Monitoring soil rapidly available potassium (RAK) reserves is extremely significant for developing precise fertilizer recommendations. The sodium tetraphenylborate (NaBPh4)-spectrophotometer method is applied to the detection of soil RAK content as a speedy, efficient and portable approach. However, the detection accuracy of this method is inferior to the ammonium acetate (NH4OAc)-flame photometer method. Here, the detection processes of the NaBPh4 turbidimetry were optimized to find the correlation with the NH4OAc method, further discussed the reasons for the different results by the two methods. The maximum relative error is 13% after conversion through the calibration curve. In addition, the accuracy of the calibration curve was verified by selecting non-fitting soil samples from two experimental sites. Therefore, our work proposes that the NaBPh4 turbidimetry, more convenient and rapid, can accurately detect the soil RAK content in the field. It provides an experimental guidance for the detection method of soil RAK content based on ultraviolet detection and on-site rapid detection devices.
Passive microwave remote sensing is a valuable tool for snow depth estimation. However, accurate retrieval is limited by nonlinear relationships between the snow depth and passive microwave brightness temperature (TB) that are caused by snow physical properties, underlying surface type, and topographical factors. Our study aims to enhance snow depth estimation in Northern Xinjiang (NX), China, utilizing Advanced Microwave Scanning Radiometer 2 TB data (with a resolution of 0.1 deg) and fractional snow cover products through a combination of wavelet transform and two artificial neural network (ANN) models: feedforward neural network (FFNN) and generalized regression neural network (GRNN). The hybrid models were trained and validated using in situ snow depth observations from 44 stations across NX. Results indicate that applying wavelet transform reduces the root-mean-square error (RMSE) by 28.88% for FFNN. In the snow season of 2013 to 2014, Wavelet-GRNN (RMSE: 7.36 cm, NSE: 0.59, R: 0.78, bias: 1.68 cm) outperforms Wavelet-FFNN (RMSE: 8.26 cm, NSE: 0.48, R: 0.75, bias: 1.69 cm) by 10.90%. However, Wavelet-FFNN exhibits superior performance, up to 13.78% than Wavelet-GRNN in complex topographic areas like Xiaoquzi station. In addition, spatial–temporal estimations demonstrate that the hybrid models surpass three well-known snow depth products and alleviate issues of excessively high or low values in NX. These findings underscore the effectiveness of hybrid models combining wavelet transform and ANNs, integrating passive microwave remote sensing and auxiliary data, for accurate snow depth estimation in mountainous regions.
Abstract Socioeconomic development, subsidence, and climate change have led to high flood risks in coastal cities, making the vulnerable, especially elderly people, more prone to floods. However, we mostly do not know how the accessibility of life-saving public resources for the elderly population will change under future scenarios. Using Shanghai as a case, this study introduced a new analytical framework to fill this gap. We integrated for the first time models of coastal flooding, local population growth, and medical resource supply-demand estimation. The results show that under an extreme scenario of coastal flooding in the year 2050, in the absence of adaptation, half of the elderly population may be exposed to floods, the supply of medical resources will be seriously insufficient compared to the demand, and the accessibility of emergency medical services will be impaired by flooding. Our methodology can be applied to gain insights for other vulnerable coastal cities, to assist robust decision making about emergency responses to flood risks for elderly populations in an uncertain future.
Abstract Pluvial flash flood (PFF) can cause serious traffic disruption in big cities. We conducted interdisciplinary research by integrating flood modeling and traffic analysis to reveal the spatiotemporal pattern of the interplay between these two processes. A simplified simulation tool, which is capable of building a road network model, assigning trip paths with the effect of road closures, and evaluating travel delay and vehicle volume redistribution in a given PFF scenario, was developed to capture the traffic disruption in the face of PFF events. Modeling outputs from a case study in the city center of Shanghai showed that the delay of vehicles diverted to dry links or trapped in flooded links may reach 0.5 to 8 times the travel time in no‐flood scenarios. Overall, approximately 1–7% of vehicle volumes on flooded links would be redistributed onto dry links (more likely major arterial roads). However, the vehicle volume variation during each time interval demonstrated evident disparity with the spatiotemporal change of flood inundations. Simulating and mapping the congestion can largely facilitate the identification of vulnerable links. Future research will test the method in other intra‐urban areas and try to bridge the gap between modeling outputs and smart city planning and management.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)