Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable ...for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change.
In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage.
Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne.
Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable ...for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change.
In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage.
Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne.
Statistical rainfall climatologies for application to problems in ecohydrology or urban flood hydrology employ indices such the average rainfall depth per rain day or rain hour, and the average daily ...or hourly rainfall intensity. These subsume fine‐temporal‐scale aspects of rainfall arrival such as sub‐hourly intermittency that vary among locations with different climatic conditions. Rainfall duration and intensity derived from data aggregated to hourly or daily level thus involve some bias, generally overestimating the duration of rainfall and underestimating rainfall rates, and this bias will vary with location. The magnitude of bias is documented for two Australian locations having rainfall records of high temporal resolution: one arid and one wet tropical.
Rainfall data aggregated to hourly or daily level yield substantial overestimates of time raining, and underestimates of rainfall rates. The magnitude of bias is shown to differ between the two field locations where on average rain is only recorded during 3.6 hr (14.9%) of a rain day at the arid location, but 8.2 hr (34%) at the wet tropical site. Resulting bias in estimates of time raining and of rainfall intensities is worse at the arid location. The significance of intermittency in particular for the detection of the effects of climate change on temporal rainfall climatology is explored. If intermittency varies with climate change, but is concealed through the use temporally aggregated rainfall data, erroneous assessments of changes in rainfall intensities may result. The use of rainfall events rather than clock‐period data appears to reduce some of these effects, but there is still a failure to reflect short‐period, high‐intensity rainfall that may be important to soil erosion and the loss of carbon, nutrients, and agrochemicals from agricultural land.
The description of temporal rainfall climatology using daily or hourly rainfall depths is analyzed. Short‐term intermittency of rain (periods of no rain) characterizes a typical “rain day” or rainfall event. This affects apparent rainfall rates, in ways that differ between arid and wet tropical locations, and which are not evident in hourly rainfall data.
Abstract The impact of climatic change on the summer monsoon season is studied to understand the rainfall pattern towards the end of the century utilizing the Coupled Model Intercomparison Project, ...Phase 6 (CMIP6) released by the World Climate Research Programme (WCRP). The analysis of model simulations from CMIP6 was carried out using 64 years of the historical period (1951–2014) and future projections till the end of the century (2015–2100). The models are compared with observed daily rainfall data from the APHRODITE (Asian Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation). The analysis revealed that most of the models show an overestimation in the annual cycle of rainfall in the historical period; however, a few of them underestimate the values. The majority of them capture the onset signal of the summer monsoon in early June, along with a good seasonality in the daily rainfall climatology. The simulations that are coherent with the observational data sets are selected on the basis of the Taylor diagram for future projections in four scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). The projections of the aforementioned scenarios are taken from the model outputs of EC‐Earth3‐Veg‐LR, INM‐CM4‐8, INM‐CM5‐0, MIROC‐ES2L and MPI‐ESM1‐2‐HR. The selected models exhibit far greater agreement among the 30 models when it comes to the features of rainfall during the summer monsoon. We have given more emphasis on summer monsoon rainfall in the historical and future projection periods since the trends are becoming more chaotic, as reported in observational studies. Over the Indian subcontinent, all of the chosen scenarios show an increased frequency of intense rainfall events with varying decadal and multidecadal features. Central India and west coastal belts are showing positive trends in extreme rainfall events towards the end of the century. At the turn of the century, the southern peninsular region experienced a decline in monsoon precipitation, whereas central India experienced an increase. The severity of rainfall variations during the monsoon season and trends in extremes are increasing as we move from the low‐emission scenario to the high‐emission scenario.
The distribution of rainfall is not uniform in various regions of the world. Rainfall study is used to recognize the characteristics, duration, and variability of temporal and spatial rainfall ...distributions. In Ethiopia, the annual and seasonal rainfall distributions are variable in space and time. This study is focused on the implication of spatiotemporal rainfall distribution on the areal rainfall characteristics evaluation under the Upper Erer Sub-basin, located in Eastern Ethiopia. In the study area, average annual rainfall amount for Dire Dawa, Harar and Haramaya, Girawa, and Gursum stations are found to be 647, 816, 801, 958, and 840 mm with coefficients of variation of 23, 20, 20, 19, and 31%, respectively. However, the rain gauges here are sparsely distributed. The rainfall occurrence and distribution at various gauging stations have been found to vary significantly both temporally and spatially. The rainfall occurrence and distribution at various gauging stations had been found to vary significantly both temporally and spatially. The spatiotemporal rainfall distribution found in the stations was assessed using a joint probability of rain days approach. The result indicated joint probability of rain days estimation approach under monthly time step has better performance than daily, decadal, and seasonal data. The joint probability approach is used along with rainfall amount under monthly rainfall for areal rainfall estimation assessment in rainfall–runoff modeling.
Rainfall‐runoff modeling is a complex nonlinear time series problem. While there is still room for improvement, researchers have been developing physical and machine learning models for decades to ...predict runoff using rainfall data sets. With the advancement of computational hardware resources and algorithms, deep learning methods such as the long short‐term memory (LSTM) model and sequence‐to‐sequence (seq2seq) modeling have shown a good deal of promise in dealing with time series problems by considering long‐term dependencies and multiple outputs. This study presents an application of a prediction model based on LSTM and the seq2seq structure to estimate hourly rainfall‐runoff. Focusing on two Midwestern watersheds, namely, Clear Creek and Upper Wapsipinicon River in Iowa, these models were used to predict hourly runoff for a 24‐hr period using rainfall observation, rainfall forecast, runoff observation, and empirical monthly evapotranspiration data from all stations in these two watersheds. The models were evaluated using the Nash‐Sutcliffe efficiency coefficient, the correlation coefficient, statistical bias, and the normalized root‐mean‐square error. The results show that the LSTM‐seq2seq model outperforms linear regression, Lasso regression, Ridge regression, support vector regression, Gaussian processes regression, and LSTM in all stations from these two watersheds. The LSTM‐seq2seq model shows sufficient predictive power and could be used to improve forecast accuracy in short‐term flood forecast applications. In addition, the seq2seq method was demonstrated to be an effective method for time series predictions in hydrology.
Key Points
An hourly runoff model was developed using the LSTM sequence‐to‐sequence learning method for 24‐hr predictions on USGS stations
The proposed model shows better performance than traditional data‐driven models and is applicable to different watersheds
The advantages and limitations of seq2seq models and how this model structure could work on the rainfall‐runoff modeling is presented
Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Available observations ...generated by automated weather stations or meteorological observatories are often limited in spatial resolution resulting in misrepresentation or absence of rainfall information at these levels. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex spatio-temporal process leading to non-linear or chaotic spatio-temporal variations, no single downscaling method can be considered efficient enough. In the domains dominated by complex topographies, quasi-periodicities, and non-linearities, deep learning (DL)–based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions. We employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods in this work. Summer monsoon season data from India Meteorological Department (IMD) and the tropical rainfall measuring mission (TRMM) data set were downscaled up to 4 times higher resolution using these methods. High-resolution data derived from deep learning-based models provide better results than linear interpolation for up to 4 times higher resolution. Among the three algorithms, namely, SRCNN, stacked SRCNN, and DeepSD, employed here, the best spatial distribution of rainfall amplitude and minimum root-mean-square error is produced by DeepSD-based downscaling. Hence, the use of the DeepSD algorithm is advocated for future use. We found that spatial discontinuity in amplitude and intensity rainfall patterns is the main obstacle in the downscaling of precipitation. Furthermore, we applied these methods for model data post-processing, in particular, ERA5 reanalysis data. Downscaled ERA5 rainfall data show a much better distribution of spatial covariance and temporal variance when compared with observation. This study is the first step towards developing deep learning-based weather data downscaling model for Indian summer monsoon rainfall data.
ABSTRACT
In situ rainfall data observed by gauges is the most important data in water resources management. However, these data have some limitations both spatially and temporally. With the ...advancements in satellite rainfall products, it is now possible to evaluate whether these products can capture the climatology of known rainfall characteristics. In this study, five satellite rainfall estimates (SREs) were evaluated against gauge data based on different rainfall regimes over Iran. The evaluated SREs are Climate Prediction Center Morphing Technique, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM), PERSIANN Climate Data Record (PERSIANN‐CDR) and the most recently available Multi‐Source Weighted‐Ensemble Precipitation (MSWEP) data. The performance of these five SREs is evaluated with respect to gauge data (total: 958 stations) in eight different climatic zones at daily, monthly, and wet/dry spells during a ten‐year period (2003–2012). Performance of SREs was evaluated using metrics of comparison based on correlation coefficient (CC), root mean square error, and relative error. The study shows that MSWEP has the highest CC (0.72) followed by TRMM (0.46) and PERSIANN‐CDR (0.43) at daily time scale. The performance of SREs varies with respect to climatic regimes, for example, the best correlation was observed in the south, the shore of Persian Gulf with ‘very hot and humid’ climate with CC values of 0.72, 0.70, and 0.82 for MSWEP, TRMM and PERSIANN‐CDR, respectively. Further, the performance of SREs was evaluated using the categorical statistics to capture the rainfall pattern based on different groups (e.g. light, moderate and heavy rainfall events). Results show that MSWEP, PERSIANN‐CDR, and TRMM performed well to distinguish rain from no‐rain condition, whereas for higher rainfall rates, PERSIANN‐CDR outperforms the other SREs.
Iran as a country with various geographical features (sea shores, high mountains, and vast deserts) could be a good climatological case study. The exist of mountains on north and west, beside deserts at the center of the country are the reason of insufficient both temporal and spatial distribution of gauge stations. Satellite rainfall estimated (SRE) datasets could be the other sources for climatological studies. In this study, the evaluation of five different SREs (CMORPH, PERSIANN, PERSIANN‐CDR, TRMM, and MSWEP) with comparison to gauge dataset is considered over Iran.
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