The link between the drop scale, high resolution measurements and precipitation estimates at the catchment scale

<p>In-situ liquid precipitation measurements are the essential source of information about the rainfall process, its spatiotemporal variability, and the expected frequency of intense events. Other sources are remote sensors or the measurement of hydrologically connected variables, such as the water flow in rivers or evaporation, but all these only provide indirect estimates of precipitation. Notwithstanding the advantage of allowing areal estimates, they still require accompanying in-situ measurements for calibration or validation purposes.</p><p>The accuracy of in-situ precipitation measurements, though understated in most research studies and hydrological applications, is imperative to substantiate both scientific achievements and decision making. Unfortunately, due to budgetary shortages and other priorities, the managers of monitoring networks rarely address accuracy and traceability issues to a significant extent, and measurements are performed at a much lower level of accuracy than the current scientific knowledge and technological development would actually permit.</p><p>The neglected precipitation measurement biases propagate through the applications or the modelling chain and their awareness is often rapidly lost, together with the reliability of the obtained results. The comparability and homogeneity of precipitation estimates and their hydrological consequences between different studies is also questionable.</p><p>High-resolution measurements, even down to the scale of the single drop, are the way to achieve better knowledge of the precipitation process and to raise the confidence of users in the accuracy of their basic source of information. In this work, based on the most recent results in precipitation measurement studies, we aim at demonstrating that the accuracy of catchment scale rainfall and snowfall estimates rely on the interpretation of high-resolution raw data from traditional sensors and on the knowledge of the drop size distribution and other microphysical parameters of the rainfall process. Drop scale measurements must be accurate as well, and this is still an open issue for the currently available disdrometers.</p>

Study on the Early Warning and Forecasting of Flash Floods in Small Watersheds Based on the Rainfall Pattern of Risk Probability Combination

Flash floods cause great harm to people's life and property safety. Rainfall is one of the main causes of flash floods in small watersheds. The uncertainty of rainfall events results in inconsistency between the traditional single rainfall pattern and the actual rainfall process, which poses a great challenge for the early warning and forecasting of flash floods. This paper proposes a novel rainfall pattern based on total rainfall and peak rainfall intensity, i.e., the rainfall pattern of risk probability combination (RPRPC). To determine the joint distribution function with the best fitting effect, copula functions are introduced and optimized. On this basis, the HEC-HMS hydrological model is used to simulate the rainfall-runoff process, a trial algorithm is used to calculate the critical rainfall (CR), and an optimistic-general-pessimistic (O-G-P) early warning mode considering the decision maker's risk preference is proposed. The small watershed of Xinxian in Henan province, China, is taken as a case study for calculation. The results show that the RPRPC is feasible and closer to the actual rainfall process than the traditional rainfall pattern (TRP) and that the HEC-HMS model can be applied to small watersheds in hilly areas. Additionally, the influence of antecedent soil moisture condition (ASMC) and rainfall pattern on critical rainfall varies with the change of peak rainfall intensity and rainfall duration. Finally, the O-G-P early warning mode is effective and provides a valuable reference for the early warning and forecasting of flash floods in small watersheds in hilly areas.

Numerical simulation of slope stability of the abandoned dreg site

A mathematical model of No. 1 abandoned dreg site of the newly-built highway from Xiangle village, Pingyao County to Hougou village, Qinyuan County was developed using GeoStudio software. The stability of the abandoned dreg site was simulated and analyzed under rainstorm. As a result, the influence of rainfall infiltration on slope seepage field is not limited to the rainfall process. The slope stability coefficient decreases significantly during the rainfall and the decreases slightly after the rainfall stopped due to continuous rainwater seepage. The minimum safety coefficient is 2.292 at 24h, greater than the allowable value, which demonstrates that the slope is stable.

Establishment of urban waterlogging pre-warning system based on coupling RBF-NARX neural networks

The optimal layout of low-impact development (LID) facilities satisfying annual runoff control for low rainfall expectation is not effective under extreme rainfall conditions and urban waterlogging may occur. In order to avoid the losses of urban waterlogging, it is particularly significant to establish a waterlogging early warning system. In this study, based on coupling RBF-NARX neural networks, we establish an early warning system that can predict the whole rainfall process according to the rainfall curve of the first 20 minutes. Using the predicted rainfall process curve as rainfall input to the rainfall-runoff calculation engine, the area at risk of waterlogging can be located. The results indicate that the coupled neural networks perform well in the prediction of the hypothetical verification rainfall process. Under the studied extreme rainfall conditions, the location of 25 flooding areas and flooding duration are well predicted by the early warning system. The maximum of average flooding depth and flooding duration is 16.5 cm and 99 minutes, respectively. By predicting the risk area and the corresponding flooding time, the early warning system is quite effective in avoiding and reducing the losses from waterlogging.

Regional landslide hazard assessment through integrating susceptibility index and rainfall process

Due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geological, fault, and lithology), it is difficult to assess the hazard of landslides at the regional scale quantitatively only considering rainfall condition. Based on detailed landslide inventory and rainfall data in the hilly area in Sichuan province, this study analyzed the effects of both rainfall process and environmental factors on the occurrence of landslides. Through analyzing environmental factors, a landslide susceptibility index (LSI) was calculated using multiple layer perceptron (MLP) model to reflect the regional landslide susceptibility. Further, the characteristics of rainfall process and landslides were examined quantitatively with statistical analysis. Finally, a probability model integrating LSI and rainfall process was constructed using logistical regression analysis to assess the landslide hazard. Validation showed satisfactory results, and the inclusion of LSI effectively improved the accuracy of the landslide hazard assessment: Compared with only considering the rainfall process factors, the accuracy of the landslide prediction model both considering the rainfall process and landslide susceptibility is improved by 3%. These results indicate that an integration of susceptibility index and rainfall process is essential in improving the timeliness and accuracy of regional landslide early warning.