Spatial Rainfall Patterns Research Articles

Revealing the relation between spatial patterns of rainfall return levels and landslide density

Abstract. It is known that the spatial rainfall pattern can mark landslide distribution across the landscape during extreme triggering events. However, the current knowledge of rainfall controls on this distribution remains limited. Here, to reveal what rainfall characteristics control landslide spatial distribution, we explore the spatiotemporal pattern of a rainfall event that triggered over 7500 landslides (area ≈ 100–104 m2) at a regional scale with an area of ≈400 km2 in Japan. Using a 5 km resolution radar-driven and gauge-adjusted hourly precipitation dataset with 32 years of records, we compared rainfall return levels for various time ranges from 1 to 72 h and landslide density in each grid cell of the precipitation dataset (≈25 km2). The results show that, even if local slope distributions within the grid cells are comparable, the number of landslides in a ≈25 km2 grid cell was substantially high when rainfall return levels exceeded the 100-year return period in all examined timespans (i.e., 1–72 h). In contrast, when only specific-duration rainfall intensities (e.g., 6–48 h) exceeded the 100-year return level, the landslide density in corresponding grid cells tended to be low. Consequently, the landslide density increased with the increase in rainfall return levels of various timespans rather than a specific rainfall intensity, such as downpours for a few hours or long-term cumulative rainfall for several days. Moreover, with the increase in the landslide density, the number of relatively large landslides exceeding ≈400 m2 increased. Therefore, the spatial differences in rainfall return levels potentially constrain the density of total landsliding and relatively large landslides. In this sense, whether rainfall intensities reach high return levels rarely experienced in a wide timespan ranging from a few hours to several days is one of the key determinants of the spatial distribution of landslides and the extent of related hazards.

Spatial Interpolations of Annual Rainfall in Ethiopia Using Simple and Universal Kriging Techniques

The study of spatial distribution of precipitation is an important aspect of water-related research like, hydrological modeling, disaster prediction and watershed management. But, spatial interpolations of annual average rainfall in Ethiopia using Simple and Universal Kriging type through different models has not yet investigated. This study was focused on to select the best fit semivarogram model for annual rainfall interpolation and to map the spatial distribution of rainfall of Ethiopia. In this study, long term mean annual rainfall which was collected from 931 meteorological stations were interpolated using different mathematical variogram models of Kriging to select the best fit variogram model useful to predict continuous surface values of annual average rainfall in Ethiopia. Spherical model was found to be the best fitted variogram model and most accurate model for interpolation using both Simple and Universal Kriging with the value of ME (0.005), RMSE (1.21) and MSDR (121.32) and ME (0.001), RMSE (0.88) and MSDR (144.11), respectively. Since the kriging results of spherical model indicate good model efficacy for this study, it may be useful to map spatial pattern of annual rainfall in Basin level in Ethiopia.

Analysis of Climate Variability in the Central District of Taraba State, North-East, Nigeria

Climatic maps have been very difficult to generate in Nigeria, not because of lack of knowledge or expertise, but due to dearth of climatic data. Most of the existing climatic maps are either not reliable or too generalized since Nigerian Meteorological Agency (NIMET) climatic data which is the only reliable climatic data are available only in the state capitals and which were often used to generate climatic maps. In this study, DivaGIS, WorldClim, NASA Power Project and globalweather climatic data of rainfall, temperature, relative humidity and solar radiation were used to generate the spatial patterns, trends and seasonal pattern of each of the aforementioned climatic elements in Taraba State Central District; comprising Bali, Gassol, Gashaka, Gembu, Kurmi and Sardauna LGAs. The results were presented in charts and using the bar chart and the kringing tools of Microsoft Excel package and ArcGIS 10.5 software respectively. Spatial pattern of rainfall of the area revealed that the rainfall pattern are influenced by relief and latitudes as Sardauna which is on the plateau in the South has more rains than Gassol in the North. Mountainous and highland areas were also found to have higher rainfall than the immediate environment. Trends in rainfall was decreasing in Bali, Gashaka and Sardauna LGAs. Rainfall is high from June to September when minimum of 200 mm monthly rainfall are received in all the five LGAs. The spatial pattern of temperature is inversely related to that of rainfall, while trends in temperature is increasing in all the LGAs. Relative humidity has similar spatial/seasonal patterns with that of rainfall, but showing decreasing trends in all the LGAs except Kurmi LGA. The spatial pattern of solar radiation was found to be affected by the aspects of the area as Kurmi LGA at the western side recorded highest amount of solar radiation. The pattern of solar radiation in the study area is seasonal because solar radiation is low (less than 20 mj/m2) in all the LGAs except Kurmi during the rainy season (June – October). It was recommended that ground station climatic data which covers larger areas than in-situ climatic data that are only available in State capitals should be encouraged among the climate analysts to alleviate the problems associated with dearth in in-situ climatic data.

Spatial downscaling of precipitation for hydrological modelling: Assessing a simple method and its application under climate change in Britain

AbstractNational or regional grid‐based hydrological models are usually run at relatively fine spatial resolutions. But the meteorological data necessary to drive such models are often coarser resolution, so some form of spatial downscaling is generally required. A 1 km hydrological model for Great Britain is used to test the performance of a simple method of downscaling precipitation based on 1 km patterns of long‐term mean annual rainfall (Standard Average Annual Rainfall; SAAR). For a range of coarser resolutions (5, 10, 25 and 50 km), a 1 km grid of multiplicative scaling factors is derived as the ratio of the 1 km grid box SAAR divided by the mean SAAR of the coarser resolution grid box that contains it. A dataset of 1 km daily observation‐based precipitation is then degraded to the coarser resolutions, and application of SAAR scaling factors is compared to no downscaling and direct use of 1 km data, for simulating river flows for a large set of catchments. SAAR‐based downscaling provides a clear improvement over no downscaling. Using monthly rather than annual long‐term mean rainfall patterns provides minimal further improvement. There are no strong relationships between performance and catchment properties, but performance using 50 km precipitation without downscaling tends to be worse for smaller, steeper catchments and those with a more south‐westerly aspect; these benefit more from SAAR‐based downscaling. An assessment using high‐resolution convection‐permitting model data shows relatively small changes in derived SAAR scaling factors between a baseline and far‐future period, suggesting that use of historical scaling factors for future periods is reasonable. Applicability of this simple downscaling method for other parts of the world should be similarly assessed, for both historical and future periods. While use of annual patterns seems to be sufficient in Britain, areas where spatial rainfall patterns are more variable through the year may require use of sub‐annual patterns.

Spatial and temporal pattern of deficient Indian summer monsoon rainfall (ISMR): impact on Kharif (summer monsoon) food grain production in India.

Despite a significant increasing trend in historical food grain production (FGP) in India, deficient Indian summer monsoon rainfall (ISMR) often causes a reduction in FGP. The present study was carried out to understand temporal and spatial variations in deficient rainfall (drought) and their impact on national and regional FGP of India. Long-term (1901-2020) percentage departure in rainfall and drought areas over the country showed nonsignificant and significant trends, respectively. Subdivisional rainfall showed significant decreasing and increasing trends in 4 and 5 subdivisions, respectively. Drought years of high frequency (once in 3-4years) and 4 to 5 consecutive drought years (once in 120years) occurred in northwest and western subdivisions of India. Departure in de-trended production of All India Kharif food grains from its normal (DDP) showed significant quadratic relationship with departure in ISMR from its normal (DRF). Besides the quadratic equation, another multiple regression model taking de-trended crop area, DRF, and drought area as predictor variables was developed for predicting DDP. Both these models, with high R2 (0.8-0.88) between observed and predicted data and low RMSE (2.6-2.7%), can be employed for advanced estimation of DDP of the country and for taking country-level policy decisions by the Indian Government. For the first time, models were formulated to estimate state-wise departure in FGP (DP). In these models, novel indices viz., (i) rainfall departure and irrigation index (RDII) and (ii) physical and socio-economic index (PSEI), were used as predictor variables. These models, with R2 (0.71-0.75) and RMSE of 11.8-14.2(< SD of observed data), hold promise for advance estimation of production loss in states, useful for regional-level planning by the Government of India, and testing them in other countries.

Impacts of tropical cyclones on summertime short-duration precipitation extremes over the middle-lower reaches of the Yangtze River valley

This study investigated variations in summertime short-duration precipitation extremes over the middle-lower reaches of the Yangtze River valley (MLYRV) and the possible impacts of tropical cyclones (TC) on these changes over the 1961–2016 period. Three short-duration precipitation types, including all events and the events in the 75th and 95th percentiles, are defined. At the 75th percentile, both the amount and number of TC-induced extreme short-duration rainfall events exhibit slightly declining trends, and the trends are not pronounced for the 95th-percentile cases. The number and amount of TC-induced 95th-percentile events are approximately one order of magnitude larger than those of the 75th-percentile cases, indicating that TCs play a more significant role on the upper tail of the rainfall distribution over the MLYRV in summer than on the rest of the precipitation distribution. A large number of the defined non-TC precipitation events are mainly distributed over the central-south MLYRV and southeastern regions. The spatial patterns of TC-induced rainfall resemble the contributions of TC-induced precipitation events to the total summer rainfall, with large values located in southeastern regions of the MLYRV. The spatial distribution of 95th-percentile events is principally attributed to the frequency of TC activities and to the TC tracks. Most of the TCs that trigger 95th-percentile events make landfall over the southeast MLYRV, and the corresponding large frequency of TC tracks is mainly situated over the ocean to the east of Taiwan. Both environmental factors and atmospheric circulations modulate the variations in extreme short-duration rainfall events. In particular, equatorward displacement of the western Pacific subtropical high impacts TC track density, resulting in more occurrences of precipitation extremes in 95th-percentile events over the MLYRV.

A comparison study on the role of urbanization in altering the short-duration and long-duration intense rainfall

Urbanization has significantly changed the regional hydrological cycle and energy balance. However, the different roles of urbanization on intense rainfall with short and long persistence need a better understanding, particularly in coastal mega cities with complex terrains. In this study, we compared the spatial and diurnal characteristics of intense rainfall across two coastal cities with different degrees of urbanization with 70 meteorological stations. The effects of anthropogenic and geographical factors on short-duration intense rainfall (SDIR) and long-duration intense rainfall (LDIR) were investigated using the statistical method. SDIR and LDIR events show different spatial patterns that SDIR events center in the highly urbanized regions while LDIR events center in the coastal mountainous regions. In terms of diurnal features, it is found that the higher occurrence frequency of SDIR in the dense urban region than that in suburbs occurs over the period with strong urban heat island intensity (UHII). It indicated that thermal contrast between urban and suburbs breeds an atmospheric environment for inspiriting the convection and SDIR events. The urban-induced increase in SDIR events depended on urbanization stages, with only dense urban regions showing a significant influence. The results of geographical detector model (GDM) also demonstrated that the synergy of build-up area and population explained 48 %–51 % of spatial heterogeneity of SDIR. Nevertheless, urbanization has little effect in modifying the diurnal features of LDIR events, while it might influence spatial rainfall patterns by enhancing rainfall peaks. The GDM results indicated that terrain positively dominates the spatial distribution for LDIR events, and the interactions of multi-factors (terrain, urbanization, or distance to the coast et al.) have enhanced explanatory power (q values up to 0.70). The results provide a fundamental understanding for the effects of anthropogenic and geographical factors on different types of rainfall events in coastal mega cities.

Spatial patterns of typhoon rainfall and associated flood characteristics over a mountainous watershed of a tropical island

The spatial distribution of rainfall in a watershed has an important impact on flood warning and forecasting. In this study, we used the K-means++ clustering method to classify the spatial characteristics of typhoon rainfall events in the Songtao reservoir watershed in the central region of Hainan Island, based on 63 typhoon rainfall event data from 1990 to 2020. Then, the relationship between the synoptic fields, spatial patterns of rainfall, and flood characteristics was explained based on tropical cyclone best-track datasets, atmospheric reanalysis data, and inflow flood series. The results showed that there are three types of cumulative rainfall patterns in the Songtao Reservoir watershed. The heavy rainfall region of pattern 1 is concentrated east of the watershed, whereas the heavy rainfall regions of pattern 2 are mainly concentrated southwest and east of the watersheds of many mountains. The heavy rainfall region of pattern 3, with a southwest-northeast orientation, is located on the west side of the watershed. The spatial coefficient of variation and index of rainfall location clearly distinguished among these three patterns. The composite analysis of typhoon tracks, wind fields, and moisture flux fields can be used to interpret the formation mechanism of these spatial patterns of rainfall events in the watershed. The spatial patterns of cumulative rainfall for each type were dominated by one or two types of hourly spatial rainfall patterns. Flood events resulting from cumulative rainfall patterns 1 and 2 have similar flood behavior metrics. Large floods in the watershed show an obvious seasonal variation, and the corresponding rainfall events mostly show the spatial characteristics of the cumulative rainfall pattern 3. These results can enhance the understanding of the linkage between typhoons, mountainous watershed rainfall, and flooding processes.

PROYEKSI PERGESERAN TIPE IKLIM OLDEMAN DI WILAYAH KALIMANTAN SELATAN PADA AKHIR ABAD 21 BERDASARKAN SIMULASI CORDEX-SEA

Climate change has the potential to alter the spatial pattern of rainfall, which is the primary variable in climate classification. The Oldeman method is one of climate classification techniques focused on agricultural management. This study's objective is to evaluate the spatial changes of the Oldeman climatic type in South Kalimantan, Indonesia assificationt of climate change. The climate in the late 21st century is simulated using data from one of the CORDEX-SEA project's products using the RCP4.5 and RCP8.5 scenarios. The shift in climate type was assesed based on the difference of the total area covered by the Oldeman climate classification during the projection period (2071-2095) and that covered during the reference period (1981-2005). The simulation data was corrected first using the linear scaling method to reduce the bias. The skill of model in reproducing Oldeman climate type was evaluated against the surface observation data from 35 sites using the percent of correct (PoC) score method. We found that the bias correction procedure successfully reduced the bias, as evidenced by a 22% rise in the correlation value of monthly rainfall and a -79% reduction in RMSE. By the end of the 21st century, both under RCP4.5 and RCP8.5 scenarios, the South Kalimantan climate is projected to be dryer, characterized by a decrease in the area covered by wet climate types (type B) and an increase in the area covered by dry and extremely dry climate types (type D and E). We discovered that the RCP8.5 scenario could result in a more tremendous shift in climate type than the RCP4.5 scenario. This study demonstrates that climate change has the potential to result in a shift in the climate classification that must be considered in agriculture policymaking.

Eddy transport, wave–mean flow interaction, and Eddy forcing during the 2013 Uttarakhand extreme event in the reanalysis and S2S retrospective forecast data

AbstractIn this study, to explore the wave–mean interaction during the monsoon season, we investigate (a) the potential role of transient eddy forcing and the wave–mean interaction on the monsoon weather during the June 2013 heavy rainfall event over the Himalayan regions (especially the Uttarakhand State of India and the nearby regions) and (b) how they are captured in a set of operational models. Some studies have pointed out how prolonged breaks can occur due to extratropical trough incursions. However, there is a lack of clarity on how transient eddy forcing associated with such interactions can lead to modulation of monsoonal circulation or whether such interaction can lead to heavy rainfall events. E‐vector fields are analysed to quantify the eddy forcing from extratropical transient eddies and the feedback mechanism between transient eddies and the mean flow during June 2013. Analysis reveals that along with local factors (orography, moisture convergence), the large‐scale heavy rainfall event over the Uttarakhand region during June 16–17, 2013 was influenced by eddy forcing due to the intrusion of extratropical Rossby waves over the Indian region. The location of eddy affects the location of regional occurrence of the eddy‐mean interaction. Model hindcast analysis results suggest that operational models cannot forecast the upper‐level eddy forced circulation patterns, and the improper representation of the E‐vector divergence field leads to the underestimation of intensity and the spatial pattern of rainfall.

ENSO effect on hydroclimate changes in southeastern China over the past two millennia

Meteorological observations indicate that both natural and anthropogenic forcing contribute to regional drought/flood in the East Asian summer monsoon (EASM) domain. However, spatiotemporal rainfall pattern and its dynamics during natural climatic variability remains unclear. Here we reconstruct a ∼3 year-resolution EASM precipitation record over the past two millennia, based on 13 230Th dates and 600 δ18O in a stalagmite from Songya Cave, southeastern China. The δ18O sequence shows a long-term decreasing trend, indicating an increasing monsoon precipitation over the past two millennia. A series of centennial-scale fluctuations are superimposed on the long-term trend, with a wetter Little Ice Age than the Medieval Warm Period. The long-term trend and centennial-scale oscillations in EASM rainfall are broadly related to El Niño-Southern Oscillation (ENSO) and Pacific decadal oscillation (PDO) variations, with an increased (decreased) EASM rainfall corresponding to El Niño-like (La Niña-like) conditions and positive (negative) phase of PDO. Comparison of δ18O records from Songya and Wanxiang Cave shows an anti-phased spatial rainfall pattern between southeastern and northwestern China. This spatiotemporal rainfall pattern, consistent with the modern observations, is possibly regulated by the ENSO, through changes in the location and strength of the Western Pacific Subtropical High (WPSH).

The Diurnal Cycle of Precipitation over Lake Titicaca Basin Based on CMORPH

This paper examines the diurnal cycle of precipitation (DCP) over Lake Titicaca basin (LTb) during the summertime months based on the high spatial–temporal resolution (8 × 8 km2 and hourly) estimates of the Climate Prediction Center Morphing technique (CMORPH). This analysis was carried out using observations from rain gauges (RgSENAMHI) as a reference for the period 2002 to 2013. The accuracy of the CMORPH product was tested with graphical comparisons and several statistical metrics, such as correlation coefficient, bias, and root mean square error. Spatial maps of these metrics and of the diurnal cycle were developed to assess the spatial dependency in the CMORPH accuracy over the LTb. On average, 43% of the total RgSENAMHI variation was explained by the CMORPH. The correlation between the CMORPH and RgSENAMHI amounts was positive over the southeastern and northern LTb and negative in the central and southern LTb. An underestimation bias was observed over most of the LTb areas, and an overestimation bias was observed at some stations (e.g., Lagunillas, Isla Suana, and Desaguadero stations). The total bias decreased when approaching the lake attaining its minimum value over the mountains consistent with previous studies. Overall, the CMORPH was able to capture the spatial patterns of rainfall over the LTb. Over the surrounding lake area, the plateau, and high mountain areas, precipitation peaks were in the late afternoon, while over low areas, such as the valleys and Lake Titicaca, it peaked around midnight to early morning. This result suggests that the DCP is closely related to the local circulation resulting from a response due to solar radiation and the complex orography. On the other hand, the high resolution CMORPH technique can depict finer regional details, such as the less coherent phase pattern over a few regions.

The Skills of Medium-Range Precipitation Forecasts in the Senegal River Basin

Reliable information on medium-range (1–15 day) precipitation forecasts is useful in reservoir operation, among many other applications. Such forecasts are increasingly becoming available from global models. The skills of medium-range precipitation forecasts derived from Global Forecast System (GFS) are assessed in the Senegal River Basin, focusing on the watershed its major hydropower dams: Manantali (located in relatively wet, Southern Sudan climate and mountainous region), Foum Gleita (relatively dry, Sahel climate and low-elevation), and Diama (a large watershed covering almost the entire basin, dominated by Sahel climate). IMERG Final, a satellite product involving rain gauge data for bias correction, is used as reference. GFS has the ability capture the overall spatial and monthly pattern of rainfall in the region. However, GFS tends to overestimate rainfall in the wet parts of the region, and slightly underestimate in the dry part. The skill of daily GFS forecast is low over Manantali (Kling-Gupta Efficiency, KGE of 0.29), but slightly higher over Foum Gleita (KGE of 0.53) and Diama (KGE of 0.59). For 15-day accumulation, GFS forecast shows higher skill over Manantali (KGE of 0.60) and Diama (KGE of 0.79) but does not change much over Foul Gleita (KGE of 0.51) compared to daily rainfall forecasts. IMERG Early, a satellite-only product available at near-real time, has better performance than GFS. This study suggests the need for further improving the accuracy of GFS forecasts, and identifies IMERG Early as a potential source of data that can help in this effort.

Relationship between Precipitation Characteristics at Different Scales and Drought/Flood during the Past 40 Years in Longchuan River, Southwestern China

In this study, the temporal and spatial patterns of rainfall in the Longchuan River basin from 1977 to 2017 were analyzed, to assess the feature of precipitation. Based on the daily precipitation time series, the Lorenz curve, precipitation concentration index (PCI), precipitation concentration degree (PCD), and the precipitation concentration period (PCP) were used to evaluate the precipitation distribution characteristics. The PCI, PCD and PCP in five categories, defined by the fixed thresholds, were proposed to investigate the concentrations, and the average values indicated the higher concentrations in the higher intensities. The indices showed strong irregularity of daily and monthly precipitation distributions in this basin. The decrease in the PCD revealed an increase in the proportion of precipitation in the dry season. The rainy days of slight precipitation in the upper and lower basins with significant downward trends (−13.13 d/10 a, −7.78 d/10 a) led to longer dry spells and an increase in the risk of drought, even severe in the lower area. In the upper basin, the increase in rainfall erosivity was supported by the upward trend in the PCIw of heavy precipitation and the simple daily intensity index (SDII) of extreme precipitation. Moreover, the PCP of light precipitation, moderate precipitation, and heavy precipitation concentrated earlier at the end of July. The results of this study can provide beneficial reference information to water resource planning, reservoir operation, and agricultural production in the basin.

Global Assessment of the Capability of Satellite Precipitation Products to Retrieve Landslide-Triggering Extreme Rainfall Events

Abstract Rainfall-induced landsliding is a global and systemic hazard that is likely to increase with the projections of increased frequency of extreme precipitation with current climate change. However, our ability to understand and mitigate landslide risk is strongly limited by the availability of relevant rainfall measurements in many landslide prone areas. In the last decade, global satellite multisensor precipitation products (SMPP) have been proposed as a solution, but very few studies have assessed their ability to adequately characterize rainfall events triggering landsliding. Here, we address this issue by testing the rainfall pattern retrieved by two SMPPs (IMERG and GSMaP) and one hybrid product [Multi-Source Weighted-Ensemble Precipitation (MSWEP)] against a large, global database of 20 comprehensive landslide inventories associated with well-identified storm events. We found that, after converting total rainfall amounts to an anomaly relative to the 10-yr return rainfall R*, the three products do retrieve the largest anomaly (of the last 20 years) during the major landslide event for many cases. However, the degree of spatial collocation of R* and landsliding varies from case to case and across products, and we often retrieved R* &gt; 1 in years without reported landsliding. In addition, the few (four) landslide events caused by short and localized storms are most often undetected. We also show that, in at least five cases, the SMPP’s spatial pattern of rainfall anomaly matches landsliding less well than does ground-based radar rainfall pattern or lightning maps, underlining the limited accuracy of the SMPPs. We conclude on some potential avenues to improve SMPPs’ retrieval and their relation to landsliding. Significance Statement Rainfall-induced landsliding is a global hazard that is expected to increase as a result of anthropogenic climate change. Our ability to understand and mitigate this hazard is strongly limited by the lack of rainfall measurements in mountainous areas. Here, we perform the first global assessment of the potential of three high-resolution precipitation datasets, derived from satellite observations, to capture the rainfall characteristics of 20 storms that led to widespread landsliding. We find that, accounting for past extreme rainfall statistics (i.e., the rainfall returning every 10 years), most storms causing landslides are retrieved by the datasets. However, the shortest storms (i.e., ∼3 h) are often undetected, and the detailed spatial pattern of extreme rainfall often appears to be distorted. Our work opens new ways to study global landslide hazard but also warns against overinterpreting rainfall derived from satellites.

A complex network approach to study the extreme precipitation patterns in a river basin.

The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes.

Developing a Formula to Estimate Precipitation at Ungauged Location and Analysis of Rainfall Pattern: Case Study for Rastra Bank Chowk, Pokhara, Nepal

Pokhara receives the second highest amount of rainfall in the country, according to Department of Hydrology and Meteorology. The rainfall distribution within this small region is random. The precipitation in Pokhara is measured from limited rain gauge stations. For understanding the spatial variation, it is necessary to access the daily rainfall data at the gauged as well as ungauged stations. In view of this problem, this study is aimed at developing the formula to estimate the rainfall value at an ungauged station, Rastrabank Chowk Pokhara using the calibration and validation method. For this purpose, the daily rainfall data of the sample station was measured using standard rain gauge for a period of four months from May 13, 2020 to September 15, 2020. The analysis of the spatial distribution and variation of daily rainfall at meteorological stations as well as sample station was done to select the data for the development of the formula. Calibration and validation of formula was performed for equal period using the statistical indices NSE, R2 and PBIAS. Calibration was performed using rainfall data of available meteorological stations of Pokhara in the first two consecutive months. From the numerous hit and trial procedures, the equation developed with rainfall data of Airport and Malepatan stations comprising of three constant values were obtained with the highest NSE and R2. PBIAS value was then calculated for maximum NSE and R2. In the next phase, validation of the equation for other two months (14 July- 15 September,2020) was performed to obtain maximum possible value of NSE and R2 and a smaller value of PBIAS. The equation was obtained with 0.980 NSE, 0.980 R2 and 6.437 % PBIAS value which indicated good agreement between observed and predicted rainfall value. Spatial rainfall pattern analysis using SURFER model demonstrated that there was a homogenous distribution of rainfall in Malepatan, Airport and sample stations. The temporal trend showed that the month July of 2020 had a higher amount of precipitation at Pokhara valley. From the HYSPLIT model, it was observed that the rainfall events in Pokhara valley was due to westerlies dominance. This study would provide the rainfall estimation techniques and hence would significantly contribute to meteorological investigations and water resource planning.

Detection of Spatial Rainfall Variation over the Andean Region Demonstrated by Satellite-Based Observations

Topography has an important role in shaping regional and global climate systems, as it acts as a mechanical barrier to the low-level moisture flow. Thus, a complex spatial pattern of rainfall can exist over the mountainous region. Moreover, it is critical to advance our understanding of the relationship between rainfall and topography in terms of rainfall timing, frequency, and magnitude. In this study, characteristics of austral summer (December–February) precipitation are analyzed using 17-year (1998–2014) high-spatial-resolution (0.05° × 0.05°) data obtained from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) over the Andean region focusing on topographic impact. We observe an interaction between precipitation patterns and topography, with clear precipitation–elevation relationships in the Andes regions. The rainfall maxima zone was observed over the higher terrain of the central and southern Andes, and the zone is attributed to frequency and intensity of rainfall, respectively. In the foothills of the central Andes, we find there was a persistent rain system when a moist, low-level flow was lifted due to topography. In contrast, steep mountain slopes and a relatively dry atmosphere modulate deep convection in the foothills of southern Andes.

U-FLOOD – Topographic deep learning for predicting urban pluvial flood water depth

This study investigates how deep-learning can be configured to optimise the prediction of 2D maximum water depth maps in urban pluvial flood events. A neural network model is trained to exploit patterns in hyetographs as well as in topographical data, with the specific aim of enabling fast predictions of flood depths for observed rain events and spatial locations that have not been included in the training dataset. A neural network architecture that is widely used for image segmentation (U-NET) is adapted for this purpose. Key novelties are a systematic investigation of which spatial inputs should be provided to the deep learning model, which hyper-parametrization optimizes predictive performance, and a systematic evaluation of prediction performance for locations and rain events that were not considered in training. We find that a spatial input dataset of only 5 variables that describe local terrain shape and imperviousness is optimal to generate predictions of water depth. Neural network architectures with between 97,000 and 260,000,000 parameters are tested, and a model with 28,000,000 parameters is found optimal. U-FLOOD is demonstrated to yield similar predictive performance as existing screening approaches, even though the assessment is performed for natural rain events and in locations unknown to the network, and flood predictions are generated within seconds. Improvements can likely be obtained by ensuring a balanced representation of temporal and spatial rainfall patterns in the training dataset, further improved spatial input datasets, and by linking U-FLOOD to dynamic sewer system models.

Improved Rainfall Data in the Philippines through Concurrent Use of GPM IMERG and Ground-Based Measurements

The availability of accurate and reliable rainfall data that are applicable to various phenomenological, climatological, and modeling studies is important, especially in the Philippines, which is considered to be highly vulnerable to natural hazards and a changing climate. The presented strategy involved constructing a dataset consisting of synoptic data, automatic rain gauge (ARG) measurements, and satellite data that are co-registered, consistent, and formatted in the same manner. Although sparse in number, the synoptic stations provide the most accurate rainfall information and were used as the baseline for creating the dataset. The ARGs that are within a distance of 1 km to the synoptic stations were used to determine the correction factors needed to make the synoptic and ARG data consistent. Subsequently, the corrected ARGs were used to make the satellite IMERG data consistent with both ARG and synoptic data. In case of the latter, only IMERG pixels with at least 10 ARGs within the relatively large footprint of the satellite sensor were used in estimating the required correction parameters derived from a combination of a power transform and linear regression correction techniques. The final results show good agreement of synoptic and corrected ARG data with correlation coefficients of 0.94 and 0.97 for the 10 day and monthly data, respectively, and improvement in the linear regression slope from 0.67 to 0.90 for 10 day data, and 0.70 to 0.94 for monthly data. In addition, the corrected ARG data agree well with the corrected IMERG data, with correlation coefficients of 0.88 and 0.93 for the 10 day and monthly data, respectively, and an improvement in slope from 0.66 to 0.87 for 10 day data, and 0.74 to 0.99 for monthly data. The merit of using a combined dataset is illustrated through comparative analyses of the IMERG data and spatially interpolated synoptic and ARG data. The results show general agreements in spatial patterns of rainfall across the datasets, especially in areas where in situ measurements are recorded. The observed discrepancy when ground data is limited emphasizes the need for satellite IMERG data to obtain the true spatial patterns of rainfall distribution.