Integrated land surface‐groundwater models are valuable tools in simulating the terrestrial hydrologic cycle as a continuous system and exploring the extent of land surface‐subsurface interactions ...from catchment to regional scales. However, the fidelity of model simulations is impacted not only by the vegetation and subsurface parameterizations, but also by the antecedent condition of model state variables, such as the initial soil moisture, depth to groundwater, and ground temperature. In land surface modeling, a given model is often run repeatedly over a single year of forcing data until it reaches an equilibrium state: the point at which there is minimal artificial drift in the model state or prognostic variables (most often the soil moisture). For more complex coupled and integrated systems, where there is an increased computational cost of simulation and the number of variables sensitive to initialization is greater than in traditional uncoupled land surface modeling schemes, the challenge is to minimize the impact of initialization while using the smallest spin‐up time possible. In this study, multicriteria analysis was performed to assess the spin‐up behavior of the ParFlow.CLM integrated groundwater‐surface water‐land surface model over a 208 km2 subcatchment of the Ringkobing Fjord catchment in Denmark. Various measures of spin‐up performance were computed for model state variables such as the soil moisture and groundwater storage, as well as for diagnostic variables such as the latent and sensible heat fluxes. The impacts of initial conditions on surface water‐groundwater interactions were then explored. Our analysis illustrates that the determination of an equilibrium state depends strongly on the variable and performance measure used. Choosing an improper initialization of the model can generate simulations that lead to a misinterpretation of land surface‐subsurface feedback processes and result in large biases in simulated discharge. Estimated spin‐up time from a series of spin‐up functions revealed that 20 (or 21) years of simulation were sufficient for the catchment to equilibrate according to at least one criterion at the 0.1% (0.01%) threshold level. Amongst a range of convergence metrics examined, percentage changes in monthly values of groundwater and unsaturated zone storages produced a slow system convergence to equilibrium, whereas criteria based on ground temperature allowed a more rapid spin‐up. Slow convergence of unsaturated and saturated zone storages is a result of the dynamic adjustment of the water table in response to a physically arbitrary or inconsistent initialization of a spatially uniform water table. Achieving equilibrium in subsurface storage ensured equilibrium across a spectrum of other variables, hence providing a good measure of system‐wide equilibrium. Overall, results highlight the importance of correctly identifying the key variable affecting model equilibrium and also the need to use a multicriteria approach to achieve a rapid and stable model spin‐up.
Key Points
Spin‐up is required to minimize impact of model initialization
Subsurface storage is a good measure of system‐wide equilibrium
Spin‐up functions can be used to determine time to equilibrium
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Woody plant encroachment (WPE) into grasslands is a global phenomenon that is associated with land degradation via xerification, which replaces grasses with shrubs and bare soil patches. It remains ...uncertain how the global processes of WPE and climate change may combine to impact water availability for ecosystems. Using a process-based model constrained by watershed observations, our results suggest that both xerification and climate change augment groundwater recharge by increasing channel transmission losses at the expense of plant available water. Conversion from grasslands to shrublands without creating additional bare soil, however, reduces transmission losses. Model simulations considering both WPE and climate change are used to assess their relative roles in a late 21
century condition. Results indicate that changes in focused channel recharge are determined primarily by the WPE pathway. As a result, WPE should be given consideration when assessing the vulnerability of groundwater aquifers to climate change.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Appropriate representation of the vegetation dynamics is crucial in hydrological modelling. To improve an existing limited vegetation parameterization in a semi-distributed hydrologic model, called ...the Soil Moisture and Runoff simulation Toolkit (SMART), this study proposed a simple method to incorporate daily leaf area index (LAI) dynamics into the model using mean monthly LAI climatology and mean rainfall. The LAI-rainfall sensitivity is governed by a parameter that is optimized by maximizing the Pearson correlation coefficient (R) between the estimated and satellite-derived LAI time series. As a result, the LAI-rainfall sensitivity is smallest for forest, shrub, and woodland regions across Australia, and increases for grasslands and croplands. The impact of the proposed method on catchment-scale simulations of soil moisture (SM), evapotranspiration (ET) and discharge (Q) in SMART was examined across six eco-hydrologically contrasted upland catchments in Australia. Results showed that the proposed method produces almost identical results compared to simulations by the satellite-derived LAI time series. In addition, the simulation results were considerably improved in nutrient/light limited catchments compared to the cases with the default vegetation parameterization. The results showed promise, with possibilities of extension to other hydrologic models that need similar specifications for inbuilt vegetation dynamics.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
A new automated workflow based computationally efficient hydrologic modeling application is developed for soil moisture and runoff simulation. The spatially distributed conceptual framework ...underpinning the Soil Moisture And Runoff simulation Toolkit (SMART) resolves water balance in large upland catchments where topography and land cover are significant drivers of rainfall-runoff transformations. SMART's computational efficiency is achieved by delineation of contiguous and topologically connected hydrologic response units and solving the water balance equation on spatially representative Equivalent Cross-Sections (ECSs). ECSs are formulated by aggregating topographic and physiographic properties of the complete or part of the first order Strahler sub-basins, thereby reducing the number of computational elements. Water balance simulations across the ECSs in two sub-basins illustrated little loss of accuracy compared to the distributed cross section delineations and soil moisture observations. A 2-dimensional Richards' equation based hydrologic model in SMART can be augmented with additional functionalities or replaced with other model structures.
•SMART is a new semi-distributed hydrologic modeling application applied to topologically connected HRUs.•SMART workflow automates sub-basin, HRU and cross section or equivalent cross section delineations of the entire catchment.•SMART reduces computational time of large catchment scale simulations by reducing the number of computational elements.•Temporal dynamics of sub-basin soil moisture are reasonably captured using parameters obtained from soil and land cover data.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Abstract
Urban areas are the primary source of human-made litter globally, and roadsides are a primary accumulation location. This study aimed to investigate how litter arrives at roadsides and ...determine the accumulation rate and composition of roadside litter. We monitored select roadsides in the Inland Empire, California, for litter abundance (count) and composition (material, item, and brand type). Receipt litter with sale time and location information was used to investigate whether wind, runoff, or human travel were dominant transport agents. Only 9% of the receipts could have experienced runoff, and wind direction was not correlated with receipt transport direction. However, human travel and receipt transport distances were similar in magnitude and distribution, suggesting that the displacement of litter from the place of purchase was predominantly affected by human travel. The median distance receipts traveled from the sale location to the litter observation location was 1.6 km, suggesting that most sources were nearby to where the litter was found. Litter accumulation rates were surprisingly stable (mean 40 349 (33 255–47 865) # km
−1
yr
−1
or 1170 (917–1447) kg km
−1
yr
−1
) despite repeated cleanups and the COVID-19 stay-at-home orders. A new approach was employed to hierarchically bootstrap litter composition proportions and estimate uncertainties. The most abundant materials were plastic and paper. Food-related items and tobacco products were the most common item types. The identified branded objects were from the primary manufacturers (Philip Morris (4, 2%–7%), Mars Incorporated (2, 1%–3%), RJ Reynolds (2, 1%–3%), and Jack in The Box (1, 1%–3%)), but unbranded objects were prevalent. Therefore, identifiable persistent labeling on all products would benefit future litter-related corporate social responsibility efforts. High-resolution monitoring on roadsides can inform urban litter prevention strategies by elucidating litter source, transport, and accumulation dynamics.
Despite the importance of mountainous catchments for providing freshwater resources, especially in semi‐arid regions, little is known about key hydrological processes such as mountain block recharge ...(MBR). Here we implement a data‐based method informed by isotopic data to quantify MBR rates using recession flow analysis. We applied our hybrid method in a semi‐arid sky island catchment in southern Arizona, United States. Sabino Creek is a 91 km2 catchment with its sources near the summit of the Santa Catalina Mountains northeast of Tucson. Southern Arizona's climate has two distinct wet seasons separated by prolonged dry periods. Winter frontal storms (November–March) provide about 50% of annual precipitation, and summers are dominated by monsoon convective storms from July to September. Isotope analyses of springs and surface water in the Sabino Creek catchment indicate that streamflow during dry periods is derived from groundwater storage in fractured bedrock. Storage‐discharge relationships are derived from recession flow analysis to estimate changes in storage during wet periods. To provide reliable estimates, several corrections and improvements to classic base flow recession analysis are considered. These corrections and improvements include adaptive time stepping, data binning, and the choice of storage‐discharge functions. Our analysis shows that (1) incorporating adaptive time steps to correct for streamflow measurement errors improves the coefficient of determination, (2) the quantile method is best for streamflow data binning, (3) the choice of the regression model is critical when the stage‐discharge function is used to predict changes in bedrock storage beyond the maximum observed flow in the catchment, and (4) the use of daily or night‐time hourly streamflow does not affect the form of the storage‐discharge relationship but will impact MBR estimates because of differences in the observed range of streamflow in each series.
Key Points
seasonal mountain block recharge estimation using stream flow recession analysis
Application of storage‐discharge relations and isotope hydrology
development of analytical solution for catchment sensitivity function
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BFBNIB, CEKLJ, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
•We presented a flexible approach for detecting precipitation and baseflow droughts.•Delayed baseflow responses to precipitation drought were up to 41 months.•We examined climatic and catchment ...properties affecting baseflow droughts.•The aridity index can differentiate the spatial patterns of baseflow droughts.•Baseflow drought have become more severe and prolonged with climate change.
Baseflow is a critical component of streamflow, as it maintains flow during meteorological drought. However, our understanding of baseflow response to meteorological droughts is limited. In this study, we presented a flexible approach for detecting precipitation and baseflow droughts and their corresponding recovery. Using this framework, we analyzed data from 358 anthropogenically unaffected catchments to characterize the droughts and recovery properties of baseflow across the United States. Results showed that baseflow droughts were more severe than the precipitation droughts, with duration ranging between 9–104 months. There were delayed responses of baseflow to precipitation droughts, showing longer-lasting effects up to 41 months after the end of precipitation droughts. Our analysis also revealed that baseflow drought is controlled by climate and hydrologic responses of a catchment, whereas baseflow recovery primarily depends on post-drought climate conditions. Furthermore, the aridity index can differentiate the spatial patterns of baseflow responses to precipitation droughts. Decadal changes in baseflow droughts revealed that baseflow droughts have become more severe and prolonged due in part to the rise in temperature highlighting the impacts of climate change on baseflow in the mild temperate zone. Overall, this study provides comprehensive insights into baseflow drought detection and its response to precipitation droughts and underscores the importance of these processes for sustainable water resource management.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Increases in greenhouse gas concentrations are expected to impact the terrestrial hydrologic cycle through changes in radiative forcings and plant physiological and structural responses. Here, we ...investigate the nature and frequency of non-stationary hydrological response as evidenced through water balance studies over 166 anthropogenically unaffected catchments in Australia. Non-stationarity of hydrologic response is investigated through analysis of long-term trend in annual runoff ratio (1984–2005). Results indicate that a significant trend (p < 0.01) in runoff ratio is evident in 20 catchments located in three main ecoregions of the continent. Runoff ratio decreased across the catchments with non-stationary hydrologic response with the exception of one catchment in northern Australia. Annual runoff ratio sensitivity to annual fractional vegetation cover was similar to or greater than sensitivity to annual precipitation in most of the catchments with non-stationary hydrologic response indicating vegetation impacts on streamflow. We use precipitation–productivity relationships as the first-order control for ecohydrologic catchment classification. A total of 12 out of 20 catchments present a positive precipitation–productivity relationship possibly enhanced by CO2 fertilization effect. In the remaining catchments, biogeochemical and edaphic factors may be impacting productivity. Results suggest vegetation dynamics should be considered in exploring causes of non-stationary hydrologic response.
•Increased drought severity lengthens recovery time of groundwater.•Time-lag between rain and groundwater drought in unconfined aquifers can be large.•Drought intensity controls the time-lag when ...groundwater is deep.•Land-atmosphere interactions control the time-lag when groundwater is shallow.
Groundwater is a life-sustaining resource that supplies water to 2 billion people worldwide and is critical for agriculture. Despite the importance of groundwater, understanding of groundwater recovery from meteorological droughts is limited. Here, we utilize daily groundwater observations from unconfined aquifers without active groundwater management across the conterminous United States to illustrate that in response to a multi-year drought, it takes on average, 3 years for shallow aquifers to recover the storage lost during the drought. This recovery time increases with higher drought severity, and is influenced by the time-lag between the initiation (termination) of a meteorological drought and initiation (termination) of a groundwater drought. There is considerable variation in the time-lag duration, up to 15 years in some aquifers, controlled by geographic properties in regions with shallow water tables and precipitation characteristics in regions with deep water tables. A machine learning algorithm finds that the most important controls on the time-lag are the drought intensity at the beginning of the precipitation drought and the mean annual recharge. Projected increases in drought severity could potentially increase groundwater recovery times to droughts in a changing climate.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The spatial and temporal variability in soil moisture modulates runoff generation and the degree of land‐atmosphere coupling. Numerous statistical and modeling approaches have been implemented to ...characterize soil moisture spatial heterogeneity at fine spatial resolution using data from sparse observational networks or distributed model simulations. This characterization has been subsequently employed to translate coarse model simulations (of the order of a few hundred meters or kilometers) to finer spatial scales for a range of ensuing applications that rely on high‐resolution characterization of soil moisture. One common feature of these disaggregation methods is that the impact of soil moisture memory is ignored. This results in both spatial and temporal persistence being poorly simulated, leading to poorer specifications of cropping and irrigation plans. To overcome this shortcoming, we developed a hybrid disaggregation method that uses the first‐order autoregressive model (AR1) constructed from fine‐resolution (60 m) soil moisture simulations to disaggregate catchment mean soil moisture obtained from remote sensing or semidistributed model simulations. Soil moisture simulations from an integrated land surface‐groundwater model, ParFlow‐Common Land Model in Baldry subcatchment, Australia, are used as virtual observations. We examined the AR1 method performance against topographic wetness index‐based methods and those developed from temporal stability method. Results illustrate that the disaggregation schemes calibrated to a 10‐day fine‐scale model simulation perform better than the topographic‐based methods in approximating soil moisture distribution at a 60‐m resolution in the catchment. Furthermore, the AR1 model is the best model (Nash‐Sutcliffe efficiency NSE > 0.45) among various alternatives explored here. Applying the hybrid univariate AR1 model is promising for disaggregating semidistributed models' soil moisture simulations while significantly reducing the computational time.
Key Points
Application of four disaggregation alternatives to disaggregate catchment mean soil moisture to fine resolution is examined
A univariate first‐order autoregressive model is the best performing model to represent spatial heterogeneity and temporal persistence
Only 10 days of fine‐resolution hydrologic model simulations is required for parameterizing the disaggregation model
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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