scholarly journals Identification of the Optimum Rain Gauge Network Density for Hydrological Modelling Based on Radar Rainfall Analysis

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1906
Author(s):  
Yeboah Gyasi-Agyei
Keyword(s):  
Scaling Laws ◽  
Rain Gauge ◽  
Hydrological Modelling ◽  
Network Density ◽  
Sampled Data ◽  
Radar Rainfall ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Performance Statistics ◽  
Rain Gauge Network

Rain gauges continue to be sources of rainfall data despite progress made in precipitation measurements using radar and satellite technology. There has been some work done on assessing the optimum rain gauge network density required for hydrological modelling, but without consensus. This paper contributes to the identification of the optimum rain gauge network density, using scaling laws and bias-corrected 1 km × 1 km grid radar rainfall records, covering an area of 28,371 km2 that hosts 315 rain gauges in south-east Queensland, Australia. Varying numbers of radar pixels (rain gauges) were repeatedly sampled using a unique stratified sampling technique. For each set of rainfall sampled data, a two-dimensional correlogram was developed from the normal scores obtained through quantile-quantile transformation for ordinary kriging which is a stochastic interpolation. Leave-one-out cross validation was carried out, and the simulated quantiles were evaluated using the performance statistics of root-mean-square-error and mean-absolute-bias, as well as their rates of change. A break in the scaling of the plots of these performance statistics against the number of rain gauges was used to infer the optimum rain gauge network density. The optimum rain gauge network density varied from 14 km2/gauge to 38 km2/gauge, with an average of 25 km2/gauge.

scholarly journals Influence of the rain gauge network on the performance of a hydrological lumped model applied at different basin scales

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Stefany Correia de Paula ◽  
Rutineia Tassi ◽  
Daniel Gustavo Allasia Piccilli ◽  
Francisco Lorenzini Neto
Keyword(s):  
Spatial Distribution ◽  
Monitoring Network ◽  
Rain Gauge ◽  
Network Density ◽  
Low Density ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Rain Gauges ◽  
The Poor ◽  
Rain Gauge Network

ABSTRACT In this study was evaluated the influence of the rainfall monitoring network density and distribution on the result of rainfall-runoff daily simulations of a lumped model (IPH II) considering basins with different drainage scales: Turvo River (1,540 km2), Ijuí River (9,462 km2), Jacuí River (38,700 km2) and Upper Uruguay (61,900 km2). For this purpose, four rain gauge coverage scenarios were developed: (I) 100%; (II) 75%; (III) 50% and (IV) 25% of the rain gauges of the basin. Additionally, a scenario considering the absence of monitoring was evaluated, in which the rainfall used in the modeling was estimated based on the TRMM satellite. Was verified that, in some situations, the modeling produced better results for scenarios with a lower rain gauges density if the available gauges presented better spatial distribution. Comparatively to the simulations performed with the rainfall estimated by the TRMM, the results obtained using rain gauges’ data were better, even in scenarios with low rain gauges density. However, when the poor spatial distribution of the rain gauges was associated with low density, the satellite’s estimation provided better results. Thus, was conclude that spatial distribution of the rain gauge network is important in the rainfall representation and that estimates obtained by the TRMM can be presented as alternatives for basins with a deficient monitoring network.

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.

scholarly journals Rainfall-Streamflow Relationship using Stepwise Method as a Basis for Rationalization of Rain Gauge Network Density

2020 ◽  
Vol 8 (5) ◽  
pp. 3814-3821
Keyword(s):  
Information Needs ◽  
Rain Gauge ◽  
Multiple Regression Model ◽  
Optimal Number ◽  
Network Density ◽  
Rainfall Variation ◽  
Rain Gauges ◽  
Stepwise Method ◽  
Streamflow Data ◽  
Rain Gauge Network

The design of rain gauge network density must be adjusted to meet the information needs of specific water uses, particularly in regard to availability of good quality and quantity of rainfall data. The study has an aim to conduct a rationalization to obtain an optimal number of rain gauge network density based on the WMO standard and the stepwise regression method. The rationalization of rain gauge network density using the stepwise method was carried out by examining the multiple correlation (r) and determination coefficient (R2 ) between rainfall and streamflow data and subsequently, to find out the rain gauges that contribute the most to the multiple regression model as a basis to determine the optimal number of rain gauge. The results found that the study area experienced a high density of rain gauge network refer to the WMO standard. The rationalization using the stepwise method showed that five rain gauges recommended as the optimal number of rain gauge. The percentage root mean square (rms) of basin rainfall showed values of 3.58% (less than 10%) which indicated that the recommended rain gauges have no significant problem regarding rainfall variation to determine basin rainfall. The study confirmed that the WMO standard and stepwise method approaches could be used as a sufficient tool to evaluate and rationalize a rain gauge network density in a river basin.

scholarly journals Citizen rain gauge improves hourly radar rainfall bias correction using a two-step Kalman filter

2021 ◽  
Author(s):  
Punpim Puttaraksa Mapiam ◽  
Monton Methaprayun ◽  
Thom Bogaard ◽  
Gerrit Schoups ◽  
Marie-Claire Ten Veldhuis
Keyword(s):  
Kalman Filter ◽  
Bias Correction ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Spatial Density ◽  
Limiting Factor ◽  
Rainfall Time Series ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Rain Gauge Network

Abstract. Low density of conventional rain gauge networks is often a limiting factor for radar rainfall bias correction. Citizen rain gauges offer a promising opportunity to collect rainfall data at higher spatial density. In this paper hourly radar rainfall bias adjustment was applied using two different rain gauge networks consisting of tipping buckets (measured by Thailand Meteorological Department, TMD) and daily citizen rain gauges in a two-step Kalman Filter approach. Radar reflectivity data of Sattahip radar station and gauge rainfall data from the TMD and citizen rain gauges located in Tubma basin, Thailand were used in the analysis. Daily data from the citizen rain gauge network were downscaled to hourly resolution based on temporal distribution patterns obtained from radar rainfall time series and the TMD gauge network. The radar rainfall bias correction factor was sequentially updated based on TMD and citizen rain gauge data using a Kalman Filter. Results show that an improvement of radar rainfall estimates was achieved by including the downscaled citizen observations compared to bias correction based on the conventional rain gauge network only. These outcomes emphasize the value of citizen rainfall observations for radar bias correction, in particular in regions where conventional rain gauge networks are sparse.

scholarly journals Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

2021 ◽  
Vol 13 (15) ◽  
pp. 2922
Author(s):  
Yang Song ◽  
Patrick D. Broxton ◽  
Mohammad Reza Ehsani ◽  
Ali Behrangi
Keyword(s):  
Snow Water Equivalent ◽  
Stage Iv ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
Correction Factors ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Snow Properties ◽  
Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

scholarly journals Time-dependent <i>Z-R</i> relationships for estimating rainfall fields from radar measurements

2010 ◽  
Vol 10 (1) ◽  
pp. 149-158 ◽  
Author(s):  
L. Alfieri ◽  
P. Claps ◽  
F. Laio
Keyword(s):  
Time Estimation ◽  
Rain Gauge ◽  
Rainfall Rate ◽  
Time Step ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Weather Radars ◽  
The North ◽  
Real Time Estimation ◽  
Power Law Equation

Abstract. The operational use of weather radars has become a widespread and useful tool for estimating rainfall fields. The radar-gauge adjustment is a commonly adopted technique which allows one to reduce bias and dispersion between radar rainfall estimates and the corresponding ground measurements provided by rain gauges. This paper investigates a new methodology for estimating radar-based rainfall fields by recalibrating at each time step the reflectivity-rainfall rate (Z-R) relationship on the basis of ground measurements provided by a rain gauge network. The power-law equation for converting reflectivity measurements into rainfall rates is readjusted at each time step, by calibrating its parameters using hourly Z-R pairs collected in the proximity of the considered time step. Calibration windows with duration between 1 and 24 h are used for estimating the parameters of the Z-R relationship. A case study pertaining to 19 rainfall events occurred in the north-western Italy is considered, in an area located within 25 km from the radar site, with available measurements of rainfall rate at the ground and radar reflectivity aloft. Results obtained with the proposed method are compared to those of three other literature methods. Applications are described for a posteriori evaluation of rainfall fields and for real-time estimation. Results suggest that the use of a calibration window of 2–5 h yields the best performances, with improvements that reach the 28% of the standard error obtained by using the most accurate fixed (climatological) Z-R relationship.

scholarly journals Evaluation of the High-Resolution CMORPH Satellite Rainfall Product Using Dense Rain Gauge Observations and Radar-Based Estimates

2012 ◽  
Vol 13 (6) ◽  
pp. 1784-1798 ◽  
Author(s):  
Emad Habib ◽  
Alemseged Tamiru Haile ◽  
Yudong Tian ◽  
Robert J. Joyce
Keyword(s):  
Rain Gauge ◽  
Random Errors ◽  
Event Scale ◽  
Entire Study ◽  
Rain Gauges ◽  
Fine Resolution ◽  
Rain Rates ◽  
Climate Prediction Center ◽  
Rain Gauge Network ◽  
Insight Into

Abstract This study focuses on the evaluation of the NOAA–NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based rainfall product at fine space–time resolutions (1 h and 8 km). The evaluation was conducted during a 28-month period from 2004 to 2006 using a high-quality experimental rain gauge network in southern Louisiana, United States. The dense arrangement of rain gauges allowed for multiple gauges to be located within a single CMORPH pixel and provided a relatively reliable approximation of pixel-average surface rainfall. The results suggest that the CMORPH product has high detection skills: the probability of successful detection is ~80% for surface rain rates &gt;2 mm h−1 and probability of false detection &lt;3%. However, significant and alarming missed-rain and false-rain volumes of 21% and 22%, respectively, were reported. The CMORPH product has a negligible bias when assessed for the entire study period. On an event scale it has significant biases that exceed 100%. The fine-resolution CMORPH estimates have high levels of random errors; however, these errors get reduced rapidly when the estimates are aggregated in time or space. To provide insight into future improvements, the study examines the effect of temporal availability of passive microwave rainfall estimates on the product accuracy. The study also investigates the implications of using a radar-based rainfall product as an evaluation surface reference dataset instead of gauge observations. The findings reported in this study guide future enhancements of rainfall products and increase their informed usage in a variety of research and operational applications.

Gridded Hourly Precipitation Analysis from High-Density Rain Gauge Network over the Yangtze–Huai Rivers Basin during the 2007 Mei-Yu Season and Comparison with CMORPH

2013 ◽  
Vol 14 (4) ◽  
pp. 1243-1258 ◽  
Author(s):  
Yali Luo ◽  
Weimiao Qian ◽  
Renhe Zhang ◽  
Da-Lin Zhang
Keyword(s):  
Heavy Rainfall ◽  
Rain Gauge ◽  
Rainfall Analysis ◽  
Hourly Precipitation ◽  
Spatial Consistency ◽  
Hourly Rainfall ◽  
Reflectivity Data ◽  
Climate Prediction Center ◽  
Rain Gauge Network

Abstract Heavy rainfall hit the Yangtze–Huai Rivers basin (YHRB) of east China several times during the prolonged 2007 mei-yu season, causing the worst flood since 1954. There has been an urgent need for attaining and processing high-quality, kilometer-scale, hourly rainfall data in order to understand the mei-yu precipitation processes, especially at the mesoβ and smaller scales. In this paper, the authors describe the construction of the 0.07°-resolution gridded hourly rainfall analysis over the YHRB region during the 2007 mei-yu season that is based on surface reports at 555 national and 6572 regional automated weather stations with an average resolution of about 7 km. The gridded hourly analysis is obtained using a modified Cressman-type objective analysis after applying strict quality control, including not only the commonly used internal temporal and spatial consistency and extreme value checks, but also verifications against mosaic radar reflectivity data. This analysis reveals many convectively generated finescale precipitation structures that could not be seen from the national station reports. A comprehensive quantitative assessment ensures the quality of the gridded hourly precipitation data. A comparison of this dataset with the U.S. Climate Prediction Center morphing technique (CMORPH) dataset on the same resolution suggests the dependence of the latter's performance on different rainfall intensity categories, with substantial underestimation of the magnitude and width of the mei-yu rainband as well as the nocturnal and morning peak rainfall amounts, due mainly to its underestimating the occurrences of heavy rainfall (i.e., &gt;10 mm h−1).

scholarly journals Estimation of convective precipitation: the meteorological radar versus an automatic rain gauge network

2005 ◽  
Vol 2 ◽  
pp. 103-109 ◽  
Author(s):  
M. C. Llasat ◽  
T. Rigo ◽  
M. Ceperuelo ◽  
A. Barrera
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Space Distribution ◽  
Convective Precipitation ◽  
Precipitation Event ◽  
Convective Rainfall ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Meteorological Radar ◽  
Rain Gauge Network

Abstract. The estimation of convective precipitation and its contribution to total precipitation is an important issue both in hydrometeorology and radio links. The greatest part of this kind of precipitation is related with high intensity values that can produce floods and/or damage and disturb radio propagation. This contribution proposes two approaches for the estimation of convective precipitation, using the β parameter that is related with the greater or lesser convective character of the precipitation event, and its time and space distribution throughout the entire series of the samples. The first approach was applied to 126 rain gauges of the Automatic System of Hydrologic Information of the Internal Basins of Catalonia (NE Spain). Data are series of 5-min rain rate, for the period 1996-2002, and a long series of 1-min rain rate starting in 1927. Rainfall events were classified according to this parameter. The second approach involved using information obtained by the meteorological radar located near Barcelona. A modified version of the SCIT method for the 3-D analysis and a combination of different methods for the 2-D analysis were applied. Convective rainfall charts and β charts were reported. Results obtained by the rain gauge network and by the radar were compared. The application of the β parameter to improve the rainfall regionalisation was demonstrated.

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