GL
G. Lin
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As the intensity and frequency of urban heat wave increases in Europe, evaporative cooling from watering pavements has been considered as a promising strategy to regulate urban temperature. The aim of this research is to include evaporative cooling modelling and enhance the surface temperature model SURF-TEMP, which is in development at the Swiss Federal Institute of Aquatic Science and Technology (Eawag), to predict the optimal use of water resources towards mitigating urban heat. In order to test various evaporation models found in literature, we first developed a simple Python model to evaluate ten evaporation models. Differences among the simulated evaporative heat fluxes and surface temperature reduction can be around two folds under the same simulation condition for different evaporative cooling models. Besides the evaporation models, three equations to obtain the convective heat transfer coefficient were tested. We tested their uncertainties and determined the equation that provides the most neutral estimation on convective heat transfer coefficient. We proceeded to conduct various simulations to understand the sensitivity of these evaporation models with respect to four factors (wind speed, relative humidity of air, initial surface temperature, and initial water height). These factors influence the evaporative cooling efficiency and therefore the optimal watering rate. Two evaporative models that represent the higher and lower limit of the simulated evaporative heat flux are incorporated into SURF-TEMP. Results show that the model that estimates the highest evaporative heat flux is in good agreement with the lab measurement published by (Parison et al.,2020) when watering rate is equal or above 1 mm per hour. When watering rate falls below 0.75 mm per hour, the models studied underestimate the evaporative heat flux and surface temperature reduction. Discrepancies during low watering rate are caused by the absence of modelling of water conduction and infiltration with the pavement, non-linear relation of evaporation rate with vapor pressure difference, and the inherent error between simulation and experimental results even under dry condition.
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As the intensity and frequency of urban heat wave increases in Europe, evaporative cooling from watering pavements has been considered as a promising strategy to regulate urban temperature. The aim of this research is to include evaporative cooling modelling and enhance the surface temperature model SURF-TEMP, which is in development at the Swiss Federal Institute of Aquatic Science and Technology (Eawag), to predict the optimal use of water resources towards mitigating urban heat. In order to test various evaporation models found in literature, we first developed a simple Python model to evaluate ten evaporation models. Differences among the simulated evaporative heat fluxes and surface temperature reduction can be around two folds under the same simulation condition for different evaporative cooling models. Besides the evaporation models, three equations to obtain the convective heat transfer coefficient were tested. We tested their uncertainties and determined the equation that provides the most neutral estimation on convective heat transfer coefficient. We proceeded to conduct various simulations to understand the sensitivity of these evaporation models with respect to four factors (wind speed, relative humidity of air, initial surface temperature, and initial water height). These factors influence the evaporative cooling efficiency and therefore the optimal watering rate. Two evaporative models that represent the higher and lower limit of the simulated evaporative heat flux are incorporated into SURF-TEMP. Results show that the model that estimates the highest evaporative heat flux is in good agreement with the lab measurement published by (Parison et al.,2020) when watering rate is equal or above 1 mm per hour. When watering rate falls below 0.75 mm per hour, the models studied underestimate the evaporative heat flux and surface temperature reduction. Discrepancies during low watering rate are caused by the absence of modelling of water conduction and infiltration with the pavement, non-linear relation of evaporation rate with vapor pressure difference, and the inherent error between simulation and experimental results even under dry condition.
Extreme rainfall brings substantial threats to lives, infrastructure, and the economy in cities. Radar rainfall nowcasting was proven able to provide forecasts up to 2 to 3 hours in advance on a catchment scale. However, an extensive evaluation of nowcasting skills for urban areas has not been performed yet. In this study, we selected 80 extreme events that occurred in 5 main Dutch cities (Amsterdam, The Hague, Groningen, Maastricht, and Eindhoven) from 2008 to 2021. We evaluated the performance of probabilistic nowcasts with 20 ensemble members applying short-term ensemble prediction system (STEPS) from Pysteps for these cities, focusing on analyzing the dependence on rainfall characteristics and city sizes. Nowcasts in Eindhoven (96 km2) and Maastricht (67 km2) had higher errors because the rainfall intensity of their events was higher. Besides, nowcasts at small areas showed higher error, especially when the size was below 100 km2. We found that forecast errors were higher and the forecast was less reliable for the 1-h event durations than for 24-h durations. Despite these differences, skillful lead times measured by Pearson correlation in all the cities were about 20 to 24 minutes for both the 1-hour and 24-hour events. CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) adjusted the bias in real-time QPE and QPF, but QPF still reduced with increasing lead time. Also, CARROTS did not adjust the rainfall spatial distribution much, so the skillful lead time did not change much. The skillful lead time in this study was shorter than the counterparts on the catchment scale because small areas are more sensitive to the displacement of forecast rainfall. Still, such lead time is similar to the findings in other research on short convective rainfall over small areas. Future research could try to apply machine learning, 3-dimensional nowcasting, or blending numerical weather prediction in the nowcasting process to better forecast the growth and decay of rainfall at a longer lead time.
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Extreme rainfall brings substantial threats to lives, infrastructure, and the economy in cities. Radar rainfall nowcasting was proven able to provide forecasts up to 2 to 3 hours in advance on a catchment scale. However, an extensive evaluation of nowcasting skills for urban areas has not been performed yet. In this study, we selected 80 extreme events that occurred in 5 main Dutch cities (Amsterdam, The Hague, Groningen, Maastricht, and Eindhoven) from 2008 to 2021. We evaluated the performance of probabilistic nowcasts with 20 ensemble members applying short-term ensemble prediction system (STEPS) from Pysteps for these cities, focusing on analyzing the dependence on rainfall characteristics and city sizes. Nowcasts in Eindhoven (96 km2) and Maastricht (67 km2) had higher errors because the rainfall intensity of their events was higher. Besides, nowcasts at small areas showed higher error, especially when the size was below 100 km2. We found that forecast errors were higher and the forecast was less reliable for the 1-h event durations than for 24-h durations. Despite these differences, skillful lead times measured by Pearson correlation in all the cities were about 20 to 24 minutes for both the 1-hour and 24-hour events. CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) adjusted the bias in real-time QPE and QPF, but QPF still reduced with increasing lead time. Also, CARROTS did not adjust the rainfall spatial distribution much, so the skillful lead time did not change much. The skillful lead time in this study was shorter than the counterparts on the catchment scale because small areas are more sensitive to the displacement of forecast rainfall. Still, such lead time is similar to the findings in other research on short convective rainfall over small areas. Future research could try to apply machine learning, 3-dimensional nowcasting, or blending numerical weather prediction in the nowcasting process to better forecast the growth and decay of rainfall at a longer lead time.