Rainfall Trends Research Articles

Rainfall and Temperature Trends and Variability in Arid and Semi-arid Lands of Kitui County, Kenya

A study was carried out to analyse rainfall and temperature trends and variability in selected agro-ecological zones in Kitui County. Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall dataset was used while temperature data was obtained from the Climatic Research Unit gridded Time Series (CRU TS) dataset. The results indicated that there was a non-significant decreasing trend (p<0.05) in average annual rainfall in all the four agro-ecological zones (p<0.05) for a 30-years period (1988-2018) in the study area. A decreasing trend in March-April-May (MAM) seasonal rainfall trend was reported in the arid and semi-arid agro-ecological zones while an increasing trend was recorded in the transitional and semi-humid zones. For the October-November-December (OND) seasonal rainfall, a non-significant decreasing trend was reported in all the four agro-ecological zones. Additionally, a higher annual rainfall variability was recorded in the drier (arid) and wetter (semi-humid) zones compared to that in semi-arid and transitional zones. Moreover, the study established that there was a spatial variation in both MAM and OND seasonal rainfall variability and that rainfall variability was higher in the OND seasonal rainfall than that of the MAM seasonal rainfall in all the agro-ecological zones. With reference to temperature trends, a statistically significant increasing trend in annual and OND seasonal average maximum and minimum temperatures was reported in all the four agro-ecological zones. Further, the study noted a non-significant increasing trend in maximum and minimum temperatures for the MAM season in all the agro-ecological zones. In regard to temperature variability, the study deduced that there was low temperature variability compared to rainfall variability in all the four agro-ecological zones. The study recommends that location-specific rainfall and temperature analysis should guide planning and implementation of adaptation strategies for effective response to climate variability. Keywords: Agro-ecological Zones, Mann-Kendall Test, Coefficient of Variation, CHIRPS, CRU TS DOI: 10.7176/JEES/12-12-05 Publication date: December 31 st 2022

Livestock farmers' perception of climate change and adaptation strategies in the Gera district, Jimma zone, Oromia Regional state, southwest Ethiopia

The aim of this study is to assess livestock farmers' perception of climate change (CC)/variability and adaptation strategies in the Gera district. Rainfall and temperature were the variables taken in the CC perception study. A total of 190 smallholder livestock farmers were sampled for the survey. Primary data were collected through semi-structured questionnaire interviews, focus group discussions (FGDs) and meteorological data series of 2001–2020. The Statistical Package for Social Sciences (SPSS) version 20.0 was used to analyze the data. The results revealed that 79.17% of respondents perceived climate change over the past 20 years. About 84.9% and 82.9% of respondents perceived increasing temperature and decreasing rainfall over the past 20 years, respectively. Farmers' perception was consistent with meteorological data of the area, which also showed increasing trend in temperature and decreasing trend in rainfall. Farmers' perceived that anthropogenic action and natural processes, anthropogenic action, natural processes, and God's anger against human sins were the main causes of CC, in decreasing order. No statistical difference (p > 0.05) was found between AEZs regarding effects of CC except for incidence of trypanosomiasis. Decreased quality and quantity of feeds, water availability, milk production, and animal fertility, and increased calving interval, number of services per conception, incidence of animal disease, and parasite were perceived as the major impacts (indicators) of CC on livestock production and productivity in their order of importance. Diversification of mixed crop-livestock, diversification of livestock species, feed conservation, reducing herd sizes, water harvesting, provision of supplementary feeds, and forage production were the most practiced adaptation strategies. Lack of technical know-how about water harvesting, shortage of land for forage production, lack of improved forage seeds, lack of supplementary feed, poor livestock management skill, lack of feed conservation practices and poor access to market were the most important barriers to CC adaptation. It is concluded that there is a need for policy makers and livestock development stakeholders to formulate and implement intervention that promote farmers' perception and adaptation abilities to CC impacts and address the identified barriers for improving livestock productivity in the study area.

Do Changes of Rainfall Trends Affect Choice of Drainage Systems?

Precipitation is one of the main factors causing surface, linear and landslide erosion. Their influence on erosion processes is determined by such parameters as: precipitation frequency, amount, duration and intensity. These features affect the rate of infiltration and the intensity of surface runoff, and thus the occurrence, course and effectiveness of erosion processes [2, 3, 4]. The aim of research was to analyse the 40-years of rainfall measurements data from the Lubuskie Voivodeship and inspect whether occurring of heavy rains are random events or maybe indicate some long period trends. To analysis was used non-parametrical Mann-Kendall test and correlation test. What is more the authors’ prepared a path of selecting the optimal dewatering system, where would be taken into consideration ground stability and changes in the rainfall trends. For this purpose, the paper contain short overview existing dewatering systems which are used to reducing risk of landslides.

Analysis of Precipitation Data Using Innovative Trend Pivot Analysis Method and Trend Polygon Star Concept: A Case Study of Soan River Basin, Potohar Pakistan

Abstract The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, the environment, and water resources. In this study, the innovative trend pivot analysis method (ITPAM) and trend polygon star concept method were applied for precipitation trend detection at 11 stations located in the Soan River basin (SRB), Potohar region, Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting, and calculating monthly shifts under different trend behaviors, that is, increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows a complex nature of behavior, and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first dataset and the second dataset in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behavior in the water resources and agricultural sectors. Significance Statement The monthly polygonal trends with risk graphs of total monthly precipitation data depicted a clear picture of climate change effects in the Potohar region of Pakistan. The monsoonal rainfall showed a significant decreasing trend in highland stations and an increasing trend in lowland stations, indicating a shifting pattern of monsoonal rainfall from highland to lowland areas.

Forecasting Rainfall and Temperature Trends in Saint Lucia Using Nonparametric Statistics and Soft Computing Techniques

Climate change and its adverse environmental impacts are major concerns for Small Island Developing States (SIDS), such as Saint Lucia. In particular, the annual atmospheric temperature and rainfall continue to receive a great deal of attention in the Caribbean and worldwide. Subsequently, looking at the spatiotemporal elements of meteorological factors with regard to the evolving environment, especially in countries where tourism and agriculture activities are dominating, is essential to evaluate climate prompted changes and propose feasible adaptation techniques. The present study attempts to examine and provide findings on the long-term deviations and fluctuation in atmospheric temperature and rainfall over the island of St. Lucia using data for the last three decades (1990-2020). The problem was analyzed using soft computing techniques of Gene Expression Programming (GEP) and a nonparametric statistical trend analysis technique of Mann Kendall (MK) and Sen's slope (Q) tests. The results showed the descriptive capabilities of the GEP output and significance as a prediction tool when compared to MK and Q tests. The results of the detailed analyses indicated that there was no significant trend of rainfall on the island over the last 30 years. However, the GEP analysis indicated that during the next 10 years, the country will experience drought-like instances of low rainfall volumes. Further analysis showed that the island's mean temperature will increase at a rate of 0.032℃ per year. Subsequently, due to the island becoming warmer with little to no increase in rainfall, the island would require in-depth planning in the area of energy consumption as well as mitigation measures due to the potential impact of increased storm activities that can be correlated with these climatic variabilities.

Rainfall Trends in the Southwestern Region of Syria Using Spatial and Statistical Analysis Methods

This study aims to analyze the rainfall trends and the spatial-temporal variations in five stations during the period (1960-2019) in the southwestern region of Syria using MK & GIS. And to Predict rainfall amounts using IDW. The results showed decreasing trends in the monthly, seasonal and annual rainfall. The annual average showed a great variation with a standard deviation ranging between 51.1 – 131.7 mm from the average annual at the Damascus International Airport station, and 232.6 – 697.7 mm from the average annual (697.7 mm) in the Quneitra station. Quneitra showed a significant negative trend during the study period at a 5% significance level in the annual rainfall series. The upper limit for the significant changes on the monthly level was in the stations (Daraa, Quneitra, and Mezzeh), the stations (Daraa and Quneitra) showed the maximum decline in the general direction; This indicates a sharp change in the amount of monthly rainfall during the study period, which amounted to (8.03 mm in Daraa, and reached (18.7 mm/month) at the significance of 5% in the series of rainfall during March. The trends of rainfall changed from winter to spring and then autumn, and the results of the MK analysis showed the presence of low negative trends in the winter season, and high negative trends in the fall and spring seasons. The Quneitra station was the most changing compared to the stations and recorded the maximum negative decrease in the amount of rainfall, which amounted to 3.9 mm in May.

Performance of CHIRPS dataset for monthly and annual rainfall-indices in Northern Argentina

In this study, we analyze the performance of CHIRPS in comparison with data from 50 rain gauges (OBS) in Northern Argentina (NA) for the 1982–2019 period. The methodology consists in a point-to-pixel comparison using the correlation coefficient (RHO), the mean relative error (MRE), the mean absolute error (MAE) and the Nash-Sutcliffe efficiency (NSE). We analyze monthly rainfall, annual rainfall indices and their trends, differentiating between anchor and no-anchor stations. A comparative analysis is performed further between two NA sub-regions: Northwestern Argentina (NWA) and Northeastern Argentina (NEA). Results indicate that CHIRPS dataset better represents the interannual variability in wetter (drier) months in NWA (NEA). For all months, RHO values are higher in NEA than NWA. For annual rainfall indices, RHO values in most of the stations of NA are non-significant or low for some number-days (with threshold of 1 mm) and very extreme indices, with the exception of the eastern extreme of NA. The less extreme indices (PRCPTOT, R95pad and R99pad) are observed to have higher RHO values (> 0.5 in all cases) in NA, as well as better MRE, MAE and NSE values. Monthly values and annual indices are underestimated in general, especially in NWA no-anchor stations. Most of the significant linear trends observed in rainfall indices are not detected with CHIRPS. As an exception, a relatively better performance for the maximum number of consecutive dry days (CDD) is observed in the sense that CHIRPS detect the positive linear trends in NWA but do not locate them with precision in comparison with OBS data. CHIRPS is not recommended for studies in NA related with the aspects (mean values, interannual variability, linear trends) of rainfall analyzed in this work, especially for the extreme rainfall.

An integrated modeling approach to predict trophic state changes in a large Brazilian reservoir

Process-based ecological models have been used to study freshwater ecosystems and water quality on a broad scale. However, it is also of pivotal importance to incorporate watershed dynamics and nutrient releases in the downstream freshwaters. Integrated modeling approaches have been used to understand the combined effect of climate warming and land use and land cover (LULC) changes in lake ecosystems. Moreover, most basin-scale water quality models require many datasets and parameters to perform reliable simulations which contributes to reduce studies in poorly monitored basins, most of them located in the Global South. In this study, we developed a coupled hydrological-biogeochemical-ecological modeling framework forced by two regionalized climate models and three LULC change scenarios to forecast trophic state changes in a subtropical multipurpose reservoir for the decade 2050-2060. The projections indicated an average air temperature increase between 2°C and 3°C and a downward trend of the average rainfall and longwave radiation for the 2050s in comparison to the last decade. We found a pattern of 28% increase in total phosphorus (TP) and total chlorophyll-a (TChla) concentrations in the reservoir compared with the historical baseline. The climate warming projections along the 2059 projected LULC and basin's increased economic development scenarios have predicted trophic state index (TSI) shifts between mesotrophic and eutrophic conditions (53.3<TSI<57.7). On the other hand, one of the climate projections along the reduced deforestation scenario indicated a trend towards oligotrophication between 2054 and 2056, however higher phosphorus availability (60µg.l−1<TP<100µg.l−1) and phytoplankton biomass (50µg.l−1<TChla<97µg.l−1) would be expected for the entire decade compared to recent years. The proposed coupled modeling framework demonstrated the potential of open-source tools in water quality management studies, especially for poorly monitored basins, based on climate change trends and human pressure.

Rainfall distribution and trends over the semi-arid Marathwada region of Maharashtra, India

Rainfall distribution and trends over the semi-arid Marathwada region of Maharashtra, India

Climate Change Trends in a European Coastal Metropolitan Area: Rainfall, Temperature, and Extreme Events (1864–2021)

This paper summarises an updated climate change trends analysis—developed for the period from 1 October 1864 to 30 September 2021 within the scope of a Horizon 2020-funded project to increase climate resilience in European coastal cities—for a representative site of the Lisbon Metropolitan Area (Portugal). By using long ground-based daily records of rainfall and surface temperature at the Lisboa-Geofísico climatological station, the analysis aimed to identify (i) long-term and recent climate trends in rainfall and temperature, (ii) changes in extreme rainfalls, heatwaves, and droughts, and (iii) possible effects of the coupled changes of minimum and maximum daily temperatures (Tmin and Tmax, respectively) on drought development based on the diurnal temperature range (DTR) indicator. To detect these trends and quantify their magnitude, the Mann−Kendall and Sen’s slope estimator tests were implemented. The analysis of the mean annual temperatures indicated that the study area has warmed ∼1.91 °C through the 157 analysed years. Results evidenced statistically significant upward trends in both Tmin and Tmax, and in the number of Tmax heatwave days. In what concerns the extreme hydrological events, the analysis of annual maximum rainfall series and peaks-over-threshold (POT) techniques showed more frequent and intense events in recent years, reaching up to ∼120.0 mm in a single day. With regard to drought, the study proved that the characterisation based on the commonly used standardised precipitation index (SPI) might differ from that based on the standardised precipitation evapotranspiration index (SPEI), as the latter can take into account not only rainfall but also temperature, an important trigger for the development of drought. According to the SPEI index, severe and extreme drought conditions have been more frequent in the last 60 years than in any other recorded period. Finally, a decreasing DTR trend towards the present was found to influence evapotranspiration rates and thus drought characteristics.

Rainfall Precipitation Pattern from 2001-2021 over Andhra Pradesh Region in India

The research focuses on a key meteorological variable, precipitation, in order to analyze the changing trend of rainfall in Andhra Pradesh, India. The State highly depends on rainfall for its agricultural activities, but the occurrence of rainfall is unprecedent and variable, causing adverse implications on the cropping system as well as negative effects on natural water resources. Daily data from the years 2001 to 2021 were analyzed to determine monthly, seasonal and yearly rainfall variability using statistical methods such as mean, standard deviation (SD) and coefficient variability (CV). District wise monthly, seasonal and annual rainfall trends have been drawn using 21-year daily data. The statistical analysis of whole reference time series data reveals that maximum mean South West Monsoon (SWM) rainfall is received in the north and central coastal districts of Srikakulam, Visakhapatnam, Vizianagaram, East Godavari, West Godavari and Krishna. North East Monsoon (NEM) rainfall contributes significantly in mean annual rainfall of southern coastal district and Rayalaseema sub division of the State.

Attribution of the human influence on heavy rainfall associated with flooding events during the 2012, 2016, and 2018 March-April-May seasons in Kenya

The changing probabilities of extreme climate and weather events, in terms of frequency, intensity, spatial extent, duration, and timing is one of the most noticeable and damaging manifestations of human-induced climate change. During the March-April-May (MAM) rainfall season of 2012, 2016 and 2018, Kenya experienced high rainfall that caused both widespread and localised flooding, resulting in human and livestock deaths, destruction of infrastructure and property, bursting of riverbanks, submerging of farmlands and emergence of isolated cases of water-borne diseases. Here, we aim to quantify how the magnitude of heavy rainfall during these seasons may have been altered by human-induced climate change. We undertake a probabilistic attribution analysis using three different approaches utilising two observational datasets and two independent climate model experiment set-ups. We analyse three different seasonal heavy rainfall indices, maximum consecutive 5-day, 10-day, and 20-day rainfall, to compare the magnitude of maxima recorded in MAM 2012, 2016 and 2018 with the magnitude of maxima in a pre-industrial climate (with little or no anthropogenic influence). We find a shift towards intensification of extreme rainfall in today's climate, although these increases are not in all cases statistically distinguishable from our estimates of magnitudes in the preindustrial climate. Although we find no significant anthropogenic climate change influence, the intensification of extreme rainfall amid the observed drying trend and the projected increases in rainfall in the MAM season in Kenya, leave the already vulnerable societies with uncertainties about how to prepare for a changing climate. This study, therefore, provides a basis for an in-depth assessment of current and future trends of extreme rainfall in East Africa in adapting to changing climate risks for sustainable development in the already vulnerable and less resilient society.

Spatiotemporal trends in daily and subdaily rainfall extremes and return levels in Turkey

AbstractIn this study, we present the long‐term daily and subdaily station data of annual extreme rainfall (1970–2015) and the trend analyses in rainfall regimes in Turkey. Trends in 5, 10, 15, and 30 min, 1, 2, 6, 12, and 24 hr of extreme rainfall in seven different rainfall regimes are estimated through nonparametric tests. The trends in return levels (2, 20, and 100 years) are defined by an appropriate three‐parameter generalized extreme value distribution and are evaluated in the climatological context of rainfall regimes. Overall, from 5‐min to 2‐hr durations, magnitudes of trends in extreme rainfall constantly increase in all rainfall regimes, which may be attributed to the intensified contribution of convective rainfall as a response to warming. Trend analysis of return levels reveals that, compared with 2‐year return levels, low‐probability high‐impact extreme rainfall events generally have the lowest estimated median trends until 30‐min to 2‐hr range. A shift in the magnitudes of trends occurs generally at 30‐min and 1‐hr durations; trends of rare intense events increase at the expense of less intense extreme rainfall. Thus, the intensification of 1–2‐hr extreme rainfall events can arise from both the increasing trends of more common events in shorter rainfall durations and from the increase in the trends of low‐probability high‐impact extreme rainfall events at this range. Moreover, explicitly in continental rainfall regimes, increases in the magnitudes of the trends in 30‐min to 2‐hr duration range are accompanied by various declines in 6–24‐hr duration range. Although coastal regimes generally have increasing trend values from 5‐min to 24‐hr durations, in northern and southern clusters, changes occur in the variability of extreme rainfall and the trend values of rarer (20‐ and 100‐year return periods) extreme rainfall exhibit increases in 6–12‐hr range.

LONG TERM CLIMATE CHANGE ASSESSMENT DURING 1907 - 2015 IN THE KUMAUN HIMALAYA REGION, UTTARAKHAND

Climate change is considered as a significant man-made global environmental challenge currently faced by the mankind today. The warming has a direct impact on the temperature sensitive snow and ice cover, geo-hazards and water supply. Objective of this study is assess the long term climate change assessment in the Kumaun Himalaya region. Monthly temperature and rainfall IMD data of years from 1907 to 2015 has been processed in the study. Monthly trend and statistical significance of rainfall and temperature were estimated using Mann-Kendall (MK) Test and the Sen’s Slope Estimator. The results show that monthly, annual and seasonal air temperature has increasing trend at significant level during the past 110 years, as against the global average of 0.5°C. The overall monthly and annual precipitation has decreasing trend at significant level in all weather station under study except Mukteshwar. Annual mean minimum temperature of the Kumaun Himalaya region showed a very significant increasing trend and has increased by 1.026°C per 110 years during the period 1907-2015. This warming is mainly contributed by the winter and post-monsoon seasons, which have shown increasing trend at significant level as compared to other seasons in the last century. The rainfall over the region showing significant decreasing trend in monthly rainfall over the time In monsoon season, rainfall is showing decreasing trend at significant level all over the Kumaun region but winter, pre monsoon and post monsoon season showing high degree of variability in trend and magnitude of rainfall.

Spatial-Temporal Analysis of Physio-Climatic changes in Tawang- Chu River Basin, Eastern Himalaya in India

The Tawang-Chu River Basin of the eastern Himalayas is a heterogenous region that is highly vulnerable to climate change. The changing temperature patterns in this mountain river basin play a vital role in changing the ecological system and inter-ecological nexus. Landsat satellite images were used to analyze spatio-temporal changes in Land Use and Land Cover (LULC) patterns in the basin. The changing trends of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were analyzed (at intervals of five years) using Landsat data. Changes in rainfall trends were analyzed using the Mann-Kendall test. The results show that fluctuations in annual temperature and increasing rainfall uncertainties are affecting LULC vegetation health.

Agroclimatic Zone-Based Analysis of Rainfall Variability and Trends in the Wabi Shebele River Basin, Ethiopia

The amount and annual distribution of rainfall caused a major socioeconomic and environmental problem where rainfed agriculture is predominant. This study assessed the long-term variability and trends of rainfall in the Wabi Shebele River Basin (WSRB), Ethiopia. The basin was discretized into 9 local agroclimatic zones (ACZ) based on annual rainfall and elevation. The coefficient of variation (CV) was used to check the variability of rainfall while modified Mann-Kendall (MK) and Innovative Trend Analysis (ITA) methods were used to detect rainfall trends. For each ACZ, stations with long-term records and less than 10% of missing data were selected for further analysis. The mean annual rainfall in the basin ranges from 227.2 mm to 1047.4 mm. The study revealed most of the ACZs showed a very high variation in Belg/Spring rainfall (CV% &gt; 30) than Kiremt/Summer and annual rainfall. Seasonal and annual rainfall trend analysis revealed that no uniform trend was detected in all ACZs. However, most of ACZs in the arid and semi-arid areas showed a non-significant decreasing trend in annual rainfall. From seasonal analysis, Belg and Kiremt rainfall showed relatively decreasing and increasing trends respectively. In comparison, a similar result was observed using MK and ITA methods.

Anomalous rainfall trends in the North-Western Indian Himalayan Region (NW-IHR)

Anomalous rainfall trends in the North-Western Indian Himalayan Region (NW-IHR)

Rainfall Spatial-Temporal Variability and Trends in the Thamirabharani River Basin, India: Implications for Agricultural Planning and Water Management

Rainfall is critical to agricultural and drinking water supply in the Thamirabharani river basin. The upper catchment areas of the Thamirabharani basin are located in high-elevated forest regions, and rainfall variability affects dam inflow and outflow. The well-known methods for rainfall analysis such as the coefficient of variation (CV), the precipitation concentration index (PCI), and trend analysis by Mann-Kendall and Sen’s slope test, as well as the Sen’s graphical innovative trend method (ITA) recently reported in several studies, were used. Rainfall data from gauge stations and the satellite-gridded Multisource Weighted Ensemble Precipitation (MSWEP) dataset were chosen for analysis at the annual and four-season time scales, namely, the Southwest Monsoon, Northeast Monsoon, winter, and summer seasons from 1991 to 2020. The mean annual PCI value reflects irregular monthly rainfall distribution (PCI &gt; 20) in all gauge stations. The spatial monthly rainfall distribution of PCI values remarkedly shows a moderate distribution in the western and an anomalous distribution in the eastern part of the basin. The annual mean rainfall ranges from 718.4 to 2268.6 mm/year, decreasing from the high altitude zone in the west to the low plains and coastal regions in the east. Seasonal rainfall contributes about 42% from the NEM, 30.6% from the SWM, 22.8% from summer, and 3.9% from winter, with moderate variability (CV less than 30%). Ground stations experienced extremely high interannual variability in rainfall (more than 60%). Trend analysis by the MK, TFPW-MK, and ITA methods shows increasing annual rainfall in the plains and coastal regions of the basin; particularly, more variations among the seasons were observed in the Lower Thamirabharani sub-basin. The NEM and summer season rainfall are statistically significant and contribute to the increasing trend in annual rainfall. The ITA method performed better in the annual and seasonal scale for detecting the rainfall trend than the MK and TFPW-MK test. The Lower Thamirabharani sub-basin in the eastern part of the basin receives more rain during the NEM than in other areas. To summarize, the low plains in the central and coastal regions in the southeast part experience an increase in rainfall with irregular monthly distribution. This study helps farmers, governments, and policymakers in effective agricultural crop planning and water management.

Annual, Seasonal, and Monthly Rainfall Trend Analysis through Non-Parametric Tests in the Sebou River Basin (SRB), Northern Morocco

This paper explores the temporal and spatial patterns of annual, seasonal, and monthly rainfall series during the period of 1961–2018 at 15 stations in the agriculturally important Sebou river basin, northern Morocco. Trends were investigated using the classical non-parametric Mann–Kendall test and the Theil–Sen approach at 90%, 95% and 99% confidence levels. A general decreasing trend was found at the annual scale, significant at the 95% confidence level at 8 stations out of 15 (53%). A particularly large decreasing trend between −30 mm and −50 mm per decade was found in the north and eastern parts of the basin. Autumn rainfall tended to increase, but this was not statistically significant. During the winter months, rainfall tended to decrease sharply (−27 mm and −40 mm per decade) in the northern slopes of the Rif mountains, while in spring, the mountainous area of the basin recorded decreases ranging between −12 mm and −16 mm per decade. During winter and spring, negative trends were significant at ten stations (66%). Summer rainfall tends toward a decrease, but the absolute change is small. These results help to understand the rainfall variability in the Sebou river basin and allow for improved mitigation strategies and water resource plans based on a prospective view of the impact of climate change on the river basin.

Sustained Water Storage in Horn of Africa Drylands Dominated by Seasonal Rainfall Extremes

AbstractRural communities in the Horn of Africa Drylands (HADs) are increasingly vulnerable to multi‐season droughts due to the strong dependence of livelihoods on seasonal rainfall. We analyzed multiple observational rainfall data sets for recent decadal trends in mean and extreme seasonal rainfall, as well as satellite‐derived terrestrial water storage and soil moisture trends arising from two key rainfall seasons across various subregions of HAD. We show that, despite decreases in total March‐April‐May rainfall, total water storage in the HAD has increased. This trend correlates strongly with seasonal totals and especially with extreme rainfall in the two dominant HAD rainy seasons between 2003 and 2016. We further show that high‐intensity October‐November‐December rainfall associated with positive Indian Ocean Dipole events lead to the largest seasonal increases in water storage that persist over multiple years. These findings suggest that developing groundwater resources in HAD could offset or mitigate the impacts of increasingly common droughts.