Rainfall Observations Research Articles

Rainfall spatio-temporal correlation and intermittency structure from micro-γ to meso-β scale in the Netherlands

AbstractWe investigate the spatio-temporal structure of rainfall at spatial scales from 7m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatio-temporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.

Assessment of GPM IMERG Satellite Precipitation Estimation under Complex Climatic and Topographic Conditions

Satellite precipitation estimation provides crucial information for those places lacking rainfall observations from ground–based sensors, especially in terrestrial or marine areas with complex climatic or topographic conditions. This is the case over much of Western China, including Upper and Middle Lancang River Basin (UMLRB), an extremely important transnational river system in Asia (the Lancang–Mekong River Basin) with complex climate and topography that has limited long–term precipitation records and high–elevation data, and no operational weather radars. In this study, we evaluated three GPM IMERG satellite precipitation estimation (IMERG E, IMERG L and IMERG F) over UMLRB in terms of multi–year average precipitation distribution, amplitude consistency, occurrence consistency, and elevation–dependence in both dry and wet seasons. Results demonstrated that monsoon and solid precipitation mainly affected amplitude consistency of precipitation, aerosol affected occurrence consistency of precipitation, and topography and wind–induced errors affected elevation dependence. The amplitude and occurrence consistency of precipitation were best in wet seasons in the Climate Transition Zone and worst in dry seasons in the same zone. Regardless of the elevation–dependence of amplitude or occurrence in dry and wet seasons, the dry season in the Alpine Canyon Area was most positively dependent and most significant. More significant elevation–dependence was correlated with worse IMERG performance. The Local Weighted Regression (LOWERG) model showed a nonlinear relationship between precipitation and elevation in both seasons. The amplitude consistency and occurrence consistency of both seasons worsened with increasing precipitation intensity and was worst for extreme precipitation cases. IMERG F had great potential for application to hydroclimatic research and water resources assessment in the study area. Further research should assess how the dependence of IMERG’s spatial performance on climate and topography could guide improvements in global precipitation assessment algorithms and the study of mountain landslides, floods, and other natural disasters during the monsoon period.

Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan

Pakistan is a flood-prone country and almost every year, it is hit by floods of varying magnitudes. This study was conducted to generate a flash flood map using analytical hierarchy process (AHP) and frequency ratio (FR) models in the ArcGIS 10.6 environment. Eight flash-flood-causing physical parameters were considered for this study. Five parameters were based on the digital elevation model (DEM), Advanced Land Observation Satellite (ALOS), and Sentinel-2 satellite, including distance from the river and drainage density slope, elevation, and land cover, respectively. Two other parameters were geology and soil, consisting of different rock and soil formations, respectively, where both layers were classified based on their resistance against water percolation. One parameter was rainfall. Rainfall observation data obtained from five meteorological stations exist close to the Chitral District, Pakistan. According to its significant importance in the occurrence of a flash flood, each criterion was allotted an estimated weight with the help of AHP and FR. In the end, all the parameters were integrated using weighted overlay analysis in which the influence value of the drainage density was given the highest value. This gave the output in terms of five flood risk zones: very high risk, high risk, moderate risk, low risk, and very low risk. According to the results, 1168 km2, that is, 8% of the total area, showed a very high risk of flood occurrence. Reshun, Mastuj, Booni, Colony, and some other villages were identified as high-risk zones of the study area, which have been drastically damaged many times by flash floods. This study is pioneering in its field and provides policy guidelines for risk managers, emergency and disaster response services, urban and infrastructure planners, hydrologists, and climate scientists.

Using satellite rainfall products to assess the triggering conditions for hydro-morphological processes in different geomorphological settings in China

Hydro-morphological processes (HMP, i.e. all processes contained within the spectrum defined between debris flows and flash floods) initiate in response to intense rainfall events. Efficient HMP hazard assessment over large regions is often hindered because of limited rainfall observations over mountainous areas. Real-time and easily accessible satellite rainfall products offer new opportunities to address the observational coverage problem in ungauged catchments. In this work, two satellite rainfall products, Global Precipitation Measurement (GPM) and Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), are investigated by taking the ground measurements as a reference. Based on a close agreement between GPM and rain gauge data, daily rainfall together with a series of antecedent rainfall values were calculated to investigate the spatio-temporal distribution of HMP occurred from 2001 to 2015 across the whole Chinese territory. Ultimately, rainfall thresholds for HMP initiation within different geomorphological settings in China were obtained by: (1) building antecedent rainfall sequences consisting of HMP and non-HMP via 100 nonparametric bootstrapped replicates; (2) testing several percentiles of the rainfall distribution as thresholds for HMP occurrence; (3) optimizing rainfall thresholds for six geomorphological macro-regions in China. This study confirmed the ability of satellite data in defining the rainfall conditions for the triggering of HMP, acknowledging the potential underestimation and/or bias that characterize any satellite rainfall products. Our findings provides new insight on rainfall conditions responsible for HMP initiation at the Chinese national scale.

Identification of a Representative Stationary Period for Rainfall Variability Description in the Sudano-Sahelian Zone of West Africa during the 1901–2018 Period

Many studies have been undertaken on climate variability in West Africa since the drastic drought of 1970s. These studies rely in many cases on different baseline periods chosen with regard to the reference periods defined by the World Meteorological Organization. A method is developed in this study to determine a stationary baseline period for rainfall variability analysis. The method is based on an application of three statistic tests (on deviation and trend) and a test of shifts detection in rainfall time series. The application of this method on six different gridded rainfall data and observations from 1901 to 2018 shows that the 1917–1946 period is the longest stationary period. An assessment of the significance of the difference between the mean annual rainfall amount during this baseline period and the annual rainfall amount during the other years shows that the “Normal” annual rainfall amount is defined by an interval delineated by ±the standard deviation (STD). With regard to this interval, a very wet/dry year is defined with a surplus/gap over/below the STD. Overall the 1901–2018 period, the 1950–1970 period presents the most important number of significant wet years and the 1971–1990 period presents the most important number of significant dry years.

Curve Number Estimation for Ungauged Watershed in Semi-Arid Region

The Benanain Watershed is located in East Nusa Tenggara with an area of 3,181 km2 and is divided into 29 sub-watersheds. The East Nusa Tenggara itself is an eastern region of Indonesia with a unique climate condition called semi-arid. The high rainfall intensity occurring in short duration results in large surface runoff and erosion. Floods and erosion in semi-arid areas due to sensitive soils to drought and heavy rainfall extremely. This paper presents the application of the Soil Conservation Services-Curve Number (SCS-CN) real-flood flows through a digital map of soil type, land use, topography, and the heterogeneity of physical condition, especially for ungauged watersheds. The method used is an approach empirical to estimate runoff from the relationship between rainfall, land use, and soil hydrology groups. This watershed has a large area that must analyze every sub-watershed. The land-use of the Benanain watershed is secondary dryland forest by 44.26% and the hydrological soil group on the B group classification with medium to high absorption potential by 46.502% from the total area. The curve number value of the Benanain Watershed ranges from 56.54 to 73.90, where the mean CN value of 65.32. The rainfall (mm) for the 29 sub-watersheds in the Benanain Watershed has decreased by about 74.65% when being surface runoff or only 25.35% of water becomes surface runoff. The relationship between rainfall depth and CN is classified as standard response and trend line (flat slope) equilibrium occurs when rainfall depth value of 56.71 mm and CN is close to 66.30. The high variability of intense rainfall between the rainy season and the dry season had a significant influence on the curve number value in a large watershed area. Further analysis will be more accurate if it is supported by long rainfall data and observation runoff data as a control. Doi: 10.28991/cej-2021-03091711 Full Text: PDF

Application of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite data for drought mitigation in Bintan island, Indonesia

The water resources of Bintan Island is limited and dependent on rainfall variability. The exposed location of Bintan small island make this island particularly vulnerable to natural disaster expecially drought. The meteorological drought related information is required for the water resources management of Bintan island. This article provides calibration of CHIRPS data. The ground-based rainfall observation data is used for CHIRPS data calibration. The Drought analysis used CHIRPS rainfall data, and ground-based observation data was carried out using the Standardized Precipitation Index (SPI). The results showed that the data set performed well in assessing drought years (1982, 1997, and 2015). The statistical Z test results showed that the CHIRPS data and ground-based observation data were not showing significantly different values. This study concludes that CHIRPS data is valuable for drought monitoring tools in Bintan island, where the ground-based observation rainfall data is very limited.

Assessment of newly-developed high resolution reanalyses (IMDAA, NGFS and ERA5) against rainfall observations for Indian region

In this study, a comprehensive assessment has been provided for the performance of three newly-developed high resolution reanalysis (IMDAA, NGFS, ERA5) datasets w.r.t. IMD rainfall observation over six homogeneous monsoon regions of India during 1999–2018. It is necessary to understand how these reanalyses rainfall datasets are performing spatio-temporally, specifically during monsoon in order to apply them in further research and developmental purposes. Focus has been given to assess their relative performances during monsoonal rainfall extremes against IMD observations. Results reveal that all these three reanalysis datasets captured the spatial patterns of observed rainfall climatology reasonably well in all the seasons, except for hilly regions where there is over-estimation against the observed patterns. Moreover, wet biases over the entire foothills of Himalayas extending up to north-east are more dominant during monsoon for IMDAA reanalysis, whereas bias is mixed type and region-specific in all seasons for NGFS and ERA5 reanalysis. Both IMDAA and NGFS reanalysis captured the probability distribution of observed daily rainfall intensity diligently especially in the range of extremes while ERA5 reanalysis underestimated the same. However, spatial distribution for rainfall extremes (based on intensity and 95th percentile) reveals that all the reanalyses have failed to capture patterns in the hilly topography as well as over peninsular Indian region. In comparison to IMDAA and ERA5 reanalysis, NGFS captured reasonably well for heavy and very heavy rainfall over Western Ghats. ERA5 reanalysis always underestimated all the extreme rainfall categories in comparison to IMDAA and NGFS reanalysis which is also confirmed from probability distribution curve. Moreover, bi-decadal (pre- and post-2008) spatial trend distribution for 95th percentile rainfall extremes indicate a reversal in trend (wetter to drier and vice versa) over India. Both IMDAA and NGFS have captured the moist and desiccated trend region-specifically along with ERA5 reanalysis against observational trends. Thus, the result provides useful insights for the climate modeling to establish appropriate standards for performing climate model evaluation over Indian region.

Impacts of Urban Canopy on Two Convective Storms With Contrasting Synoptic Conditions Over Nanjing, China

AbstractDiverse urban‐induced rainfall anomalies highlight the need for improved understanding on extreme rainfall in cities. In this study, we examine urban modification of rainfall over Nanjing, China. Our results are based on climatological analyses of hourly rainfall observations and high‐resolution Weather Research and Forecasting model simulations coupled with different urban physics schemes. A gridded dataset of urban canopy parameters (UCPs) was developed to better characterize the geometrical features of downtown Nanjing. Two convective storms were investigated to shed light on the impacts of urban canopy on spatial and temporal rainfall variabilities for storms with contrasting synoptic conditions. We show that the model simulations coupled with the multi‐layer urban physics scheme and the gridded UCPs can noticeably reduce biases in surface thermal and dynamic fields, but its performance in rainfall patterns varies with storm events. There is a strong convergence zone over the urban‐rural interface induced by building complexes, leading to intensified convection for the storm with strong synoptic conditions but bifurcated moisture fluxes for the storm with weak synoptic conditions. Lagrangian analysis of storm elements further illustrates the role of urban canopy in deflecting storm cells approaching the city for the storm under weak synoptic conditions. The magnitudes of positive rainfall anomalies induced by urban canopy range from 60% to 100% of the storm‐total rainfall. Our study highlights the necessity of improved characterization of urban canopy and the perturbed atmospheric boundary layer processes in examining urban convective rainfall.

The Peranan Pergerakan Air Dalam-Tanah dalam Menurunkan Aliran Permukaan

The main cause of flooding and erosion that creates critical land in various regions in Indonesia is surface runoff. The surface runoff will occur if rainfall exceeds the capacity of soil water absorption (infiltration capacity). The soil management of each land use could improve the infiltration capacity. The water movement both on the surface and in the soil determines the water infiltration. This study predicts surface runoff based on the infiltration rate of various land uses and rainfall in the Ciliwung Watershed. A series of studies were performed in the upstream and middle areas of the watershed. Observations of soil properties, water movement, and rainfall were carried out in various dry land uses. The results showed that the soil's physical properties mostly determine the constant infiltration rate, which affected the water movement in the ground. The initial water content, the degree of saturation of the initial moisture content, and the soil's physical properties determine the time of constant infiltration rate. The value of constant rate infiltration and the time of its achievement define the amount of surface runoff that occurs.
 Keywords: hydraulic conductivity, run off, soil management, time achievement of infiltration capacity

Benefits from high-density rain gauge observations for hydrological response analysis in a small alpine catchment

Abstract. Spatial rainfall patterns exert a key control on the catchment-scale hydrologic response. Despite recent advances in radar-based rainfall sensing, rainfall observation remains a challenge, particularly in mountain environments. This paper analyzes the importance of high-density rainfall observations for a 13.4 km2 catchment located in the Swiss Alps, where rainfall events were monitored during 3 summer months using a network of 12 low-cost, drop-counting rain gauges. We developed a data-based analysis framework to assess the importance of high-density rainfall observations to help predict the hydrological response. The framework involves the definition of spatial rainfall distribution metrics based on hydrological and geomorphological considerations and a regression analysis of how these metrics explain the hydrologic response in terms of runoff coefficient and lag time. The gained insights on dominant predictors are then used to investigate the optimal rain gauge network density for predicting the streamflow response metrics, including an extensive test of the effect of down-sampled rain gauge networks and an event-based rainfall–runoff model to evaluate the resulting optimal rain gauge network configuration. The analysis unravels that, besides rainfall amount and intensity, the rainfall distance from the outlet along the stream network is a key spatial rainfall metric. This result calls for more detailed observations of stream network expansions and the parameterization of along-stream processes in rainfall–runoff models. In addition, despite the small spatial scale of this case study, the results show that an accurate representation of the rainfall field (with at least three rain gauges) is of prime importance for capturing the key characteristics of the hydrologic response in terms of generated runoff volumes and delay for the studied catchment (0.22 rain gauges per square kilometer). The potential of the developed rainfall monitoring and analysis framework for rainfall–runoff analysis in small catchments remains to be fully unraveled in future studies, potentially also including urban catchments.

Advances in Urban Stormwater Management in Japan: A Review

The series of annual flood disasters that struck Japan in recent years pose challenges to urban stormwater management. Japan has been implementing nation-wide hydrometeorological observation through a dense network of rain gauges. Since the recent decade, ground radars have been deployed to observe heavy rainfall with high spatiotemporal resolution as a countermeasure. While commercial software is popular in designing stormwater drainage systems, several integrated urban flood models have been developed domestically and are applicable in stormwater management. A paradigm shift with the rise of Internet of Things (IoT) provides an inexperienced opportunity in hydrological observation, and has been implemented for monitoring sewer network conditions. Despite this broad scope of research works and technological innovations, such advancement is not internationally recognized yet. The present study aims to review the development and role of science and technology in stormwater management in Japan, focusing specifically on rainfall observation, integrated urban flood modelling, and emerging technologies for stormwater monitoring. In addition, the possible future direction of stormwater management is envisioned. Considering the series of record-breaking rainfall events that struck Japan, we will have to face more severe challenges in urban flood management alongside the impact of global climate change. As compared to structural measures, which are subject to budgetary constraints, the relative importance of non-structural measures is increasing; therefore, effective application of numerical modeling techniques is required. A common weakness of the urban flood modeling framework is the limited availability of observations in sewer networks, which can be relaxed by emerging IoT based observations. The fusion of IoT based observations with an integrated urban flood modeling technique appears to the emerging technology for stormwater management.

Evaluation of Real-Time Water Level Prediction Technology Using Statistical Models for Reducing Urban Flood Risk

The frequency of localized short-term torrential rains that exceed the planned rainfall is increasing along with inundation damage due to inland flooding. Stepwise inundation measures utilizing existing stock and disaster prevention/mitigation for excessive rainfall are required. In this study, we describe the results of empirical research using a statistical model constructed based on rainfall and water level observation data as a highly accurate water level prediction method suitable for real-time prediction. This is aimed at application in flood control activities and operation support of pump facilities. By comparing and verifying the prediction accuracy between the water level prediction model and the statistical model by Convolutional Neural Network (CNN), which is generally used as an image recognition technology, the usefulness of the statistical model was confirmed. Further improvement in accuracy and widespread use of these water level prediction models are expected.

Uncertainty in different precipitation products in the case of two atmospheric river events

One of the World Climate Research Programme Grand Challenges is to evaluate whether existing observations are enough to underpin the assessment of weather and climate extremes. In this study, we focus on extreme associated with atmospheric rivers (ARs). ARs are characterized by intense moisture transport usually from the tropics to the extra-tropics. They can either be beneficial, providing critical water supply, or hazardous, when excessive precipitation accumulation leads to floods. Here, we examine the uncertainty in gridded precipitation products included in the Frequent Rainfall Observations on GridS (FROGS) database during two atmospheric river events in distinct Mediterranean climates: one in California, USA, and another in Portugal. FROGS is composed of gridded daily-precipitation products on a common 1° × 1° grid to facilitate intercomparison and assessment exercises. The database includes satellite, ground-based and reanalysis (RE) products. Results show that the precipitation products based on satellite data, individually or combined with other products, perform least well in capturing daily precipitation totals over land during both cases studied here. The RE and the gauge-based products show the best agreement with local ground stations. As expected, there is an overall underestimation of precipitation by the different products. For the Portuguese AR, the multi-product ensembles reveal mean absolute percentage errors between −25% and −60%. For the western US case, the range is from −60% to −100%.

Demonstration of Stormwater Management Technology by Short-Term Rainfall Prediction and Real-Time Runoff Analysis System Using Small X-Band Radar

We developed a system that combines urban area rainfall radar (small X-band, dual-polarization radar), short-term rainfall prediction model, and real-time runoff analysis technology, and the demonstration study was conducted on the drainage districts in Fukui City and Toyama City. We demonstrated the effectiveness of the flood damage, by providing the real-time information on rainfall prediction, water level in sewerage pipes, and inland flood prediction to the operators of drainage pump of stormwater storage pipes, and residents in flood-prone areas. During the study for about two years, it was confirmed that the accuracy of the radar rainfall observation was comparable to that of the X-band dual-polarization Doppler weather radar managed by the Ministry of Land, Infrastructure, Transport and Tourism. In the operation of the drainage pump for the Tsukimiminori Stormwater Storage Pipe in Fukui City, we were able to secure the storage capacity for the next rainfall based on the forecast information by maximizing the drainage capacity of the discharge destination. In addition, it was also confirmed that the residents themselves could secure the lead time for setting up water-stop sandbags and moving their vehicles to higher ground.

The heavy precipitation event of 14–15 October 2018 in the Aude catchment: a meteorological study based on operational numerical weather prediction systems and standard and personal observations

Abstract. The case of the heavy precipitation event on 14 and 15 October 2018 which has led to severe flash flooding in the Aude watershed in south-western France is studied from a meteorological point of view using deterministic and probabilistic numerical weather prediction systems, as well as a unique combination of observations from both standard and personal weather stations. This case features typical characteristics of Mediterranean heavy precipitation events such as its classic synoptic situation and its quasi-stationary convective precipitation that regenerates continuously, as well as some peculiarities such as the presence of a former hurricane and a pre-existing cold air mass close to the ground. Mediterranean Sea surface temperature and soil moisture anomalies are briefly reviewed, as they are known to play a role in this type of hydrometeorological events. A study of rainfall forecasts shows that the event had limited predictability, in particular given the small size of the watersheds involved. It is shown that the stationarity of precipitation, whose estimation benefits from data from personal stations, is linked to the presence near the ground of a trough and a strong potential virtual temperature gradient, the stationarity of both of which is highlighted by a combination of observations from standard and personal stations. The forecast that comes closest to the rainfall observations contains the warmest, wettest, and fastest low-level jet and also simulates near the ground a trough and a marked boundary between cold air in the west and warm air in the east, both of which are stationary.

Predictive Performance Analysis of PDF – IDF Model Types Using Rainfall Observations from Fourteen Gauged Stations

The design of structures for flood mitigation depends on the adequate estimation of rainfall intensity over a given catchment which is achieved by the rainfall intensity duration frequency modelling. In this study, an extensive comparative analyses were carried out on the predictive performance of three PDF – IDF model types, namely: Gumbel Extreme Value Type 1 (GEVT – 1), Log-Pearson Type 3 (LPT – 3) and Normal Distribution (ND) in 14 selected cities in Southern Nigeria. This is to rank the order of best performance. The principle of general model development was adopted in which rainfall intensities at different durations and specified return periods were used as input data set. This is not same as return period specific model that involves rainfall intensities for various durations and a given return period. The predicted rainfall intensity values with the PDF – IDF model types indicate high goodness of fit (R2) and Mean Squared Errors (MSE) ranging from: (a) R2 = 0.875 – 0.992; MSE = 33.17 – 224.6 for GEVT – 1; (b) R2 = 0.849 – 0.990; MSE = 65.34 – 405.5 for LPT – 3 and (c) R2 = 0.839 – 0.992; MSE = 29.23 – 200.2 for ND. The comparative analysis of all the 42 general models (14 locations versus 3 model types) considered showed that the order of best performance is LPT – 3 1st, GEVT - 1 2nd and ND 3rd for each return period (10, 50 and 100 years). The Kruskal Wallis test of significance indicates that no significant difference exists in the predictive performance of the three General models across the board. This may be due to the fact that the fourteen locations of the study area are bordering with the Atlantic Ocean and seems to have similar climatology. These developed General models are recommended for the computation of intensities in the fourteen locations for the design of flood control structures; and the order of preference should be LPT – 3 > GEVT – 1 > ND.

Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

To develop a progressive multimetric configuration optimisation method for WRF simulations of extreme rainfall events over Egypt

The Weather Research and Forecasting (WRF) model can help to improve our understanding of analysing and forecasting hydrometeorological disasters. Especially for some regions like the Nile Delta, which faces growing climate hazards but has inadequate in situ rainfall observations. However, identifying an optimal configuration to run the WRF model is often a challenge. In this study, the WRF model was used to simulate extreme rainfall events at high spatial and temporal resolutions centered around Alexandria, in northern Egypt. In particular, a progressive multimetric configuration optimisation (PMCO) method is proposed to identify the possible optimal configurations of WRF in the aspect of domain size, numbers of vertical levels, nesting ratio, spin-up times, and physical parameterization schemes (microphysics, planetary boundary layer, and cumulus), based on 48 specifically designed experiments. The simulation performances are quantified and sorted by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). All WRF simulations use the ERA5 reanalysis dataset as boundary conditions and the WRF results are verified against the Integrated Multi-satellitE Retrievals for GPM (IMERG). The results show that the rainfall distribution and magnitude are most sensitive to the spin-up time and physical parameterization schemes. It is also observed that the improvement of WRF's reproducibility of rainfall intensity is usually accompanied by a decrease in the reproducibility of rainfall distribution. The best model configuration for the study area comprises of three-level nesting (D01 80x80; D02 112x112; D03 88X88), 58 vertical levels, 1:3:3 downscaling ratio, 48h spin-up time, WRF Single-Moment 6-class microphysics scheme, Mellor-Yamada-Janjic planetary boundary layer scheme, and Grell-Freitas cumulus. The stability of this configuration is also verified with the other three extreme rainfall events over Egypt. The results show that there exists a common WRF configuration set in Egypt that produces the relatively good simulations for extreme rainfall events.

An Enhanced Innovative Triangular Trend Analysis of Rainfall Based on a Spectral Approach

The world is currently witnessing high rainfall variability at the spatiotemporal level. In this paper, data from three representative rain gauges in northern Algeria, from 1920 to 2011, at an annual scale, were used to assess a relatively new hybrid method, which combines the innovative triangular trend analysis (ITTA) with the orthogonal discrete wavelet transform (DWT) for partial trend identification. The analysis revealed that the period from 1950 to 1975 transported the wettest periods, followed by a long-term dry period beginning in 1973. The analysis also revealed a rainfall increase during the latter decade. The combined method (ITTA–DWT) showed a good efficiency for extreme rainfall event detection. In addition, the analysis indicated the inter- to multiannual phenomena that explained the short to medium processes that dominated the high rainfall variability, masking the partial trend components existing in the rainfall time series and making the identification of such trends a challenging task. The results indicate that the approaches—combining ITTA and selected input combination models resulting from the DWT—are auspicious compared to those found using the original rainfall observations. This analysis revealed that the ITTA–DWT method outperformed the ITTA method for partial trend identification, which proved DWT’s efficiency as a coupling method.