Check dam construction is one of the most effective and popular methods for sediment trapping in erosion-prone areas. Quantitative estimation of the sediment trapping by check dams is necessary for evaluating the effects of check dams. In this study, we proposed a new framework, SWAT-DCDam (Soil and Water Assessment Tool-Dynamic Check Dam), for modeling sediment deposition caused by check dams by integrating the widely-used SWAT model and a newly developed module, DCDam. We then applied this framework to a typical loess watershed, the YanRB (Yan River Basin), to assess the time-varying effects of check dam networks along past 60 years (1957-2016). The DCDam module generates a specific check dam network to conceptualize the complex connections at each time step (monthly), and streamflow and sediment load simulated by the SWAT model were used to force the sediment routing in the check dam network. The evaluation results showed that the SWAT-DCDam framework performed satisfactorily in simulating sediment trapped by check dams. In the YanRB, our study suggested that the designed structural parameters of check dams have evolved during the past 60 years, with higher dam but smaller controlled area in recent years. Sediment trapped by check dams increased with the intensity of soil erosion, but their relationship varied in different time periods. Further, annual amount of sediment deposition increased with the available storage, and their relationship is clearer when the available storage is less than 115Í10⁶ m³, which may be a critical storage for the YanRB, and sediment trapped by check dams could be restricted when the available storage is below the critical level. Besides, our simulation results showed that more than 75% check dams are almost full, indicating the demand for new check dams in this watershed. In brief, our developed framework can be a promising tool for check-dam effects study.

Understanding and identifying pathways and processes affecting sediment, nutrient and faecalcontaminant inputs from agricultural catchments to streams can improve environmental management strategies and provide a base for estimating the performance of various edge-of-field mitigations, such as riparian buffers and constructed wetlands. It can also provide estimates of the time lag between when changes in land use practices occur are implemented and when water quality effects that result from these changes are likely to be observed. The objective of this study was to characterise watersheds according to the major flow pathways:  overland flow, shallow sub-surface flow, a mix of overland and shallow subsurface flows, or deep ground water flow. Our goal was to characterise these flow pathways using publicly available spatial and temporal data for New Zealand, such as digital elevation maps, fundamental soil layers, rainfall maps, stream flow records and other physical characteristics of catchments.  In this study, a novel, combined data and modelling approach was employed to partition stream flow. The approach comprised a digital filtering technique to separate baseflow from total stream flow, machine learning to predict a baseflow index for all streams with Strahler 1^st order and higher, and hydrological modelling to partition the flow into five flow components: surface runoff, interflow, tile drainage, shallow groundwater, and deep groundwater. We further developed a web-based tool called Hydrological Flow path Explorer to visualize different flow pathways for all streams with Strahler 1^st order and higher for New Zealand rivers. The flow pathway analysis results can support the development of recommendations for riparian buffer design.

Eucalyptus is one of the most planted trees world-wide for hardwood production. In Australia, E.globulus (blue gum) represents 51.7% of all plantations trees for hardwood production, and the largest planted areas are in South Australia and Victoria. The E. globulus plantations expansion concerns governments because of their high water use. Especially in semi-arid regions, factors including population growth and limited precipitation rates result in pressure for regulating water allocation for commercial plantation activity. Water accounting models for plantation establishments have been developed in South Australia over an 11-years management cycle. The models are based on limited experimental evidence on mature plantations. There is insufficient information regarding the water use and growth of stands in the early years after establishment and, thus, management practices are difficult to be defined over the entire management cycle. This study aims to quantify the trade-offs between water use and CO₂ assimilation in a blue gum plantation in the first few years after establishment. The study site is located in southwestern Victoria, Australia, where energy and CO₂ fluxes as well as environmental variables were continuously measured above the tree canopy during the first 3 years after the plantation establishment. The results reveal the effect of tree growth on the water use efficiency (WUE) of a young plantation. During the first three years after establishment, understory vegetation and ecosystem respiration had a major impact on the net ecosystem exchange (NEE). After that period, the trees grew enough to dominate the contributions to NEE, with the plantation becoming a more consistent carbon sink during the entire year, and not only during growing season. Consequently, annual increases in the gross primary productivity (GPP) and WUE were observed.

Current and potential risks to the overall ecological integrity of freshwater ecosystems due to changes in environmental conditions and impacts of anthropogenic activities require exigent and well‐informed actions. Up to now, most of the efforts undertaken have focused on evaluating the physicochemical properties of water, whereas biological evaluation determining the health of a river ecosystem is less attended to. Therefore, this study has aimed to apply a macroinvertebrate-based index to the biological assessment of Lai Chi Wo. The resulting candidate metrics have then been evaluated in terms of redundancy, sensitivity, and responsiveness to environmental changes, using stepwise procedures.  Then five candidate metrics were selected out of 41 for M-IBI development after evaluating their sensitivity and appropriateness. The results of M-IBI showed that human interferences may lead to macroinvertebrate variance. After studying the relationship between monthly M-IBI and environmental factors, M-IBI was found that could comprehensively reflect river health, human intervention, and environmental change. The M-IBI will be a referable river health assessment criterion for supporting long-term monitoring and protecting ecological integrity of streams in Hong Kong and mainland of China.

Understanding the climate change impacts on water and carbon cycles is of great importance for comprehensive watershed management. Although many studies have been conducted on the future climate change impacts on either water cycle or carbon cycle, the potential impacts on water-carbon coupling cycles are still poorly understood. This study used an integrated hydro-biochemical model (SWAT-DayCent) to quantitatively investigate the climate change impacts on water-carbon coupling cycles with a case study of typical loess hilly-gully watershed-the Jinghe River Basin (JRB) on the Loess Plateau. We used climate scenarios data derived under the three Representative Concentration Pathways (RCPs2.6, 4.5 and 8.5) by five downscaled Global Circulation Models (GCMs) and set two future periods of 2020–2049 (near future, NF) and 2070–2099 (far future, FF). It was projected that the annual precipitation would generally decrease slightly during the NF period but increase by 4–11% during the FF period, while the maximum/minimum air temperatures would increase significantly. The average annual streamflow would decrease (with up to 20.1% under RCP8.5) and evapotranspiration (ET) would remain almost unchanged during the NF period; however, both of them would increase during the FF period. The net primary production (NPP) would be generally higher due to the CO₂ fertilization, whereas the soil organic carbon (SOC) would decrease across all scenarios due to the warmer climate. The decrease in SOC was primarily nonlinearly controlled by the increased air temperature and soil water content. The NPP-ET was projected to be closely coupled across all scenarios, and this coupling was mainly controlled by the inter-annual variability (IAV) of precipitation. Moreover, the precipitation IAV combined with NPP-ET coupling could also jointly control the NPP variability in the JRB. 

Variation in streamflow is subjected to variability in climate and anthropogenic activities. Accurate computation of relative contribution these factors involving variation in the streamflow remain an issue of debate. This study computes the influence of climate variability and anthropogenic activities on streamflow at a watershed scale, using streamflow time series, in combination with break point recognition, trend analysis, and hydrological sensitivity analysis. A conceptual framework has been adopted to investigate the relative proportion of effects. The Innovative trend analysis test and Pettit test were applied to examine trend and break point in the time series for a period of (1965-2016). After detecting the breck point in the year 1996, the time series was separated into two sub-series, pre-change or natural period (1965-1996) and postchange or anthropogenic-induced period (1997-2016). The hydrological model was initially calibrated with pre-change period data as input and operated for reconstruction of streamflow in the post-change period. The relative contribution of climate variability and anthropogenic activities were quantified individually using a framework. An Upward trend observed in both precipitation and streamflow time series. The climate variability was the dominating component with influencing the variation in the streamflow contributing 74.27% and the contribution of anthropogenic activities was reported 25.8% to total variation in the watershed streamflow. Although, the contribution anthroponomic active were comparatively less with respect to climate change but it was found significantly increasing in post-change period. The outcomes of this study suggest the climate variability is leading factor associate to variation in streamflow, However, anthropogenic activities were also found sensitive to streamflow variation in the watershed after break point. This knowledge gives a peer insight and would be useful to initiate any project in the watershed in future.   Acknowledgment: This work was supported by the National Research Foundation of the Korean government (Grant No. NRF-2020R1C1C1014636).

Cloud seeding, which is the most popular artificial rainfall technology, is costly and may be inefficient in some situations. Therefore, due to its particular characteristics of environmentally friendly and low cost, acoustic induced precipitation has drawn widely attentions from researchers in recent years. Low-frequency acoustic field can be used to stimulate rainfall by evoking wavy motion of air particles in the cloud which will significantly promote the process of collision coalescence and lead to the volume increase of cloud droplets. Nevertheless, there is still a lack of effective methods to evaluate the effect of acoustic rainfall enhancement in field experiments. A nearly two- month acoustic rainfall experiment with 43 trials was carried out by our research team in Linzhi City, Tibet Province. Based on the comparative analysis of the rain-gauge data, it can be concluded that the stimulation of sound waves on rainfall depends on the duration of precipitation. The promoting effect of acoustic field is more significant in the long duration rainfall process. Meanwhile, there are obvious differences between the precipitation process under acoustic wave and that of natural rainfall in the initial stage.

We present the development and application of a (~1 km) distributed hydrological model, wflow_sbm, with global spatial data and parameterization for the upper region of the Greater Chao Phraya River (GCPR) basin in Thailand. Our main aim was to test the competence of global data to overcome in situ data scarcity often occurring in Southeast Asia. The model was then used for estimating daily streamflow and further assessing effects of reservoir operation. We forced the model with the MSWEP V2 precipitation and eartH2Observe potential evapotranspiration datasets. Seamless distributed parameter maps based on pedotransfer functions (PTFs) and literature review were applied, leaving only the KsatHorFrac parameter, determining the lateral subsurface flow to be calibrated. A target storage-and-release-based reservoir operation module (ROM) was implemented to simulate reservoir releases. The model can reconstruct daily streamflow in the upper GCPR basin, especially for natural flows (KGE = 0.78). The ROM can capture the seasonal variability of reservoir releases, but not very accurately at the daily timescale (KGE = 0.43) since the actual reservoir operations are too complex. Different PTFs and KsatHorFrac values only introduce little uncertainty in the streamflow results. Therefore, the proposed model provides an opportunity for streamflow estimation in other ungauged or data-scarce basins in Southeast Asia. Further model application to quantify reservoir effects revealed that while reservoirs in the upper GCPR basin mitigated many historical extreme flow incidents in terms of both magnitudes and frequencies, their timing became more variable and difficult to predict. Altogether, the results highlighted the difficulty in representing the reservoir system in hydrological models and the importance of effective decision making for real-time reservoir operation, which remain challenging in both modeling and practice. This abstract is based on our recently published 2-part articles entitled 'Daily flow simulation in Thailand'.

In the past several decades, the Pearl River Basin (PRB) has experienced rapid urbanization, and, as a result, water pollution from the point source (PS) has resulted in poor water quality in some reaches of the river. Meanwhile, non-point source (NPS) pollution from large agricultural lands is still a main factor of causing water pollution at large. To get an insightful understanding of water quality in the PRB, it is important to identify the influence of PS and NPS on water pollution in different subbasins. In this study, PS pollution data including urban and industrial sewage from 36 main cities over the PRB in 2011 and 2017 are collected. The hydrological model, SWAT (Soil and Water Assessment Tool), is used to simulate the instream nutrients transport including nitrogen (N) and phosphorus (P) in the West River, North River and East River of the PRB. The study would help to assess the primary source and type of water pollution in different watersheds and contribute to provide suggestions for local land use management and trends of eutrophication in the Pearl River Estuary (PRE).

Development of technology for artificial stimulation of precipitation needs more attention in order to optimize water resources management. As a promising technology, acoustic-induced precipitation has received wide attention due to its low operating cost and free pollution. The main principle is to emit directional low-frequency sound waves to target cloud layer, causing cloud-droplet to collide and agglomerate into large-sized partials and form rainfall drops. However, it is difficult to systematically investigate acoustic agglomeration optimization of cloud droplet only relying on physical experiments. With wide application of numerical simulation in the field of multiphase flow, numerical modeling can be adopted to analyze aerosol particle dynamics and acoustic agglomeration process for the lack of experimental research. In this research, a multi-physics coupling model, COMSOL, was adopted to simulate the process of acoustic agglomeration. The results show the acoustic parameters, such as acoustic frequency and intensity, significantly influence the agglomeration behavior of aerosol droplets through changing the acoustic pressure structure in an agglomeration chamber and changing the acoustophoretic force exerted on the droplet parcels. The environmental parameters such as operating pressure and ambient temperature indirectly affect the interaction processes of air flow and aerosol droplets. It is expected that the preliminary simulation model will be applied to obtain a comprehensive understanding of the process of acoustic-induced precipitation.

The Tibetan Plateau (TP), as the Asian water tower, is the highest in the world and it represents one of the most complex terrain of the earth which has undergone significant warming during the past five decades. In the same period, Indian Summer Monsoon (ISM), which has a great impact on the climate over TP especially over Yarlung Zangbo river basin, has seen a weakening trend. In this context, it is significant to investigate the regional climate change over southern TP and the relationship between climate here and ISM. In this study, spatial and temporal distribution of temperature, precipitation and meridional wind are analyzed, and base on the analysis, we use Weather Research Forecasting model to simulate the impact of meridional wind on regional climate change by increasing/decreasing V-component wind by 10%, 20% and 30% in the driving data. Furthermore, a case was selected to study the impact of meridional wind on the regional rainstorm. The results show that the decreasing of meridional wind can make the most region warmer and reduce the precipitation significantly and meridional wind plays an important role in the location and duration of rainstorm.

Hydroclimatic time series analysis focuses on a few feature types (e.g., autocorrelations, trends, extremes), which describe a small portion of the entire information content of the observations. Aiming to exploit a larger part of the available information and, thus, to deliver more reliable results (e.g., in hydroclimatic time series clustering contexts), here we approach hydroclimatic time series analysis differently, i.e., by performing massive feature extraction. In this respect, we develop a big data framework to facilitate complete hydroclimatic variable behaviour characterizations. This new framework is fully automatic (in the sense that it does not depend on the hydroclimatic process at hand) and relies on approximately 60 diverse features that are mostly sourced from scientific fields beyond geoscience and environmental science (e.g., the fields of neuroscience, biology, biomedicine and forecasting), thereby constituting a new concept for our fields. We empirically prove the high practical relevance of this new concept in hydrological and hydroclimatic contexts by applying our framework to three global hydroclimatic datasets, which together contain 40-year-long time series originating from over 13 000 stations. Our big data analyses provide a useful basis for extracting interpretable knowledge (e.g., on seasonality, trends, autocorrelation, long-range dependence, entropy and on feature types that are met less frequently in the geoscientific literature, as well as on the relationships between all the computed features) at the global scale, and for comparing the examined hydroclimatic variable types in terms of this knowledge. Overall, we feel that, by moving a step further from the traditional approach to feature extraction in hydroclimatic research (as made in this work), we might gain additional insights into the nature of hydroclimatic regimes and changes.