Towards actionable climate and flood prediction : understanding and advancing land surface modeling with enriched geospatial information

Land surface models (LSMs) are central to our understanding and prediction of the terrestrial hydrological cycle. This dissertation focuses on using enriched geospatial information from remote sensing (RS) and geographic information system (GIS) to advance the snow and river routing component of state-of-the-art LSMs, and assessing their roles in predicting temperature, precipitation, and streamflow. In Chapters 2 and 3, the first systematic studies are conducted to quantify the role of land snow data assimilation (DA) in seasonal climate forecast. Using 7-yr DA products that assimilated the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS), I find a local improvement of 5%–25% in the temperature forecast, where the delayed improvement at higher latitudes is explained by incoming solar radiation that is key to the snow–atmosphere coupling. Focusing on the Asia monsoon, I detect an improvement in the precipitation forecast, which is more robust over central north India with sensor-dependent behaviors in different seasons. The results clarify that to successfully translate DA to useful atmospheric prediction skill, the regional snow–atmosphere coupling, the DA uncertainties, and the monsoon sensitivity to thermal forcing over land need to be jointly considered. In Chapters 4 and 5, I introduce a vector-based river routing model to be coupled with traditional grid-based LSMs. By conducting comprehensive model evaluations in the Texas “Flash Flood Alley” in high-impact historical floods, I identify the model strengths and weaknesses in simulating flood discharges. The best modeling results are then used to reveal the hydrometeorological factors responsible for a record-breaking local flood, which includes the rainfall location and basin physiographic features, the initial wetness in the deeper soil layer, and the flow velocity in the river network. The assessed modeling advancements have actionable societal implications because they apply to the Community Land Model 4 (CLM4) and the Noah model with multi-parameterizations (Noah-MP), both LSMs are adopted by major operational forecasting centers. They may also inform future LSM developments that aim to unify the “top-down” atmospheric modeling and the “bottom-up” hydrological modeling approaches in a generic framework

Irrigation-Induced Environmental Changes around the Aral Sea: An Integrated View from Multiple Satellite Observations

The Aral Sea basin (ASB) is one of the most environmentally vulnerable regions to climate change and human activities. During the past 60 years, irrigation has greatly changed the water distribution and caused severe environmental issues in the ASB. Using remote sensing data, this study investigated the environmental changes induced by irrigation activities in this region. The results show that, in the past decade, land water storage has significantly increased in the irrigated upstream regions (13 km 3 year -1 ) but decreased in the downstream regions (-27 km 3 year -1 ) of the Amu Darya River basin, causing a water storage decrease in the whole basin (-20 km 3 year -1 ). As a result, the water surface area of the Aral Sea has decreased from 32,000 in 2000 to 10,000 km2 in 2015. The shrinking Aral Sea exposed a large portion of the lake bottom to the air, increasing (decreasing) the daytime (nighttime) temperatures by about 1 °C year -1 (0.5 °C year -1 ). Moreover, there were other potential environmental changes, including drier soil, less vegetation, decreasing cloud and precipitation, and more severe and frequent dust storms. Possible biases in the remote sensing data due to the neglect of the shrinking water surface area of the Aral Sea were identified. These findings highlight the severe environmental threats caused by irrigation in Central Asia and call attention to sustainable water use in such dryland regions. Keywords: environmental issues; the shrinking Aral Sea; irrigation; desertification; dust storm; remote sensing; NDVI; GRACE; MODI

Irrigation-Induced Environmental Changes around the Aral Sea: An Integrated View from Multiple Satellite Observations

The Aral Sea basin (ASB) is one of the most environmentally vulnerable regions to climate change and human activities. During the past 60 years, irrigation has greatly changed the water distribution and caused severe environmental issues in the ASB. Using remote sensing data, this study investigated the environmental changes induced by irrigation activities in this region. The results show that, in the past decade, land water storage has significantly increased in the irrigated upstream regions (13 km3 year−1) but decreased in the downstream regions (−27 km3 year−1) of the Amu Darya River basin, causing a water storage decrease in the whole basin (−20 km3 year−1). As a result, the water surface area of the Aral Sea has decreased from 32,000 in 2000 to 10,000 km2 in 2015. The shrinking Aral Sea exposed a large portion of the lake bottom to the air, increasing (decreasing) the daytime (nighttime) temperatures by about 1 °C year−1 (0.5 °C year−1). Moreover, there were other potential environmental changes, including drier soil, less vegetation, decreasing cloud and precipitation, and more severe and frequent dust storms. Possible biases in the remote sensing data due to the neglect of the shrinking water surface area of the Aral Sea were identified. These findings highlight the severe environmental threats caused by irrigation in Central Asia and call attention to sustainable water use in such dryland regions

Recent changes to Arctic river discharge

Arctic rivers drain ~15% of the global land surface and significantly influence local communities and economies, freshwater and marine ecosystems, and global climate. However, trusted and public knowledge of pan-Arctic rivers is inadequate, especially for small rivers and across Eurasia, inhibiting understanding of the Arctic response to climate change. Here, we calculate daily streamflow in 486,493 pan-Arctic river reaches from 1984-2018 by assimilating 9.18 million river discharge estimates made from 155,710 satellite images into hydrologic model simulations. We reveal larger and more heterogenous total water export (3-17% greater) and water export acceleration (factor of 1.2-3.3 larger) than previously reported, with substantial differences across basins, ecoregions, stream orders, human regulation, and permafrost regimes. We also find significant changes in the spring freshet and summer stream intermittency. Ultimately, our results represent an updated, publicly available, and more accurate daily understanding of Arctic rivers uniquely enabled by recent advances in hydrologic modeling and remote sensing

Clinical factors of post-chemoradiotherapy as valuable indicators for pathological complete response in locally advanced rectal cancer

OBJECTIVES: Pathological complete response has shown a better prognosis for patients with locally advanced rectal cancer after preoperative chemoradiotherapy. However, correlations between post-chemoradiotherapy clinical factors and pathologic complete response are not well confirmed. The aim of the current study was to identify post-chemoradiotherapy clinical factors that could serve as indicators of pathologic complete response in locally advanced rectal cancer. METHODS: This study retrospectively analyzed 544 consecutive patients with locally advanced rectal cancer treated at Sun Yat-sen University Cancer Center from December 2003 to June 2014. All patients received preoperative chemoradiotherapy followed by surgery. Univariate and multivariate regression analyses were performed to identify post-chemoradiotherapy clinical factors that are significant indicators of pathologic complete response. RESULTS: In this study, 126 of 544 patients (23.2%) achieved pathological complete response. In multivariate analyses, increased pathological complete response rate was significantly associated with the following factors: post-chemoradiotherapy clinical T stage 0-2 (odds ratio=2.098, 95% confidence interval=1.023-4.304, p=0.043), post-chemoradiotherapy clinical N stage 0 (odds ratio=2.011, 95% confidence interval=1.264-3.201, p=0.003), interval from completion of preoperative chemoradiotherapy to surgery of >;7 weeks (odds ratio=1.795, 95% confidence interval=1.151-2.801, p=0.010) and post-chemoradiotherapy carcinoembryonic antigen ≤2 ng/ml (odds ratio=1.579, 95% confidence interval=1.026-2.432, p=0.038). CONCLUSIONS: Post-chemoradiotherapy clinical T stage 0-2, post-chemoradiotherapy clinical N stage 0, interval from completion of chemoradiotherapy to surgery of >;7 weeks and post-chemoradiotherapy carcinoembryonic antigen ≤2 ng/ml were independent clinical indicators for pathological complete response. These findings demonstrate that post-chemoradiotherapy clinical factors could be valuable for post-operative assessment of pathological complete response

No relationship between 2',3'-cyclic nucleotide 3'-phosphodiesterase and schizophrenia in the Chinese Han population: an expression study and meta-analysis

<p>Abstract</p> <p>Background</p> <p>2',3'-Cyclic nucleotide 3'-phosphodiesterase (<it>CNP</it>), one of the promising candidate genes for schizophrenia, plays a key part in the oligodendrocyte function and in myelination. The present study aims to investigate the relationship between <it>CNP </it>and schizophrenia in the Chinese population and the effect of different factors on the expression level of <it>CNP </it>in schizophrenia.</p> <p>Methods</p> <p>Five <it>CNP </it>single nucleotide polymorphisms (SNPs) were investigated in a Chinese Han schizophrenia case-control sample set (n = 180) using direct sequencing. The results were included in the following meta-analysis. Quantitative real-time polymerase chain reaction (PCR) was conducted to examine <it>CNP </it>expression levels in peripheral blood lymphocytes.</p> <p>Results</p> <p>Factors including gender, genotype, sub-diagnosis and antipsychotics-treatment were found not to contribute to the expression regulation of the <it>CNP </it>gene in schizophrenia. Our meta-analysis produced similar negative results.</p> <p>Conclusion</p> <p>The results suggest that the <it>CNP </it>gene may not be involved in the etiology and pathology of schizophrenia in the Chinese population.</p

A Single E627K Mutation in the PB2 Protein of H9N2 Avian Influenza Virus Increases Virulence by Inducing Higher Glucocorticoids (GCs) Level

While repeated infection of humans and enhanced replication and transmission in mice has attracted more attention to it, the pathogenesis of H9N2 virus was less known in mice. PB2 residue 627 as the virulent determinant of H5N1 virus is associated with systemic infection and impaired TCR activation, but the impact of this position in H9N2 virus on the host immune response has not been evaluated. In this study, we quantified the cellular immune response to infection in the mouse lung and demonstrate that VK627 and rTsE627K infection caused a significant reduction in the numbers of T cells and inflammatory cells (Macrophage, Neutrophils, Dendritic cells) compared to mice infected with rVK627E and TsE627. Further, we discovered (i) a high level of thymocyte apoptosis resulted in impaired T cell development, which led to the reduced amount of mature T cells into lung, and (ii) the reduced inflammatory cells entering into lung was attributed to the diminished levels in pro-inflammatory cytokines and chemokines. Thereafter, we recognized that higher GCs level in plasma induced by VK627 and rTsE627K infection was associated with the increased apoptosis in thymus and the reduced pro-inflammatory cytokines and chemokines levels in lung. These data demonstrated that VK627 and rTsE627K infection contributing to higher GCs level would decrease the magnitude of antiviral response in lung, which may be offered as a novel mechanism of enhanced pathogenicity for H9N2 AIV