The United Arab Emirates (UAE) is located in a region characterized by high temperatures and very low precipitation [1
]. Thus, the freshwater resource of the country, which is mainly available as groundwater, is very limited, but the water demand continues to soar due to the improvement in the living standard, population increase, and economic growth. The water shortage is exacerbated by excessive withdrawal for municipal and agricultural use. Rainfall is very scarce with an annual average of 110 mm and sporadic spatial distribution [2
]. The extremely scant surface water resources are too unreliable to be considered in water resources planning and management, because of the high rate of evaporation and prolonged drought conditions [3
]. To meet the increasing water demand of these sectors, the UAE deploys several conventional and non-conventional sources of water within its water supply management system. However, strikingly low availability of natural water resources has encouraged the UAE to meet its requirements through desalination plants, which account for 22% of the water produced in the UAE [4
]. Although current water demands are adequately satisfied with the available water resources, the UAE is set to face challenges in the future owing to the depletion of natural water sources, population growth, increasing urbanization, and the impacts of global warming [5
The stress on water resources of the UAE is increasing as the gap between demand and resource amount diverges. Hence, there is a great need to update and optimize the water resource management strategies that are currently in place with the most recent and reliable data. Understanding the short- and long-term trends of the precipitation by employing high-resolution data in both spatial and temporal domains can provide invaluable information for regulating and managing agricultural and municipal water use. The goal of updating water resource management strategies is to protect the groundwater aquifers from being over-pumped to an irreversible state and to mitigate aquifer salinity. In-land aquifers located in Al Ain and Al Dhaid cities are depleting rapidly and coastal aquifers are experiencing seawater intrusion attributed to the oil industry and agricultural activities [6
]. Sherif [3
] states that seawater intrusion is the most critical issue to the freshwater aquifers and is highly related directly to pumping, especially for coastal aquifers. The decline of water resources in the UAE is captured by the Gravity Recovery And Climate Experiment (GRACE), twin satellites used to detect a change in groundwater quantity by examining the change in gravity [7
]. Other advantages of data-informed water resource management include improvement of the water quality, enhancement of the overall health of aquifer systems, and water conservation [6
]. Moreover, Ahmed [9
] reported that agricultural activities caused a negative impact on water resources in the UAE, and the sector of agriculture needs significant improvement. Implementation of methods that minimize water consumption such as using advanced irrigation technologies, construction of groundwater-recharge dams, and growing salt-tolerant crops will need accurate hydrometeorological data.
Furthermore, studies have shown that there is a strong link between different ecosystem variables and precipitation trends. Stefanidis [10
] showed that the soil erosion decreased significantly as the precipitation decreased by 15% and temperature increased by 5% over two decades over mountainous catchment in central Greece. Zhang [11
] employed satellite-based evapotranspiration (ET) estimates and precipitation to assess the regional water balance over the pan-Arctic basin and Alaska. They reported that the ET exhibited positive trends over most of the region; however, areas (32%) occupied by boreal forests showed negative ET trends.
In general, there are fewer hydrometeorological studies conducted over arid and semi-arid regions than over other regions of the world due to the scarce amount of rainfall and the very limited distribution of rain gauges. A number of studies examined the rainfall trends in the UAE using rain gauge data (e.g., Ouarda [12
], Merabtene [13
], and Donat [14
]). Ouarda [12
] found that the rainfall time series data of four rain gauges in the UAE showed a significant downward shift in 1999 with increasing trends before and after the shift. This observation was also supported by Merabtene [13
] who found a significant breakpoint in the time-series of another rain gauge in Sharjah in 1998. These studies also found that the amount of average annual rainfall in the early 21st century was much lower than the average annual rainfall of the final decades of the 20th century in all the stations. This reduction was attributed to the significant drop in winter rainfall. These studies highlighted a need for reevaluating the current status of the water resources and the urge for developing an integrated framework for water resources planning. From a regional perspective, the spatial distribution of the trend of extreme precipitation events indicates that the eastern Middle East and North Africa (MENA) region is projected to have a drier climate whereas the western region is expected to experience wetter conditions [14
]. Most of the studies conducted over the MENA region indicated that precipitation was decreasing in most of the rain gauges during the 20th century. Sixty-seven percent of the 145 rain gauges studied by Modarres [15
] showed a decreasing trend in annual precipitation even though only 19% showed a significant negative trend. Törnros [16
] found that only 14% of the 37 stations displayed a statistically significant negative trend in the southeastern Mediterranean region, and Kwarteng’s [17
] analysis of data from 31 stations revealed a decrease in rainfall over Oman but no significant negative trend. Another study conducted in the mountainous range of Central Pindus (Greece) indicated that the annual precipitation showed a negative trend on average in nine rain gauges over the last half-century [18
]. Analysis of the spatial and temporal distribution of 559 stations spanning from 1917 to 2006 in southern Italy revealed a significant reduction of precipitation in the winter months while showing a relatively smaller rate of increase in summer months [19
]. These and other studies indicate that the water resources in the region are declining.
In the last two decades, remotely sensed precipitation products have played a vital role as reliable input for weather forecasts and hydrologic models. The products helped to improve the outcome and accuracy of the models because of their spatial and temporal coverage and resolution. Moreover, the quality of remotely sensed products has been improving over time [1
]. Their temporal and spatial resolutions have become finer and have emerged as competitors to the conventional methods i.e., rain gauges. Currently, there are two main techniques of remotely sensed precipitation estimation, namely weather radar measurements and satellite estimates [20
]. Satellite-based products have an advantage over radar data as they are not susceptible to obstruction caused by topography or other physical barriers, and due to their global coverage, uniform spatial distribution can be achieved (not range-dependent). Hydrologic application of these products includes flood forecasting and mitigation, designing hydraulic structures, emergency response systems (during extreme events), and many other applications [21
]. Sorooshian [22
] emphasized the importance of precipitation remote-sensing products and predicted that the resolutions of 4 km and 30 min with reasonable accuracy could be attained in the near future. All of the validation studies conducted on satellite-based precipitation lauded their potential. However, they all agreed that the accuracy of the precipitation products was not consistent for different regions and climatic conditions.
The Integrated Multi-Satellite Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), one of the most highly cited satellite-based precipitation products, was identified by many researchers as one of the most accurate data products. Li [23
] concluded that the IMERG product performed best after examining five satellite-based precipitation products over mainland China using a gridded, ground-based precipitation product compiled from 2400 rain gauges as a ground reference. A similar study over the same region (China) by Tang [24
] found that the latest IMERG product was outperforming all other eight products assessed in the study. The climate prediction center Morphing technique (CMORPH), another highly cited precipitation product, was found to be the second-best product. Alsumaiti [25
] concluded that IMERG had a slightly higher correlation with ground records of 71 stations in the UAE than the CMORPH. In a few other studies, the conclusion was the opposite, citing that CMORPH was outperforming IMERG, especially the latest version of CMORPH. Li [20
] reported that the correlation between a rain gauge network and CMORPH was the highest amongst the four satellite-based products (CMORPH, PERSIANN, IMERG, and TRMM Multi-satellite Precipitation Analysis (TMPA)) over the Yangtze River in China. Duan [26
] also noted that CMORPH was the best product over the Adige Basin in Italy. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), another widely used satellite-based precipitation product, tended to overestimate the rain events in arid and semi-arid regions in some studies [27
]. IMERG products apparently show a significant improvement in detecting and capturing the storms over time as noted by Asong [29
] in Southern Canada; Sungmin [30
] in Southeastern Austria; Khodadoust Siuki [31
] in Iran; and Wang [32
] in South China. This advancement is attributed mainly to the improvement of the algorithms, the finer resolution, and the integration of ground measurements.
Arid and semi-arid regions lack comprehensive high-resolution spatial and temporal analysis of precipitation. The advancement in remote-sensing-based precipitation products and the accumulation of long enough records trigger the question of how useful the products are in time-series analysis. The main objectives of this research are to (1) investigate the long-term (2003–2019) trends of precipitation over the UAE; (2) assess the ability of satellite-based precipitation products to detect trends compared to ground observation; (3) explore the spatial distribution of precipitation frequency using satellite-based precipitation products; and (4) evaluate the seasonal variability of the precipitation quantity and frequency over the UAE. The products used in this research are the latest versions of the GPM-IMERG, CMORPH, and PERSIANN-Cloud Classification System (PERSIANN-CCS). Statistical analysis of single-time change-point detection is carried out using Pettitt’s change-point detection test. The existence of trends and their significance are also investigated using the Correlated Seasonal Mann–Kendall Trend Test. Lastly, Theil–Sen’s slope test is employed to estimate the magnitude of the trends in the precipitation data.
This study examines the long-term precipitation trends over the UAE using three of the most highly cited satellite-based precipitation products. We analyzed 17 years (2003 to 2019) of data from the IMERG, CMORPH, and PERSIANN-CCS products and compared them with rain gauge data observed at 18 stations. The analysis included an assessment of the performance of satellite-based precipitation products in comparison to ground observations. The use of high-resolution satellite precipitation products revealed information on the spatial distribution of the precipitation trends and frequency at multiple temporal resolutions. The results show that the areal average annual precipitation of the UAE is significantly lower in the early 21st century than that of the late 20th century, even though it shows an increasing trend by all the products including rain gauges over the study period (2003–2019). The spatial distribution of the annual precipitation suggests that the coastal regions receive significantly higher precipitation relative to the inland as reported by all the products.
The rainfall frequency analysis based on hourly precipitation data shows that the UAE received an average of 120 wet hours annually according to the IMERG product. CMORPH and PERSIANN estimated much lower numbers of wet hours (42 and 51, respectively). The seasonal distribution of the rainfall frequency indicates that IMERG products overestimate the occurrence of rainfall in the spring and winter seasons. In general, the PERSIANN product was not able to capture the spatial variability of the rainfall frequency over the country for all seasons. In terms of capturing the precipitation frequency with its spatial and seasonal variability, the CMORPH product seems to match the rain gauge observations slightly better than the other satellite products.
Pettitt’s change-point test indicated that the majority of the country did not experience a significant change-point in their rainfall time-series throughout the study period (2003–2019). Only two rain gauge stations (out of 18) and less than 15% of the country, according to the satellite-based products, demonstrated a change point. The Correlated Seasonal Mann–Kendall trend test indicates positive trends in six rain gauge stations and negative trends in two stations (out of 18 stations), all of which are located in the wetter Eastern part of the UAE.
Overall, the IMERG product showed good agreement with the rain gauge data in describing the monthly trends. However, IMERG tends to overestimate light precipitation and, as a result, over-detects the occurrence of rainfall in the country (higher false positives), especially in the spring and winter seasons. On the other hand, CMORPH seems to reasonably capture the rainfall frequency but fails in the trend analysis. Lastly, PERSIANN failed to capture the spatial variability of the rainfall amount and frequency across the country.
In conclusion, satellite products have great potential for improving the spatial aspect of rainfall frequency analysis. Moreover, thanks to their very fine temporal resolution, they can complement rain gauge data to develop rainfall intensity–duration–frequency (IDF) curves in very dry regions, where an installation of dense rain gauge networks is not feasible. This can be done in the near future as more satellite data are collected and the records become long enough for in-depth statistical analysis. The results also show that satellite precipitations products can be very useful for several water resources planning and management applications in water-stressed countries. However, more research is needed to verify whether the apparent overestimation and over-detection of light rainfall by satellite products are real or related to the well-known rainfall under-catchment by rain gauges in dry and warm conditions. More future research is needed to understand the uncertainty of satellite precipitation products and their interaction with the uncertainty of rain gauge observations used in calibration and validation. Future research can also evaluate the potential application of semi-real time satellite products in hydrometeorological prediction as well as water resources planning and management.