Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R2 of 0.80 and an RMSE of 76.03 g/m2. (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R2 was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R2 was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m2 to 72.36 g/m2. (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R2 was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m2 to 72.36 g/m2. This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data.
<p>Runoff generation in semi-humid regions is always characterized by a complex nonlinear process influenced by both saturation excess mechanism and infiltration excess mechanism. A hybrid runoff generation module is proposed in this study to delineate the mixed rainfall-runoff process by integrating an infiltration module, based on a modified Horton equation, with the saturation excess runoff generation module of Xinanjiang model at grid scale. A new distributed hydrological model, termed grid-Xinanjiang-infiltration-excess (GXAJ-IE) model, is subsequently developed in the context of grid-Xinanjiang model. Not only in semi-humid regions, but GXAJ-IE model is also expected to achieve acceptable performance in other hydrometeorological zones due to its superimposed runoff generation structure. Thus GXAJ-IE model is tested in four watersheds across different hydrometeorological zones (humid, semi-humid, semi-arid and arid) of China, and two models with single runoff generation mode, grid-Xinanjiang (GXAJ) model and grid-infiltration-excess (GIE) model, are set as benchmarks for comparison purpose. The results indicate that compared with the two benchmark models, GXAJ-IE model has higher flexibility and robustness in reproducing the flood hydrographs, especially the flood peaks, driven by various rainfall patterns in the semi-humid Dongwan and Maduwang watersheds. Furthermore, GXAJ-IE model could well capture the spatiotemporal characteristic of the saturation and infiltration excess runoff components, and delineate the evolution of their contributing areas within a flood event. Yet rainfall input with low spatiotemporal resolution still remains a limitation to give full play to the advantage of GXAJ-IE model. None of the models performs well in the arid and semi-arid Suide watershed, even though, GXAJ-IE model shows comparable simulation accuracy with GIE model whereas GXAJ model absolutely loses its edge. In the humid Tunxi watershed, GXAJ-IE model produces comparably good performance with GXAJ model while GIE model is slightly inferior. Overall, GXAJ-IE model is fairly adaptable to different hydrometeorological regions in China and shows great potential for universal application, with an especially promising prospect in improving the flood forecasting accuracy for the semi-humid watersheds.</p>
Abstract. With the booming big data techniques, large-sample hydrological analysis on streamflow regime is becoming feasible, which could derive robust conclusions on hydrological processes from a big-picture perspective. However, there is not a comprehensive global large-sample dataset for components of the streamflow regime yet. This paper presents a new time series dataset on global streamflow indices calculated from daily streamflow records after data quality control. The dataset contains 79 indices over seven major components of streamflow regime (i.e., magnitude, frequency, duration, changing rate, timing, variability, and recession) of 5548 river reaches globally. The indices time series in the dataset are available until 2021, the lengths of which vary from 30 to 215 years with an average of around 66 years. Restricted-access streamflow data of typical river basins in China are included in the dataset. Compared to existing global datasets, this global dataset covers more indices, especially those characterizing the frequency, duration, changing rate, and recession of streamflow regime. With the dataset, research on streamflow regime will become easier without spending time handling raw streamflow records. This comprehensive dataset will be a valuable resource to the hydrology community to facilitate a wide range of studies, such as studies of hydrological behaviour of a catchment, streamflow regime prediction in data-scarce regions, as well as variations in streamflow regime from a global perspective.
The use of porous ceramic filters is promoted globally for household water treatment, but these filters are ineffective in removing viruses from water. In order to increase virus removal, we combine a promising natural coagulant, chitosan, as a pretreatment for ceramic water filters (CWFs) and evaluate the performance of this dual barrier water treatment system. Chitosan is a non-toxic and biodegradable organic polymer derived by simple chemical treatments from chitin, a major source of which is the leftover shells of crustacean seafoods, such as shrimp, prawns, crabs, and lobsters. To determine the effectiveness of chitosan, model test water was contaminated with Escherichia coli K011 and coliphage MS2 as a model enteric bacterium and virus, respectively. Kaolinite clay was used to model turbidity. Coagulation effectiveness of three types of modified chitosans was determine at various doses ranging from 5 to 30 mg/L, followed by flocculation and sedimentation. The pre-treated supernatant water was then decanted into the CWF for further treatment by filtration. There were appreciable microbial removals by chitosan HCl, acetate, and lactate pretreatment followed by CWF treatment, with mean reductions (95% CI) between 4.7 (± 1.56) and 7.5 (± 0.02) log10 for Escherichia coli, and between 2.8 (± 0.10) and 4.5 (± 1.04) log10 for MS2. Turbidity reduction with chitosan treatment and filtration consistently resulted in turbidities < 1 NTU, which meet turbidity standards of the US EPA and guidance by the World Health Organization (WHO). According to WHO health-based microbial removal targets for household water treatment technology, chitosan coagulation achieved health protective targets for both viruses and bacteria. Therefore, the results of this study support the use of chitosan to improve household drinking water filtration processes by increasing virus and bacteria reductions.
Abstract The impacts of future climate change on the watershed streamflow and total dissolved nitrogen (TDN) fluxes upstream of the Danjiang River were estimated. The newest shared socioeconomic pathways (SSP) in CMIP6 were used as a climate change scenario. The ensembles of downscaled GCM outputs from WorldClim were used as future climate change information. A combined modeling approach is proposed, including the Long Ashton Research Station Weather Generator (LARS-WG) model as a weather generator and the generalized watershed loading function (GWLF) model for watershed hydrochemical process model and scenario analysis. The results show that there is generally less annual streamflow but more annual TDN flux under future climate change scenarios. The monthly streamflow and TDN flux increased from May to July and decreased from August to October. Changes in streamflow and TDN fluxes were the greatest in the worst uncontrolled scenario of SSP 5-85, with a 12.1% decrease in annual streamflow in the 2070s and a 4.82% increase in annual TDN flux in the 2090s. This indicates that active climate policies can mitigate the impact of climate change on watersheds. Furthermore, the source apportionments of TDN from agricultural sources will increase to nearly 50% by the 2090s, and targeted management strategies should be implemented.
Abstract. With the booming big data techniques, large-sample hydrological analysis on streamflow regime is becoming feasible, which could derive robust conclusions on hydrological processes from a big-picture perspective. However, there is not a comprehensive global large-sample dataset for components of the streamflow regime yet. This paper presents a new time series dataset on global streamflow indices calculated from daily streamflow records after data quality control. The dataset contains 79 indices over seven major components of streamflow regime (i.e., magnitude, frequency, duration, changing rate, timing, variability, and recession) of 5548 river reaches globally. The indices time series in the dataset are available until 2021, the lengths of which vary from 30 to 215 years with an average of around 66 years. Restricted-access streamflow data of typical river basins in China are included in the dataset. Compared to existing global datasets, this global dataset covers more indices, especially those characterizing the frequency, duration, changing rate, and recession of streamflow regime. With the dataset, research on streamflow regime will become easier without spending time handling raw streamflow records. This comprehensive dataset will be a valuable resource to the hydrology community to facilitate a wide range of studies, such as studies of hydrological behaviour of a catchment, streamflow regime prediction in data-scarce regions, as well as variations in streamflow regime from a global perspective.
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