Response of hydrologic calibration to replacing gauge-based with NEXRAD-based precipitation data in the USEPA Chesapeake Bay Watershed model

This study investigated the response of hydrologic calibration to replacing gauge-based with radar-based precipitation data in the USEPA Chesapeake Bay Program (CBP) Watershed (CBW) model over the Potomac River Basin. Specific objectives were to (1) compare gauge-based and NEXRAD radar-based (Multisensor Precipitation Estimator, MPE) data at the (a) point-pixel and (b) spatially aggregated level; (2) evaluate the model's calibration accuracy using the different precipitation data sets; and (3) examine the response of model hydrology. Hourly gauge-point and MPE-pixel data were compared at 80 locations. The CBP's interpolated and aggregated precipitation data at the model unit (county) level were compared with MPE data aggregated to the same 114 county-based spatial segments. The model calibration followed the CBP's automated approach, using observed streamflow at 37 gauge stations. Model performance was evaluated using calibration and hydrologic statistics, and GIS-aided spatial information. Calibrated parameters and model hydrologic fluxes were compared. The average annual gauge-point and MPE-pixel values (excluding hours when either was missing) agreed well. Differences in average annual values between the spatially aggregated data sets were, however, significant in parts of the study area. When parameter constraints were relaxed to allow calibration to adjust to the smaller volume of precipitation, the model using MPE outperformed the model calibrated to CBP precipitation data at 65% of the 37calibration sites. The model response was controlled largely by the seasonal difference in precipitation inputs: (1) calibration process could not compensate for large differences in seasonal flow bias caused by the seasonal volume of precipitation; (2) seasonal flow bias affected the lower zone nominal soil moisture storage parameter (LZSN), mainly affecting interflow and groundwater flow. The surface flow component was generally the same for the different precipitation inputs. The two precipitation data types can be used interchangeably to simulate surface-flow dominated processes, but care must be taken in simulations where subsurface pathways and residence times are important. MPE is a strong alternative to gauge-based precipitation data because of its spatiotemporal coverage and rare missing records. Using MPE in hydrologic modeling is appealing because of the improved calibration accuracy of the CBW model demonstrated in this study

Mitigation of Greenhouse Gas Emissions from Urban Environmental Infrastructures

The world’s population will increase to 9.4 billion people by 2050 and 70% of whom will be living in urban areas. Such urbanization with population growth and industrial development demands in turn create a need for the planning, design, and construction of environmental infrastuctures (e.g., water and wastewater treatment plants: WTPs and WWTPs). The environmental infrastructures are essential to provide cities and towns with water supply, waste disposal, and pollution control services. During the operation of WTPs and WWTPs, massive amount of energy, fuels, and chemicals are consumed. Therefore, they could be major contributors to urban greenhouse gas (GHG) emissions (i.e., 17% of GHGs are generated from water and sewer sector in urban area). To make cities resilient and sustainable, the emission of GHGs from WTPs and WWTPs should be estimated as accurately as possible and effective mangement plans should be set up as soon as possible. A comprehensive model was developed to quantitatively estimate on-site and off-site GHGs generated from WTPs and WWTPs. The model was applied to an advanced WTP (treating 200,000 m³/d of raw water with micro-filtration membrane) and a hybrid WWTP (treating 5,500 m³/d of municipal wastewater with five-stage Bardenpho processes). The overall on-site and off-site GHG emissions from the advanced WTP and hybrid WWTP were 0.193 and 2.337 kgCO2e/d*m3. The major source of GHG generation in the advanced WTP was off-site GHG emissions (98.6%: production of chemicals consumed for on-site use and electricity consumed for unit-process operation). On the other hand, on-site GHG emissions related to biochemical reactions (64%) was the main GHG source of the hybrid WWTP. Reducing electricity consumption in advanced WTPs could be the best option for generating less GHG emissions and acquiring better water quality. Various options (CO2 capture and conversion to other useful materials, recovery and reused of CH4, and operation of WWTPs at optimal conditions) could significanlty reduce the total amount of GHG emissions in hybrid WWTPs. The results could be applied to the development of green and sustainable technology, leading to a change in paradigm of urban environmental infrastructure

JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech

In neural text-to-speech (TTS), two-stage system or a cascade of separately learned models have shown synthesis quality close to human speech. For example, FastSpeech2 transforms an input text to a mel-spectrogram and then HiFi-GAN generates a raw waveform from a mel-spectogram where they are called an acoustic feature generator and a neural vocoder respectively. However, their training pipeline is somewhat cumbersome in that it requires a fine-tuning and an accurate speech-text alignment for optimal performance. In this work, we present end-to-end text-to-speech (E2E-TTS) model which has a simplified training pipeline and outperforms a cascade of separately learned models. Specifically, our proposed model is jointly trained FastSpeech2 and HiFi-GAN with an alignment module. Since there is no acoustic feature mismatch between training and inference, it does not requires fine-tuning. Furthermore, we remove dependency on an external speech-text alignment tool by adopting an alignment learning objective in our joint training framework. Experiments on LJSpeech corpus shows that the proposed model outperforms publicly available, state-of-the-art implementations of ESPNet2-TTS on subjective evaluation (MOS) and some objective evaluations.Comment: Submitted to INTERSPEECH 202

The Characteristics and Impacts of Imperviousness From a GIS-based Hydrological Perspective

With the concern that imperviousness can be quantified differently depending on data sources and methods, regression equations to translate between imperviousness estimates using land use and land cover were developed. In addition, this study examined how quantitatively different imperviousness estimates affect the prediction of hydrological response. The regressions between indicators of hydrological response and imperviousness-descriptors were evaluated by examining goodness-of-fit measures such as explained variance or relative standard errors. The results show that imperviousness estimates using land use are better predictors of hydrological response than imperviousness estimates using land cover. Also, this study reveals that flow variability is more sensitive to spatially distributed models than lumped models, while thermal variability is equally responsive to both models. The findings from this study can be further examined from a policy perspective with regard to policies that are based on a threshold concept for imperviousness impacts on the ecological and hydrological system

The Effect of Robo-taxi User Experience on User Acceptance: Field Test Data Analysis

With the advancement of self-driving technology, the commercialization of Robo-taxi services is just a matter of time. However, there is some skepticism regarding whether such taxi services will be successfully accepted by real customers due to perceived safety-related concerns; therefore, studies focused on user experience have become more crucial. Although many studies statistically analyze user experience data obtained by surveying individuals' perceptions of Robo-taxi or indirectly through simulators, there is a lack of research that statistically analyzes data obtained directly from actual Robo-taxi service experiences. Accordingly, based on the user experience data obtained by implementing a Robo-taxi service in the downtown of Seoul and Daejeon in South Korea, this study quantitatively analyzes the effect of user experience on user acceptance through structural equation modeling and path analysis. We also obtained balanced and highly valid insights by reanalyzing meaningful causal relationships obtained through statistical models based on in-depth interview results. Results revealed that the experience of the traveling stage had the greatest effect on user acceptance, and the cutting edge of the service and apprehension of technology were emotions that had a great effect on user acceptance. Based on these findings, we suggest guidelines for the design and marketing of future Robo-taxi services

Kaon BB-parameter from improved staggered fermions in Nf=2+1N_f=2+1 QCD

We present a calculation of the kaon $B$-parameter, $B_K$, using lattice QCD. We use improved staggered valence and sea fermions, the latter generated by the MILC collaboration with $N_f=2+1$ light flavors. To control discretization errors, we use four different lattice spacings ranging down to $a\approx 0.045\;$fm. The chiral and continuum extrapolations are done using SU(2) staggered chiral perturbation theory. Our final result is $\hat{B}_K = 0.727 \pm 0.004 (\text{stat}) \pm 0.038 (\text{sys})$, where the dominant systematic error is from our use of truncated (one-loop) matching factors.Comment: 4 pages, 4 figures, 2 tables, updated references, updated grant number