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 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 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 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 The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model

2018 ◽  
Vol 07 (01) ◽  
pp. 27-50
Author(s):  
George Andiego ◽  
Muhammad Waseem ◽  
Muhammad Usman ◽  
Nithish Mani
Keyword(s):  
Hydrological Model ◽  
Rain Gauge ◽  
Network Density ◽  
Rain Gauge Network

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.

The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model

2018 ◽  
Vol 08 (01) ◽  
pp. 27-50
Author(s):  
George Andiego ◽  
Muhammad Waseem ◽  
Muhammad Usman ◽  
Nithish Mani
Keyword(s):  
Hydrological Model ◽  
Rain Gauge ◽  
Network Density ◽  
Rain Gauge Network

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.

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