Groundwater discharge to river networks makes up a major source of riverine CO2 emission, available evidence however comes mainly from headwater streams which are directly connected to terrestrial ...ecosystems and spatially limited in terms of system size. Here relying on coupled water and CO2 mass balances, we quantified the groundwater-mediated CO2 input to the Yangtze River mainstem on an annual basis, where the mass balance of water provided physical constraints on CO2 exchange between the river and groundwater. A landscape topographic control of the groundwater-river interaction was proposed where mountain reaches preferentially receive water and CO2 discharge from the groundwater while plain alluvial reaches predominantly lose water to the aquifers. Groundwater CO2 inputs were however small in magnitude on all reaches (0.3–14% of the total CO2 emission and transport by the river) and unable to account for the discrepancy between surface evasion and internal metabolism in the river. Minor direct groundwater discharge to the reaches in comparison to smaller streams (negative to < 3.5% of the surface water flows) was concluded to be the main reason for low groundwater-sourced CO2 in the large river reaches.
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•Coupled water and CO2 mass balances were conducted on a large river on an annual basis.•Groundwater CO2 inputs are small in magnitude on all reaches and not able to explain all discrepancies.•Groundwater CO2 inputs depends largely on landscape terrains the river reaches flow through.•River damming reverts groundwater and CO2 exchanges in years of dam construction.
Abstract The propagation of meteorological droughts to soil droughts poses a substantial threat to water resources, agricultural production, and social systems. Understanding drought propagation ...process is crucial for early warning and mitigation, but mechanisms of the propagation from meteorological drought to soil drought, particularly at varying soil depths, remain insufficiently understood. Here, we employ the maximum correlation coefficient method and the random forest (RF) model to investigate the spatiotemporal patterns and drivers of propagation time (PT) from meteorological drought to soil drought at four different depths across China from 1980 to 2018. Our findings reveal consistently higher PT in northern China and lower PT in southern China across varying soil depths, with more pronounced spatial heterogeneity with increasing soil depth. Furthermore, we identify temperature and precipitation as determinants of spatial patterns of PT in surface and deeper soil layers, respectively. Additionally, precipitation emerges as the dominant factor influencing changes in PT between different soil layers. Our study highlights a discernible shift in PT drivers from temperature to precipitation as soil depth increases and the significant impact of precipitation on exacerbating spatial heterogeneity in PT. This study contributes to an enhanced comprehension of the propagation process from meteorological drought to soil drought at different depths, which can aid in establishing practical drought mitigation measures and early warning systems.
Soil moisture is an important indicator that is widely used in meteorology, hydrology, and agriculture. Two key problems must be addressed in the process of downscaling soil moisture: the selection ...of the downscaling method and the determination of the environmental variables, namely, the influencing factors of soil moisture. This study attempted to utilize machine learning and data mining algorithms to downscale the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture data from 25 km to 1 km and compared the advantages and disadvantages of the random forest model and the Cubist algorithm to determine the more suitable soil moisture downscaling method for the middle and lower reaches of the Yangtze River Basin (MLRYRB). At present, either the normalized difference vegetation index (NDVI) or a digital elevation model (DEM) is selected as the environmental variable for the downscaling models. In contrast, variables, such as albedo and evapotranspiration, are infrequently applied; nevertheless, this study selected these two environmental variables, which have a considerable impact on soil moisture. Thus, the selected environmental variables in the downscaling process included the longitude, latitude, elevation, slope, NDVI, daytime and nighttime land surface temperature (LST_D and LST_N, respectively), albedo, evapotranspiration (ET), land cover (LC) type, and aspect. This study achieved downscaling on a 16-day timescale based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. A comparison of the random forest model with the Cubist algorithm revealed that the R2 of the random forest-based downscaling method is higher than that of the Cubist algorithm-based method by 0.0161; moreover, the root-mean-square error (RMSE) is reduced by 0.0006 and the mean absolute error (MAE) is reduced by 0.0014. Testing the accuracies of these two downscaling methods showed that the random forest model is more suitable than the Cubist algorithm for downscaling AMSR-E soil moisture data from 25 km to 1 km in the MLRYRB, which provides a theoretical basis for obtaining high spatial resolution soil moisture data.
Precipitation plays an important role in the global water cycle, in addition to material and energy exchange processes. Therefore, obtaining precipitation data with a high spatial resolution is of ...great significance. We used a geographically weighted regression (GWR)-based downscaling model to downscale Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data over the middle and lower reaches of the Yangtze River Basin (MLRYRB) from a resolution of 0.25° to 1 km on an annual scale, and the downscaled results were calibrated using the geographical differential analysis (GDA) method. At present, either the normalized difference vegetation index (NDVI) or a digital elevation model (DEM) is selected as the environmental variable in the downscaling models. However, studies have shown that the relationship between the NDVI and precipitation gradually weakens when precipitation exceeds a certain threshold. In contrast, the enhanced vegetation index (EVI) overcomes the saturation shortcomings of the NDVI. Therefore, this study investigated the performances of EVI-derived and NDVI-derived downscaling models in downscaling TRMM precipitation data. The results showed that the NDVI performed better than the EVI in the annual downscaling model, possibly because this study used the annual average NDVI, which may have neutralized detrimental saturation effects. Moreover, the accuracy of the downscaling model could be effectively improved after correcting for residuals and calibrating the model with the GDA method. Subsequently, the downscaled rainfall was closer to the actual weather station rainfall observations. Furthermore, the downscaled results were decomposed into fractions to obtain monthly precipitation data, showing that the proposed method by utilizing the GDA method could improve not only the spatial resolution of remote sensing precipitation data, but also the accuracy of data.
During recent decades, more frequent flood-drought alternations have been seen in China as a result of global climate change and intensive human activities, which have significant implications on ...water and food security. To better identify the characteristics of flood-drought alternations, we proposed a modified dry-wet abrupt alternation index (DWAAI) and applied the new method in the middle and lower reaches of the Yangtze River Basin (YRB-ML) to analyze the long-term spatio-temporal characteristics of dry-wet abrupt alternation (DWAA) events based on the daily precipitation observations at 75 rainfall stations in summer from 1960 to 2015. We found that the DWAA events have been spreading in the study area with higher frequency and intensity since 1960. In particular, the DWAA events mainly occurred in May and June in the northwest of the YRB-ML, including Hanjiang River Basin, the middle reaches of the YRB, north of Dongting Lake and northwest of Poyang Lake. In addition, we also analyzed the impact of El Niño Southern Oscillation (ENSO) on DWAA events in the YRB-ML. The results showed that around 41.04% of DWAA events occurred during the declining stages of La Niña or within the subsequent 8 months after La Niña, which implies that La Niña events could be predictive signals of DWAA events. Besides, significant negative correlations have been found between the modified DWAAI values of all the rainfall stations and the sea surface temperature anomalies in the Nino3.4 region within the 6 months prior to the DWAA events, particularly for the Poyang Lake watershed and the middle reaches of the YRB. This study has significant implications on the flood and drought control and water resources management in the YRB-ML under the challenge of future climate change.
Low-impact development (LID) has been widely used at both site-specific and local scales to try and mitigate the impact of urban stormwater runoff caused by increasing impervious urban areas. ...Recently, the concept of a “sponge city” was proposed by the Chinese government, which includes LID controls at the source, a pipe drainage system midway, and a drainage system for excess stormwater at the terminal. There is a need to evaluate the effectiveness of sponge city construction at the large urban catchment scale, particularly with different spatial distributions of LIDs that reduce directly connected impervious areas (DCIAs). In this paper, the performances of five design scenarios with different spatial distributions but same sizes of LID controls at the urban catchment scale were analyzed using a geographic information system (GIS) of the United States Environmental Systems Research Institute (ESRI)—based Storm Water Management Model (SWMM) of the United States Environmental Protection Agency (USEPA) and MIKE 11 of Danish Hydraulic Institute (DHI) in Xining City, China. Results confirmed the effectiveness of sponge city construction in reducing the urban stormwater runoff. The hydrological performance reduction was positively correlated and linearly dependent on DCIA reduction. Peak flow reduction was most sensitive to DCIA reduction, followed by runoff volume and peak time. As rainfall intensity increased, the hydrological performance was more sensitive to rainfall intensity than DCIA reduction. Results of this study provide new insights for stormwater managers to implement LID more effectively at the urban catchment scale.
•The concept of event-based extreme precipitation (EEP) was proposed.•Spatio-temporal changes and possible non-stationarity of EEP events were analyzed.•The proposed EEP concept was more appropriate ...in the extreme precipitation analysis.
Extreme precipitations (EP) could induce a series of social, environmental and ecological problems. Traditional EP analysis usually investigated the characteristics based on a fixed time scale and therefore ignored the continuity of EP occurrence. As a result, a comprehensive assessment on the influence and consequence of the EP occurring during consecutive time periods were largely eliminated. On the other hand, the characteristics of EP, including variables such as frequency, intensity and extreme volume, were commonly defined without sufficient consideration of the local tolerance capacity (which can be represented by a threshold level of EP) and therefore would sometimes be inappropriate. In this study, we proposed a concept of event-based extreme precipitation (EEP) by considering the continuity of EP and defined the statistical variables for the characteristics of an EEP event by taking account of local tolerance capacity. An EEP was identified as a collection of precipitation data over the consecutive time period in which all the precipitation amounts are above the pre-defined threshold, and EEP events are separated by at least one time step (e.g., day or hour) with precipitation amount below the threshold. As a case study which in fact motivated our proposal, we investigated the changes and variations of EEP with the consideration of potential non-stationarity in the Hanjiang River Basin of China (HJRB) during the time period of 1960–2013. Results showed that the concept of EEP, which could reflect the impact of continuity of EP occurrence and mirror the differences of local tolerance capacity, was more appropriate than the traditional method in the EP analysis.
Soil moisture has a significant influence on water, energy, and carbon biogeochemical cycles. A numerical method for solving Richards' equation is usually used for simulating soil moisture. Selection ...of a lower boundary condition for Richards' equation will further affect the simulation results for soil moisture, water cycle, energy balance, and carbon biogeochemical processes. In this study, the soil water movement dynamic sub-model of a hydrologically based land surface model, the variable infiltration capacity (VIC) model, was modified using the finite difference method (FDM) to solve a mixed form of Richards' equation. In addition, the VIC model was coupled with a terrestrial biogeochemical model, the Carnegie Ames Stanford Approach model of carbon, nitrogen, and phosphorus (CASACNP model). The no-flux boundary (NB) and free-drainage boundary (FB) were selected to investigate their impacts on simulations of the water, energy, and soil carbon cycles based on the coupling model. The NB and FB had different influences on the water, energy, and soil carbon simulations. The water and energy simulations were more sensitive, while the soil carbon simulation was less sensitive to FB than to NB. Free-drainage boundary could result in lower soil moisture, evaporation, runoff, and heterotrophic respiration and higher surface soil temperature, sensible heat flux, and soil carbon content. The impact of the lower boundary condition on simulation would be greater with an increase in soil permeability. In the silt loam soil case, evaporation, runoff, and soil respiration of FB were nearly 16%, 13%, and 1% smaller, respectively, compared to those of NB.
Multi‐physics ensembles have emerged as a promising approach to hydrological simulations. As multi‐physics ensembles are constructed by perturbing the model physics, the ensemble members share a ...substantial portion of the same physics and hence are not independent of each other. It is unknown whether and to what extent this nonindependence affects the skill gain of the ensemble method, especially compared with the multi‐model ensemble approach. This study compares a multi‐physics ensemble configured from the Noah land surface model with multi‐parameterization options (Noah‐MP) with the North American Land Data Assimilation System (NLDAS) multi‐model ensemble. The two ensembles are evaluated in terms of the annual cycle and interannual anomaly at 12 River Forecast Centers over the conterminous United States. The ensemble skill gain is measured by the difference between the performance of the ensemble mean and the average of the ensemble members' performance, and the inter‐member independence is measured by error correlations. Results show that, due to the improved model physics, the Noah‐MP configurations outperform, on average, the NLDAS models, especially in the snow‐dominated areas. The Noah‐MP ensemble almost always obtains an outstanding member that performs the best among the two ensembles, reflecting its dense sampling of the feasible model physics space. However, these two performance superiorities do not lead to a superiority of the ensemble mean. The Noah‐MP ensemble has a lower ensemble skill gain, which corresponds to the lower inter‐member independence. These results highlight the importance of inter‐member independence, particularly when most hydrological ensemble methods have overlooked it.
Key Points
Multi‐physics ensembles can sophisticatedly treat the regional difference in dominant processes with a dense sampling of model physics space
The overlap in the physics among the multi‐physics ensemble members leads to a lower inter‐member independence and ensemble skill gain
There is a need for advancing ensemble methods to account for both performance and independence, especially for a multi‐physics ensemble
In this study, to improve the efficiency of the original Palmer Drought Severity Index (PDSI_original), we coupled the Soil and Water Assessment tool (SWAT) and PDSI_original to construct a drought ...index called PDSI_SWAT. The constructed PDSI_SWAT is applied in the Wei River Basin (WRB) of China during 1960–2012. The comparison of the PDSI_SWAT with four other commonly used drought indices reveals the effectiveness of the PDSI_SWAT in describing the drought propagation processes in WRB. The whole WRB exhibits a dry trend, with more significant trends in the northern, southeastern and western WRB than the remaining regions. Furthermore, the drought frequencies show that drought seems to occur more likely in the northern part than the southern part of WRB. The principle component analysis method based on the PDSI_SWAT reveals that the whole basin can be further divided into three distinct sub-regions with different drought variability, i.e., the northern, southeastern and western part. Additionally, these three sub-regions are also consistent with the spatial pattern of drought shown by the drought frequency. The wavelet transform analysis method indicates that the El Niño-Southern Oscillation (ENSO) events have strong impacts on inducing droughts in the WRB. The results of this study could be beneficial for a scientific water resources management and drought assessment in the current study area and also provide a valuable reference for other areas with similar climatic characteristics.
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