Abstract. Climate warming exacerbates the degradation of the mountain cryosphere, including glacier retreat, reduction in snow cover area, and permafrost degradation. These changes dramatically alter the local and downstream hydrological regime, posing significant threats to basin-scale water resource management and sustainable development. However, there is still a lack of systematic research that evaluates the variation of cryospheric elements in mountainous catchments and their impacts on future hydrology and water resources. In this study, we developed an integrated cryospheric-hydrologic model, referred to as the FLEX-Cryo model. This model comprehensively considers glaciers, snow cover, frozen soil, and their dynamic impacts on hydrological processes in the mountainous Hulu catchment located in the Upper Heihe river of China. We utilized the state-of-the-art climate change projection data from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate the future changes in the mountainous cryosphere and their impacts on hydrology. Our findings showed that the two glaciers in the Hulu catchment will completely melt out around the years 2045–2051. By the end of the 21st century, the annual maximum snow water equivalent is projected to decrease by 41.4 % and 46.0 %, while the duration of snow cover will be reduced by approximately 45 and 70 days. The freeze onset of seasonal frozen soil is expected to be delayed by 10 and 22 days, while the thaw onset of permafrost is likely to advance by 19 and 32 days. Moreover, the maximum freeze depth of seasonal frozen soil is projected to decrease by 5.2 and 10.9 cm per decade, and the depth of the active layer will increase by 8.2 and 15.5 cm per decade. Regarding hydrology, runoff exhibits a decreasing trend until the complete melt-out of glaciers, resulting in a total runoff decrease of 15.6 % and 18.1 %. Subsequently, total runoff shows an increasing trend, primarily due to an increase in precipitation. Permafrost degradation causes the duration of low runoff in the early thawing season to decrease, and the discontinuous baseflow recession gradually transitions into linear recessions, leading to an increase in baseflow. Our results highlight the significant changes expected in the mountainous cryosphere and hydrology in the future. These findings enhance our understanding of cold-region hydrological processes and have the potential to assist local and downstream water resource management in addressing the challenges posed by climate change.
Spatial downscaling is an effective way to obtain precipitation with sufficient spatial details. The performance of downscaling is typically determined by the empirical statistical relationships between precipitation and the used auxiliary variables. In this study, we conducted a comprehensive comparison of five empirical statistical methods for spatial downscaling of GPM IMERG V06B monthly and annual precipitation with a relatively long time series from 2001 to 2015 over a typical semi-arid to arid area (Gansu province, China). These methods included two parametric regression methods (univariate regression, UR; multivariate regression, MR) and three machine learning methods (artificial neural network, ANN; support vector machine, SVM; random forests, RF), which were used to downscale the satellite precipitation from 0.1° (~10 km) to 1 km spatial resolution. Five commonly used indices which were normalized differential vegetation index (NDVI), elevation, land surface temperature (LST), latitude and longitude, were selected as auxiliary variables. The downscaled results were validated using a total of 80 rain gauge station data during 2001-2015. Results showed that latitude had the overall largest correlation with IMERG annual precipitation, also evidenced by feature importance measurements in RF. The downscaled results at monthly scale were overall consistent with the results at annual scale. The machine learning based methods had better predictive ability of the original IMERG precipitation than parametric regression methods, with larger coefficient of determination (R2) and smaller root mean square error (RMSE) as well as relative root mean square error (RRMSE). The downscaled 1 km IMERG precipitation by parametric regression methods had obvious underestimations (positive residual errors) in the south and east of Gansu province and overestimations (negative residual errors) in the west. In addition, the validation results of parametric regression downscaling methods showed large improvements after residual correction, while the improvements were small in the machine learning based methods. However, the interpolation algorithm included in residual correction can cause certain errors in the downscaled results due to the ignorance of precipitation spatial heterogeneity. The machine learning based RF downscaling had the smallest residual errors and the overall best validation results, showing great potentials to provide accurate precipitation with high spatial resolution.
Abstract Modeling of riverine dissolved organic carbon (DOC) dynamics is of great importance for the global carbon budget. Impoundment interception changes the travel time of water and DOC from upslope contributing areas, exerting substantial influence on riverine DOC dynamics in the catchments with many impoundments. However, the impact of impoundment interception representation on riverine DOC modeling has not been evaluated so far. This study investigated to what extent impoundment interception representation affects DOC simulations using a newly developed catchment‐scale DOC model, which can represent the upslope contributing areas of impoundments and the impoundment interception process. The results showed that streamflow and DOC load simulation were well simulated regardless of whether impoundment interception was represented, but the simulation of DOC concentrations was satisfiable only when impoundment interception was taken into account. The simulation without impoundment interception produced unrealistic fluctuation of DOC concentration due to the direct mixing of DOC from different sources with contrasting concentration gradients. These results underscored the significance of employing an appropriate model structure for riverine DOC simulation. It is strongly recommended that DOC concentration be utilized for model evaluation in order to attain robust simulation outcomes. Moreover, the newly developed model in this study keeps a balance between the completeness of process presentation and model complexity, occupying a unique “ecological niche” among catchment‐scale riverine DOC models.
Mercury is a severe pollutant in China. A novel mobile laser spectroscopy laboratory was constructed and used in differential absorption lidar mapping in major Chinese cities, a heavily polluted mining area and an archeological site.
Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to different retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential Climate Variables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l'Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency's Climate Change Initiative (ESA CCI) soil moisture, the study shows that the QA4ECV FAPAR can support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.
<p>Model realism testing is of vital importance in science of hydrology, in terms of realistic representation of hydrological processes and reliability of future prediction. We conducted three modeling case studies in cold regions of China, i.e. the upper Heihe River basin, the Urumqi Glacier No.1 basin, and the Yigong Zangbu River basin, to test the importance of stepwise modeling and internal fluxes validation to improve model realism.</p><p>In the upper Heihe River basin, we used four progressively more complex hydrological models (FLEXL, FLEXD, FLEXT0 and FLEXT), to stepwisely account for distributed forcing inputs, tailor-made model structure for different landscapes, and the realism constraints of parameters and fluxes. We found that the stepwise modeling framework helped hydrological processes understanding, and the tailor-made model structure and realism constraints improved model transferability to two nested basins.</p><p>In the Urumqi Glacier No. 1 basin, with 52% of the area covered by glaciers, we developed a conceptual glacier-hydrological model (FLEXG) and tested its performance to reproduce the hydrograph, and separate the discharge into contributions from glacier and nonglacier areas, and establish estimates of the annual glacier mass balance (GMB), the annual equilibrium line altitude (ELA), and the daily snow water equivalent (SWE). We found that the FLEXG model, involving effects of topography aspect, was successfully transferred and upscaled to a larger catchment without recalibration.</p><p>In the Yigong Zangbu River basin, with 41.4% glacier area, we designed three models (FLEXD, FLEX-S, FLEX-SG) to stepwisely understand the impact of snow, glacier to reproduce historic streamflow. We found that by involving snow and glacier modules, the model performance was dramatically improved. Although the daily streamflow of FLEX-SG reached up to 0.93 Kling-Gupta Efficiency (KGE) in calibration, it significantly overestimated snow cover area (SCA) and glacier mass balance (GMB). With satellite measured precipitation lapse rate, we improved FLEX-SG model realism not only to reproduce hydrography but also SCA and GMB.</p>
Abstract. Future global temperature change would have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore the future climate change. However, ESMs still exist great uncertainty and often run at a coarse spatial resolution (The majority of ESMs at about 2 degree). Accurate temperature data at high spatial resolution are needed to improve our understanding of the temperature variation and for many applications. We innovatively apply the deep-learning(DL) method from the Super resolution (SR) in the computer vision to merge 31 ESMs data and the proposed method can perform data merge, bias-correction and spatial-downscaling simultaneously. The SR algorithms are designed to enhance image quality and outperform much better than the traditional methods. The CRU TS (Climate Research Unit gridded Time Series) is considered as reference data in the model training process. In order to find a suitable DL method for our work, we choose five SR methodologies made by different structures. Those models are compared based on multiple evaluation metrics (Mean square error(MSE), mean absolute error(MAE) and Pearson correlation coefficient(R)) and the optimal model is selected and used to merge the monthly historical data during 1850–1900 and monthly future scenarios data (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) during 2015–2100 at the high spatial resolution of 0.5 degree. Results showed that the merged data have considerably improved performance than any of the individual ESM data and the ensemble mean (EM) of all ESM data in terms of both spatial and temporal aspects. The MAE displays a great improvement and the spatial distribution of the MAE become larger and larger along the latitudes in north hemisphere, presenting like a ‘tertiary class echelon’ condition. The merged product also presents excellent performance when the observation data is smooth with few fluctuations in time series. Additionally, this work proves that the DL model can be transferred to deal with the data merge, bias-correction and spatial-downscaling successfully when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632 (Wei et al., 2021).
Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.
We use a 785 nm shifted excitation Raman difference (SERDS) technique to measure the Raman spectra of the conidia of 10 mold species of especial toxicological, medical, and industrial importance, including Stachybotrys chartarum , Penicillium chrysogenum , Aspergillus fumigatus , Aspergillus flavus , Aspergillus oryzae , Aspergillus niger , and others. We find that both the pure Raman and fluorescence signals support the hypothesis that for an excitation wavelength of 785 nm the Raman signal originates from the melanin pigments bound within the cell wall of the conidium. In addition, the major features of the pure Raman spectra group into profiles that we hypothesize may be due to differences in the complex melanin biosynthesis pathways. We then combine the Raman spectral data with neural network models to predict species classification with an accuracy above 99%. Finally, the Raman spectral data of all species investigated is made freely available for download and use.
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