Abstract:Accurate estimation of precipitation and its spatial variability is crucial for reliable discharge simula-tions. Although radar and satellite based techniques are becoming increasingly widespread, quantitative precipita-tion estimates based on point rain gauge measurement inter-polation are, and will continue to be in the foreseeable future, widely used. However, the ability to infer spatially distributed data from point measurements is strongly dependent on the number, location and reliability of meas-urement… Show more
“…When grouping the events by the maximum measured hourly intensity of any rain gauge of each event as done in Figure 4 into four equally sized groups of events, it is apparent that more intense events show a much higher spatial variability than the less intense ones. This is supported by the results of Girons Lopez et al [43] who came to the same conclusion. This does not mean that the deviation is negligible for events with smaller intensities than 26 mm/h (see Figure 4a,b).…”
Section: Resultssupporting
confidence: 77%
“…Using precipitation interpolations from a dense rain gauge network and comparing it to a combination of radar and rain gauge data Girons Lopez et al [43] showed a significant improvement of the catchment-average interpolation error by using higher measurement densities. This behavior was even more apparent for higher precipitation intensities.…”
Rainfall runoff models are frequently used for design processes for urban infrastructure. The most sensitive input for these models is precipitation data. Therefore, it is crucial to account for temporal and spatial variability of rainfall events as accurately as possible to avoid misleading simulation results. This paper aims to show the significant errors that can occur by using rainfall measurement resolutions in urban environments that are too coarse. We analyzed the spatial variability of rainfall events from two years with the validated data of 22 rain gauges spread out over an urban catchment of 125 km2. By looking at the interstation correlation of the rain gauges for different classes of rainfall intensities, we found that rainfall events with low and intermediate intensities show a good interstation correlation. However, the correlation drops significantly for heavy rainfall events suggesting higher spatial variability for more intense rainstorms. Further, we analyzed the possible deviation from the spatial rainfall interpolation that uses all available rain gauges when reducing the number of rain gauges to interpolate the spatial rainfall for 24 chosen events. With these analyses we found that reducing the available information by half results in deviations of up to 25% for events with return periods shorter than one year and 45% for events with longer return periods. Assuming uniformly distributed rainfall over the entire catchment resulted in deviations of up to 75% and 125%, respectively. These findings are supported by the work of past research projects and underline the necessity of a high spatial measurement density in order to account for spatial variability of intense rainstorms.
“…When grouping the events by the maximum measured hourly intensity of any rain gauge of each event as done in Figure 4 into four equally sized groups of events, it is apparent that more intense events show a much higher spatial variability than the less intense ones. This is supported by the results of Girons Lopez et al [43] who came to the same conclusion. This does not mean that the deviation is negligible for events with smaller intensities than 26 mm/h (see Figure 4a,b).…”
Section: Resultssupporting
confidence: 77%
“…Using precipitation interpolations from a dense rain gauge network and comparing it to a combination of radar and rain gauge data Girons Lopez et al [43] showed a significant improvement of the catchment-average interpolation error by using higher measurement densities. This behavior was even more apparent for higher precipitation intensities.…”
Rainfall runoff models are frequently used for design processes for urban infrastructure. The most sensitive input for these models is precipitation data. Therefore, it is crucial to account for temporal and spatial variability of rainfall events as accurately as possible to avoid misleading simulation results. This paper aims to show the significant errors that can occur by using rainfall measurement resolutions in urban environments that are too coarse. We analyzed the spatial variability of rainfall events from two years with the validated data of 22 rain gauges spread out over an urban catchment of 125 km2. By looking at the interstation correlation of the rain gauges for different classes of rainfall intensities, we found that rainfall events with low and intermediate intensities show a good interstation correlation. However, the correlation drops significantly for heavy rainfall events suggesting higher spatial variability for more intense rainstorms. Further, we analyzed the possible deviation from the spatial rainfall interpolation that uses all available rain gauges when reducing the number of rain gauges to interpolate the spatial rainfall for 24 chosen events. With these analyses we found that reducing the available information by half results in deviations of up to 25% for events with return periods shorter than one year and 45% for events with longer return periods. Assuming uniformly distributed rainfall over the entire catchment resulted in deviations of up to 75% and 125%, respectively. These findings are supported by the work of past research projects and underline the necessity of a high spatial measurement density in order to account for spatial variability of intense rainstorms.
“…Precipitation is indeed the main input driving many if not most models of hydrological processes (McDonnell & Beven, ; Seibert & McDonnell, ). However, although we know that precipitation varies greatly in time and even over short distances in space (Girons Lopez, Wennerström, Nordén, & Seibert, ; Goodrich, Faurès, Woolhiser, Lane, & Sorooshian, ; Vieux, ), uniformity is often still assumed for small areas. This assumption applies not only to the precipitation amount but also to its isotopic composition (D and 18 O), which has become a common tool in tracer hydrological investigations (McGuire & McDonnell, ).…”
The isotopic composition of precipitation (D and 18O) has been widely used as an input signal in water tracer studies. Whereas much recent effort has been put into developing methodologies to improve our understanding and modelling of hydrological processes (e.g., transit‐time distributions or young water fractions), less attention has been paid to the spatio‐temporal variability of the isotopic composition of precipitation, used as input signal in these studies. Here, we investigated the uncertainty in isotope‐based hydrograph separation due to the spatio‐temporal variability of the isotopic composition of precipitation. The study was carried out in a Mediterranean headwater catchment (0.56 km2). Rainfall and throughfall samples were collected at three locations across this relatively small catchment, and stream water samples were collected at the outlet. Results showed that throughout an event, the spatial variability of the input signal had a higher impact on hydrograph separation results than its temporal variability. However, differences in isotope‐based hydrograph separation determined preevent water due to the spatio‐temporal variability were different between events and ranged between 1 and 14%. Based on catchment‐scale isoscapes, the most representative sampling location could also be identified. This study confirms that even in small headwater catchments, spatio‐temporal variability can be significant. Therefore, it is important to characterize this variability and identify the best sampling strategy to reduce the uncertainty in our understanding of catchment hydrological processes.
“…Rainfall in center-west and northern Brazil is monitored through a relatively sparse rain gauge data network (15 or fewer rain gauges per 10 4 km 2 ), comparable to inland regions of South America; sub-Saharan Africa; and central, east, and southeast-Asia 25 . These low densities are likely to result in non-trivial differences between regional rainfall data products 26 (in Switzerland, rain gauge densities of >24 rain gauges per 1,000 km 2 were required to avoid density-dependent biases 9 ).Figure 1Study region and locations of in - situ (IS) rainfall and streamflow gauges. Panel (a) shows the Amazon-Cerrado transition states of Brazil.…”
Studies of the hydroclimate at regional scales rely on spatial rainfall data products, derived from remotely-sensed (RS) and in-situ (IS, rain gauge) observations. Because regional rainfall cannot be directly measured, spatial data products are biased. These biases pose a source of uncertainty in environmental analyses, attributable to the choices made by data-users in selecting a representation of rainfall. We use the rainforest-savanna transition region in Brazil to show differences in the statistics describing rainfall across nine RS and interpolated-IS daily rainfall datasets covering the period of 1998–2013. These differences propagate into estimates of temporal trends in monthly rainfall and descriptive hydroclimate indices. Rainfall trends from different datasets are inconsistent at river basin scales, and the magnitude of index differences is comparable to the estimated bias in global climate model projections. To address this uncertainty, we evaluate the correspondence of different rainfall datasets with streamflow from 89 river basins. We demonstrate that direct empirical comparisons between rainfall and streamflow provide a method for evaluating rainfall dataset performance across multiple areal (basin) units. These results highlight the need for users of rainfall datasets to quantify this “data selection uncertainty” problem, and either justify data use choices, or report the uncertainty in derived results.
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