scholarly journals Spatial Diagnosis of Rain Gauges’ Distribution and Flood Impacts: Case Study in Itaperuna, Rio de Janeiro—Brazil

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1120 ◽  
Author(s):  
Priscila Celebrini de Oliveira Campos ◽  
Igor Paz
Keyword(s):  
Fractal Dimension ◽  
Rio De Janeiro ◽  
Rainfall Variability ◽  
Probability Of Detection ◽  
Extreme Weather Events ◽  
Small Scale ◽  
Essential Information ◽  
Rain Gauges ◽  
Flood Susceptibility ◽  
Susceptibility Maps

The global increase of urban areas highlights the need to improve their adaptation to extreme weather events, in particular heavy rainfall. This study analyzes the impacts of in-situ rain gauges’ distribution (by applying the fractal dimension concept) associated with a spatial diagnosis of flood occurrences in the municipality of Itaperuna, Rio de Janeiro–Brazil, performing an investigation of flood susceptibility maps based on transitory (considering precipitation) and on permanent factors (natural flood susceptibility). The fractal analysis results pointed out that the rain gauges’ distribution presented a scaling break behavior with a low fractal dimension ( 0.416 ) at the small-scale range, highlighting the incapacity of the local instrumentation to capture the spatial rainfall variability. Thereafter, the cross-tabulation method was used to validate both predictive maps with recorded data of the major January 2020 event, which indicated that the transitory factors’ flood map presented an unsatisfactory Probability of Detection of floods ( P O D = 0.552 ) when compared to the permanent factors’ map ( P O D = 0.944 ) . These issues allowed to consider the hydrological uncertainties associated with the sparse gauge network distribution and its impacts on the use of flood susceptibility maps. Such methodology enables the evaluation of other municipalities and regions, constituting essential information in aid of territorial management.

Fractal analysis of rain gauges’ inhomogeneous distribution and the associated hydrological impacts: the case study of Muriaé River basin

2021 ◽  
Author(s):  
Priscila Celebrini de O. Campos ◽  
Igor Paz ◽  
Maria Esther Soares Marques ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer
Keyword(s):  
Fractal Dimension ◽  
Spatial Analysis ◽  
Fractal Analysis ◽  
Probability Of Detection ◽  
Susceptibility Map ◽  
Rain Gauges ◽  
Flood Susceptibility ◽  
Susceptibility Maps ◽  
Cross Tabulation ◽  
The Cross

<p>The urban population growth requires an improvement in the resilient behavior of these areas to extreme weather events, especially heavy rainfall. In this context, well-developed urban planning should address the problems of infrastructure, sanitation, and installation of communities, primarily related to insufficiently gauged locations. The main objectives of this study were to analyze the impacts of in-situ rain gauges’ distribution associated with the elaboration of a spatial diagnosis of the occurrence of floods in the municipality of Itaperuna, Rio de Janeiro – Brazil. The methodology consisted of the spatial analysis of rain gauges’ distribution with the help of the fractal dimension concept and investigation of flood susceptibility maps prepared by the municipality based on transitory factors (which consider precipitation in the modeling) and on permanent factors (natural flood susceptibility). Both maps were validated by the cross-tabulation method, crossing each predictive map with the recorded data of flood spots measured during a major rainfall event. The results pointed that the fractal analysis of the rain gauges’ distribution presented a scaling break behavior with a low fractal dimension at the small-scale range, mostly concerned in (semi-)urban catchments, highlighting the incapacity of the local instrumentation to capture the spatial rainfall variability. Thereafter, the cross-tabulation validation method indicated that the flood susceptibility map based on transitory factors presented an unsatisfactory probability of detection of floods when compared to the map based on permanent factors. These results allowed us to take into account the hydrological uncertainties concerning the insufficient gauge network and the impacts of the sparse distribution on the choice and elaboration of flood susceptibility maps that use rainfall data as input. Finally, we performed a spatial analysis to estimate the population and habitations that can be affected by floods using the flood susceptibility map based on permanent factors.</p>

A dense network of rain gauges within an urban area: Rainfall uncertainty and variability

2021 ◽  
Author(s):  
Luc Neppel ◽  
Pierre Marchand ◽  
Pascal Finaud-Guyot ◽  
Vincent Guinot ◽  
Christian Salles
Keyword(s):  
Spatial Variability ◽  
Urban Area ◽  
Urban Areas ◽  
Rainfall Variability ◽  
Temporal Structure ◽  
Drainage System ◽  
Spatiotemporal Variability ◽  
Small Scale ◽  
Rain Gauges ◽  
Rain Gauges Network

<p>This study presents a new high density rain gauges network installed in urban area to study spatio-temporal structure and variability of precipitation at small scales. The preliminary results concerning gauges calibration and characterization of the rainfall spatial variability at fine scale are discussed.</p><p>In urban areas, the impervious surfaces connected to the drainage system leads to highly dynamic flows. The flood and runoff risk characterization requires  fine spatiotemporal scale to describe hydrological model input data :rainfall within spatial scale of less than 1km and temporal scale close to 1minis necessary for urban hydrological applications and risk assessment. In order to characterize small-scale rainfall spatiotemporal variability, a dense rain gauges network is deployed at Montpellier (France) with inter-gauges distances from 100m to 1km. Currently, 9 tipping bucket rain gauges  associated with 9 anemometers are acquiring rainfall and wind norm intensity every minutes. The network density and extension will be increased soon.</p><p>The first year measurements highlight a spatial variability of the 1-minute rainfall at the subkilometer scale. This observed variability is analyzed in view of the measurement uncertainty (gauge calibration, gauge error, bias due to the gauge location) to identify the natural rainfall variability.</p><p>This contribution presents the new densely extensive rainfall  network measurement, the typing bucket raingauge calibration and highlights that the observed 1-minute rainfall intensity variability  is significant and cannot be only explained by the measurement uncertainties.</p>

scholarly journals A Field Experiment on the Small-Scale Variability of Rainfall Based on a Network of Micro Rain Radars and Rain Gauges

2015 ◽  
Vol 54 (1) ◽  
pp. 243-255 ◽  
Author(s):  
Yong Chen ◽  
Huizhi Liu ◽  
Junling An ◽  
Ulrich Görsdorf ◽  
Franz H. Berger
Keyword(s):  
Spatial Scales ◽  
Rainfall Variability ◽  
Summer Rainfall ◽  
Small Scale ◽  
Precipitation Radar ◽  
River Catchment ◽  
Rain Gauges ◽  
Straight Line ◽  
Areal Rainfall ◽  
Xilin River Catchment

AbstractSmall-scale summer rainfall variability in a semiarid zone was studied by deploying five vertically pointing Micro Rain Radars (MRRs) along a nearly straight line and by using 12 rain gauges in the study area of the Xilin River catchment in China. The spatial scales of 4 and 9 km correspond to the resolution of precipitation radar and rainfall products from satellites. The dataset of the MRRs and rain gauges covers two months in the summer of 2009. Three parameters, that is, spatial correlation, intermittency, and the coefficient of variation (CV), were used to describe the rainfall variability as based on the data from the MRRs and rain gauges. The probability of partial beamfilling in a 4-km (9 km) pixel over a 30-min temporal scale was 17%–20% (28%–37%). More accurate equipment can measure lower rainfall intermittency. For scales of 4 and 9 km, the median CV of the accumulation times that were longer than 3 h with rainfall > 1 mm was 0.17–0.42. The accuracy of areal rainfall measured by different quantities of equipment was also evaluated. One MRR was sufficient for measuring the daily areal rainfall at a 4-km scale, with a fraction of prediction within a factor of 2 of observations of 1.0 and a correlation coefficient of ≥0.58 when daily mean rainfall was >1 mm.

scholarly journals Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network

2013 ◽  
Vol 17 (6) ◽  
pp. 2195-2208 ◽  
Author(s):  
N. Peleg ◽  
M. Ben-Asher ◽  
E. Morin
Keyword(s):  
Temporal Variability ◽  
Rainfall Variability ◽  
Rain Gauge ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Daily Scale ◽  
Rain Gauge Network ◽  
Hydrological Applications

Abstract. Runoff and flash flood generation are very sensitive to rainfall's spatial and temporal variability. The increasing use of radar and satellite data in hydrological applications, due to the sparse distribution of rain gauges over most catchments worldwide, requires furthering our knowledge of the uncertainties of these data. In 2011, a new super-dense network of rain gauges containing 14 stations, each with two side-by-side gauges, was installed within a 4 km2 study area near Kibbutz Galed in northern Israel. This network was established for a detailed exploration of the uncertainties and errors regarding rainfall variability within a common pixel size of data obtained from remote sensing systems for timescales of 1 min to daily. In this paper, we present the analysis of the first year's record collected from this network and from the Shacham weather radar, located 63 km from the study area. The gauge–rainfall spatial correlation and uncertainty were examined along with the estimated radar error. The nugget parameter of the inter-gauge rainfall correlations was high (0.92 on the 1 min scale) and increased as the timescale increased. The variance reduction factor (VRF), representing the uncertainty from averaging a number of rain stations per pixel, ranged from 1.6% for the 1 min timescale to 0.07% for the daily scale. It was also found that at least three rain stations are needed to adequately represent the rainfall (VRF < 5%) on a typical radar pixel scale. The difference between radar and rain gauge rainfall was mainly attributed to radar estimation errors, while the gauge sampling error contributed up to 20% to the total difference. The ratio of radar rainfall to gauge-areal-averaged rainfall, expressed by the error distribution scatter parameter, decreased from 5.27 dB for 3 min timescale to 3.21 dB for the daily scale. The analysis of the radar errors and uncertainties suggest that a temporal scale of at least 10 min should be used for hydrological applications of the radar data. Rainfall measurements collected with this dense rain gauge network will be used for further examination of small-scale rainfall's spatial and temporal variability in the coming years.

Mapping global water projects: improving access to donor investment information on the web

Water Policy ◽  
2003 ◽  
Vol 5 (3) ◽  
pp. 203-212
Author(s):  
J. Lisa Jorgensona
Keyword(s):  
Water Resources ◽  
Web Sites ◽  
Large Scale ◽  
Technical Assistance ◽  
Water Withdrawal ◽  
Small Scale ◽  
Water Projects ◽  
Essential Information ◽  
Water Project ◽  
The Web

This paper discusses a series of discusses how web sites now report international water project information, and maps the combined donor investment in more than 6000 water projects, active since 1995. The maps show donor investment:  • has addressed water scarcity,  • has improved access to improvised water resources,  • correlates with growth in GDP,  • appears to show a correlation with growth in net private capital flow,  • does NOT appear to correlate with growth in GNI. Evaluation indicates problems in the combined water project portfolios for major donor organizations: •difficulties in grouping projects over differing Sector classifications, food security, or agriculture/irrigation is the most difficult.  • inability to map donor projects at the country or river basin level because 60% of the donor projects include no location data (town, province, watershed) in the title or abstracts available on the web sites.  • no means to identify donor projects with utilization of water resources from training or technical assistance.  • no information of the source of water (river, aquifer, rainwater catchment).  • an identifiable quantity of water (withdrawal amounts, or increased water efficiency) is not provided.  • differentiation between large scale verses small scale projects. Recommendation: Major donors need to look at how the web harvests and combines their information, and look at ways to agree on a standard template for project titles to include more essential information. The Japanese (JICA) and the Asian Development Bank provide good models.

scholarly journals A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

2021 ◽  
Vol 13 (6) ◽  
pp. 1208
Author(s):  
Linfei Yu ◽  
Guoyong Leng ◽  
Andre Python ◽  
Jian Peng
Keyword(s):  
Comprehensive Evaluation ◽  
Probability Of Detection ◽  
Light Rain ◽  
Spatial And Temporal Scales ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Major River ◽  
Low Levels ◽  
Rain Events ◽  
Early Late

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

Impact of small-scale spatial rainfall variability on runoff modeling

1995 ◽  
Vol 173 (1-4) ◽  
pp. 309-326 ◽  
Author(s):  
Jean-Marc Faurès ◽  
D.C. Goodrich ◽  
David A. Woolhiser ◽  
Soroosh Sorooshian
Keyword(s):  
Rainfall Variability ◽  
Small Scale ◽  
Runoff Modeling

Climate change could intensify Sahel conflicts

2022 ◽  
Keyword(s):  
Climate Change ◽  
Security Policy ◽  
Rainfall Variability ◽  
Political Stability ◽  
Climate Impacts ◽  
Small Scale ◽  
Content Type ◽  
Droughts And Floods ◽  
Political Conditions ◽  
Small Scale Irrigation

Significance It will increase rainfall variability and extreme events such as droughts and floods, as well as raising temperatures. These effects may trigger cascading risks to economic, social and political stability. Impacts The EU could play a key role in moderating climate effects as it shapes migration and security policy in the Sahel. The likelihood and severity of climate impacts will depend on socio-economic and political conditions in the region. Small-scale irrigation, climate-adapted seeds and traditional soil conservation techniques can help increase resilience to climate change.

A Comparative Assessment of Multi-criteria Decision Analysis for Flood Susceptibility Modeling 

2021 ◽  
Author(s):  
Ehsan Shahiri Tabarestani ◽  
Hossein Afzalimehr
Keyword(s):  
Characteristic Curve ◽  
Drainage Density ◽  
Weight Of Evidence ◽  
Mazandaran Province ◽  
Conditioning Factors ◽  
Flood Susceptibility ◽  
Susceptibility Modeling ◽  
Susceptibility Maps ◽  
Reliable Model ◽  
First Time

Abstract Floods are one of the most damaging natural disasters throughout the world. The purpose of this study is to develop a reliable model for identification of flood susceptible areas. Three Multi-criteria decision-making techniques, namely Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Attributive Border Approximation Area Comparison (MABAC) methods combined with weight of evidence (WOE) were used in Mazandaran Province, Iran. MABAC method is applied to determine the flood susceptibility in this study, for the first time. At first, 160 flood locations were identified in the study area, of which 112 (70%) locations were selected randomly for modeling, and the remaining 48 (30%) locations were used for validation. Using Geographic Information System (GIS) with eight conditioning factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood susceptibility maps were prepared. The results showed that the area under receiver operating characteristic curve (AUROC) for the test data of AHP-WOE, TOPSIS-WOE-AHP, and MABAC-WOE-AHP methods were 75.3%, 91.6%, and 86.1%, respectively, which indicate the reasonable accuracy of models. High accuracy of the proposed new model (MABAC) clarifies its applicability for preventive measures.

scholarly journals Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

2017 ◽  
Vol 10 (3) ◽  
pp. 1383-1402 ◽  
Author(s):  
Paolo Davini ◽  
Jost von Hardenberg ◽  
Susanna Corti ◽  
Hannah M. Christensen ◽  
Stephan Juricke ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Rainfall Variability ◽  
Global Climate Model ◽  
Small Scale ◽  
Low Resolution ◽  
The Impact ◽  
Stochastic Physics

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

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