Hydrological Processes最新文献

Mountains have a critical role in freshwater supply for downstream populations. As the climate changes, groundwater stored in mountains may help buffer the impacts to declining water resources caused by decreased snowpack and glacier recession. However, given the scarcity of groundwater observation wells in mountain regions, it remains unclear how mountain groundwater is being impacted by climate change across ecoregions. This study quantifies temporal trends in mountain groundwater levels and explores how various climatic, physiographic and anthropogenic factors affect these trends. We compiled data from 171 public groundwater observation wells within mountain regions across Canada and the United States, for which at least 20 years of monthly data is available. The Mann-Kendall test for monotonic trend revealed that 54% of these wells have statistically significant temporal trends (p < 0.05) over the period of record, of which 69% were negative and therefore indicating overall declining groundwater storage. Wells in the western mountain ranges showed stronger trends (both positive and negative) than the eastern mountain ranges, and higher elevation wells showed fewer negative trends than the low elevation (<400 m asl) wells (p < 0.05). Correlation, Kruskal-Wallis tests, stepwise multiple linear regression and random forest regression were used to identify factors controlling groundwater trends. Statistical analysis revealed that lower-elevation mountain regions with higher average annual temperatures and lower average annual precipitation have the greatest declines in groundwater storage under climate change. Trends in temperature and precipitation, and ecoregion were also important predictors on groundwater level trends, highlighting geographic differences in how mountain wells are responding to climate change. Furthermore, sedimentary bedrock aquifers showed markedly more negative trends than crystalline bedrock aquifers. The findings demonstrate that the impact of climate change on mountain water resources extends to the subsurface, with important implications for global water resources.

Isotope-enabled models provide a means to generate robust hydrological simulations. However, daily isotope-enabled rainfall-runoff models applied to larger spatial scales (>100 km²) require more input data than conventional non-isotope models in the form of precipitation isotope time series, which are difficult to generate even with point station measurements. Spatially distributed isotope data can be circumvented by isotope-enabled climate models. Here, we evaluate the hydrological simulations of the J2000-isotope enabled hydrological model driven with data from corrected and un-corrected isotope-enabled global and regional climate models (isotope-enabled global spectral model [IsoGSM] and isotope-enabled regional spectral model [IsoRSM], respectively) compared with 1 year of measured reference station and a yearly average precipitation isotope input for a pilot site, the data-scarce sub-humid Eerste River catchment in South Africa. The models driven by all input products performed well for upstream and downstream discharge gauges with Nash Sutcliffe efficiency (NSE) from 0.58 to 0.85 and LogNSE of 0.66 to 0.93. The simulated δ²H stream isotopes using the reference J2000-iso and J2000-isoRSM were good for the main river with a stream Kling Gupta efficiency (KGE) of between 0.4–0.9 and the top 100 Monte Carlo simulations varying by around 5‰ for δ²H. For smaller tributaries the model was unable to capture the measured stream isotopes due to biased precipitation isotope inputs. Adjusting the J2000-iso with a bias corrected IsoRSM improved the stream and groundwater isotope simulation and outperformed the model driven by an average yearly precipitation isotope input. Differences in simulated hydrological processes were only evident between the models when evaluating percolation with unrealistic simulations for the standard J2000 model. While the regional climate model is computationally more intensive than its global counterpart, it provided better stream isotope simulations and improvements to simulated percolation. Our results indicate that isotope-enabled climate models can provide useful input data in data scarce regions for hydrological models, where improved water management to address climate change impacts is needed.

Evaluation metrics play a pivotal role in the calibration process of hydrological models, serving as objective functions that directly influence the final values of model parameters and significantly affect users' perceptions of model performance. However, the choice and interpretation of evaluation metrics are subjective; therefore, this study provides a more objective framework for assessing model performance. This paper initially explored the applicability of various commonly used evaluation metrics, providing an overview of their limitations. Following this, we decomposed errors by analysing their physical significance and geometric representation in scatter plots, categorizing them into systematic and unsystematic errors. Through the decomposition and derivation of the Nash–Sutcliffe efficiency (NSE) formula, we established the quantitative relationship among various evaluation metrics. The soil and water assessment tool (SWAT) model was utilized to simulate monthly runoff in the Baishan basin (China), for the period 1994–2017, with NSE serving as the objective function for calibration. Our findings are consistent with previous studies, indicating that the model tends to slightly underestimate high flows while significantly overestimating low flows. Further analysis through error decomposition and the examination of relationships among various evaluation metrics revealed that unsystematic errors were dominant during the spring snowmelt runoff period, while systematic errors prevailed in the dry season. By evaluating the runoff series based on the magnitude of runoff or by categorizing it according to seasons and months, a more stringent assessment of the model's performance was achieved. These findings not only highlight the necessity for careful selection of evaluation metrics but also underscore the significance of our methodological advancements in enhancing hydrological model precision and reliability.

Floodplains are essential ecosystems that provide a variety of economic, hydrologic, and ecologic services. Within floodplains, surface water-groundwater exchange plays an important role in facilitating biogeochemical processes and can have a strong influence on stream hydrology through infiltration or discharge of water. These functions can be difficult to assess due to the heterogeneity of floodplains and monitoring constraints, so numerical models are useful tools to estimate fluxes, especially at large spatial extents. In this study, we use the SWAT+ (Soil and Water Assessment Tool) ecohydrological model to quantify magnitudes and spatiotemporal patterns of floodplain surface water-groundwater exchange in a mountainous watershed using an updated version of the gwflow module that directly calculates floodplain-aquifer exchange rates during periods of floodplain inundation. The gwflow module is a spatially distributed groundwater modelling subroutine within the SWAT+ code that uses a gridded network and physically based equations to predict groundwater storage, groundwater head, and groundwater fluxes. We used SWAT+ to model the 7516 km² Colorado River headwaters watershed and streamflow data from USGS gages for calibration and testing. Models that included floodplain-groundwater interactions outperformed those without such interactions and provided valuable information about floodplain exchange rates and volumes. Our analyses on the location of floodplain fluxes in the watershed also show that wider areas of floodplains, “beads” (e.g., like beads on a necklace), exchanged a higher net and per area volume of water, as well as higher rates of exchange, compared to narrower areas, “strings.” Study results show that floodplain channel-groundwater exchange is a valuable process to include in hydrologic models, and model outputs could inform land conservation practises by indicating priority locations, such as beads, where substantial hydrologic exchange occurs.

Dissolved oxygen (DO) is a critical water quality constituent that governs habitat suitability for aquatic biota, biogeochemical reactions and solubility of metals in streams. Recently introduced high-frequency sensors have increased our ability to measure DO, but we still lack the capacity to understand and predict DO concentrations at high spatial resolutions or in unmonitored locations. Machine learning (ML) has been a commonly used approach for modelling DO, however, conventional ML models have no representation of the limnological processes governing DO dynamics. Here we implement and evaluate two process-guided deep learning (PGDL) approaches for predicting daily minimum, mean and maximum DO concentrations in rivers from the Delaware River Basin, USA. In both cases, a multi-task approach was taken in which the PGDL models predicted stream metabolism and gas exchange rates in addition to the DO concentrations themselves. Our results showed that for these sites, the PGDL approaches did not improve upon baseline predictions in temporal and spatially similar holdout experiments. One of the approaches did, however, improve predictions when applied to spatially dissimilar sites. Although this particular PGDL approach did not improve predictive accuracy in most cases, our results suggest that process guidance, perhaps a more constrained approach, could benefit a data-driven DO model.

The role of runoff in providing nutrients to runon loci in deserts was not extensively explored. Here we report 2 years of measurements of the chemical composition of rain and runoff (three events for each year, which correspond to the long-term runoff events) in plots that were constructed over four biocrust types in the Negev Desert, Israel. The enrichment ratios showed high variability, being high for K⁺ (3.7), moderately high for NH₄⁺ (1.6) and slightly high for Mg²⁺ and SO₄²⁻ (1.2). It was low for Cl⁻ and NO₃⁻ (0.5), moderately lower for Ca²⁺ (0.7) and slightly lower for Na⁺ and HCO₃⁻ (0.8). When examined per rain event, significant higher concentrations were found for K⁺ and Mg²⁺ while NO₃⁻ exhibited significantly lower concentration. The high enrichment of K⁺ and the enrichment of Mg²⁺ may point to a biogenic origin. While K⁺ enrichment is suggested to result from K⁺ excretion by the cyanobacteria, bacteria, and possibly by the mosses following cell wetting and the K⁺ role in cell osmoregulation, decomposition and erosion of the chlorophyll pigment may result in Mg²⁺ release. On the other hand, the data point out that despite the crust capability to fix nitrogen and thus to provide its own needs for nitrogen, NO₃⁻ was depleted from the runoff water, a phenomena that may be explained by the crust preference to utilise available low-cost nitrogen provided by rain. Due to runoff accumulation at small depressions within the interdune and at the dune-interdune interface, runoff may contribute additional amount of nutrients to these habitats. For the dune-interdune interface it may account for an addition of 273.8% and 35.3% of the total potassium and nitrogen, respectively. The addition of water and nutrients may have important contribution to the growth of the moss-dominated biocrusts and the shrubs at the dune-interdune interface, being responsible for the formation of ‘mantles and islands of fertility’ at the dune-interdune interface. It also points to the possible role that biocrusts may play in agroforestry practices.

Significant advancements in satellite-based precipitation retrieval algorithms have led to the development of continuous, quasi-global precipitation products, offering unique opportunities for hydrometeorological and climate research. In this context, six satellite-based precipitation products (SPPs), including CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were thoroughly investigated for their application in hydrological simulations over the Wardha River basin in India. The observed gridded precipitation product developed by India Meteorological Department (IMD) has been used as reference data to evaluate the performance of SPPs. Hydrological variables such as runoff, soil moisture (SM) and evapotranspiration (ET) were simulated using the Variable Infiltration Capacity model. The performances of SPPs are critically assessed using various statistical metrics, including Pearson's correlation coefficient (R), Klinga-Gupta Efficiency (KGE), Percent Bias (PBIAS), root mean square error (RMSE), and the RMSE to standard deviation ratio (RSR). The model-simulated discharge was compared with streamflow observations at a single gauging site, while a spatially distributed comparison was conducted between simulated SM and ET and satellite-based SM and ET. The IMD dataset consistently shows superior performance for discharge simulation at both daily and monthly scales, with KGE values of 0.85 and 0.91, respectively. Among SPPs, MSWEP and CHIRPS excel in simulating daily and monthly discharge, respectively. For ET simulation, CHIRPS outperforms IMD and other SPPs, achieving an overall KGE value of 0.37. In contrast, PERSIANN is the most effective for simulating SM compared to other precipitation products.

Northwest China is both an important coal storage base and an ecologically fragile area, and soil water content (SWC) is a key factor limiting the ecological development of Northwest China. Revealing the characteristics of spatial and temporal distribution changes of soil water content in mining areas under coal mining disturbance and the influencing mechanism is crucial for the protection of water resources in mining areas. In this study, the soil water content (depth 0–1000 cm) of a typical coal mine subsidence area in the western part of Shendong Coal Group was monitored in situ for 1 year after mining, and the absolute value, variability, and spatial distribution of soil water content were temporally analysed by combining classical statistics, 2D Ordinary Kriging and 3D Empirical Bayesian kriging spatial interpolation. sequence analysis. The results showed that the shallow SWC (0 ~ 60 cm) was distributed horizontally in bands, and gradually increased along the direction from northwest to southeast; with the increase of coal mining time, the absolute value of SWC decreased by 0.81% ~ 33.58%, and the coefficient of variation decreased and then increased, with the range of variation from 2.19% ~ 34.49%. The deep SWC (100 ~ 1000 cm) was stratified vertically and increased with soil depth, and the shallow and deeper soil moisture would gradually migrate to the middle layer under the influence of coal mining. In addition, this paper accurately portrays the three-dimensional spatial and temporal distribution of soil water content by the 3D EBK model, which further reveals the mechanism of coal mining's influence on soil water content. This study can provide technical and data support for predicting and evaluating the potential impacts of mining activities on the water cycle, and help mining areas to formulate policies for managing and protecting water resources.

To maintain a reasonable sediment regulation system in the Loess Plateau, it is critical to determine the effects of check dam construction on sediment production and topographic changes. An indoor simulation experiment was conducted to investigate sediment production at the outlet section of the gully and micro-topographic changes within the channel before and after dam construction. The results showed that check dam significantly affected the run-off and sediment transport processes in the watershed. Specifically, the cross-sectional morphology index (η) and the width-depth ratio increased by 10.23% and 40.44%, respectively, while sediment content and particle size decreased by 39.29% and 18.58%, respectively. Additionally, the relative importance of section parameters and micro-topographic parameters that affect sediment production rate and particle size was ranked using the random forest algorithm. The roughness after check dam construction was identified as a relatively important topographic factor affecting sediment production and particle selection by erosion. These findings provide valuable information for future check dam construction and development in the Loess Plateau region.

Although there is a general understanding of how afforestation impact on streamflow, there is also requirement for additional empirical data both on a small and large basins scale, primarily due to the significant variability found in global data sets. A multi-method approach is proposed to understand the impact of plantation forest cover changes on stream flow, aiming to address individual approach weaknesses and enable cross-validation. Focusing on Uruguay as a case study, the data based analysis indicated a significant decrease in streamflow within four highly afforested large basins of the north region, occurring approximately when the afforestation reached 15% of the total basin area. Also three of those basins showed a significant decreasing trend in streamflow after the change-point. A stronger link between afforestation increase and lower runoff-rainfall ratio was found in autumn-winter season compared to spring–summer, due to higher soil water availability. The model residual approach effectively isolated land use and land cover effects in large basins with gradual afforestation, utilizing long data series. Consistently, statistically significant trends in the residual series indicated decreased streamflow in the after-afforestation period in the four large basins. Finally a clear difference in the magnitude of the change emerged between the large and small basins, highlighting the influence of rainfall heterogeneity, landscape control and forest management on the scaling behaviour of streamflow and runoff ratio.