the results were spatially classifed using ArcGIS software after interpolating using the inverse distance weighting method.A higher coefcient of variation indicates greater variability, while a lower coefcient of variation suggests more consistent or stable monthly rainfall [33].
Te precipitation concentration index (PCI) is a statistical measure used to assess the distribution of precipitation over time within a specifc region and it provides information about the temporal concentration or unevenness of rainfall throughout the year [34].Te index helps in understanding whether precipitation is evenly distributed across months or if it is concentrated in a specifc period [35] and PCI is used to quantify the relative distribution of the rainfall patterns [36].Te precipitation concentration index (PCI) is a very important parameter to evaluate the  Advances in Meteorology concentration of rainfall over the season and that may be applied monthly or annually as classifed in Table 3. Precipitation concentration index (PCI) was evaluated according to [37] as described in the following equation: (5)

Results and Discussion
3.1.Bias Correction of Rainfall.DAV satellite product rainfall data bias exceeding 30% has the potential to considerably afect the precision and dependability of precipitation information.Tis underscores the necessity for corrective measures to address the bias, especially since the selected station in the current studies exhibited a bias percentage exceeding 30% [38].Based on [39] showing the highest level of underestimation.Meteorological droughts might be underestimated or overlooked due to the underestimated rainfall data, and this can hinder early drought detection and preparedness eforts and overestimation can lead to false alarms of impending drought conditions [40].Te bias percentage in stellate product rainfall data can reach as high as −63%, with variations potentially dependent on the specifc location of the study area [38].In the current study, the maximum underestimation and overestimation biases were recorded as −83.3% and 86.4%, respectively, at the Nekemt station which was the maximum compared with earlier fndings.Te percentage of bias observed in the present study varied across months, even for stations with similar characteristics.Te fuctuation in bias percentages within stellate product rainfall data from one month to the next can be ascribed to various factors, including seasonal patterns [41], climate oscillations, and potential errors in instrumentation and measurement processes [42].Te percentage of bias in the current study varied from one station to another station which may be due to the location of the station, local climate characteristics, and the quality of ground-based gauged rainfall data used for comparison.Tese biases highlight the importance of considering station-specifc bias correction when using satellite rainfall data for various applications in diferent regions.In Bahirdar station, the R 2 values improved from 0.74 to 0.93 after bias correction, indicating a signifcant enhancement in the correlation between rainfall data that is described in Figures 2(a) and 2(b).Similarly, in Markos station, the R 2 values increased from 0.72 to 0.89 after bias correction, indicating a notable improvement in the correlation of rainfall data (Figures 2(c) and 2(d)).Te R 2 values in the Nekemt station were improved from 0.71 to 0.96 after bias correction, suggesting a substantial enhancement in the correlation between rainfall data in the Nekemt station as presented in Figures 2(e) and 2(f ).Lastly, the R 2 values of Muger station were increased from 0.69 to 0.84 after bias correction, indicating a considerable improvement in the correlation of rainfall data as described in Figures 2(g) and 2(h).Te minimum value of coefcients of determination after bias correction (R 2 ) was 0.84 which indicates a better ft between observed and DAV rainfall.Te fndings indicate the efectiveness of the correction method in improving the accuracy of the satellite-derived rainfall data [39].All evaluated coefcients of determination (R 2 ) after bias correction showed that DAV rainfall data were in a high linear relationship with the data measured from the precipitation observation station.Overall, the results demonstrate that bias correction techniques have successfully improved the correlation between rainfall data in all four stations.
After bias correction, the spatial annual rainfall distribution was interpolated through the Nile River basin as discussed in Figure 3. Te average annual rainfall across the Nile River basin exhibited considerable variability, ranging from 0.65 mm in Egypt to 1455.5 mm in Ethiopia, and the result showed that the amount of annual rainfall in the Nile River basin decreased from Ethiopia toward Egypt.Te observed variation in average annual rainfall across the Nile River basin can be attributed to several climatic and geographical factors.Ethiopia was located at the headwaters of the Nile, experiencing a more diverse and elevated topography, contributing to higher precipitation levels.In contrast, downstream Sudan and Egypt grapple with a more arid climate and lower elevations, resulting in diminished annual rainfall.Tis spatial distribution in the basin is used to understand regional variations in rainfall within the area to suggest possible mitigation measures [34].

Standardized Precipitation
Index.Te research conducted involved a historical analysis of the standard precipitation index (SPI) for stations located in the Nile basin from 2001 to 2021.During the period from 2007 to 2009, the Nile River basin exhibited the highest coverage under extreme drought conditions, reaching a maximum of 35% as presented in Figure 4(c).Tis observation suggests a heightened susceptibility of the basin to arid conditions during that specifc time frame.In contrast, the availability of extreme wetlands from 2018 to 2021 (Figure 4(g))   A minimal area (3.81%) of the Nile River basin experienced extremely wet conditions, indicating regions with an abundance of precipitation.A signifcant portion of the Nile River basin (9.01%) was experiencing severely wet conditions.Wetness characteristics of the basin could lead to issues such as fooding, soil erosion, and potential impacts on agriculture and infrastructure [43], and understanding the causes of severe wetness is important for water resource management and disaster preparedness [44].Another substantial part of the basin exhibits moderate wetness (7.36%).While not as extreme as severe wetness, this still indicates a notable surplus of water.Moderate wet conditions can infuence water availability and ecosystem health and may have implications for various sectors, including agriculture and water supply [45].Approximately 9.97% of the Nile River basin was currently experiencing drought conditions classifed as near normal.Te nearnormal drought suggests that the conditions are drier than average but not to an extreme extent [46].In this study, the result was that approximately 21.20% of the Nile River basin exhibited conditions indicative of moderate drought, pointing towards a substantial reduction in precipitation.Tis important fnding highlights the impact of climatic variations on the hydrological dynamics of the region, shedding light on the vulnerabilities within the Nile River basin.Te classifcation of moderate drought in a signifcant proportion of the basin emphasizes the pressing need for adaptive water resource management strategies to mitigate potential adverse efects on ecosystems and human activities [47].Te reported result indicating that 17.11% of the Nile River basin was experiencing severe drought 6 Advances in Meteorology conditions suggests a heightened and critical shortage of water availability in this specifc geographic area.Drought conditions are often characterized by an extended period of below-average precipitation coupled with increased evaporation, leading to a defcit in water resources [48].Te classifcation of 31.54% of the Nile River basin as extremely drought-prone emphasizes the critical need for efective water management strategies, sustainable land use practices, and climate-resilient policies to address and mitigate the impacts of severe water scarcity in these areas, especially within the latitude range of 20 °0′0″ to 30 °0′0″ North.Te observed spatial distribution of drought highlights the heterogeneity of climatic conditions within the Nile basin [40].Variation of standard precipitation index in Nile River basin may be due to climate change and human activity infuence [49].Te annual value of the standard precipitation index indicated that the Nile River basin in Sudan and Egypt tributaries received several drought events, and the most severe event was during the year 1984 [50].
Notably, the region of the Nile River basin in Egypt was presently undergoing an extreme drought, indicating a severe lack of precipitation that posed substantial challenges to water availability and agriculture throughout the Nile River basin.In Sudan, the drought status of the Nile basin varied across the spectrum from near normal to extremely dry, highlighting a mixed scenario where some areas experienced regular precipitation while others faced severe drought conditions.Meanwhile, the Nile River basin in Ethiopia (called as Upper Blue Nile basin) exhibited predominantly wet to moderately wet conditions, suggesting an excess of rainfall that could have positive implications for water availability and agriculture in the Nile River basin.Tese results highlight the importance of region-specifc water management strategies and the need for continuous monitoring to address the diverse hydrological challenges faced by diferent parts of the Nile basin.Further research and proactive measures are essential to mitigate the impacts of drought, particularly in regions experiencing extreme conditions, and to enhance the overall resilience of the Nile River basin to varying climatic patterns.

Drought Duration and Frequency.
Te duration and frequency of droughts in the Nile River basin exhibited signifcant variability across diferent regions within the study period.Specifcally, in Ethiopia, there were instances where no drought was observed (0 months), whereas in Egypt, drought durations extended up to 12 months (Figure 5(a)).In addition, the frequency of these drought events ranged from none (0 occurrences) to as many as 21 times throughout the period of study (Figure 5(b)).Analysis of annual SPI (standardized precipitation index) data reveals a greater prevalence of drought conditions in Egypt, and these dry periods were notably observed in the   Te Nile River basin exhibits signifcant variability in rainfall, particularly in Sudan and Egypt, especially within the latitude range of 22 °0′ to 25 °0′ North and this region experiences notable fuctuations in precipitation levels.Te mean coefcients of variation for the wet season, dry season, and annual precipitation in the Upper Blue Nile River basin (in Ethiopia) between 1901 and 2000 were 8.8%, 11.4%, and 7.5%, respectively.Tese values, all below 20%, suggest relatively less variability in rainfall availability [52].In the recent investigation covering the years 2001-2021, a similar trend of less rainfall variability was observed in the Blue Nile River basin, which constitutes a portion of the larger Nile basin.Tis consistency implies a continued pattern of relatively stable precipitation levels in the region.Tis study contributes to the existing knowledge of rainfall variability in the Nile River basin, highlighting the spatial diferences observed across Ethiopia, Sudan, and Egypt.Te identifed categories of variability serve as a valuable tool for water resource planners and policymakers to develop targeted strategies for sustainable water management and climate resilience in the region [53].
Based on the precipitation concentration index, the analysis reveals that 34.61% of the study area experiences a high irregular distribution of precipitation (Figure 6(b)).Tis indicates areas where precipitation events are sporadic and exhibit signifcant variability.In addition, 27.70% of the area demonstrates a moderate precipitation distribution, suggesting a more balanced but still variable pattern.Notably, 21.17% of the region exhibits very high irregular distribution, emphasizing areas with extreme fuctuations in precipitation levels.A smaller percentage, 16.52%, was characterized by a uniform distribution of precipitation, indicative of regions with consistent and evenly distributed rainfall.In the Upper Blue Nile River basin, the annual precipitation concentration index ranges from 11.43% to 28.39%, with an average of 21.41%.Tis average signifes a moderate distribution of rainfall in the area [54], and the current study observed variability of the upper reach of the Nile River basin (in Ethiopia) ranging from uniform to moderate distribution.Te precipitation concentration index of the Nile River basin increased from Ethiopia toward Sudan and Egypt.In contrast, the precipitation concentration in Sudan and Egypt indicates an erratic distribution of rainfall in these regions, and the precipitation distribution pattern within the Nile River basin in Egypt exhibits a highly irregular nature.A considerable portion of the Nile River basin in Sudan displays a distinct high level of irregularity in precipitation distribution, especially when compared to other classifcation patterns.Understanding the precipitation concentration index is important for assessing the Advances in Meteorology variability and reliability of rainfall patterns, which has implications for water resource management, agriculture, and other sectors in the region [55].Both rainfall variability and concentration vary due to factors like land use changes, water bodies [56], geography, and topography [57].

Conclusion and Recommendation
In this paper, spatial distribution, frequency and duration of meteorological drought, rainfall variability, and concentration were discussed in Nile River basin as summarized in the following: (1) Tis study focused on evaluating meteorological drought, rainfall variability, and the precipitation concentration in the Nile River basin, utilizing rainfall data from the Power Data Access Viewer for the period 2001-2021.In order to enhance the accuracy of the analyses involving the abovementioned indices, a rigorous bias correction process was employed on the satellite-derived rainfall data, ensuring its alignment with data from selected meteorological stations.(2) Te application of bias correction on the satellitederived rainfall data within the Data Access Viewer yielded a notable 26.6% enhancement in the R 2 value.Tis improvement shows a substantial increase in the accuracy of the satellite product for rainfall.Following the bias correction, the spatial distribution of annual rainfall across the Nile River basin showcased signifcant variability, ranged from 0.65 mm in Egypt to 1455.5 mm in Ethiopia.(3) Based on the standard precipitation index, approximately 30.15% of the Nile River basin displayed a spectrum of conditions, ranging from extremely wet to near normal.On the other hand, a substantial portion, accounting for 69.85% of the total area, exhibited variations indicative of moderate to extreme drought that show available monthly or annual rainfall was minimal compared with the longterm mean value of rainfall.Te maximum extent of extreme drought conditions observed from the 2007 to 2009 time period covered an average of 35% of the Nile basin.Te duration and frequency of droughts in the Nile River basin also increased from Ethiopia towards Sudan and Egypt.
(4) In the study of the basin's rainfall patterns, it was found that approximately 59.1% of the area demonstrates a lower variability in precipitation, indicating a more uniform and predictable pattern of rainfall.On the other hand, the precipitation concentration index indicates that 34.61% of the basin is characterized by a markedly uneven distribution of rainfall, pointing to a greater degree of unpredictability in precipitation trends.
(5) Future research should explore the drivers and impacts of extreme droughts in the Nile River basin, focusing on adaptive strategies for water resource management in vulnerable areas.

Figure 1 :
Figure 1: Location of study area.

Figure 3 :
Figure 3: Spatial distribution of total annual rainfall in Nile River basin after bias correction.

Figure 6 :
Figure 6: Average coefcient of variation (a) and precipitation concentration index (b) of the Nile River basin.

Table 3 :
Category of precipitation concentration index.