Conventional approaches to microcomponent identification are typically confined to analysing limited periods of flow data (e.g. selected one to two week periods) for houses with up to 5–6 occupants where clear repeating patterns in the flow signal can be identified and associated with particular water use devices. However, this approach is not feasible when there are large numbers of occupants in single households (e.g. 15–20 occupants for extended families) due to the complex nature of the flow signal. In order to address these limitations, two innovative developments were undertaken. Firstly, sound frequency monitoring of water use devices in multiple rooms in a single house in Kuwait was undertaken, and this additional information was used to enhance microcomponent identification. Secondly, new data processing and microcomponent identification software was developed which can utilize both flow and sound frequency data for microcomponent identification, and which also allows the automated processing of large data records for extended periods. A probabilistic approach to identification was developed which quantifies the uncertainty in attributing a sound signal to a water use device. A procedure was also developed whereby the results obtained from detailed flow and sound data from one house were extrapolated to a set of 18 houses for which flow data only were available. The main result from this study is that the mean consumption for the set of 19 Kuwaiti extended family houses in the study area is 246 ± 27 (95

Despite the apparent simplicity, it is notoriously difficult to measure rainfall accurately because of the challenging environment within which it is measured. Systematic bias caused by wind is inherent in rainfall measurement and introduces an inconvenient unknown into hydrological science that is generally ignored. This paper examines the role of rain gauge shape and mounting height on catch efficiency (CE), where CE is defined as the ratio between nonreference and reference rainfall measurements. Using a pit gauge as a reference, we have demonstrated that rainfall measurements from an exposed upland site, recorded by an adjacent conventional cylinder rain gauge mounted at 0.5 m, were underestimated by more than 23% on average. At an exposed lowland site, with lower wind speeds on average, the equivalent mean undercatch was 9.4% for an equivalent gauge pairing. An improved-aerodynamic gauge shape enhanced CE when compared to a conventional cylinder gauge shape. For an improved-aerodynamic gauge mounted at 0.5 m above the ground, the mean undercatch was 11.2% at the upland site and 3.4% at the lowland site. The mounting height of a rain gauge above the ground also affected CE due to the vertical wind gradient near to the ground. Identical rain gauges mounted at 0.5 and 1.5 m were compared at an upland site, resulting in a mean undercatch of 11.2% and 17.5%, respectively. By selecting three large rainfall events and splitting them into shorter-duration intervals, a relationship explaining 81% of the variance was established between CE and wind speed. Plain Language Summary This study was motivated by how challenging it is to measure rainfall accurately, despite it appearing to be very simple. Rainfall measurement is important to society because it has so many everyday uses, such as food production and weather forecasting, and applications that are critical to life, such as flood warning and effective management of water resources. Rainfall is difficult to measure because it varies so much in time and space, and the measurement of rain is highly affected by how windy it is, which also varies in time and space. Therefore, when it rains at the same time as being very windy, which is common during many storms, rainfall measurements are greatly underestimated. The uplands generally receive more rainfall and higher wind speeds than the lowlands, therefore it follows that we underestimate rainfall by more in the uplands. This is important because rainfall measurements in the uplands are sparse, yet it is in such areas where many floods originate. This study shows that the underestimation of rainfall at a site in the windy Scottish uplands was more than 23% on average. It then suggests some techniques that can be implemented to improve the measurement of rainfall.

The airflow surrounding any catching‐type rain gauge when impacted by wind is deformed by the presence of the gauge body, resulting in the acceleration of wind above the orifice of the gauge, which deflects raindrops and snowflakes away from the collector (the wind‐induced undercatch). The method of mounting a gauge with the collector at or below the level of the ground, or the use of windshields to mitigate this effect, is often not practicable. The physical shape of a gauge has a significant impact on its collection efficiency. In this study, we show that appropriate “aerodynamic” shapes are able to reduce the deformation of the airflow, which can reduce undercatch. We have employed computational fluid‐dynamic simulations to evaluate the time‐averaged airflow realized around “aerodynamic” rain gauge shapes when impacted by wind. Terms of comparison are provided by the results obtained for two standard “conventional” rain gauge shapes. The simulations have been run for different wind speeds and are based on a time‐averaged Reynolds‐Averaged Navier‐Stokes model. The shape of the aerodynamic gauges is shown to have a positive impact on the time‐averaged airflow patterns observed around the orifice compared to the conventional shapes. Furthermore, the turbulent air velocity fields for the aerodynamic shapes present “recirculating” structures, which may improve the particle‐catching capabilities of the gauge collector.

Despite the apparent simplicity, it is notoriously difficult to measure rainfall accurately because of the challenging environment within which it is measured. Systematic bias caused by wind is inherent in rainfall measurement and introduces an inconvenient unknown into hydrological science that is generally ignored. This paper examines the role of rain gauge shape and mounting height on catch efficiency (CE), where CE is defined as the ratio between nonreference and reference rainfall measurements. Using a pit gauge as a reference, we have demonstrated that rainfall measurements from an exposed upland site, recorded by an adjacent conventional cylinder rain gauge mounted at 0.5 m, were underestimated by more than 23% on average. At an exposed lowland site, with lower wind speeds on average, the equivalent mean undercatch was 9.4% for an equivalent gauge pairing. An improved-aerodynamic gauge shape enhanced CE when compared to a conventional cylinder gauge shape. For an improved-aerodynamic gauge mounted at 0.5 m above the ground, the mean undercatch was 11.2% at the upland site and 3.4% at the lowland site. The mounting height of a rain gauge above the ground also affected CE due to the vertical wind gradient near to the ground. Identical rain gauges mounted at 0.5 and 1.5 m were compared at an upland site, resulting in a mean undercatch of 11.2% and 17.5%, respectively. By selecting three large rainfall events and splitting them into shorter-duration intervals, a relationship explaining 81% of the variance was established between CE and wind speed. Plain Language Summary This study was motivated by how challenging it is to measure rainfall accurately, despite it appearing to be very simple. Rainfall measurement is important to society because it has so many everyday uses, such as food production and weather forecasting, and applications that are critical to life, such as flood warning and effective management of water resources. Rainfall is difficult to measure because it varies so much in time and space, and the measurement of rain is highly affected by how windy it is, which also varies in time and space. Therefore, when it rains at the same time as being very windy, which is common during many storms, rainfall measurements are greatly underestimated. The uplands generally receive more rainfall and higher wind speeds than the lowlands, therefore it follows that we underestimate rainfall by more in the uplands. This is important because rainfall measurements in the uplands are sparse, yet it is in such areas where many floods originate. This study shows that the underestimation of rainfall at a site in the windy Scottish uplands was more than 23% on average. It then suggests some techniques that can be implemented to improve the measurement of rainfall.

(1) Newcastle University, Civil Engineering and Geosciences, Newcastle, United Kingdom (m.d.pollock@ncl.ac.uk), (2) EML, Newcastle-Upon-Tyne, United Kingdom (michael@emltd.net), (3) WMO/CIMO Lead Centre on Precipitation Intensity, University of Genova, Genoa, Italy (matteo.colli@unige.it), (4) University of Dundee, Dundee, United Kingdom (a.z.black@dundee.ac.uk), (5) James Hutton Institute, Aberdeen, United Kingdom (Mark.Wilkinson@hutton.ac.uk)

Accurate precipitation measurement is a fundamental requirement in a broad range of applications including flood risk management and hydrological studies. At present, the most widely used method of measuring precipitation is the ‘rain gauge’, which is often also considered to be the most accurate. In the context of hydrological modelling, measurements from rain gauges are interpolated to produce an areal representation, which forms an important input to drive hydrological models. The results of these models may be applied in a variety of contexts, such as evaluating the hydrological impacts of climate change. In each stage of such a process another layer of uncertainty is introduced. The initial measurement errors are propagated through this chain, compounding the overall uncertainty. This study looks at the fundamental source of error, the precipitation measurement itself, and specifically addresses the systematic ‘wind-induced’ error.

Rainfall measurement has an extensive historical precedent. Attempts have been made to standardise measurement procedures. This has never been successfully achieved. There are many sources of measurement error, some of which are compounded by poor rain gauge siting and a variation in gauge height. By far the worst cause of measurement inaccuracy is due to windinduced undercatching. Some solutions have been proposed to tackle this problem but none have been fully implemented, and little has been done on the topic for several decades. Rain gauge intercomparisons performed for different types of rain gauges situated at a standard height showed that the design shape of the gauge is significant, in terms of measured rainfall catch. The type of rainfall event was also shown to be significant, with events typical of west-coast UK uplands shown to be more susceptible to wind-effects than a large convective event on the UK’s east coast. Different types of instruments were also demonstrated to have varying degrees of sensitivity to rainfall, further complicating the measurement process. A number of key findings are presented which pertain to; the aerodynamics of the gauge itself, the type of rainfall event and wind conditions, the siting of the rain gauge, and why there is a pressing need for standardisation of rainfall measurement. Implications of the findings describe an inconvenient truth in hydro-meteorology which transcends a variety of applications of rainfall data, from real-time flood forecasting to Numerical Weather Prediction models. Having discussed the problem and the importance of solving it, the experimental design of a multisite field experiment is discussed, supplemented by the use of Computational Fluid Dynamics (CFD) simulations.

The most widely used device to measure rainfall is the tipping bucket rain gauge (TBR), although there is no standard design. The precision and accuracy of TBR measurements vary, and calibration procedures are dependent upon the organisation or institution operating a network. Consequently, rainfall datasets may be heterogeneous and not easily comparable. Environmental conditions at the gauge orifice also adversely influence the accuracy of measurement. The height at which the gauge is mounted has a significant influence on the gauge catch. Reference measurements can be made using a rain gauge in a pit structure, with the gauge orifice positioned at ground level. Different types of rainfall events, occurring in differing geographical and micro-topographical contexts, vary the influence of the wind on rainfall measurement. Hence, it is difficult to develop and apply an allencompassing correction procedure for wind using empirical observational methods alone. Computational Fluid Dynamics (CFD) provides an ideal framework within which to develop an understanding of how wind affects catch accuracy. Observational data from the field can be used to validate CFD simulations and enhance correction algorithms.