Spatial Distribution Of Rainfall Research Articles

Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone

Abstract. Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how the spatial and temporal distribution of rainfall and snowmelt affects discharge in rain–snow transition zones. These zones experience large year-to-year variations in precipitation phase, cover a significant area of mountain catchments globally, and might extend to higher elevations under future climate change. We used observations from 11 weather stations and snow depths measured from one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semiarid, 1.8 km2 headwater catchment. We focused on surface water input (SWI; the summation of rainfall and snowmelt on the soil) for 4 years with contrasting climatological conditions (wet, dry, rainy, and snowy) and compared simulated SWI to measured discharge. A strong spatial agreement between snow depth from the lidar survey and model (r2 = 0.88) was observed, with a median Nash–Sutcliffe efficiency (NSE) of 0.65 for simulated and measured snow depths at snow depth stations for all modeled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the 4 years, with north-facing slopes producing 1.09–1.25 times more SWI than south-facing slopes, and snowdrifts producing up to 6 times more SWI than the catchment average. Annual discharge in the catchment was not significantly correlated with the fraction of precipitation falling as snow; instead, it was correlated with the magnitude of precipitation and spring snow and rain. Stream cessation depended on total and spring precipitation, as well as on the melt-out date of the snowdrifts. These results highlight the importance of the heterogeneity of SWI at the rain–snow transition zone for streamflow generation and cessation, and emphasize the need for spatially distributed modeling or monitoring of both snowpack and rainfall dynamics.

Using CHIRPS Satellite-Based Data for Spatio-Temporal Variability of Rainfall in Dashti Hawler District – Kurdistan Region of Iraq 1982 – 2019

Abstract Rainfall is one of the most essential climatic indicators of climate change, The changes in rainfall in space and time would impact runoff, soil moisture, and groundwater reserves. The study of the climate change effects on water resource planning and management necessitates the analysis of precipitation changes (Khayyat, et. al, p. 35, 2019; M. Nawaz, et. al., 2020, p.2). The availability of long-term and temporally consistent rainfall time series is one of the most important criteria for undertaking a detailed study of regional and temporal variation and trends in rainfall in any location. Agriculture activities, flood disaster risk management and reduction, drought, water harvesting, water supply to communities, and other human activities related to the spatiotemporal distribution of rainfall in any region benefit from the analysis of long-term climate time-series data at a high temporal and spatial resolution. Water resource management, agricultural productivity, and climate change mitigation benefit from understanding the spatial distribution of rainfall and its temporal trends (Morales-Acuña, et al., 2021, p. 1). Rainfall data and measurements from traditional ground weather stations have traditionally been the major sources of such climate data; however, due to limited or nonexistent station networks in many regions of the world, historical records from station observations are insufficient. As a result, satellite-based rainfall data are increasingly being utilized to supplement or replace station observations.

Dual-Polarization Radar-Based Quantitative Precipitation Estimation of Mountain Terrain Using Multi-Disdrometer Data

The precipitation systems that pass over mountains develop rapidly due to the forcible ascent caused by the topography, and spatial rainfall distribution differences occur due to the local development of the system because of the topography. In order to reduce the damage caused by orographic rainfall, it is essential to provide rainfall field data with high spatial rainfall accuracy. In this study, the rainfall estimation relationship was calculated using drop size distribution data obtained from 10 Parsivel disdrometers that were installed along the long axis of Mt. Halla (oriented west–east; height: 1950 m; width: 78 km; length: 35 km) on Jeju Island, South Korea. An ensemble rainfall estimation relationship was obtained using the HSA (harmony search algorithm). Through the linear combination of the rainfall estimation relationships determined by the HSA, the weight values of each relationship for each rainfall intensity were optimized. The relationships considering KDP, such as R(KDP) and R(ZDR, KDP), had higher weight values at rain rates that were more than 10 mm h−1. Otherwise, the R(ZH) and R(ZH, ZDR) weights, not considering KDP, were predominant at rain rates weaker than 5 mm h−1. The ensemble rainfall estimation method was more accurate than the rainfall that was estimated through an independent relationship. To generate the rain field that reflected the differences in the rainfall distribution according to terrain altitude and location, the spatial correction value was calculated by comparing the rainfall obtained from the dual-polarization radar and AWS observations. The distribution of Mt. Halla’s rainfall correction values showed a sharp difference according to the changes in the topographical elevation. As a result, it was possible to calculate the optimal rain field for the orographic rainfall through the ensemble of rainfall relationships and the spatial rainfall correction process. Using the proposed methodology, it is possible to create a rain field that reflects the regional developmental characteristics of precipitation.

Impact of spatial distribution information of rainfall in runoff simulation using deep learning method

Abstract. Rainfall-runoff modeling is of great importance for flood forecast and water management. Hydrological modeling is the traditional and commonly used approach for rainfall-runoff modeling. In recent years, with the development of artificial intelligence technology, deep learning models, such as the long short-term memory (LSTM) model, are increasingly applied to rainfall-runoff modeling. However, current works do not consider the effect of rainfall spatial distribution information on the results. Focusing on 10 catchments from the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset, this study compared the performance of LSTM with different look-back windows (7, 15, 30, 180, 365 d) for future 1 d discharges and for future multi-day simulations (7, 15 d). Secondly, the differences between LSTMs as individual models trained independently in each catchment and LSTMs as regional models were also compared across 10 catchments. All models are driven by catchment mean rainfall data and spatially distributed rainfall data, respectively. The results demonstrate that regardless of whether LSTMs are trained independently in each catchment or trained as regional models, rainfall data with spatial information improves the performance of LSTMs compared to models driven by mean rainfall data. The LSTM as a regional model did not obtain better results than LSTM as an individual model in our study. However, we found that using spatially distributed rainfall data can reduce the difference between LSTM as a regional model and LSTM as an individual model. In summary, (a) adding information about the spatial distribution of the data is another way to improve the performance of LSTM where long-term rainfall records are absent, and (b) understanding and utilizing the spatial distribution information can help improve the performance of deep learning models in runoff simulations.

Event-based landslide susceptibility models in Shihmen watershed, Taiwan: accounting for the characteristics of rainfall events.

When performing a landslide susceptibility analysis, a model is usually established on the basis of a multi-temporal or event-triggered landslide inventory. Because multi-temporal landslide inventories for most areas are rarely available, an event-triggered landslide inventory is often used, but the result depends on the selection of single event. In order to establish a landslide susceptibility model with a good prediction performance, the present study tried to find out how to select a single event-triggered landslide inventory, and investigated the effect of various combinations of event inventories. We selected Shihmen reservoir watershed as the research area, conducted a logistic regression analysis to build 23 event-based landslide susceptibility models and one multi-year landslide susceptibility model, and estimated the performance of these models. In addition, this study further assessed the influence of event characteristics on the model prediction performance, used the above results to merge two different events, and then established models based on these combinations. The results indicated that when establishing an event-based landslide susceptibility model, selecting events with suitable rainfall return periods and landslide density can yield robust models with relatively high predictive ability. Furthermore, the combination of two events which negatively correlate with each other in rainfall spatial distributions can enhance a model's predictive ability and modeling efficiency.

Peak discharges per unit area increase with catchment area in a high-relief mountains with permeable sedimentary bedrock

To improve the accuracy of flood-flow prediction under changing climate conditions, understanding the storm-flow generation at various scales in a catchment is essential. Flood-flow prediction is conducted assuming that peak discharge per unit area (hereafter referred to as peak unit discharge) decreases with catchment area, based on observations. However, this assumption may not be applicable in steep high-relief mountain catchments with permeable bedrock, although this has not been confirmed due to the difficulty of flood-flow measurement in high-relief mountains. We obtained continuous discharge data for three nested catchments with areas of 0.58 km2, 2.2 km2 and 94 km2 in the high-relief Chichibu Mountains, which have permeable bedrock, and tested the hypothesis that peak unit discharge and direct runoff; flow caused by and directly following a rainfall and forms the major part of the flood hydrograph excluding base flow per unit area (hereafter referred to as unit direct runoff) were greater in larger catchments. The effect of the spatial distribution of rainfall on the spatial distribution of storm runoff was evaluated based on radar-rain gauge analyzed precipitation data after verification using ground-based rainfall data. We obtained data for 67 storms, with a depth of rainfall-event ranging from 26.5 to 231.5 mm and a return period ranging from 10-3 to 3.2 y. During significant floods, with peak unit discharge at largest catchment was greater than 3 mm/h; peak unit discharge and unit direct runoff were always larger at larger catchment. Differences in peak unit discharge and unit direct runoff among measured catchments were often several times and sometimes more than an order of magnitude, depended on the storm events. While, the catchment mean depth of rainfall-events was up to 1.26 times greater in the largest catchment, relative to the two smaller catchments, implying that spatial variations in storm rainfall have little effect on the differences in flood flow among catchments. Our study and previous research indicate that this increasing storm runoff per unit area with catchment area is characteristic of catchments with permeable bedrock, where some rainfall that infiltrates bedrock on hillslopes is transported downstream within the bedrock to create storm flow in larger catchments.

Hydrological response of a peri-urban catchment exploiting conventional and unconventional rainfall observations: the case study of Lambro Catchment

Abstract. Commercial microwave links (CMLs) can be used as opportunistic and unconventional rainfall sensors by converting the received signal level into path-averaged rainfall intensity. As the reliable reconstruction of the spatial distribution of rainfall is still a challenging issue in meteorology and hydrology, there is a widespread interest in integrating the precipitation estimates gathered by the ubiquitous CMLs with the conventional rainfall sensors, i.e. rain gauges (RGs) and weather radars. Here, we investigate the potential of a dense CML network for the estimation of river discharges via a semi-distributed hydrological model. The analysis is conducted in a peri-urban catchment, Lambro, located in northern Italy and covered by 50 links. A two-level comparison is made between CML- and RG-based outcomes, relying on 12 storm/flood events. First, rainfall data are spatially interpolated and assessed in a set of significant points of the catchment area. Rainfall depth values obtained from CMLs are definitively comparable with direct RG measurements, except for the spells of persistent light rain, probably due to the limited sensitivity of CMLs caused by the coarse quantization step of raw power data. Moreover, it is shown that, when changing the type of rainfall input, a new calibration of model parameters is required. In fact, after the recalibration of model parameters, CML-driven model performance is comparable with RG-driven performance, confirming that the exploitation of a CML network may be a great support to hydrological modelling in areas lacking a well-designed and dense traditional monitoring system.

Stochastic daily rainfall generation on tropical islands with complex topography

Abstract. Stochastic rainfall generators are probabilistic models of rainfall space–time behavior. During parameterization and calibration, they allow the identification and quantification of the main modes of rainfall variability. Hence, stochastic rainfall models can be regarded as probabilistic conceptual models of rainfall dynamics. As with most conceptual models in earth sciences, the performance of stochastic rainfall models strongly relies on their adequacy in representing the rain process at hand. On tropical islands with high elevation topography, orographic rain enhancement challenges most existing stochastic models because it creates localized precipitations with strong spatial gradients, which break down the stationarity of rain statistics. To allow for stochastic rainfall modeling on tropical islands, despite non-stationarity of rain statistics, we propose a new stochastic daily multi-site rainfall generator specifically for areas with significant orographic effects. Our model relies on a preliminary classification of daily rain patterns into rain types based on rainfall space and intensity statistics, and sheds new light on rainfall variability at the island scale. Within each rain type, the distribution of rainfall through the island is modeled by combining a non-parametric resampling of past analogs of a latent field describing the spatial distribution of rainfall, and a parametric gamma transform function describing rain intensity. When applied to the stochastic simulation of rainfall on the islands of O`ahu (Hawai`i, United States of America) and Tahiti (French Polynesia) in the tropical Pacific, the proposed model demonstrates good skills in jointly simulating site-specific and island-scale rain statistics. Hence, it provides a new tool for stochastic impact studies in tropical islands, in particular for watershed water resource management.

Atmospheric and oceanic conditions associated with early and late onset for Eastern Africa short rains

AbstractTiming of the rainy season is essential for a number of climate sensitive sectors over Eastern Africa. This is particularly true for the agricultural sector, where most activities depend on both the spatial and temporal distribution of rainfall throughout the season. Using a combination of observational and reanalysis datasets, the present study investigates the atmospheric and oceanic conditions associated with early and late onset for Eastern Africa short rains season (October–December). Our results indicate enhanced rainfall in October and November during years with early onset and rainfall deficit in years with late onset for the same months. Early onset years are found to be associated with warmer sea surface temperatures (SSTs) in the western Indian Ocean, and an enhanced moisture flux and anomalous low‐level flow into Eastern Africa from as early as the first dekad of September. The late onset years are characterized by cooler SSTs in the western Indian Ocean, anomalous westerly moisture flux and zonal flow limiting moisture supply to the region. The variability in onset date is separated into the interannual and decadal components, and the links with SSTs and low‐level circulation over the Indian Ocean basin are examined separately for both timescales. Significant correlations are found between the interannual variability of the onset and the Indian Ocean dipole mode index. On decadal timescales the onset is shown to be partly driven by the variability of the SSTs over the Indian Ocean. Understanding the influence of these potentially predictable SST and moisture patterns on onset variability has huge potential to improve forecasts of the East African short rains. Improved prediction of the variability of the rainy season onset has huge implications for improving key strategic decisions and preparedness action in many sectors, including agriculture.

More intense daily precipitation in CORDEX‐SEA regional climate models than their forcing global climate models over Southeast Asia

AbstractThe ability of regional climate models (RCMs) to accurately simulate the current climate is increasingly important for impact assessments over Southeast Asia (SEA), identified as one of the world's most vulnerable regions to climate change. In this study, we evaluate the performance of a set of regional high‐resolution simulations from the Coordinated Regional Climate Downscaling Experiment‐SEA (CORDEX‐SEA) in simulating rainfall over the region. Simulations of the 1982–2005 seasonal mean climatology of daily precipitation and precipitation distribution over land are compared to observations from different sources (i.e., in situ‐based and satellite‐based). We also evaluate to what extent the precipitation distribution in RCMs is closer to observations than their associated forcing global climate models (GCMs). Observational estimates of precipitation over SEA have large uncertainties, making the model evaluations complicated. Despite these difficulties, our results highlight that RCMs can reproduce some complexities in the spatial distribution of seasonal rainfall but generally have a larger wet bias than GCMs. This is particularly true for the extremes in which RCMs show a large overestimation of rainfall intensity. There are some precipitation quantiles and grid points in which RCMs show limited reductions in biases compared to observations, but there is no consistency across all simulations and RCMs are generally further away from observations than their forcing GCMs. We find that greater intensity in RCMs over CORDEX‐SEA compared to their associated forcing GCMs is firstly associated with the increased supply of moisture from both local and large‐scale sources. Second, a widespread increase in convective precipitation is found across the region in RCMs. Our findings suggest that a model's ability to simulate precipitation over the region relies more on the RCM setup itself (e.g., parameterization scheme), rather than its forcing GCM. This should be considered when assessing the reliability of RCM precipitation simulations for future projections.

A high-resolution climate experiment over part of Vietnam and the Lower Mekong Basin: performance evaluation and projection for rainfall

This study first evaluates the performance of three model experiments in representing rainfall over part of Vietnam and the Lower Mekong Basin for the historical period 1986-2005. The three experiments include the Coupled Model Intercomparison Project Phase 5 (CMIP5) EC-EARTH Global Climate Model (GCM) and two downscaling runs based on a regional climate model at 25km resolution with the GCM forcing (RCM-25km) and at 5km resolution with the RCM-25km forcing (RCM-5km). Verifications against observations show that the experiments generally capture the spatial distribution of climatological rainfall. While the GCM well represents the observed average rainfall cycles, its coarse resolution limits its capability in reproducing extreme rainfall values. The downscaling experiments do not clearly show their advantage in simulating average rainfall but exhibit significant added values when representing extreme rainfall in the study region. The RCM-5km does not outperform its driving 25km experiment in representing the mean and extreme rainfall values, suggesting that having a better resolution may not compensate for having a good model configuration with appropriate physical schemes. Analysis of climate projection for the far future period 2080-2099 under two representative concentration pathways (RCP) scenarios, RCP4.5 and RCP8.5, reveals that the downscaling experiments can modify the change direction of future rainfall obtained with the GCM. While the EC-EARTH GCM generally projects wetter tendencies of up to 50%, the downscaling experiments project a general decrease of down to -50% under both scenarios over the study domain. Regarding extreme rainfall, the annual maximum 1-day rainfall amount (RX1day) is projected to increase for the three experiments. The simple daily intensity index (SDII) future changes follow those of the annual rainfall values.

Forecasting rainfed rice yield with biomass of early phenophases, peak intercepted PAR and ground based remotely sensed vegetation indices

Rice is the main staple food of the country but crop productivity in some years declines due to erratic monsoon and non-uniformity in spatial and temporal distribution of rainfall. Hence, assessing productivity of the rice crop in advance using meteorological and plant physiological attributes will be helpful for planners to take decision on contingency measures. In this investigation, dry biomass of early phenophases (active tillering, panicle initiation, boot leaf stages, flowering),peak intercepted photosynthetically active radiation (IPAR), peak spectral reflectance based vegetation indices of 3 rice varieties under 3 nitrogen levels (50, 100 and 130 kg ha-1) were made correlated with grain yield.Based on interrelationship it was found that biomass of flowering period was better correlated with grain yield with the R2 value of 0.75.Inter-relationship between peak IPAR(%), remotely sensed peak simple ratio index (IR/R) and normalized difference vegetation index (NDVI)with the rice yield were also established. Multiple regression modelwas developed by interrelating yield as dependant variable with dry biomass of flowering stage, peak IPAR(%) and peak IR/R and NDVI as independent variables which may be used as an effective tool for early prediction of rice yield, at least 30-40 days in advance. The grain yield was also estimated through developed algorithm using MODIS satellite derived NDVI and compared with that of actual yield.

Improved understanding of regional groundwater drought development through time series modelling: the 2018–2019 drought in the Netherlands

Abstract. The 2018–2019 drought in north-western and central Europe caused severe damage to a wide range of sectors. It also emphasised the fact that, even in countries with temperate climates, adaptations are needed to cope with increasing future drought frequencies. A crucial component of drought management strategies is to monitor the status of groundwater resources. However, providing up-to-date assessments of regional groundwater drought development remains challenging due to the limited availability of high-quality data. This limits many studies to small selections of groundwater monitoring sites, giving an incomplete image of drought dynamics. In this study, a time series modelling-based method for data preparation was developed and applied to map the spatio-temporal development of the 2018–2019 groundwater drought in the south-eastern Netherlands, based on a large set of monitoring data. The data preparation method was evaluated for its usefulness and reliability for data validation, simulation, and regional groundwater drought assessment. The analysis showed that the 2018–2019 meteorological drought caused extreme groundwater drought throughout the south-eastern Netherlands, breaking 30-year records almost everywhere. Drought onset and duration were strongly variable in space, and higher-elevation areas suffered from severe drought well into 2020. Groundwater drought development appeared to be governed dominantly by the spatial distribution of rainfall and the landscape type. The time series modelling-based data preparation method was found to be a useful tool to enable a spatially detailed record of regional groundwater drought development. The automated time series modelling-based data validation improved the quality and quantity of useable data, although optimal validation parameters are probably context dependent. The time series simulations were generally found to be reliable; however, the use of time series simulations rather than direct measurement series can bias drought estimations, especially at a local scale, and underestimate spatial variability. Further development of time-series-based validation and simulation methods, combined with accessible and consistent monitoring data, will be valuable to enable better groundwater drought monitoring in the future.

Analysis of Rainstorm Flood Induced Flash Flood in Wende River Basin

Due to the joint influence of climate and topography, flash flood disasters in the Wende River Basin of Jilin Province occurred repeatedly, causing serious casualties and huge economic losses; in July 2017, three floods with large rainfall intensity and profound influence occurred in a row in the main stream of Wende River. Based on official measured data of the Wende River basin, three typical rainfall processes (“July 13”, “July 20” and “, “August 3 “) are selected to discuss the rainfall characteristics. The triggered characteristics, induced factors and the disaster mechanism of the basin are preliminarily explored from the aspects of rainfall weather system, temporal and spatial distribution of rainstorm and flood, geographical factors to analyze the best countermeasures for flash flood prevention. The above will provide reference for the non-engineering measure project of flash flood prevention currently under construction.

Spatial and Temporal Assessment of Rainfall Variability using GIS Approach in Trichy District of Tamil Nadu

Precipitation is one of the transportation components in hydrological cycle. The magnitude of precipitation swings with time and space. Majorly India receives precipitation in the form of rainfall. Precipitation plays a key role in the rainfed agriculture. The present study deals with the rainfall characteristics of Tiruchirappalli district, Tamil Nadu. Seasonal rainfall data from eighteen rain gauge stations (1971-2012) have been taken for analysis of seasonal and annual rainfall pattern of Tiruchirappalli district. Mean rainfall of the district is about 696 mm. The highest rainfall of 1247 mm recorded in the year 2005 and the lowest precipitation of 227 mm recorded in the year 1976. About 48 percent and 35 percent of the rainfall received in North East and South West Monsoon, respectively. Spatial rainfall distribution was studied with the help of Kriging interpolation technique and respective maps were prepared with Geographical Information System. The percentage departure of annual rainfall is classified under the category of excess, normal and large excess category. South East and central part of Tiruchirappalli receives moderate to low rainfall. North East parts of Tiruchirappalli district such as pullambadi, Lalgudi and nearby areas received maximum rainfall during North East Monsoon and South West Monsoon. In winter season Manapparai and Vaiyampatti region received more rainfall while in summer season Thottiam and Mayanur area received more rainfall. The two major highlighted crops in Trichy district are Banana and Onion. Tiruchirappalli district is one of the Banana growing belts in Tamil Nadu. Spatial distribution of rainfall maps will be helpful to form a crop plan for different crops to increase the agricultural productivity of Tiruchirappalli district and to ensure the food security.

Simulation of Rainfall over Bangladesh Using Regional Climate Model (RegCM4.7)

Regional climate model is a scientific tool to monitor present climate change and to provide reliable estimation of future climate projection. In this study, the Regional Climate Model version 4.7 (RegCM4.7) developed by International Centre for Theoretical Physics (ICTP) has been adopted to simulate rainfall scenario of Bangladesh. The study examines model performance of rainfall simulation through the period of 1991-2018 with ERA-Interim75 data of 75 km horizontal resolution as lateral boundaries, downscaled at 25km resolution using the mixed convective precipitation scheme; MIT-Emanuel scheme over land and Grell scheme with Fritsch-Chappell closure over ocean. The simulated rainfall has been compared both at spatial and temporal scales (monthly, seasonal and annual) with observed data collected from Bangladesh Meteorological Department (BMD) and Climate Research Unit (CRU). Simulated annual rainfall showed that the model overestimated in most of the years. Overestimation has been observed in the monsoon and underestimation in pre-monsoon and post-monsoon seasons. Spatial distribution of simulated rainfall depicts overestimation in the southeast coastal region and underestimation in the northwest and northeast border regions of Bangladesh. Better estimation of rainfall has been found in the central and eastern parts of the country. The simulated annual rainfall has been validated through the Linear Scaling bias correction method for the years of 2016, 2017, and 2018 considering the rainfall of 1991-2015 as reference. The bias correction with linear scaling method gives fairly satisfactory results and it can be considered in the future projection of rainfall over Bangladesh.

The Event-specific Geomorphological Instantaneous Unit Hydrograph (E-GIUH): The basin hydrological response characteristic of a flood event

In the field of hydrology, the Geomorphological Instantaneous Unit Hydrograph (GIUH) is central to describing a watershed response. The application of GIUH is extended to individual hydrological events by accounting for the influence of rainfall spatial distribution. A method is proposed herein to identify the Geomorphological Instantaneous Unit Hydrograph specific to each flood event (i.e. E-GIUH), when runoff of effective rainfall is the dominant process. The E-GIUH is derived from observational data, namely: rainfall field time series, and a hydrograph. Such an identification process is formulated as an inverse problem with parameters such as the E-GIUH velocity and coefficient of dispersion, as well as the hyetograph of rainfall excess. The proposed method is applied to several flood events across two mountainous catchments within the Cevennes-Vivarais Mediterranean Hydrometeorological Observatory territory prone to flash flooding. Results indicate that the E-GIUHs display significant variability over the two basins, and the E-GIUH parameters appear to be correlated with the flood event magnitude. The E-GIUH synthetizes the basin response to rain forcing and can be considered as a signature of flood events. A sensitivity study suggests that E-GIUH identification is fairly robust, even with respect to the a priori hyetograph of effective rainfall.

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.

Estimation of Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) Using GSSHA Model (Case Study Area Upper Citarum Watershed)

Probable Maximum Flood (PMF) used in the design of hydrological structures reliabilities and safety which its value is obtained from the Probable Maximum Precipitation (PMP). The objectives of this study are to estimate PMP and PMF value in Upper Citarum Watershed and understand the impact from different PMP value to PMF value with two scenarios those are Scenario A and B. Scenario A will calculate the PMP value from each Global Satellite Mapping of Precipitation (GSMaP) rainfall data grid and Scenario B calculate the PMP value from the mean area rainfall. PMP value will be obtained by the statistical Hershfield method, and the PMF will be obtained by employed the PMP value as the input data in Gridded Surface Subsurface Hydrologic Analysis (GSSHA) hydrologic model. Model simulation results for PMF hydrographs from both scenarios show that spatial distribution of rainfall in the Upper Citarum watershed will affect the calculated discharge and whether Scenario A or B can be applied in the study area for PMP duration equal or higher than 72 hours. PMF peak discharge for Scenario A is averagely 13,12% larger than Scenario B.

Retraction Note to: The characteristics of rainfall in coastal areas and the intelligent library book push system oriented to the Internet of Things

Driven by economic globalization and the development of a low-carbon economy, the Internet of Things has brought new hope to countries around the world. In the context of climate change and rapid urbanization, the health of rivers in coastal areas of China is facing huge risks. The water cycle in coastal areas is complex and highly dependent on human activities. At the same time, the relative lack of long-term measured hydrological data makes it difficult to model the flow of coastal rivers, and also difficult to predict and analyze water quality. Therefore, this paper establishes a method to evaluate the latent heat structure based on the percentage of precipitation or the occurrence frequency of various systems in order to quantify the time, space distribution, and climatic conditions of the latent heat structure of the deep convective system under different regions and weather conditions. In addition, this research first analyzes the rainfall distribution rules and rainfall characteristics in coastal areas, then compares the physical mechanism-based SOBEK hydrological model with the neural network model, and conducts modeling and model characteristics analysis of the coastal areas. In response to these challenges, this article first analyzes and summarizes the temporal and spatial distribution of coastal rainfall and typical rainfall characteristics. This article focuses on the intelligent data mining push service system used by academic libraries at home and abroad. After careful research, academic libraries based on data mining will promote the development of intelligent push services. With the help of SQLServer2019 database management system, Android technology, and PHP component architecture, combined with collaborative filtering algorithms, intelligent book recommendation is realized. The recommendation principle is based on user characteristics and effectively promotes interest classification to create recommendation models and service systems.