Effect of rain gauge density over the accuracy of rainfall: a case study over Bangalore, India

Mishra, Anoop Kumar

doi:10.1186/2193-1801-2-311

Cited by 44 publications

(26 citation statements)

References 4 publications

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“…World Meteorological Organization (WMO) guidelines indicated a number of stations per km 2 ranging from one station per 100 km 2 for complex, mountain terrain to one station per 10,000 km 2 in arid and polar deserts [60]. Nonetheless, uncertainties are often calculated with different methods and metrics depending on the region of interest and the applications for which precipitation is needed (e.g., [61,62]). Mishra, for instance, suggested that for southern India the acceptable rain gauge density for reproducing significantly total precipitation was around 1 station per 350 km 2 [61].…”

Section: Several Reasons Determined the Choice Of The Datasetsmentioning

confidence: 99%

Validation of Satellite, Reanalysis and RCM Data of Monthly Rainfall in Calabria (Southern Italy)

2019

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Skills in reproducing monthly rainfall over Calabria (southern Italy) have been validated for the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) satellite data, the E-OBS dataset and 13 Global Climate Model-Regional Climate Model (GCM-RCM) combinations, belonging to the ENSEMBLES project output set. To this aim, 73 rainfall series for the period 1951–1980 and 79 series for the period 1981–2010 have been selected from the database managed by Multi-Risk Functional Centre of the Regional Agency for Environmental Protection (Regione Calabria). The relative mean and standard deviation errors, and the Pearson correlation coefficient have been used as validation metrics. Results showed that CHIRPS satellite data (available only for the 1981–2010 validation period) and RCMs based on the ECHAM5 Global Climate performed better both in mean error and standard deviation error compared to other datasets. Moreover, a slight appreciable improvement in performance for all ECHAM5-based models and for the E-OBS dataset has been observed in the 1981–2010 time-period. The whole validation-and-assessment procedure applied in this work is general and easily applicable where ground data and gridded data are available. This procedure might help scientists and policy makers to select among available datasets those best suited for further applications, even in regions with complex orography and an inadequate amount of representative stations.

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Section: Several Reasons Determined the Choice Of The Datasetsmentioning

confidence: 99%

Validation of Satellite, Reanalysis and RCM Data of Monthly Rainfall in Calabria (Southern Italy)

2019

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“…Gauge data are traditionally the most widely used data type for hydrological modelling. However, they pose multiple problems such as gauge undercatch [9] and the high costs of supporting dense networks of gauges which are crucial for reliable areal precipitation estimates [10], in particular during intensive and spatially variable rainfall events causing flash floods [11]. Reanalysis data products such as the WATCH Forcing Data (Water and Global Change, "WFD"; [12]) are promising in that they usually cover long time periods (100 years in the case of the WFD), but their spatial resolution is not sufficient for modelling of small and medium-sized catchments.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Hydrological Simulations by Applying Daily Precipitation Interpolation Schemes in Meso-Scale Catchments

Szcześniak

Piniewski

2015

Water

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Abstract:Ground-based precipitation data are still the dominant input type for hydrological models. Spatial variability in precipitation can be represented by spatially interpolating gauge data using various techniques. In this study, the effect of daily precipitation interpolation methods on discharge simulations using the semi-distributed SWAT (Soil and Water Assessment Tool) model over a 30-year period is examined. The study was carried out in 11 meso-scale (119-3935 km 2 ) sub-catchments lying in the Sulejów reservoir catchment in central Poland. Four methods were tested: the default SWAT method (Def) based on the Nearest Neighbour technique, Thiessen Polygons (TP), Inverse Distance Weighted (IDW) and Ordinary Kriging (OK). =The evaluation of methods was performed using a semi-automated calibration program SUFI-2 (Sequential Uncertainty Fitting Procedure Version 2) with two objective functions: Nash-Sutcliffe Efficiency (NSE) and the adjusted R 2 coefficient (bR 2 ). The results show that: (1) the most complex OK method outperformed other methods in terms of NSE; and (2) OK, IDW, and TP outperformed Def in terms of bR 2 . The median difference in daily/monthly NSE between OK and Def/TP/IDW calculated across all catchments ranged between 0.05 and 0.15, while the median difference between TP/IDW/OK and Def ranged between 0.05 and 0.07. The differences between pairs of interpolation methods were, however, spatially variable and a part of this variability was attributed to catchment properties: catchments characterised by low station density and low OPEN ACCESSWater 2015, 7 748 coefficient of variation of daily flows experienced more pronounced improvement resulting from using interpolation methods. Methods providing higher precipitation estimates often resulted in a better model performance. The implication from this study is that appropriate consideration of spatial precipitation variability (often neglected by model users) that can be achieved using relatively simple interpolation methods can significantly improve the reliability of model simulations.

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“…Several researchers such as, refs. [10][11][12] pointed out the increasing uncertainties regarding spatial estimation of precipitation from point measurements mainly for short periods. Ref.…”

Section: Spatial And/or Temporal Continuitymentioning

confidence: 99%

Climatic Uncertainty in the Mediterranean Basin and Its Possible Relevance to Important Economic Sectors

Kutiel

2019

Atmosphere

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The Mediterranean Basin is among the densest populated regions of the world with forecasts for a further population increase in the coming decades. Agriculture and tourism are two main economic activities of this region. Both activities depend highly on climate and weather conditions. Climate and weather in turn, present a large variability both in space and in time which results in different uncertainty types. Any change in weather and or climate conditions in the coming decades due to climate change may increase this uncertainty. Temporal uncertainty is discussed in detail and different ways of how to exhibit it are presented with examples from various locations in the Mediterranean basin. Forecasted increased uncertainty may in turn increase future challenges for long term planning and managing of agriculture and tourism in that part of the world.

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Effect of rain gauge density over the accuracy of rainfall: a case study over Bangalore, India

Cited by 44 publications

References 4 publications

Validation of Satellite, Reanalysis and RCM Data of Monthly Rainfall in Calabria (Southern Italy)

Validation of Satellite, Reanalysis and RCM Data of Monthly Rainfall in Calabria (Southern Italy)

Improvement of Hydrological Simulations by Applying Daily Precipitation Interpolation Schemes in Meso-Scale Catchments

Climatic Uncertainty in the Mediterranean Basin and Its Possible Relevance to Important Economic Sectors

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