Assessing the influence of weather parameters on rainfall to forecast river discharge based on short-term

Ali, Danladi; stephen, mark rigby and adam; Aliyu, B.M.; Gaya, G.K.; Silikwa, N.W.; Machael, Y.

doi:10.1016/j.aej.2017.03.004

Cited by 27 publications

(11 citation statements)

References 3 publications

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“…This phenomenon of crossing variables was verified through the analysis of 1237 storms. Recent studies show that the time series of temperature and humidity, when properly combined, can generate a trusted precipitation forecasting model [61].…”

Section: Discussionmentioning

confidence: 99%

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Gutiérrez-López

Cruz-Paz

Mandujano

2019

Climate

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Forecasting extreme precipitations is one of the main priorities of hydrology in Latin America and the Caribbean (LAC). Flood damage in urban areas increases every year, and is mainly caused by convective precipitations and hurricanes. In addition, hydrometeorological monitoring is limited in most countries in this region. Therefore, one of the primary challenges in the LAC region the development of a good rainfall forecasting model that can be used in an early warning system (EWS) or a flood early warning system (FEWS). The aim of this study was to provide an effective forecast of short-term rainfall using a set of climatic variables, based on the Clausius–Clapeyron relationship and taking into account that atmospheric water vapor is one of the variables that determine most meteorological phenomena, particularly regarding precipitation. As a consequence, a simple precipitation forecast model was proposed from data monitored at every minute, such as humidity, surface temperature, atmospheric pressure, and dewpoint. With access to a historical database of 1237 storms, the proposed model allows use of the right combination of these variables to make an accurate forecast of the time of storm onset. The results indicate that the proposed methodology was capable of predicting precipitation onset as a function of the atmospheric pressure, humidity, and dewpoint. The synoptic forecast model was implemented as a hydroinformatics tool in the Extreme Precipitation Monitoring Network of the city of Queretaro, Mexico (RedCIAQ). The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems all over Mexico, mainly during hurricanes and flashfloods.

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Section: Discussionmentioning

confidence: 99%

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Gutiérrez-López

Cruz-Paz

Mandujano

2019

Climate

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“…The use of reanalysis data would enable the calculation of PET through the more accurate combined methods (such as PM). However, the uncertainties associated with reanalysis data should be carefully examined, as some of the modelled variables can display large errors (Reichler and Kim, 2008), and PM has also shown sensitivity to input data inaccuracy (Oudin et al, 2005;Debnath et al, 2015;Estévez et al, 2009;Gong et al, 2006).…”

Section: Further Findings and Future Workmentioning

confidence: 99%

Response to referee#2 (Mark McCarthy)

Tanguy¹

2018

Preprint

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Potential evapotranspiration (PET) is a necessary input data for most hydrological models and is often needed at a daily time step. An accurate estimation of PET requires many input climate variables which are, in most cases, not available prior to the 1960s for the UK, nor indeed most parts of the world. Therefore, when applying hydrological models to earlier periods, modellers have to rely on PET estimations derived from simplified methods. Given that only monthly observed temperature data is readily available for the late 19th and early 20th century at a national scale for the UK, the objective of this work was to derive the best possible UK-wide gridded PET dataset from the limited data available. To that end, firstly, a combination of (i) seven temperature-based PET equations, (ii) four different calibration approaches and (iii) seven input temperature data were evaluated. For this evaluation, a gridded daily PET product based on the physically based Penman-Monteith equation (the CHESS PET dataset) was used, the rationale being that this provides a reliable "ground truth" PET dataset for evaluation purposes, given that no directly observed, distributed PET datasets exist. The performance of the models was also compared to a "naïve method", which is defined as the simplest possible estimation of PET in the absence of any available climate data. The "naïve method" used in this study is the CHESS PET daily long-term average (the period from 1961 to 1990 was chosen), or CHESS-PET daily climatology. The analysis revealed that the type of calibration and the input temperature dataset had only a minor effect on the accuracy of the PET estimations at catchment scale. From the seven equations tested, only the calibrated version of the McGuinness-Bordne equation was able to outperform the "naïve method" and was therefore used to derive the gridded, reconstructed dataset. The equation was calibrated using 43 catchments across Great Britain. The dataset produced is a 5 km gridded PET dataset for the period 1891 to 2015, using the Met Office 5 km monthly gridded temperature data available for that time period as input data for the PET equation. The dataset includes daily and monthly PET grids and is complemented with a suite of mapped performance metrics to help users assess the quality of the data spatially. This dataset is expected to be particularly valuable as input to hydrological models for any catchment in the UK.

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“…Predicting rainfall, on the other hand, is extremely difficult. A variety of factors influence rainfall, including humidity, maximum and minimum temperatures, wind speed and direction, and so on (Danladi et al 2018;Elwell & Stocking 2010). The pattern of these parameters can be used to forecast rainfall.…”

Section: Introductionmentioning

confidence: 99%

Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia

2021

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Rainfall prediction is a critical task because many people rely on it, particularly in the agricultural sector. Rainfall forecasting is difficult due to the ever-changing nature of weather conditions. In this study, we carry out a rainfall predictive model for Jimma, a region located in southwestern Oromia, Ethiopia. We proposed a Long Short-Term Memory (LSTM)-based prediction model capable of forecasting Jimma's daily rainfall. Experiments were conducted to evaluate the proposed models using various metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) Nash-Sutcliffe model efficiency (NSE), and R2, and the results were 0.01, 0.4786 0.81 and 0.9972, respectively. We also compared the proposed model to existing machine learning regressions like Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The RMSE of MLP was the lowest of the four existing learning models i.e., 0.03. The proposed LSTM model outperforms the existing models, with an RMSE of 0.01. The experimental results show that the proposed model has a lower RMSE and a higher R2.

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Assessing the influence of weather parameters on rainfall to forecast river discharge based on short-term

Cited by 27 publications

References 3 publications

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Response to referee#2 (Mark McCarthy)

Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia

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