Special Issue "Modelling Precipitation in Space and Time"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 10595

Special Issue Editor

Dr. Gabriele Buttafuoco
E-Mail Website
Guest Editor
National Research Council of Italy, Institute for Agricultural and Forest Systems in the Mediterranean (ISAFOM), 87036 Rende, CS, Italy
Interests: hydrology and water resources; geostatistics; uncertainty analysis

Special Issue Information

Dear Colleagues,

Modeling precipitation in space and time over specified areas, such as a hydrological catchment or a grid-cell of various climatic, hydrologic, and ecological models is of great interest. In modeling precipitation, the key issue is the choice of an interpolation approach. In areas with low relief, even distribution of rain gauges and abundant data, most interpolation techniques give similar results. Unfortunately, such conditions are rarely met, and when data are sparse, especially in mountainous areas, the implicit or explicit underlying assumptions about the variation among measured points may differ significantly even at relatively reduced scales. Moreover, modeling precipitation enables making inferences about the knowledge of the precipitation process, and caution is required in using information on precipitation relying only on statistical relationships.

Exhaustive information, such as topographic attributes, can be used as auxiliary variables (covariates) in multivariate approaches and may improve the estimation of precipitation or may allow understanding factors controlling the distribution of precipitation. However, auxiliary variables are often associated with different support sizes, and combining them with precipitation data requires solving the problem of how best to integrate (fuse) such information.

Conclusions of climate and hydrological studies may be potentially biased when variation in precipitation is caused by nonclimatic factors related to nonhomogeneity of long climate records.

Potential topics include but are not limited to the following:

  • Interpolating precipitation in space, in time, and in space and time;
  • Methods for quantifying uncertainty;
  • Accounting for missing data in precipitation series;
  • Using environmental covariates to improve precipitation modeling.

Dr. Gabriele Buttafuoco
Guest Editor

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Published Papers (8 papers)

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Research

Article
Evaluation of ERA5 Precipitation Accuracy Based on Various Time Scales over Iran during 2000–2018
Water 2021, 13(18), 2538; https://doi.org/10.3390/w13182538 - 16 Sep 2021
Cited by 2 | Viewed by 871
Abstract
In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the [...] Read more.
In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales. Full article
(This article belongs to the Special Issue Modelling Precipitation in Space and Time)
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Article
Recent Changes in Temperature and Precipitation of the Summer and Autumn Seasons over Fujian Province, China
Water 2021, 13(14), 1900; https://doi.org/10.3390/w13141900 - 09 Jul 2021
Cited by 3 | Viewed by 966
Abstract
Based on the observation data of daily temperature and precipitation in summer and autumn of 68 representative meteorological stations in Fujian Province from 1971 to 2018, using the climate Tendency Rate, Mann-Kendall trend test, Morlet wavelet analysis and other methods, this paper analyzes [...] Read more.
Based on the observation data of daily temperature and precipitation in summer and autumn of 68 representative meteorological stations in Fujian Province from 1971 to 2018, using the climate Tendency Rate, Mann-Kendall trend test, Morlet wavelet analysis and other methods, this paper analyzes the variation trends of air temperature and annual precipitation and the wavelet periodic variation characteristics of annual precipitation time series in summer and autumn of Fujian Province over a period of approximately 48 years. The results show that over the approximately 48 years, the temperature and precipitation in summer and autumn in Fujian showed an obvious upward trend, which had a mutation around 2000, but the mutation time was different, and the precipitation was slightly earlier. The annual temperature and precipitation in summer and autumn experienced three oscillations on the 28a scale. In the 28a time scale of summer autumn seasonal oscillation, there are three negative centers and two positive centers. According to the characteristics of annual average temperature and annual precipitation in the first major cycle, the annual precipitation in summer and autumn will continue to increase in the future. Full article
(This article belongs to the Special Issue Modelling Precipitation in Space and Time)
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Article
Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size
Water 2021, 13(6), 830; https://doi.org/10.3390/w13060830 - 18 Mar 2021
Cited by 1 | Viewed by 784
Abstract
Accounting for secondary exhaustive variables (such as elevation) in modelling the spatial distribution of precipitation can improve their estimate accuracy. However, elevation and precipitation data are associated with different support sizes and it is necessary to define methods to combine such different spatial [...] Read more.
Accounting for secondary exhaustive variables (such as elevation) in modelling the spatial distribution of precipitation can improve their estimate accuracy. However, elevation and precipitation data are associated with different support sizes and it is necessary to define methods to combine such different spatial data. The paper was aimed to compare block ordinary cokriging and block kriging with an external drift in estimating the annual precipitation using elevation as covariate. Block ordinary kriging was used as reference of a univariate geostatistical approach. In addition, the different support sizes associated with precipitation and elevation data were also taken into account. The study area was the Calabria region (southern Italy), which has a spatially variable Mediterranean climate because of its high orographic variability. Block kriging with elevation as external drift, compared to block ordinary kriging and block ordinary cokriging, was the most accurate approach for modelling the spatial distribution of annual mean precipitation. The three measures of accuracy (MAE, mean absolute error; RMSEP, root-mean-squared error of prediction; MRE, mean relative error) have the lowest values (MAE = 112.80 mm; RMSEP = 144.89 mm, and MRE = 0.11), whereas the goodness of prediction (G) has the highest value (75.67). The results clearly indicated that the use of an exhaustive secondary variable always improves the precipitation estimate, but in the case of areas with elevations below 120 m, block cokriging makes better use of secondary information in precipitation estimation than block kriging with external drift. At higher elevations, the opposite is always true: block kriging with external drift performs better than block cokriging. This approach takes into account the support size associated with precipitation and elevation data. Accounting for elevation allowed to obtain more detailed maps than using block ordinary kriging. However, block kriging with external drift produced a map with more local details than that of block ordinary cokriging because of the local re-evaluation of the linear regression of precipitation on block estimates. Full article
(This article belongs to the Special Issue Modelling Precipitation in Space and Time)
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Article
Impact of the Grid Resolution and Deterministic Interpolation of Precipitation on Rainfall-Runoff Modeling in a Sparsely Gauged Mountainous Catchment
Water 2021, 13(2), 230; https://doi.org/10.3390/w13020230 - 19 Jan 2021
Cited by 11 | Viewed by 1157
Abstract
Precipitation is a key variable in the hydrological cycle and essential input data in rainfall-runoff modeling. Rain gauge data are considered as one of the best data sources of precipitation but before further use, the data must be spatially interpolated. The process of [...] Read more.
Precipitation is a key variable in the hydrological cycle and essential input data in rainfall-runoff modeling. Rain gauge data are considered as one of the best data sources of precipitation but before further use, the data must be spatially interpolated. The process of interpolation is particularly challenging over mountainous areas due to complex orography and a usually sparse network of rain gauges. This paper investigates two deterministic interpolation methods (inverse distance weighting (IDW), and first-degree polynomial) and their impact on the outputs of semi-distributed rainfall-runoff modeling in a mountainous catchment. The performed analysis considers the aspect of interpolation grid size, which is often neglected in other than fully-distributed modeling. The impact of the inverse distance power (IDP) value in the IDW interpolation was also analyzed. It has been found that the best simulation results were obtained using a grid size smaller or equal to 750 m and the first-degree polynomial as an interpolation method. The results indicate that the IDP value in the IDW method has more impact on the simulation results than the grid size. Evaluation of the results was done using the Kling-Gupta efficiency (KGE), which is considered to be an alternative to the Nash-Sutcliffe efficiency (NSE). It was found that KGE generally tends to provide higher and less varied values than NSE which makes it less useful for the evaluation of the results. Full article
(This article belongs to the Special Issue Modelling Precipitation in Space and Time)
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Article
Use of Ensemble-Based Gridded Precipitation Products for Assessing Input Data Uncertainty Prior to Hydrologic Modeling
Water 2020, 12(10), 2751; https://doi.org/10.3390/w12102751 - 02 Oct 2020
Cited by 3 | Viewed by 1192
Abstract
The spatial and temporal performance of an ensemble of five gridded climate datasets (precipitation) (North American Regional Reanalysis, European Centre for Medium-Range Weather Forecasts interim reanalysis, European Union Water and Global Change (WATCH) Watch Forcing data ERA-Interim, Global Forcing Data-Hydro, and The Australian [...] Read more.
The spatial and temporal performance of an ensemble of five gridded climate datasets (precipitation) (North American Regional Reanalysis, European Centre for Medium-Range Weather Forecasts interim reanalysis, European Union Water and Global Change (WATCH) Watch Forcing data ERA-Interim, Global Forcing Data-Hydro, and The Australian National University spline interpolation) was evaluated towards quantifying gridded precipitation data ensemble uncertainty for hydrologic model input. Performance was evaluated over the Nelson–Churchill Watershed via comparison to two ground-based climate station datasets for year-to-year and season-to-season periods (1981–2010) at three spatial discretizations: distributed, sub-basin aggregation, and full watershed aggregation. All gridded datasets showed spatial performance variations, most notably in year-to-year total precipitation bias. Absolute minimum and maximum realizations were generated and assumed to represent total possible uncertainty bounds of the ensemble. Analyses showed that high magnitude precipitation events were often outside the uncertainty envelope; some increase in spatial aggregation, however, enveloped more observations. Results suggest that hydrologic models can reduce input uncertainty with some spatial aggregation, but begin to lose information as aggregation increases. Uncertainty bounds also revealed periods of elevated uncertainty. Assessing input ensemble bounds can be used to include high and low uncertainty periods in hydrologic model calibration and validation. Full article
(This article belongs to the Special Issue Modelling Precipitation in Space and Time)
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Article
Mapping the Daily Rainfall over an Ungauged Tropical Micro-Watershed: A Downscaling Algorithm Using GPM Data
Water 2020, 12(6), 1661; https://doi.org/10.3390/w12061661 - 10 Jun 2020
Cited by 5 | Viewed by 1101
Abstract
In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data [...] Read more.
In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds. Full article
(This article belongs to the Special Issue Modelling Precipitation in Space and Time)
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Article
Role of Cluster Validity Indices in Delineation of Precipitation Regions
Water 2020, 12(5), 1372; https://doi.org/10.3390/w12051372 - 12 May 2020
Cited by 3 | Viewed by 1975
Abstract
The delineation of precipitation regions is to identify homogeneous zones in which the characteristics of the process are statistically similar. The regionalization process has three main components: (i) delineation of regions using clustering algorithms, (ii) determining the optimal number of regions using cluster [...] Read more.
The delineation of precipitation regions is to identify homogeneous zones in which the characteristics of the process are statistically similar. The regionalization process has three main components: (i) delineation of regions using clustering algorithms, (ii) determining the optimal number of regions using cluster validity indices (CVIs), and (iii) validation of regions for homogeneity using L-moments ratio test. The identification of the optimal number of clusters will significantly affect the homogeneity of the regions. The objective of this study is to investigate the performance of the various CVIs in identifying the optimal number of clusters, which maximizes the homogeneity of the precipitation regions. The k-means clustering algorithm is adopted to delineate the regions using location-based attributes for two large areas from Canada, namely, the Prairies and the Great Lakes-St Lawrence lowlands (GL-SL) region. The seasonal precipitation data for 55 years (1951–2005) is derived using high-resolution ANUSPLIN gridded point data for Canada. The results indicate that the optimal number of clusters and the regional homogeneity depends on the CVI adopted. Among 42 cluster indices considered, 15 of them outperform in identifying the homogeneous precipitation regions. The Dunn, D e t _ r a t i o and Trace( W 1 B ) indices found to be the best for all seasons in both the regions. Full article
(This article belongs to the Special Issue Modelling Precipitation in Space and Time)
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Article
Modeling Spatiotemporal Rainfall Variability in Paraíba, Brazil
Water 2019, 11(9), 1843; https://doi.org/10.3390/w11091843 - 05 Sep 2019
Cited by 7 | Viewed by 1579
Abstract
The purpose of this study was to provide a detailed framework to use the spatiotemporal kriging to model the space-time variability of precipitation data in Paraíba, which is located in the northeastern region of Brazil (NEB). The NEB is characterized by an irregular, [...] Read more.
The purpose of this study was to provide a detailed framework to use the spatiotemporal kriging to model the space-time variability of precipitation data in Paraíba, which is located in the northeastern region of Brazil (NEB). The NEB is characterized by an irregular, highly variable distribution of rainfall in space and time. In this region, it is common to find high rates of rainfall at locations adjacent to those with no record of rain. Paraíba experiences localized periods of drought within rainy seasons and distinct precipitation patterns among the state’s mesoregions. The mean precipitation values observed at several irregularly spaced rain gauge stations from 1994 to 2014 showed remarkable variations among the mesoregions in Paraíba throughout the year. As a consequence of this behavior, there is a need to model the rainfall distribution jointly with space and time. A spatiotemporal geostatistical methodology was applied to monthly total rainfall data from the state of Paraíba. The rainfall data indicate intense spatial and temporal variabilities that directly affect the water resources of the entire region. The results provide a detailed spatial analysis of sectors experiencing precipitation conditions ranging from a scarcity to an excess of rainfall. The present study should help drive future research into spatiotemporal rainfall patterns across all of NEB. Full article
(This article belongs to the Special Issue Modelling Precipitation in Space and Time)
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