The main objective of this study was to use an uncertainty version of a widely used monthly time step, semi-distributed model (the Pitman model) to explore the equifinalities in the way in which the main hydrological processes are simulated and any identifiable linkages with uncertainties in the available observational data. The study area is the Zambezi River basin and 18 gauged sub-basins have been included in the analyses. Unfortunately, it is not generally possible to quantify some of the observational uncertainties in such a data scarce area and mostly we are limited to identifying where these data are clearly deficient (i.e. erroneous or non-representative). The overall conclusion is that the equifinalities in the model are hugely dominant in terms of the uncertainties in the relative occurrence of different runoff generating processes, although water use uncertainties in the semi-arid parts of the basin can contribute to these uncertainties. The identification of landscape features that suggest the occurrence of saturation excess surface runoff provides some information to constrain the model. Improved independent estimates of groundwater recharge is also identified as a key source of observational data that would help a great deal in constraining the model parameter space and therefore reducing some of the model equifinality.

To study the hyporheic exchange driven by a single peak flood-induced water level fluctuation (i.e. flood wave), a method combining numerical simulation with theoretical derivation was proposed based on the Inbuk Stream, Korea, where flooding occurs frequently. The hyporheic exchanges induced by different flood waves were investigated by varying amplitude (A), duration (T), wave type parameter (r), and rising duration (tp), which were adopted from the real-time stream stage fluctuations. Additionally, the idea of constant upstream flood volume (CUFV) condition for flood waves was put forward, and the effects of “Botan” (T/A) and peak number (N) on hyporheic exchange were studied. The results showed that the hyporheic exchange flux (q) was controlled by the water level h (sine-type) and its change rate v (cosine-type), and was proportional to the polynomial of them q“∝” (ω∙h+v), where ω is the angular frequency of the flood wave. Based on this mechanism, the influence principles on hyporheic exchanges of the typical flood wave parameters (A, T, r and tp) as well as T/A and N under CUFV condition were clarified. The main characteristic variables of hyporheic exchange, which were maximum aquifer storage and residence time, were positively correlated. They also had positive relations to the integral of the flood wave over time, which increased when the wave became higher, wider, rounder and less skewed. However, when CUFV condition was imposed, the residence time was positively correlated with T/A, whereas the maximum aquifer storage was negatively correlated with T/A. With the increase in N, water exchanged more frequently and some water returned to the stream early, leading to the slight decrease in maximum aquifer storage and residence time. These findings enriched the theory of hyporheic exchange driven by surface water fluctuation and be of great significance to enhance pollutant degradation in the hyporheic zone downstream of reservoirs.

Irrigation activities are a major control on water movement and storage in irrigated river valleys in the Intermountain West, USA. Particularly in dry years, surface water diversions can deplete streams over the summer irrigation season, leading to more variable stream temperatures and increased risk for resident aquatic species. Cooler lateral inflows derived from irrigation activities can mitigate the impacts of depletion by buffering main channel stream temperatures. Given the increasing susceptibility of depleted streams to climate and land use changes, understanding stream temperature patterns and controls in these systems is critical. We used intensive field monitoring over three summers and thermal aerial imagery to characterize stream temperature patterns and irrigation influences in a 2.5 km reach of a small agricultural stream in northern Utah. Considering variable hydrology, weather, channel morphology, diversions, and lateral inflows we found stream temperatures to be relatively insensitive to flow depletion or lateral inflows in a wet year but very sensitive in drier years. Irrigation-related lateral inflows reduced longitudinal warming and diel variability during drier years and at times prevented temperatures from reaching stressful or lethal limits. Reaches with substantial lateral inflow contributions also had a greater areal proportion of low temperatures and spatial temperature diversity. These trends were enhanced by differences in channel morphology, with greater spatial and temporal variability in multi-thread than single-thread reaches. Study results highlight critical flow and weather conditions driving increased temperature variability that will likely become more extreme with additional climate change related reductions in baseflow. Regardless of the cause, this study highlights that decreased instream flows increase the importance of identifying, quantifying, and maintaining lateral inflows to maintain instream temperatures and preservation of these inflows should be considered in future water management decisions.

Fully coupled atmospheric-hydrological models allow a more realistic representation of the land surface–boundary layer continuum, representing both high-resolution land-surface/subsurface water lateral redistribution and the related feedback towards the atmosphere. This study evaluates the potential contribution of the fully coupled approach in extended-range mesoscale hydrometeorological ensemble forecasts. Previous studies have shown, for deterministic simulations, that the effect of fully coupling for short-range forecasts is minor compared to other sources of uncertainty, however, it becomes not negligible when increasing the forecast period. Through a proof-of-concept consisting of an ensemble (50 members from the ECMWF Ensemble Prediction System) seven-days-in-advance forecast of a high impact event affecting the Calabrian peninsula (southern Italy, Mediterranean basin) on November 2019, the paper elucidates the extent to which the improved representation of the terrestrial water lateral transport in the Weather Research and Forecasting (WRF) – Hydro modeling system affects the ensemble water balance, focusing on the precipitation and the hydrological response, in terms of both soil moisture dynamics and streamflow in 14 catchments spanning over 42% of the region. The fully coupled approach caused an increase of surface soil moisture and latent heat flux from land in the days preceding the event, partially affecting the lower Planetary Boundary Layer. However, when shoreward moisture transport from surrounding sea rapidly increased becoming the dominant process, only a weak signature of soil moisture contribution could be detected, resulting in only slightly higher precipitation forecast and not clear variation trend of peak flow, even though the latter variable increased up to 10% in some catchments. Overall, this study highlighted a remarkable performance of the medium-range ensemble forecasts, suggesting a profitable use of the fully coupled approach for forecasting purposes in circumstances in which soil moisture dynamics is more relevant and needs to be better addressed.

Snow density is one of the essential properties to describe snowpack characteristics. To obtain the spatial variability of snow density and estimate it accurately in different periods of snow season still remain as challenges, particularly in the mountains. This study analyzed the spatial variability of snow density with in-situ measurements in three different periods (i.e. accumulation, stable, melt period) of snow seasons 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The performance of multiple linear regression model (MLR) and three machine learning models (i.e. Random Forest (RF), Extreme Gradient Boosting (XGB) and Light Gradient Boosting Machine (LGBM)) to simulate snow density has been evaluated. It was found that the snow density in melt period (0.27 g cm-3) was generally greater than that in stable (0.20 g cm-3) and accumulation period (0.18 g cm-3), and the spatial variability of snow density in melt period was slightly smaller than that in other two periods. The snow density in mountainous areas was generally higher than that in plain or valley areas, and snow density increased significantly (p < 0.05) with elevation in the accumulation and stable periods. Besides elevation, latitude and ground surface temperature also had critical impacts on the spatial variability of snow density in the middle Tianshan Mountains, China. In this work, the machine learning model, especially RF model, performed better than MLR on snow density simulation in three periods. Compared with MLR, the determination coefficients of RF promoted to 0.61, 0.51 and 0.58 from 0.50, 0.1 and 0.52 in accumulation period, stable period and melt period respectively. This study provide a more accurate snow density simulation method for estimating regional snow mass and snow water equivalent, which allows us to achieve a better understanding of regional snow resources.

Flooding is a frequent disaster that has a wide-spread footprint globally with significant financial and societal impacts. With availability of Earth observation data from private and public entities at varying spatial, temporal, and spectral resolution as well as data from crowdsourcing, there is no shortage of models. In fact, models and algorithms are abundant and proliferating. However, the question remains where is a global flood model when we need one? Just because models are available does not mean they are usable or accessible and adequate for emergency managers, first responders and other stakeholders who use the model outputs for preparedness, response and resource planning. Often the issue of usability stems from the fact that the models are not always reproducible or replicable. The accuracy and uncertainty associated with the models and how they change based on the scale of analysis and the resolution of input and output datasets are often not communicated properly to stakeholders so they can be part of their decision-making process. The proliferation of machine learning and data driven models that rely on historical data also adds to this problem. This paper discusses several important issues associated with global flood models and provides recommendations that could be used to increase the usability of these models.

It is widely recognized that multi-year drought can induce changes in catchment hydrological behaviors. However, at present, our understanding about multi-year, drought-induced changes in catchment hydrological behaviors and its driving factors at the process level is still very limited. This study proposed a new approach using a data assimilation technique with a process-based hydrological model to detect multi-year drought-induced changes in catchment hydrological behaviors and to identify driving factors for the changes in an unimpaired Australian catchment (Wee Jasper) which experienced prolonged drought from 1997 to 2009. Modelling experiments demonstrated that the multi-year drought caused a significant change in the catchment rainfall-runoff relationship, indicated by significant step changes in the estimated time-variant hydrological parameters SC (indicating catchment active water storage capacity) and C (reflecting catchment evapotranspiration dynamics), whose average values increased 23.4% and 10.2%, respectively, due to drought. The change in the rainfall-runoff relationship identified by the data assimilation method is consistent with that arrived at by a statistical examination. The proposed method provides insights about the drivers of the changes in the rainfall-runoff relationship at the processes level. Declining groundwater and deep soil moisture depleted by persistent evapotranspiration of deep-rooted woody vegetation during drought are the main driving factors for the catchment behaviors change in the Wee Jasper catchment. The new method proposed in this study was found to be an effective technique for detecting both the change of hydrological behaviors induced by prolonged drought and its driving factors at the process level.

Soil–rock mixtures are widely encountered in geotechnical engineering projects. The instability and failure mechanism of grap-graded soil–rock mixtures under rainfall conditions has always been the focus of geological disaster research. To deeply explore the mechanism of seepage deformation of soil–rock mixtures, an indoor physical permeability test that considers soil–rock mixtures with different fine contents was conducted, and a particle-scale numerical simulation test of the permeability evolution was carried out using the coupling model of PFC3D and ABAQUS. The test results showed that the spatial distribution of fine particle loss along the height direction could be divided into three areas: top loss, middle uniform, and bottom loss area. The “island” effect of coarse particles, which is caused by excessive fine content and makes the fine particles bear more load, was eliminated with the loss of fine particles. In this preset working condition of coarse and fine particle diameters, setting FC to 35% may be the best way to fill the voids between the coarse particles. Particle migration leads to a change in the load-bearing skeleton structure, thereby causing seepage deformation. Therefore, the particle-scale numerical test method can better reproduce the seepage deformation process of grap-graded soil–rock mixtures.

The effect of climate change on water resources has been an area of continued research, especially in Australia. Previous studies have suggested significant trends in rainfall, and these are amplified causing larger changes in streamflow. However, most of the previous analysis was based on annual time scales or modelled data and did not account for changes in land cover, which could interact with changes in climate. Climate data and streamflow data sourced from 13 fully forested small catchments (<250 km2) was analysed for trends. Non-parametric Mann-Kendall trend analysis, generalised additive mixed modelling and rainfall-runoff modelling were combined for the analysis. This indicates consistent increases in maximum temperature and varied decreases in rainfall. Limited to a small number of catchments in south eastern Australia there were small, but significantly amplified, decreases in streamflow. In general, overall decreases are much smaller than predicted in earlier research.

The magnitude and spatial heterogeneity of snow deposition are difficult to model in mountainous terrain. Here, we investigated how snow patterns from a 32-year (1985 – 2016) snow reanalysis in the Tuolumne, Kings, and Sagehen Creek, California Sierra Nevada watersheds could be used to improve simulations of winter snow deposition. Remotely-sensed fractional snow-covered area (fSCA) from dates following peak-snowpack timing were used to identify dates from different years with similar fSCA, which indicated similar snow accumulation and depletion patterns. Historic snow accumulation patterns were then used to 1) relate snow accumulation observed by snow pillows to watershed-scale estimates of mean snowfall, and 2) estimate 90 m snow deposition. Finally, snow deposition fields were used to force snow simulations, the accuracy of which were evaluated versus airborne lidar snow depth observations. Except for water-year 2015, which had the shallowest snow estimated in the Sierra Nevada, normalized snow accumulation and depletion patterns identified from historic dates with spatially correlated fractional snow-covered area agreed on average, with absolute differences of less than 10%. Watershed-scale mean winter snowfall inferred from the relationship between historic snow accumulation patterns and snow pillow observations had a ±13% interquartile range of biases between 1985 and 2016. Finally, simulations using 1) historic snow accumulation patterns, and 2) snow accumulation observed from snow pillows, had snow depth coefficients of correlations and mean absolute errors that improved by 70% and 27%, respectively, as compared to simulations using a more common forcing dataset and downscaling technique. This work demonstrates the real-time benefits of satellite-era snow reanalyses in mountainous regions with uncertain snowfall magnitude and spatial heterogeneity.

Concentration-discharge (C-Q) relationships can provide insight into how catchments store and transport solutes, but analysis is often limited to long-term behavior assessed from infrequent grab samples. Increasing availability of high-frequency sensor data has shown that C-Q relationships can vary substantially across temporal scales, and in response to different hydrologic drivers. Here, we present four years of dissolved organic carbon (DOC) and nitrate-nitrogen (NO3-N) sensor data from a snowmelt- dominated catchment in the Rocky Mountains of Colorado. We assessed both the direction (enrichment vs. dilution) and hysteresis in C-Q relationships across a range of time scales, from interannual to sub-daily. Both solutes exhibited a seasonal flushing response, with concentrations initially increasing as solute stores are mobilized by the melt pulse, but then declining as these stores are depleted. The high-frequency data revealed that the seasonal melt pulse was composed of numerous individual daily melt pulses. The solute response to daily melt pulses was relatively chemostatic, suggesting mobilization and depletion to be progressive rather than episodic processes. In contrast, rainfall-induced pulses produced short-lived but substantial enrichment responses, suggesting they may activate alternative solute sources or transport pathways. The results clearly demonstrate that solute responses to individual events may differ significantly from the longer-term behavior these events combine to generate, something which only becomes apparent when high-frequency data are used.  

In this study, both laboratory experiments and numerical simulations were conducted to investigate the effect of density-driven flow on the transport of high-concentration pollutants in the hyporheic zone. The results show that the density gradient can change the flow of pore water and the strong density-driven flow can lead to an unstable flow, which increases the effect of preferential flow and thus causes the appearance of solute fingers in the hyporheic zone. Notably, these solute fingers become more obvious with the increase of depth. The appearance of solute fingers depends on the relative strength of the pumping exchange and density gradient, which are represented by the dimensionless number M* and N* respectively. Finger flows appear near the interface when M* is less than 0.5 N*. This study may contribute to better understanding the transport and destination of solutes and thus may provide some insights into the assessment on pollution incidents.

Rapid urbanization and global climate change are likely to exacerbate urban flooding intensity, frequency, and uncertainty. Thus, it is fundamental and crucial to investigate the dominant influencing factors for the mitigation of urban flooding. However, the influence of building patterns on urban flooding remains limited. Taking Beijing, a typical megacity, as a case study area, we quantified the importance of building patterns and their interaction effect at the subwatershed scale using the boosted regression tree (BRT) and geographical detector model (GeoD). The results indicated that (1) the landscape shape index, slope, green space ratio and waterbody ratio were the most important influencing factors determining urban flooding, with a total relative contribution of 67.23%, (2) building metrics had a certain impact on urban flooding, and the sum of the relative contribution can reach 21.03%, (3) with urban flooding density, the landscape shape index, slope, and green space ratio exhibited a combination of negative and positive correlation, and (4) an enhancement effect existed between building metrics, especially the building congestion degree and building density. These findings provide quantitative insights that rational urban morphology planning can improve stormwater management and promote urban sustainability in megacities.

Stream thermal regimes are critical to the stability of freshwater habitats. There is growing concern that climate change will result in stream warming due to rising air temperatures, decreased shading in forested areas due to wildfires, and changes in streamflow. Groundwater plays an important role in controlling stream temperatures in mountain headwaters, where it makes up a considerable portion of discharge. This study investigated the controls on the thermal regime of a headwater stream, and the surrounding groundwater processes, in a catchment on the eastern slopes of the Canadian Rocky Mountains. Groundwater discharge to the headwater spring is partially sourced by a seasonal lake. Spring, stream, and lake temperature, water level, discharge and chemistry data were used to build a conceptual model of the system. Meteorological data was used to set up a stream temperature model. A tracer test was carried out to estimate hyporheic exchange along the study reach. This study presents a unique example of an indirectly lake-headed stream i.e., where the interaction of groundwater and lake water, and the hydraulic gradient determine the resulting stream temperature. Energy balance of the stream is mainly controlled by radiation. Sensible and latent heat fluxes play a secondary role, but their effects generally cancel out. Hyporheic exchange is present but plays only a minor role in the energy balance. During snowfall events, the latent heat associated with melting of direct snowfall onto the water surface was responsible for rapid stream cooling. An increase in advective inputs from groundwater and hillslope pathways did not result in observed cooling of stream water during rainfall events. The results from this study will assist water resource and fisheries managers in adapting to stream temperature changes under a warming climate.

The continuous exploitation of groundwater has made wetland degradation an ecological and geological environmental problem that cannot be ignored and which has had impacts on the ecological environment and human production and life. In this study, with the help of Visual MODFLOW software, we used numerical simulation technology to simulate the wetland–aquifer interaction during the multiyear pumping process, establish a quasi-ideal model of wetlands based on the actual area of the Baiyangdian Basin, simulate the relationship of water quantity change between wetlands and piedmont plain aquifers during groundwater exploitation and its natural recovery process, and quantify the attenuation of the contribution of groundwater to wetlands caused by groundwater overexploitation. The results show that the impact of groundwater overexploitation on wetland degradation is mainly divided into two parts: one is the reduced base flow from the piedmont plain to the wetland, and the other is the induced infiltration caused by the reverse recharge of groundwater from the wetland due to the pumping effect. At the beginning of pumping, the effect of reduced base flow on wetland degradation is dominant, but with a longer pumping time, the effect of induced infiltration on wetland degradation exceeds the effect of reduced base flow. After stopping pumping, the effect of induced infiltration on wetland degradation responds instantly and decreases rapidly, while the effect of reduced base flow on wetland degradation continues for a long time. The total water reduction of wetlands increases with increasing hydraulic conductivity, and in actual wetland areas, if groundwater overexploitation is not restricted or artificial supply measures are not taken, the amount of wetland water will gradually decrease until it is exhausted.

Recent decades have seen a change in the runoff characteristics of the Suntar River in Eastern Siberia. This study attempts to attribute these changed hydrological conditions through parameterizing a hydrological model based on historical short-term observations conducted in 1957-1959 at the Suntar-Khayata research station. The Hydrograph model is applied as it has the advantage of using observed physical properties of landscapes as its parameters. The developed parametrization of the goltsy landscape (rocky-talus) is verified by comparison of the results of simulations of variable states of snow and frozen ground with observations carried out in 1957-1959. Continuous simulations of streamflow on a daily time step are conducted for the period 1957-2012 in the Suntar River (area 7680 km2, altitude 828-2794 m) with mean and median values of Nash-Sutcliff criteria reaching 0.58 and 0.67 respectively. The results of simulations have shown that the largest component of runoff (about 70%) is produced in the high-altitude area which comprises only 44% of the Suntar River basin area. The simulated streamflow reproduces the patterns of recently observed changes, including the increase in low flows, suggesting that the increase in the proportion of liquid precipitation in autumn due to air temperature rise is an important factor in driving streamflow changes in the region. The data presented are unique for the vast mountainous parts of North-Eastern Eurasia which play an important role in global climate system. The results indicate that parameterizing a hydrological model based on observations rather than blind calibration allows the model to be used in studying the response of river basins to climate change with greater confidence.

Many large rivers worldwide are enriched with high levels of suspended solids (SS), which are known to be hotspots of many nitrogen (N) transformation processes (e.g., denitrification, nitrification). However, the influence of SS on microbial ammonium (NH4+) recycling remains unclear. Water column NH4+ regeneration rates (REGs) and potential uptake rates (Upots) as well as community biological NH4+ demand (CBAD) was measured in the river-estuary continuum of the third longest river in the world—Yangtze River, where shows dramatic SS gradients. We found that, REGs, Upots, and CBAD all showed increasing trends along the river flow, with higher REGs, Upots, and CBAD in the estuary than in the river sections. The regeneration and uptake of NH4+ were nearly balanced in the river sections, while the positive CBAD in the estuary indicated obvious NH4+ demand of microbes. Concentrations of SS, which also controls the content of chemical oxygen demand and particulate N, were the main factor influencing NH4+ recycling rates and CBAD. SS induced regenerated NH4+ in the river-estuary continuum of Yangtze River was estimated to be 21.81 × 108 kg N yr−1 and accounted for about 25% of total N inputs, suggesting that regenerated NH4+ is an important N source for microbes and may influence nutrient dynamics in lower coasts. To our knowledge, this is the first to report NH4+ recycling in Yangtze River with an emphasis on its influencing factors and contribution to N budgets.

The weakened connectivity of wetland systems is the key factor leading to the destruction, degradation, and disappearance of wetlands. The study of the change of wetland system connectivity enables understanding the hydrological process in wetland system and providing significant support for the study of ecological water demand. However, research on the connectivity of wetland systems has primarily focused on the intuitive connectivity in terms of hydrology and geomorphology in recent years, while the impact of wetland systems on habitat has been ignored. In this study, an innovative method was applied to evaluate and regulate the stereoscopic spatial connectivity (SSC) of the wetland system in the Heilongjiang River Basin in China (HRBC). In this method, the water requirements of typical organisms in the region were considered, and the hydrological trend in the wetland system as well as the health conditions of the SSC were analyzed using remote sensing image. A regulation mode for improving the stereoscopic spatial connectivity index (SSCI) was proposed. The results revealed that over the past 35 years, the wetland system in the study area shrank significantly, with the SSCI decreasing from 41.30% in 1980 to 35.08% in 2015. By comparing the correlation among temperature, precipitation, agricultural land, construction land, and the wetland system during the same period, it was proven that human activity is the major driving force behind the observed wetland system shrinkage. Subsequently, the key protected areas required to maintain the SSC of the wetland system were clarified, and the key recovery areas were determined according to the three scenarios of ‘high–medium–low’ feasibility, which greatly improved the SSCI and generalization route (GR) after regulation. In general, the proposed SSC evaluation methods can fully reflect the ecohydrological process of wetland systems. The methods also scientifically quantify the significant effects of the regulation mode, which has certain relevance for the evaluation and regulation of wetland systems in other regions.