Trend Analysis of Rainfall Using Gridded Data over a Region of Southern Italy

Caloiero, Tommaso; Coscarelli, Roberto; Pellicone, Gaetano

doi:10.3390/w13162271

Cited by 8 publications

(3 citation statements)

References 52 publications

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“…In the field of prevention and management of extreme events, accurate precipitation mapping is of great importance, as it represents a key data for hydrological modeling and simulation of climatic scenarios necessary for the prevention of natural hazards such as floods, hydraulic erosion, desertification, and land movements (Caloiero et al, 2021). However, these phenomena are often studied in very large areas where direct rainfall measurements from rainfall stations are spotty and poorly distributed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Precipitation Spatial Interpolation Techniques using GIS for Better Prevention of Extreme Events: Case of the Assaka Watershed (Southern Morocco)

Khaddari¹,

Bouziani²,

Moussa³

et al. 2022

EEC

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The spatial distribution of precipitation is a key data for the prevention and management of extreme events that threaten the Assaka watershed. This area is characterized by a scarcity of climatological data, an unevenly distributed rainfall observation network and low density. However, spatial interpolation methods of point precipitation measurements could overcome these aspects. For this reason, this research consists in determining the most adequate method in terms of efficiency and practical use in order to accurately map the maximum daily precipitation for a period of 30 years (1990 -2020). In this context four interpolation techniques (Thiessen polygons, inverse distance weighting, ordinary kriging and linear regression) were applied in a GIS environment. The cross-validation allows to evaluate the global performance of each method using statistical indicators (RMSE, MAE) as well as adjustment diagrams between observed and predicted values. Indeed, this analysis has allowed to qualify the method of multiple linear regression (MLR), as the best interpolator (RMSE=1.67mm and MEA=1.40mm). These results are judged by the fact that this technique integrates geographical factors (topography, latitude, proximity to the ocean) related to the formation of precipitation in the study area. Other methods are considered unsuitable in this anisotropic environment where the density of observation points is very low. These results constitute exploitable approaches by scientists and decision-makers in the prevention and management of extreme events (floods, landslides, water erosion) as well as land management (water resources, agriculture and environment).

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Section: Introductionmentioning

confidence: 99%

Evaluation of Precipitation Spatial Interpolation Techniques using GIS for Better Prevention of Extreme Events: Case of the Assaka Watershed (Southern Morocco)

Khaddari¹,

Bouziani²,

Moussa³

et al. 2022

EEC

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“…In the past decade, several long-term gridded rainfall datasets have been developed and continuously improved. Hence, they have become more accessible for hydro-climatic studies, driving hydrological models, and validation of climate model simulations [4][5][6][7][8][9][10]. Moreover, most of the gridded datasets are constructed based on rain gauge station datasets that are unevenly distributed in space and sparse in some areas.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Spatial-Temporal Characteristics of Rainfall Variations over Thailand Inferred from Different Gridded Datasets

Torsri

Lin

Dike

et al. 2022

Water

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The fidelity of gridded rainfall datasets is important for the characterization of rainfall features across the globe. This study investigates the climatology, interannual variability, and spatial-temporal variations of seasonal rainfall over Thailand during the 1970–2007 period using station data obtained from the Thai Meteorological Department (TMDstn). In addition, the performance of three gridded rainfall datasets, namely APHRODITE, CRU, and GPCC, in reproducing these seasonal rainfall features were intercompared and further validated with the results derived from the TMDstn. Results show that the gridded datasets can reproduce the spatial distribution of the TMDstn’s summer mean rainfall. However, large systematic underestimation is seen in APHRODITE, while GPCC shows better agreement with TMDstn as compared to others. In the winter, the spatial distribution of the seasonal mean of rainfall is well captured by all gridded data, especially in the upper part of Thailand, while they failed to capture high rainfall intensity in the south and the eastern parts of Thailand. Meanwhile, all the gridded datasets underestimated the interannual variability of summer and winter season rainfall. Using EOF analysis, we demonstrate that all the gridded datasets captured the first two dominant modes of summer rainfall, while they underestimated the explained variance of EOF-1. In the winter season, a good agreement is found between the first two modes of the TMDstn and the gridded datasets for both the spatial pattern and temporal variation. Overall, the GPCC data show relatively better performance in reproducing the spatial distribution of rainfall climatology and their year-to-year variation over Thailand. Furthermore, the performance of the gridded datasets over Thailand is largely dependent on the season and the complexity of the topography. However, this study indicates the existence of systematic bias in the gridded rainfall datasets when compared with TMDstn. Therefore, this indicates the need for users to pay attention to the reliability of gridded rainfall datasets when trying to identify possible mechanisms responsible for the interannual variability of seasonal rainfall over Thailand.

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“…These additional volumes could come from conventional resources (either by increasing the exploitation of surface and groundwater) or from unconventional resources (by resorting to water transfers from other basins or the desalination of seawater). For this reason, it is necessary to promote the use of integrated resources management and the use of decision making-tools and simulation models, for a better management under uncertainty scenarios and more resilient resources allocation [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Effects of Climate Change on Water Quality in the Jucar River Basin (Spain)

Martínez

Galiano²,

Rubio³

et al. 2021

Water

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The Mediterranean region is a climate change hotspot, especially concerning issues of hydrological planning and urban water supply systems. In this context, the Jucar River Basin (Spain) presents an increase of frequency, intensity and duration of extreme meteorological phenomena, such as torrential rains, droughts or heat waves, which directly affect the quantity and quality of raw water available for drinking. This paper aims to analyze the effects of climate change on the raw water quality of the Jucar River Basin District, which mainly supplies the city of Valencia and its metropolitan area, in order to adapt drinking water treatments to new conditions and opportunities. For this purpose, we used observed data of water quality parameters from four stations and climate drivers from seven Earth system models of the latest Coupled Model Intercomparison Project—Phase 6. To model water quality (turbidity and conductivity) in the past and future scenarios, this study employs a backward stepwise regression taking into account daily values of mean temperature, maximum temperature, total rainfall and minimum and maximum relative humidity. Results showed that the model performance of the water quality simulation is more adequate for short moving-average windows (about 2–7 days) for turbidity and longer windows (about 30–60 days) for conductivity. Concerning the future scenarios, the most significant change was found in the projected increase of conductivity for the station of the Júcar river, between 4 and 11% by 2100, respectively, under the medium (SSP2–4.5) and pessimistic (SSP5–8.5) emission scenarios. The joint use of these types of management and monitoring tools may help the managers in charge of carrying out the different water treatments needed to apply a better plan to raw water and may help them identify future threats and investment needs to adapt the urban water supply systems to the changing conditions of raw water, such as turbidity or conductivity, as a consequence of climate change.

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Trend Analysis of Rainfall Using Gridded Data over a Region of Southern Italy

Cited by 8 publications

References 52 publications

Evaluation of Precipitation Spatial Interpolation Techniques using GIS for Better Prevention of Extreme Events: Case of the Assaka Watershed (Southern Morocco)

Evaluation of Precipitation Spatial Interpolation Techniques using GIS for Better Prevention of Extreme Events: Case of the Assaka Watershed (Southern Morocco)

Evaluation of Spatial-Temporal Characteristics of Rainfall Variations over Thailand Inferred from Different Gridded Datasets

Effects of Climate Change on Water Quality in the Jucar River Basin (Spain)

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