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 A Study of the Influence of the Spatial Distribution of Rain Gauge Networks on Areal Average Rainfall Calculation

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1635 ◽  
Author(s):  
Jiho Lee ◽  
Soojun Kim ◽  
Hwandon Jun
Keyword(s):  
Spatial Distribution ◽  
Estimation Error ◽  
Case Analysis ◽  
Rain Gauge ◽  
Spatial Characteristic ◽  
Rain Gauges ◽  
First Case ◽  
Average Rainfall ◽  
Major River ◽  
Rain Gauge Network

Estimating the AAR (Areal Average Rainfall) is an essential process when determining the accurate amount of available water resources and building the input data which is integral to the Rainfall-Runoff Analysis. To estimate the AAR, using rain gauge networks that are spatially well distributed is ideal. In this study, the spatial characteristics of the rain gauge networks for the five major river basins in South Korea are considered and the amount of influence the spatial distribution has on the estimation of the AAR is evaluated. For this purpose, the estimation error for AAR is calculated for two cases. The first case (Analysis 1) compares the value of the estimation error of the AAR from two different basins where one has well distributed rain gauges while the other does not. The second case (Analysis 2) estimates the estimation error of two different rain gauge distributions for the same basin. The spatial characteristic of the rain gauge network is evaluated by using the NNI (Nearest Neighbour Index), while the Arithmetic Mean Method, Thiessen Method and the Estimation Theory are applied to calculate the AAR. From Analysis 1, we are able to prove that the estimation error of the AAR is relatively small in the basins with that have spatially well distributed rain gauge networks whereas the estimation error is relatively large when the spatial distribution of the rain gauge network is clustered. Also, results from Analysis 2 showed that not only is the spatial distribution of the rain gauge networks important, but that the density has a significant influence on accurately calculating the AAR. The results from this study can be applied towards the ideal establishment of the rain gauge networks.

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 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 Benefits from high-density rain gauge observations for hydrological response analysis in a small alpine catchment

2021 ◽  
Vol 25 (4) ◽  
pp. 2301-2325
Author(s):  
Anthony Michelon ◽  
Lionel Benoit ◽  
Harsh Beria ◽  
Natalie Ceperley ◽  
Bettina Schaefli
Keyword(s):  
Rain Gauge ◽  
High Density ◽  
Swiss Alps ◽  
Hydrologic Response ◽  
Hydrological Response ◽  
Rainfall Runoff ◽  
Analysis Framework ◽  
Stream Network ◽  
Rain Gauges ◽  
Rain Gauge Network

Abstract. Spatial rainfall patterns exert a key control on the catchment-scale hydrologic response. Despite recent advances in radar-based rainfall sensing, rainfall observation remains a challenge, particularly in mountain environments. This paper analyzes the importance of high-density rainfall observations for a 13.4 km2 catchment located in the Swiss Alps, where rainfall events were monitored during 3 summer months using a network of 12 low-cost, drop-counting rain gauges. We developed a data-based analysis framework to assess the importance of high-density rainfall observations to help predict the hydrological response. The framework involves the definition of spatial rainfall distribution metrics based on hydrological and geomorphological considerations and a regression analysis of how these metrics explain the hydrologic response in terms of runoff coefficient and lag time. The gained insights on dominant predictors are then used to investigate the optimal rain gauge network density for predicting the streamflow response metrics, including an extensive test of the effect of down-sampled rain gauge networks and an event-based rainfall–runoff model to evaluate the resulting optimal rain gauge network configuration. The analysis unravels that, besides rainfall amount and intensity, the rainfall distance from the outlet along the stream network is a key spatial rainfall metric. This result calls for more detailed observations of stream network expansions and the parameterization of along-stream processes in rainfall–runoff models. In addition, despite the small spatial scale of this case study, the results show that an accurate representation of the rainfall field (with at least three rain gauges) is of prime importance for capturing the key characteristics of the hydrologic response in terms of generated runoff volumes and delay for the studied catchment (0.22 rain gauges per square kilometer). The potential of the developed rainfall monitoring and analysis framework for rainfall–runoff analysis in small catchments remains to be fully unraveled in future studies, potentially also including urban catchments.

Evaluation of optimal rain gauge network density for rainfall-runoff modelling

2015 ◽  
Author(s):  
Valeria Montesarchio ◽  
Dario Orlando ◽  
Denise Del Bove ◽  
Francesco Napolitano ◽  
Stefano Magnaldi
Keyword(s):  
Rain Gauge ◽  
Network Density ◽  
Rainfall Runoff ◽  
Rain Gauge Network

scholarly journals Evaluation of Satellite Based Precipitations and Their Applicability for Rainfall Runoff Modelling in Narayani Basin of Nepal

2016 ◽  
Vol 8 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Sunil Ghaju ◽  
Knut Alfredsen
Keyword(s):  
Bias Correction ◽  
Rain Gauge ◽  
Extensive Study ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Satellite Precipitation ◽  
Precipitation Estimates ◽  
Set Up ◽  
Rain Gauge Network ◽  
Precipitation Station

High spatial variability of precipitation over Nepal demands dense network of rain-gauge stations. But to set-up a dense rain gauge network is almost impossible due to mountainous topography of Nepal. Also the dense rain gauge network will be very expensive and some time impossible for timely maintenance. Satellite precipitation products are an alternative way to collect precipitation data with high temporal and spatial resolution over Nepal. In this study, the satellite precipitation products TRMM and GSMaP were analyzed. Precipitation was compared with ground based gauge precipitation in the Narayani basin, while the applicability of these rainfall products for runoff simulation were tested using the LANDPINE model for Trishuli basin which is a sub-basin within Narayani catchment. The Nash-Sutcliffe efficiency calculated for TRMM and GSMaP from point to pixel comparison is negative for most of stations. Also the estimation bias for both the products is negative indicating under estimation of precipitation by satellite products, with least under estimation for the GSMaP precipitation product. After point to pixel comparison, satellite precipitation estimates were used for runoff simulation in the Trishuli catchment with and without bias correction for each product. Among the two products, TRMM shows good simulation result without any bias correction for calibration and validation period with scaling factor of 2.24 for precipitation which is higher than that for gauge precipitation. This suggests, it could be used for runoff simulation to the catchments where there is no precipitation station. But it is too early to conclude by just looking into one catchment. So extensive study need to be done to make such conclusion.Journal of Hydrology and Meteorology, Vol. 8(1) p.22-31

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 >2 mm h−1 and probability of false detection <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.

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.

scholarly journals Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

2017 ◽  
Author(s):  
Igor Paz ◽  
Bernard Willinger ◽  
Auguste Gires ◽  
Laurent Monier ◽  
Christophe Zobrist ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Radar Data ◽  
Rain Gauge ◽  
Distributed Hydrological Model ◽  
Space Resolution ◽  
Rain Gauges ◽  
X Band ◽  
Distributed Simulations ◽  
Rain Gauge Network ◽  
Catchment Model

This paper presents a comparison between rain gauges, C-band and X-band radar data over an instrumented and regulated catchment of the Paris region, as well as their respective hydrological impacts with the help of flow observations and a semi-distributed hydrological model. Both radars confirm the high spatial variability of the rainfall down to their space resolution (respectively one kilometer and 250 m) and therefore underscore limitations of semi-distributed simulations. The use of the polarimetric capacity of the Météo-France C-band radar was limited to corrections of the horizontal reflectivity and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, neither calibration was performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (below called ENPC X-band radar), nor any optimization of its scans. In spite of that and the non-negligible fact that the catchment was much closer to the C-band radar than to the X-band radar (20 km vs. 40 km), the latter seems to perform at least as well as the former, but with a higher scale resolution. This characteristic was best highlighted with the help of a multifractal analysis of the respective radar data, which also shows that the X-band radar was able to pick up a few extremes that were smoothed out by the C-band radar.

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