Assessment of potential sediment disasters and resilience management of mountain roads using environmental indicators

In recent years, extreme rainfall events occur frequently, causing serious watershed sediment disasters, destroying mountain roads, and endangering the safety of residents' lives and property. This study aims to deal with the spatial change of potential sediment movement on the road slope pre-disaster and to screen disaster hot spots for early warning and control system. The conceptual model is used to simulate the distribution of primary and/or derived disasters on a watershed scale to assess the impact of sediment disasters caused by heavy rain event. Correlation analysis shows that the models in assessment of primary disaster and derived disaster are significantly correlated with the collapse ratio and disaster ratio, respectively. Since the primary disaster has been considered when calculating the derived disaster risk, the terrain subdivision along Provincial Highway 21 (Tai-21) is extracted to understand the derived sediment disaster on the road slope. The model can effectively evaluate the road sections prone to disasters. According to the risk level, the hot spot of road slope disasters and the management of disaster resilience are determined and can be the reference for disaster prevention and control.

Characteristics of Precipitation Diurnal Cycle over a Mountainous Area of Sumatra Island including MJO and Seasonal Signatures Based on the 15-Year Optical Rain Gauge Data, WRF Model and IMERG

In this study we investigate the characteristics of the diurnal precipitation cycle including the Madden–Julian oscillation (MJO) and seasonal influences over a mountainous area in Sumatra Island based on the in situ measurement of precipitation using the optical rain gauge (ORG). For comparison with ORG data, the characteristics based on the Global Precipitation Measurement (GPM) mission (IMERG) and Weather Research and Forecasting (WRF) simulations were also investigated. Fifteen years of ORG data over a mountainous area of Sumatra, namely, at Kototabang (100.32° E, 0.20° S), were analyzed to obtain the characteristics of the diurnal cycle of precipitation in this region. The diurnal cycle of precipitation presented a single peak in the late afternoon, and the peak time difference was closely related to the rain event duration. The MJO acts to modulate the diurnal amplitude but not the diurnal phase. A high precipitation amount (PA) and frequency (PF) were observed during phases 2, 3, and 4, along with an increase in the number of longer-duration rain events, but the diurnal phase was similar in all MJO phases. In terms of season, the highest PA and PF values were observed during pre-southwest and pre-northeast monsoon seasons. WRF simulation reproduced the diurnal phase correctly and more realistically than the IMERG products. However, it largely overestimated the amplitude of the diurnal cycle in comparison with ORG. These disagreements could be related to the resolution and quality of IMERG and WRF data.

Assessing the Impact of Dropsonde Data on Rain Forecasts in Taiwan with Observing System Simulation Experiments

This paper presents an observing system simulation experiment (OSSE) study to examine the impact of dropsonde data assimilation (DA) on rainfall forecasts for a heavy rain event in Taiwan. The rain event was associated with strong southwesterly flows over the northern South China Sea (SCS) after a weakening tropical cyclone (TC) made landfall over southeastern China. With DA of synthetic dropsonde data over the northern SCS, the model reproduces more realistic initial fields and a better simulated TC track that can help in producing improved low-level southwesterly flows and rainfall forecasts in Taiwan. Dropsonde DA can also aid the model in reducing the ensemble spread, thereby producing more converged ensemble forecasts. The sensitivity studies suggest that dropsonde DA with a 12-h cycling interval is the best strategy for deriving skillful rainfall forecasts in Taiwan. Increasing the DA interval to 6 h is not beneficial. However, if the flight time is limited, a 24-h interval of DA cycling is acceptable, because rainfall forecasts in Taiwan appear to be satisfactory. It is also suggested that 12 dropsondes with a 225-km separation distance over the northern SCS set a minimum requirement for enhancing the model regarding rainfall forecasts. Although more dropsonde data can help the model to obtain better initial fields over the northern SCS, they do not provide more assistance to the forecasts of the TC track and rainfall in Taiwan. These findings can be applied to the future field campaigns and model simulations in the nearby regions.

Nitrous Oxide Emission Fluxes in Coffee Plantations during Fertilization: A Case Study in Costa Rica

The main source of N2O emissions is agriculture, and coffee monocultures have become an important part of these emissions. The demand for coffee has increased in the last five decades. Thus, its production in agricultural fields and the excess of fertilizers have increased. This study quantified N2O emissions from different dose applications and types of nitrogen fertilizer in a region of major coffee production in Costa Rica. A specific methodology to measure N2O fluxes from coffee plants was developed using Fourier-transform infrared spectroscopy (FTIR). Measurements were performed in a botanical garden in Germany and plots in Costa Rica, analyzing the behavior of a fertilizer in two varieties of coffee (Catuai and Geisha), and in a field experiment, testing two types of fertilizers (chemical (F1) and physical mixture (F2)) and compost (SA). As a result, the additions of synthetic fertilizer increased the N2O fluxes. F2 showed higher emissions than F1 by up to 90% in the field experiment, and an increase in general emissions occurred after a rain event in the coffee plantation. The weak levels of N2O emissions were caused by a rainfall deficit, maintaining low water content in the soil. Robust research is suggested for the inventories.

A Detention Reservoir Reduced Combined Sewer Overflows and Bathing Water Contamination Due to Intense Rainfall

Combined sewer overflows (CSOs) close to water bodies are a cause of grave environmental concern. In the past few decades, major storm events have become increasingly common in some regions, and the meteorological scenarios predict a further increase in their frequency. Consequently, CSO control and treatment according to best practices, the adoption of innovative treatment solutions and careful sewer system management are urgently needed. A growing number of publications has been addressing the quality, quantity and types of available water management and treatment options. In this study, we describe the construction of an innovative detention reservoir along the Arzilla River (Fano, Italy) whose function is to store diluted CSO wastewater exceeding the capacity of a combined drain system. River water sampling and testing for microbial contamination downstream of the tank after a heavy rain event found a considerable reduction of fecal coliform concentrations, which would have compounded the impact of stormwater on the bathing site. These preliminary results suggest that the detention tank exerted beneficial environmental effects on bathing water by lowering the microbial load.

Verification of heavy rainfall warning over Bihar and Uttar Pradesh

India Meteorological Department (IMD) issues heavy rainfall warning for a meteorological sub-division when the expected 24 hours rainfall over any rain gauge station in that sub-division is likely to be 64.5 mm or more. Though these warnings have been provided since long and are also now being issued for smaller spatial scales, very few attempts have been made for quantitative evaluation of these warnings. Hence, a study is undertaken to verify the heavy rainfall warning over the representative meteorological sub-divisions of east Uttar Pradesh (UP), west UP and Bihar during main monsoon months of July and August. For this purpose data of the recent 5 years (2001-2005) and also for another epoch of 5 years in the early 1970s has been taken into consideration. In this connection, the day when heavy rainfall is recorded over atleast two stations in a sub-division, has been considered as a heavy rainfall day for that sub-division. This study of verification shows that probability of detection of heavy rainfall is 64% over Bihar, 52% over east UP and 53% over west UP for the recent 5 years. Compared to early 1970s, there has been slight improvement in the forecast skill during 2001-2005 with probability of detection increasing by about 10-20% and with decrease in missing rate and false alarm rate. However, the false alarm rates are still large indicating higher bias towards over-prediction. The synoptic conditions associated with the heavy rainfall events have been collected for the period 2001-05 and analysed. The analysis of the unanticipated heavy rainfall events suggests that though proper interpretation of synoptic charts and NWP outputs could improve the warnings, the forecast system available even today is still not capable to capture every heavy rain event in advance.

Spatial and Time Warping for Gauge Adjustment of Rainfall Estimates

Many satellite-based estimates use gauge information for bias correction. In general, bias-correction methods are focused on the intensity error and do not explicitly correct possible position or timing errors. However, position and timing errors in rainfall estimates can also lead to errors in the rainfall occurrence or the intensity. This is especially true for localized rainfall events such as the convective rainstorms occurring during the rainy season in sub-Saharan Africa. We investigated the use of warping to correct such errors. The goal was to gauge-adjust satellite-based estimates with respect to the position and the timing of the rain event, instead of its intensity. Warping is a field-deformation method that transforms an image into another one. We compared two methods, spatial warping focusing on the position errors and time warping for the timing errors. They were evaluated on two case studies: a synthetic rainfall event represented by an ellipse and a rain event in southern Ghana during the monsoon season. In both cases, the two warping methods reduced significantly the respective targeted (position or timing) errors. In the southern Ghana case, the average position error was decreased by about 45 km by the spatial warping and the average timing error was decreased from more than 1 h to 0.2 h by the time warping. Both warping methods also improved the continuous statistics on the intensity: the correlation went from 0.18 to at least 0.62 after warping in the southern Ghana case. The spatial warping seems more interesting because of its positive impact on both position and timing errors.

Impact of climate change on the wind-driven rain exposure of a historical building

Due to climate change, considering future rain event patterns and increased average temperatures, wind-driven rain exposure of buildings can increase. In order to assess the future damage risk related to moisture, it is essential to take the future wind-driven rain load into account. Computational fluid dynamics simulations of wind-driven rain are performed on a historical building located in Victoria, BC, Canada using the current and future weather data. The results show an increased wind-driven rain exposure of the building by up to 20%, especially in façade regions which are already exposed to a higher amount of rain.