Weather radar rainfall data in urban hydrology

Abstract

Abstract. Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology, necessitate an updated review of the state of the art in radar rainfall for urban hydrological applications. Three key areas of research have been identified as especially important in application of radar data in urban hydrology, given their significant advances over the past decade: 1) Temporal and spatial resolution of rainfall data required for different hydrological applications, 2) Rainfall estimation, radar data adjustment and data quality, and 3) Nowcasting of radar rainfall and real-time applications. Based on these three fields of research, the paper provides recommendations based on an updated overview of shortcomings, gains, and novel developments in relation to urban hydrological applications. The paper reviews how the focus in urban hydrology as a field of research has shifted over the last decade to fields such as urban resilience to hydrological extremes, climate change impacts, and on-line warning/prediction systems. It is discussed how radar rainfall data can contribute to existing hydrological fields and add value to the aforementioned emerging fields in current and future applications.

Prediction of river temperature surges is dependent on precipitation method

“…Differences in precipitation between datasets (weather station, citizen science, and radar) were more pronounced than the differences between point‐based and catchment‐average calculation methods, which reflect precipitation variability over short distances during the events. Single precipitation gauge data are, therefore, often unrepresentative of multiple water temperature sample sites unless strict consideration is given to catchment size and strategic siting (Gabriele et al, ; Thorndahl et al, ; Villarini et al, ).…”

“…Furthermore, as the process of heat exchange between low‐heat capacity surfaces and surface run‐off occurs over short durations (Herb et al, ), this effect is likely to be better captured by the highest temporal resolution datasets. In contrast, point‐based methods provide inaccurate precipitation estimates as they fail to account for variations in storm intensity across the catchment, which may result in high spatial variation in rainfall, particularly in urban areas (Thorndahl et al, ). Point‐based methods are unlikely to be an adequate proxy of thermally charged surface run‐off, as they fail to represent the export of heated water from individual subcatchments.…”

“…From the weather station data, only constant precipitation across the catchment could be derived, which misleadingly shows water temperature surges taking place before any precipitation was observed in both the high‐intensity event and the low‐intensity event. The assumption of a constant precipitation value across the catchment is likely to be highly inaccurate and leads to difficulties quantifying precipitation‐driven processes (Thorndahl et al, ). The degree to which water temperature surges are controlled by precipitation may be underestimated or masked entirely when using single gauge data depending on catchment area, river network properties, storm direction, and rate of passage through the catchment.…”

“…However, precipitation typically exhibits high spatial variability within catchments at different scales (Dixon & Mote, ; Salvadore, Bronders, & Batelaan, ). This is particularly evident in urban catchments where the combination of the urban heat island effect and changes in urban wind field alter precipitation patterns (Dixon & Mote, ; Salvadore et al, ) leads to variations in rainfall intensity and duration (Gabriele, Chiaravalloti, & Procopio, ; Pedersen et al, ; Thorndahl et al, ; Villarini, Mandapaka, Krajewski, & Moore, ). Consequently, the precipitation processes that drive water temperature fluxes during individual events can be difficult to quantify, particularly when using data from a single rainfall gauge.…”

“…However, to‐date concerns over data quality have inhibited their uptake for research purposes (Barthel, Seidl, Nickel, & Büttner, ; Buytaert et al, ; Starkey et al, ). Radar precipitation data can also yield high temporal and spatial resolution precipitation estimates (Biggs & Atkinson, ; Gabriele et al, ; Thorndahl et al, ; Villarini et al, ). For example, in the United Kingdom, the NIMROD system generates radar‐derived precipitation estimates at 5‐min temporal and 1‐km 2 spatial resolution (Villarini et al, ).…”