Description: The availability of water resources in Central Asia depends greatly on snow accumulation in the mountains of Tien-Shan and Pamir. It is important to precisely forecast water availability as it is shared by several countries and has a transboundary context. The impact of climate change in this region requires improving the quality of hydrological forecasts in the Naryn river basin. This is especially true for the growing season due to the unpredictable climate behavior. A real-time monitoring and forecasting system based on hydrological watershed models is widely used for forecast monitoring. The study’s main objective is to simulate hydrological forecasts for three different hydrological stations (Uch-Terek, Naryn, and Big-Naryn) located in the Naryn river basin, the main water formation area of the Syrdarya River. We used the MODSNOW model to generate statistical forecast models. The model simulates the hydrological cycle using standard meteorological data, discharge data, and remote sensing data based on the MODIS snow cover area. As for the forecast at the monthly scale, the model considers the snow cover conditions at separate elevation zones. The operation of a watershed model includes the effects of climate change on river dynamics, especially snowfall and its melting processes in different altitude zones of the Naryn river basin. The linear regression models were produced for monthly and yearly hydrological forecasts. The linear regression shows R2 values of 0.81, 0.75, and 0.77 (Uch-Terek, Naryn, and Big-Naryn, respectively). The correlation between discharge and snow cover at various elevation zones was used to examine the relationship between snow cover and the elevation of the study. The best correlation was in May, June, and July for the elevation ranging from 1000–1500 m in station Uch-Terek, and 1500–3500 m in stations Naryn and Big-Naryn. The best correlation was in June: 0.87; 0.76; 0.84, and May for the elevation ranging from 1000–3500 m in station Uch-Terek, and 2000–3000 m in stations Naryn and Big-Naryn. Hydrological forecast modeling in this study aims to provide helpful information to improve our under-standing that the snow cover is the central aspect of water accumulation.

Description: The popular probability integral transform (PIT) uniform plot presents informative empirical illustrations of five types of ensemble forecasts, i.e., reliable, under-confident, over-confident, negatively biased and positively biased. This paper has built a novel two-stage framework upon the PIT uniform plot to quantitatively examine the forecast attributes of bias and reliability. The first stage utilizes the test statistic on bias to examine whether the mean of PIT values is equal to the theoretical mean of standard uniform distribution. Then, the second stage uses the test statistic on reliability to examine whether the mean squared deviation from the theoretical mean is equal to the theoretical variance of standard uniform distribution. Therefore, by using the two-tailed bootstrap hypothesis testing, the first stage identifies unbiased ensemble forecasts, negatively biased forecasts and positively biased forecasts; the second stage focuses on unbiased ensemble forecasts to furthermore identify reliable forecasts, under-confident forecasts and over-confident forecasts. Numerical experiments are devised for the National Centers for Environmental Prediction (NCEP)’s Climate Forecast System version 2 (CFSv2) ensemble forecasts of global precipitation. The results highlight the existence of various shapes of the PIT uniform plots. Due to extreme values of observed precipitation, the PIT uniform plots in some cases can substantially deviate from the 1:1 line even though the mean and variance of ensemble forecasts are respectively in accordance with the mean and variance of observations. Nevertheless, the two-stage framework along with the two test statistics serves as a robust tool for the verification of ensemble hydroclimatic forecasts.

Description: The normal modes (i.e. Earth's free oscillations) are long-period low-frequency seismic signals, which are excited by a variety of factors, such as earthquakes, volcanic eruption, landslide, avalanche and so on, are an essential vehicle for global seismic tomography to elucidate large-scale heterogeneities within the deep Earth. Accurate extraction of signals on normal mode spectrum is a prerequisite for the imaging inversion, providing the differences between the observed and synthetic normal mode spectrum. However, the normal mode spectrum has great complexity due to many structural factors within the Earth, so unacceptable false and dismissed selections of the signals always occur, which hinder the development of exploration of the deep Earth’s deep interior based on normal mode data. To address these problems, we build a deep-learning based neural network, named ModeNet, which is capable of precisely and efficient selecting the frequency windows to cover the target normal modal signals on a noisy spectrum, which could outperform the conventional spectrum-FLEXWIN method without relying on comparisons with synthetics. We also define our own method to evaluate the performance of ModeNet on the testing set and obtain a precision as high as ~0.98. Moreover, ModeNet achieves good generalization in processing seismograms of different events with different noise levels, components, and time window data, as well as superconductivity-gravimeter observations. Therefore, ModeNet could be implemented as a valuable tool for the future deep Earth inversion.

Description: The 15 January 2022 eruption of the Hunga-Tonga volcano generated huge explosions in the atmosphere, which excited various kinds of atmospheric disturbances detected globally by ground-based or spaceborne instruments. Here we show our simulations of these atmospheric waves along with a summary of observation findings. The modelling is achieved by a complete synthetic seismogram calculator (QSSP) considering a hybrid approach of the normal mode theory and matrix method in a layered spherical Earth model including the atmosphere. The synthetic waveform profile, the spectra for nearby stations, as well as the dispersion curves extracted in an array all show good agreement with the observations. Based on these results, especially the dispersion curves for phase speed, we propose a new interpretation for the horizontally propagating Lamb wave. In this model, the Lamb wave can be regarded as the fundamental gravity modes in low frequency band and the fundamental acoustic modes in high frequency band, respectively. The findings may provide new perspectives on the analysis of general atmospheric waves and show the potential of seismic techniques on atmospheric problems.

Description: Intensified climate change is already affecting water quality around the world. The fundamental goal of water quality engineering is to ensure water safety to human and the environment. Traditional water quality engineering includes monitoring, evaluation, and control of key water quality parameters, most of which however do not consider potential health impact of mixture pollutants - a reality faced by terminal water users. To focus on one of the original goals of water quality engineering—human health and environmental protection—here we advocate toxicity-oriented water quality monitoring and control. This presentation showcases some of our efforts to achieve this goal. Specifically, as a complement to traditional water quality parameters, we assessed water toxicity using high-sensitivity toxicological endpoints, and subsequently investigated the performance of some water quality intervention strategies in modulating water toxicity. Furthermore, we applied the toxicity concept to existing water treatment design theories to facilitate toxicity-oriented water quality control designs. Suggestions for the next steps are also discussed. We hope that our work will stimulate the interest of water quality scientists and engineers in improving and adopting the toxicity-oriented approach to water quality monitoring and control.

Description: Water scarcity and climate change are two of the most pressing problems facing the world. A growing amount of research and practice on water rights trading (WRT) and carbon emissions trading (CET) is being conducted worldwide in order to ensure water quality, quantity and a sustainable climate. Further, since the production sector cannot function without water inputs and carbon emissions, there is a high degree of consistency between the sectors involved in these two types of resource trading. This study built a tripartite evolutionary game model based on agriculture, industry, and forestry sectors (AIFS) is constructed to simulate the strategic evolution process and interrelationship among the sectoral stakeholders to investigate the complex water-carbon nexus mechanism between these production sectors in the context of WRT and CET. Furthermore, for empirical simulation, four resource trading market scenarios are constructed, along with data from four cities in the Dongjiang River Basin (DRB) in southern China are selected. The results indicated that there is a significant impact of agricultural net benefits in the DRB on the choice of AIFS strategy, which in turn leads to better WRT that promotes the mutual coupling of AIFS strategies. In addition, strategy coupling brings higher relative returns and risks to the game system, and multiple markets enable AIFS to hedge losses that may result from single market failures in the mutual game. Furthermore, AIFS' behavioral strategies are not necessarily aligned with market expectations, which has implications for us as we guide the development of WRTs and CETs.

Description: Salt-tide invasion in the dry season seriously threatens freshwater availability in a tidal river area. Freshwater availability is associated intensely with natural factors and human activities. Therefore, a new framework, i.e. regulation by avoiding saltwater withdrawal (RASW), which developed relationships among saltwater intrusion, upstream streamflow and local water supply, was established. The RASW contains three phases, i.e. estuary salinity-exceedance simulation, upstream streamflow distribution design, and local water supply security analysis, which was applied in Zhuhai-Macao water supply system of the Guangdong-Hong Kong-Macao Great Bay Area, China. Results demonstrate that a hybrid data-driven method coupling wavelet transform and random forest was more accurate for the salinity-exceedance simulation. Upstream streamflow and the time of excessive salinity in the waterway were connected, and the time of withdrawal avoiding saltwater was obtained. Critical upstream streamflow for various scenarios of water demand in the dry season were identified. However, the security of water supply is majorly associated with the distribution of upstream streamflow in the dry season. The meta-Gaussian copula efficiently simulates the six-dimensional distribution of monthly streamflow and is appropriate for design streamflow distribution. Water supply security benefits greatly from the joint river-reservoir regulation mode. For a given exceedance frequency of average streamflow, the modes and security situations are diverse, due to various streamflow distributions, i.e. extreme low streamflow and its occurrence time. The proposed framework facilitates integrated decision-making for water supply security in coastal area.

Description: Water and energy are basic natural resources that play a significant role in regional sustainable development. Carbon emission, which affects climate change, is also an important environmental element. In the context of economic globalization, international trade drives the transfer of water, energy and carbon footprints spatially. Thus, it’s critical to understand the water-energy-carbon(WEC) nexus embodied in global trade. However, few studies have investigated the coupling of multi-environmental elements and considered cross-regional correlation effects. In this paper, multi-regional input-output tables were developed to quantify WEC transfers embodied in trade among 39 major economies, and the spatiotemporal characteristics and coupling relationships of WEC flows were analyzed. Then, a logarithmic mean divisia index (LMDI) method was conducted to explore driving factors of WEC transfer changes for individual countries. The changes of WEC export and import were decomposed into five driving factors, including economic effect, intensity effect, structure effect, population effect and external dependence effect. The main results are: (1) WEC showed similar transfer paths, with its main outflows from China and BRIIAT (Brazil, Russia, India, Indonesia, Australia, Turkey) countries to East Asia, NAFTA and EU countries. (2) The transfers of virtual WEC were positively correlated. Both W-E and W-C showed two types of linear relationships with different slopes. (3) Economic effect, dependence effect and population effect promoted WEC export, while intensity effect and structural effect drove negatively. However, structural effect was a positive factor for WEC import. Economic effect and intensity effect showed the highest contribution, while population effect showed the least.

Description: In normal mode seismology, the atmosphere and the solid Earth are generally considered to be decoupled, and the seismic wave energy from the Earth's interior is difficult to propagate into the atmosphere. However, a growing number of studies have shown that earthquakes, volcanoes and tsunamis can disturb the Earth's atmosphere due to various coupling effects. Therefore, it is an open question whether normal mode (especially in low frequency) signals can be detected in atmospheric observations. In our study, experiments were conducted to investigate the existence of normal mode signals in surface atmospheric observations using the improved multi-taper method, based on the seismographs and ground-based atmospheric observations of the Global Seismic Network before and after the 2021 Mw8.2 Alaska earthquake. Then, the results showed that the normal mode signal can be detected in the pressure observations by comparing the pre-earthquake, post-earthquake and theoretical calculations. However, since the signal-to-noise ratio varies greatly between stations and modes, direct correction of the barometric pressure by the admittance method may lead to large deviations in the estimation of normal mode parameters. Furthermore, we investigate the generation mechanism of seismic normal modes in surface atmospheric observations, which suggests that these signals originate from crust-atmosphere coupling in the same frequency band, and this possible coupling should be considered in normal mode seismology research.

Description: Upper tropospheric (UT) clouds play a crucial role in the climate system by modulating the Earth's energy budget and heat transport. These clouds are most abundant in the tropics, where they often form as cirrus anvils from convective outflow, building mesoscale systems. The radiative heating of the cirrus anvils may be critical to cloud climate feedback.We are building a coherent long-term 3D dataset which describes tropical UT cloud systems for process and climate studies. For this purpose we used cloud data from the Atmospheric InfraRed Sounder and the Infrared Atmospheric Sounding Inferometer and atmospheric and surface properties from the meteorological reanalyses ERA-Interim and machine learning techniques. The different artificial neural network models were trained on collocated radar – lidar data from the A-Train. The rain intensity classification has an accuracy of about 65 to 70% and allows us to build objects of strong precipitation, used to identify convective organization. This rain intensity classification is more efficient to detect large latent heating than cold cloud temperature. In combination with a cloud system analysis we found that deeper convection leads to larger heavy rain areas and a larger detrainment, with a slightly smaller thick anvil emissivity. This kind of analysis can be used for a process-oriented evaluation of convective precipitation parameterizations in climate models. Furthermore we show the usefulness of our data to investigate tropical convective organization metrics. Selected results on the relation between horizontal and vertical structure of the convective systems and on temporal anomalies will be presented.