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• Impact of hydraulic and fracture geometric properties on deflection flow were characterized. • The mechanism of deflection flow is revealed. • The conditions limiting the occurrence of deflection flow were predicted. Deflection flow, which represents the process of flow redistribution at fracture intersections, is essential for accurately predicting fluid infiltration and contaminant transport in fracture networks. However, previous studies focused on the outcome of flow redistribution but neglected the process, leading to poor knowledge of deflection flow at intersections. In this study, numerical investigations of the deflection flow in cross fractures were carried out based on Navier-Stokes equations and the results were compared with previous experiments. Geometrical characteristics such as angle(φ), aperture ratio(k), and hydraulic characteristics like inlet flow ratio(n) were considered to explore the mechanism of deflection flow. The deflection flow intensity(Xφ) was introduced to characterize the magnitude and direction of the deflection flow. The results showed that the deflection flow is caused by unequal local head loss. The effect of φ on Xφ is limited by n and k. Increasing n or k enhances the deflection flow but the directions of the induced deflection flow are reversed. The obtained relationship was further supported by theoretical results derived from the cubic law. Subsequently, sensitivity analysis of impact factors to the deflection flow was carried out by the Morris method, the sensitivity in descending order was k , n , φ. Finally, based on the analysis of Xφ , the conditions to limit the occurrence of deflection flow was obtained as a linear positive correlation between the critical inlet flow ratio(n c) and aperture ratio(k). The results from this study may provide insights and physical constraints on construction of flow models based on discrete fracture networks. [ABSTRACT FROM AUTHOR]

[Display omitted] • Microbial growth boosts N 2 O release from hyporheic zones (HZs) into water column. • Carbon source availability affects electron competition in denitrification. • HZs with coarse sediments also support N 2 O release due to microbial growth. • Microbial activity is the primary factor influencing N 2 O source and sink. • A new Damköhler number is proposed to discern N 2 O source/sink function of HZs. The hyporheic zones (HZs) are key sites of the production of nitrous oxide (N 2 O), a potent ozone-depleting greenhouse gas. Denitrification is the primary process of N 2 O production in HZs, including four reduction steps (NO 3 −→NO 2 −→NO→N 2 O→N 2). Electron competition occurs between the four reduction steps and can significantly impact the production of N 2 O. However, denitrification was typically considered as simplified two step reactions for investigating the release of N 2 O, neglecting the electron competition among the four-step reactions. Moreover, the N 2 O production/consumption patterns are regulated by both hydraulic and biogeochemical conditions in HZs. Dynamic microbial growth cannot only mediate the biogeochemical reactions, but also change hydraulic properties spatiotemporally by bioclogging. But microbial growth is rarely considered for investigating N 2 O dynamics of HZs. To assess these effects on hyporheic N 2 O dynamics and source-sink function, we establish a novel numerical model of N 2 O dynamics of HZs, coupling porous flow, reactive transport, electron competition, microbial growth and bioclogging. The results show that the weak electron competitiveness of N 2 O reductase results in a less allocation of electrons to the N 2 O reduction process, particularly in situations with limited carbon sources, thus increasing the release of N 2 O into the rives. Microbial growth significantly influences N 2 O release from HZs into rivers, increasing by more than two orders of magnitude on average compared to the model neglecting microbial dynamics. In contrast to the classical knowledges that HZs in coarse sediments tending to short residence time cannot act as sources of N 2 O, dynamic microbial growth obviously increases the potential for N 2 O release from HZs in coarse sediments to the rivers. The global Monte Carlo regional sensitivity analyses indicate that microbial biomass is the most critical factor determined the hyporheic source-sink function for N 2 O, followed by carbon oxidation rate and residence time. These are significantly different from previous knowledge that the residence time and oxygen/nitrogen uptake rate are the most sensitive parameters, which may lead to misunderstanding of the key controlling factors of N 2 O release from HZs. In addition, we propose a new Damköhler number ( Da O 2 ∗ ) of dissolved oxygen by multiplying the classical Da O 2 with a dimensionless microbial modification factor for identifying N 2 O source-sink function of HZs, with Da O 2 ∗ < 1 for N 2 O sink, while Da O 2 ∗ > 1 for N 2 O source. [ABSTRACT FROM AUTHOR]

• Global Sensitivity Analysis (GSA) was utilized to identify key parameters and processes governing redox zonation in riparian zone. • A deep learning-based surrogate model was implemented to mitigate computational burdens, enhancing efficiency. • Groundwater flow and reactive transport processes emerge as pivotal processes influencing variations in the thickness of redox zones. The riparian zone constitutes an intricate redox environment, giving rise to distinct redox zones characterized by dissolved oxygen (DO), nitrate, manganese dioxide (Mn (IV)), iron hydroxide (Fe(III)), and sulfate. Processes encompassing river fluctuation, groundwater flow, microbial growth and death, as well as solute reactive transport, exert substantial influences on the spatiotemporal zonation of these redox zones in the riparian zone. Nonetheless, understanding remains elusive regarding how these processes govern the thickness of these zones. In this study, we built a one-dimensional (1-D) model which is adapted from Zhu et al. (2023) that integrates these processes to simulate the temporal variation of the thicknesses of different redox zones in riparian zone. Sobol's sensitivity analysis method and the hierarchical sensitivity analysis framework based on Bayesian Networks (BNs) were then employed to quantify the sensitivities of the 17 selected parameters and the four generalized processes governing redox zonation, respectively. To mitigate the computational costs, multi-layer perceptron (MLP) was employed to establish the surrogate models, efficiently predicting model outputs for sensitivity analysis. The results of sensitivity analysis highlight the groundwater flow and reactive transport processes as the most important processes affecting thickness variations of the redox zones. Parameters such as the initial hydraulic conductivity (K s0) and DOC concentration (C DOC) play a predominant role in governing the thickness variations. Parameters linked to river fluctuation and microbe growth processes exhibit very limited effects on redox zonation. These findings offer valuable insights into the factors controlling redox zones in the riparian environment. [ABSTRACT FROM AUTHOR]

• Baseline case was performed using coarse material to validate apparatus feasibility. • Stratified layers' arrangement with different permeabilities (0.165–0.457 cm/sec). • Longitudinal dispersion (1 cm), transverse dispersion (0.1 cm) and Péclet number (1.5) were noted. • Use of under-surface barrier was incorporated to remediate the intrusion. • Sensitivity analysis to record the variation in ratio of toe length/height (T L \H) of intrusion. Passive seawater intrusion is recorded due to geological heterogeneity, such as stratification of aquifers. The purpose of this study is to check the performance of a new kind of barrier system for stratified layers with different permeabilities under the influence of an inclined ocean-aquifer interface. The value of permeability ranges from 0.165–0.457 cm/sec. A base case of homogenous aquifer was used to check the feasibility of the glass box model. Coarser material was employed to validate the results with literature by showing similar wedge formation with faster intrusion that reaches the boundary. Toe length to height ratio was in the range of 2.0 to 3.14. Longitudinal dispersion of 1 cm, transverse dispersion of 0.1 cm, and Péclet number of 1.5 were calculated, which falls under the range defined in literature. Results show that the under-surface barrier is much more successful in stopping intrusion when materials with monotonically increasing permeability are considered, as the toe length increases with increasing permeability. For perpendicular stratification, the use of conventional barrier is much less efficient as compared to an under-surface barrier as the toe length to height ratio decreases. Height of intrusion after using an under-surface barrier in different layers decreases subsequently as compared to without the barrier. Application of these results to real-life problems considering factors such as scaling, heterogeneity, applicability of boundary conditions, and temporal dynamics is suggested. Under-surface barriers have merits like ease of installation, fewer complications, etc. Results exhibit that the use of an under-surface barrier starting from the point where the equilibrium of water has reached and extending towards the impermeable strata is the most efficient approach to follow. Sensitivity analysis to check the behavior of saltwater intrusion under various height openings shows that the toe length is affected by the presence of material with higher permeability. [ABSTRACT FROM AUTHOR]

• Heterogeneous soils, transient flow, and multicomponent biodegradation simulated. • Soil heterogeneity may reduce contaminant leaching without increasing travel times. • Effects of heterogeneity are stronger when reactant mixing limits biodegradation. • Effects of heterogeneity on leaching not significantly affected by transient flow. • Mean residence times and initial biodegradation rate are informative on leaching. Biodegradation is an important mechanism of contaminant removal from soils. We use numerical simulations to study the contaminant transport in heterogeneous soils subject to transient flow conditions and anaerobic multicomponent biodegradation. These processes and their interactions affect contaminant travel times, the extent of reactant mixing, solute and microbial biomass distributions in the soil, and biodegradation outcomes. Especially when flux variations are large, the combination of soil heterogeneity and transient flow gives rise to dynamic preferential flow zones, which affects reactant mixing and biodegradation outcomes. Results show that soil heterogeneity may reduce contaminant leaching due to enhanced reactant mixing, especially when biodegradation is more limited by reactant mixing. Furthermore, unlike under steady-state flow, under transient flow soil heterogeneity does not substantially reduce contaminant residence times. As preferential flow zones change dynamically, the spatio-temporally averaged transport, mixing, and biodegradation experienced by various parts of contaminant plumes become homogenized. Therefore, knowing the initial biodegradation rate of a contaminant upon infiltration, and its mean residence time in the soil, enables a relative sensitivity analysis. This allows biodegradation outcomes of various scenarios to be approximately ranked, even under soil heterogeneity and transient flow, using information that is straightforward to measure or estimate in the field. [ABSTRACT FROM AUTHOR]

[Display omitted] • Improved rainfall predictions in hyper-arid regions. • Monthly and seasonal rainfall prediction analysis. • Multivariate analysis for enhanced predictive capabilities. • Sensitivity analysis identifies key influential factor in rainfall prediction. • Significance for water resources management and climate adaptation. Arid regions like the United Arab Emirates (UAE) face a dire challenge of scarce water resources and unpredictable climate patterns. This study investigates the efficacy of advanced Machine Learning (ML) techniques in enhancing rainfall prediction within hyper-arid environments. Leveraging an extensive 30-year dataset from 1991 to 2020, this study harnessed the power of XGBoost, LSTM, Random Forest (RF), Gradient Boost (GB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Linear Regression (LR), and ensemble methods to significantly enhance the prediction accuracy of monthly rainfall over UAE. In the initial univariate analysis, focused solely on rainfall as the predictor, the ML models displayed encouraging performance during the training phase, achieving an impressive correlation coefficient (CC) of 0.88 for both XGBoost and the ensemble models. However, their predictive efficacy witnessed a decline during the testing phase, where the maximum CC reached 0.45. In contrast, traditional models like Linear Regression and SVM, yielded subpar results in both training and testing, exhibiting correlation values lower than 0.3. To address these limitations, a multivariate analysis is conducted by incorporating additional meteorological parameters, including wind speed, temperature, humidity, and evapotranspiration. This augmentation proved highly beneficial as it substantially enhanced the models' predictive capacities during the testing period. The XGB achieves a CC of 0.76, LSTM improves from 0.21 to 0.71, and stacked models exhibit promising behavior jumping from an average of 0.44 to 0.82 during the testing periods. Additionally, we performed a sensitivity analysis utilizing LASSO regression, which revealed that wind speed and minimum temperature emerged as the most influential parameters for monthly rainfall prediction in the arid context. These two meteorological factors exerted a substantial impact on the accuracy of our predictive models, underscoring their significance in understanding and forecasting rainfall patterns in hyper-arid regions, such as the United Arab Emirates. The identification of these key drivers further strengthens the foundation for effective water resource management and climate adaptation strategies in such challenging environments. This study provides valuable insights for water resource planning, agriculture, and climate resilience strategies in hyper-arid regions. Further research can build upon these results to enhance rainfall prediction models and support sustainable development in arid regions. [ABSTRACT FROM AUTHOR]

• Inverse modeling for simultaneous DNAPL-source characterization and contaminant transport parameter calibration. • A BHK-ELM is proposed for reliable surrogate modeling of the DNAPL-transport numerical simulation. • An SRD-SI algorithm is proposed to conduct enhanced identification for the sensitivity-varied variables. • A nonlinear homotopy-variational mechanism is construct for reasonably guiding the SRD-SI searching paths. Accurate source characterization and transport parameter estimation is important when seeking to predict the spatiotemporal distribution of dense non-aqueous phase liquid (DNAPL) contaminants in groundwater. However, this is a complex multimodal search problem prone to equifinality and premature convergence, which leads to considerable error. To address this, a sensitivity-relevant dynamic swarm intelligence (SRD-SI) algorithm embedded in a homotopy-variation mechanism is proposed in the present study to rationally balance the inversion processes of sensitivity-varied source characteristics and DNAPL transport parameters. In this approach, global optima are progressively approached in conjunction with the homotopy variation of the search space. Furthermore, to avoid computationally expensive numerical model repetition during the sensitivity analysis and inverse iterations, a Bayesian-based optimization framework that combines multiple kernel functions in a kernel extreme learning machine (KELM) model is designed considering the complex site conditions and statistical characteristics of the input variables, thus creating the Bayesian hybrid KELM (BHK-ELM) model for the reliable surrogate modeling of numerical DNAPL-transport simulations. The results show that the BHK-ELM model recognizes and effectively reconstructs the complex input − output mapping of the numerical model by increasing the determination coefficient R 2 to 0.9988 while improving the computational efficiency approximately 4500-fold. Because source characteristics and boundary conditions are far more sensitive than transport parameters to the contaminant distribution, conventional inverse modeling methods struggle to accurately identify these transport parameters. In contrast, the proposed inverse modeling system combining sensitivity analysis, swarm intelligence, and homotopy variation is more stable and provides significantly more accurate estimations for all unknown variables. Compared with the traditional SI algorithm, the homotopy-variation SRD-SI reduced the maximum inversion relative error from 46.22 % to 9.53 %, while the mean inversion relative error was reduced from 11.09 % to 3.90 %. [ABSTRACT FROM AUTHOR]

• Geological-entropy-based hydraulic properties of DFN are investigated. • A unified relation between anisotropic permeability and entropy index is proposed. • There is a power-law relationship of hydraulic REV size versus entropy scale. To investigate the intrinsic association between geometrical properties and permeability of anisotropic fractured media, a methodology of geological entropy is applied to anisotropic fracture networks for the evaluation of seepage fields. By integrating the synthetic effect of fracture geometrical parameters (e.g., trace length, spacing, dip angle, and aperture), the global connectivity of fracture networks is quantified by the entropy scale (H s), while the directional entropy scale (H s , α ) is used to characterize the local geometrical anisotropy in arbitrary orientations. A series of hydraulic representative elementary volumes (REVs) of fracture patterns with various geometrical properties are acquired based on continuum analysis. The permeability in the continuum scale is obtained from steady-state flow simulations. Sensitivity analyses are performed to understand the geometrical dependence of permeability parameters regarding entropy indices. It is found that a systematic increase in trace length, spacing, and dip angle is correlated with an increase, decrease, and equilibrium in entropy, respectively, which corresponds to a change in hydraulic REV sizes that can be expressed as a power-law function related to the H s. Moreover, H s , α can well explain the permeability anisotropy resulting from the spatial order of fracture networks, and a unified mathematical relationship for the effective permeability coefficients in arbitrary orientations versus the H s , α is proposed, independent of variations in fracture patterns and scales. The results indicate that geological entropy, related to the geometrical properties and spatial distribution of fractures, is appropriate to characterize the permeability of anisotropic systems. [ABSTRACT FROM AUTHOR]

• Either climate data or streamflow data alone is not sufficient to accurately estimate SSL. • Gradient boosting models accurately predict SSL using hydro-climate data. • Hydrological metrics improve the accuracy SSL modeling during model calibration. • The prediction uncertainty of SSL increases with the basin's area. • Average ensemble modeling enhanced the accuracy of models in rivers. Machine learning (ML) models have the potential to improve the accuracy of Suspended Sediment Load (SSL) predictions. However, their ability to incorporate climate data to enhance SSL predictions remains underexplored. This study investigates the efficacy of ML models in modeling sediment transport using hydro-climate variables as input data. Boosted decision tree models, including Xgboost, LightGBM, and Catboost, were employed to estimate SSL using hydro-climate variables such as streamflow and precipitation, as well as maximum and minimum temperatures. The models were trained, optimized, and evaluated using either climate/streamflow data or a combination of both, encompassing 47 gauges across the contiguous United States (CONUS) between 1950 and 2022. The results demonstrate that streamflow data alone is insufficient for accurate SSL estimation in all rivers. Consequently, integrating climate data and streamflow data proved effective in predicting SSL at the daily scale across diverse climate zones with commendable accuracy. The models achieved an average Nash-Sutcliffe Efficiency (NSE) of > 0.66, Kling-Gupta Efficiency (KGE) of > 0.67, and Willmott Index (WI) of > 0.83 during the unseen (test) phase. These findings highlight the potential of ML models, particularly when combining climate data with streamflow, to enhance SSL predictions and improve our understanding of sediment transport dynamics. [ABSTRACT FROM AUTHOR]

• Storage models could be more sensitive to A S /A compared to other parameters. • For dispersion-dominant streams, the calibration of MADE has lower uncertainty. • In advection-dominant river, the estimated storage parameters have lower uncertainty. Researchers must determine the storage parameter values to understand the storage processes and compare them across streams. These parameters are highly variable in time and space, making them challenging to measure. Individual studies of a single stream cannot characterize the interrelations among the processes involved in solute transport, hydraulic variables, and physical stream characteristics. Despite decades of research, a comprehensive framework is lacking. This study uses a simple and effective Monte Carlo approach to propose a general framework within which most solute transport models can undergo sensitivity analysis. This framework provides a comprehensive depiction of model performance under various scenarios, such as flow properties, stream geomorphology, and solute status. The relative and absolute sensitivities of three solute transport models, namely, the modified advection–dispersion equation (MADE), transient storage model (TSM), and variable residence time (VART), were evaluated and compared within the proposed framework. The main findings can be summarized as follows. In most conditions, storage models are more sensitive to the ratio of the storage zone cross-section to the stream cross-section than other parameters. Increasing the exchange rate between the stream and storage zones reduces the sensitivity to its indicative parameter and, consequently, its impact on the concentration time series. MADE's capability to identify modification parameters is higher for streams where dispersion dominates over advection, and their estimates have lower uncertainty. On the other hand, as advection becomes more dominant over dispersion in TSM and VART, estimated storage parameters have lower uncertainty." [ABSTRACT FROM AUTHOR]

• We present a two-step BN based GSA for NRTM to identify its dominant process. • A real modeling case of EPIC-Nitrogen2D was used for demonstration. • The results show that the dominant processes were diverse for different outputs. Process-based nitrogen reactive transport models (NRTMs) are vital tools for simulating complex nitrogen reactive transport processes in soils and groundwater to reduce nutrient loss and contamination risk. To develop and improve these models, global sensitivity analysis (GSA) is used to identify key model parameters and processes. Because existing GSA approaches focus mainly on model parameters and not model processes, in this study, we developed a two-step GSA method to improve our understanding of model processes. First, we applied the Morris method to identify less influential parameters. Then, we developed a Bayesian network (BN)-based GSA method to identify the dominant processes for different model predictions. The BN-GSA method accounts for the hierarchical uncertainty in the structure of the model system and flexibly groups uncertain inputs into processes based on their characteristics. The two-step GSA method was applied to a real-world nitrogen reactive transport model with 68 uncertain parameters in three different climate scenarios. The results show that the dominant processes differed for various nitrogen and agricultural outputs; thus, different processes should be emphasized based on specific modeling purposes. [ABSTRACT FROM AUTHOR]

• Guidelines for the use of snow data combined with runoff to calibrate models. • Improvement of the snow simulations without impairing runoff performance. • The improvement in the model consistency is not accompanied by lower equifinality. • Using snow data does not improve model robustness to climate variability. This paper compares different calibration strategies for using snow data combined with streamflow records to constrain model optimisation in mountain catchments. In particular, it assesses to what extent the use of snow observations makes it possible to improve the consistency, identifiability and robustness of the calibrated parameters. To answer this question, a semi-distributed snow and ice model was used on top of a rainfall-runoff model using the SAFRAN meteorological reanalysis as input dataset on several catchments in the Alps and Pyrenees. Model calibration and control were based on streamflow observations, remotely-sensed snow-covered areas and in-situ snow water equivalent (SWE) measurements. The snow and rainfall-runoff parameters were calibrated sequentially and simultaneously against objective functions integrating different combinations of runoff, snow cover and SWE criteria. Statistical assessment of model performances in independent evaluation periods showed that sequential calibration of the snow parameters gives too much weight to snow compared to runoff. Instead, incorporating snow data in the simultaneous calibration of the parameters improves snow simulations without impairing runoff performance. This can be achieved by limiting the weight of the snow criteria to 25% in the objective function. Although local SWE measurements were found to be more useful than satellite observations for identifying more consistent parameters, it is advisable to include both in the calibration process through a compromise objective function. However, the improved model consistency was not accompanied by a significant reduction in equifinality and optimisation times. On the other hand, improving internal consistency made it possible to reduce the interdependence between the parameters of the snow model and those of the rainfall-runoff model. This also made it possible to identify the least sensitive snow parameters in order to fix them at general values without impairing model performance while reducing equifinality with a more parsimonious model. Finally, there was no evidence that using snow observations in the calibration process improves model robustness with respect to climate variability. [ABSTRACT FROM AUTHOR]

• The Surface flux equilibrium-Nonparametric (SFE-NP) method is suitable for different water supply conditions, which is proposed. • The accuracy of SFE-NP method by using FLUXNET2015 data, which is evaluated. • The applicably temporal scale of SFE-NP method is analyzed. • The sensitivity of the input variables of the SFE-NP method to ET estimation is explored. Developing suitable evapotranspiration (ET) methods for different water supply conditions is a major challenge. As a diagnostic approach, the non-parametric (NP) method avoids the parameterization of resistances, while having the advantages of clear physical mechanisms and simple input data. However, the applicability of the NP method in arid areas, especially at short temporal scales, is controversial. To address this problem, a type of equilibrium ET estimation method, surface flux equilibrium (SFE), was introduced into the NP method to obtain a new approach for estimating ET: the proposed SFE-NP. The accuracy of the SFE-NP method was validated based on 30 globally distributed FLUXNET2015 eddy covariance tower sites, and its applicability and sensitivity were analyzed. Results demonstrate that the performance of the SFE-NP method in arid areas is satisfactory, involving a significant reduction of the ET estimation error of more than 24.00 % (relative error: 32.31 %; bias: 10.17 W/m2) compared with the Priestley–Taylor (PT), NP and Surface Flux Equilibrium (SFE) methods. And the accuracy of SFE-NP method is similar to that of Surface Flux Equilibrium Maximum Entropy Production (SFE-MEP) method in the arid areas. Furthermore, in non-arid areas, the accuracy of the SFE-NP method (relative error: 14.45 %) was better than that of the PT method and similar to that of the NP, SFE and SFE-MEP method. The SFE-NP method should be used at the daily scale or longer. The ET derived by the SFE-NP method introduced more errors at the hourly scale (increased error: 12.15 W/m2) than at the daily scale. Air temperature and land surface temperature were the most sensitive parameters in the SFE-NP method, particularly in arid areas, with their sensitivities under arid conditions being twice as high as those under humid conditions. The SFE-NP method can satisfy the requirements for ET retrieval under different underlying surfaces and water supply conditions, and provides a simple but efficient method for high-precision ET estimation. [ABSTRACT FROM AUTHOR]

• Roles of recharge rate and flow regime on conduit flow are quantified. • Roles of conduit properties, density and morphology on spring discharge. • The percentage of conduit flow over spring discharge is quantified for Silver Springs. The physical controls on the contribution of conduit to spring discharge, quantified as the ratio of conduit flow to spring discharge (Q c / Q), are evaluated through numerical simulations using MODFLOW-Conduit Flow Process. The controlling factors include the ratio of conduit surface roughness to conduit diameter (ε / D), the hydraulic conductivity ratio between the conduit wall and the porous matrix (K c / K), the ratio of groundwater recharge to the hydraulic conductivity of the porous matrix, and conduit density (C d) defined as the ratio of total conduit length to springshed area. For a single conduit, Q c / Q increases with K c / K when flow in the conduit is laminar. When flow is turbulent, Q c / Q is stable and then decreases with ε / D , increases with K c / K , and decreases with R / K due to the limited exchange capacity between matrix and conduit (i.e., small K c / K). The results of hypothetical conduit networks show that Q c / Q increases with C d but is not sensitive to the two sets of network structure evaluated. The sensitivity analysis for the Silver Springs in Florida shows that the spatial heterogeneity of conduit diameter causes higher Q c / Q , and Q c / Q is most sensitive to the conduit density, followed by R / K , and then K c / K. [ABSTRACT FROM AUTHOR]

• Freshwater resources in elliptical islands are assessed analytically. • Dispersive correction factor applied to floating island lenses for the first time. • Islands with aspect ratios closest to unity have larger fresh groundwater storage. • Sea-level rise may increase the freshwater storage of a truncated lens. The vulnerability of island freshwater lenses to rising sea levels has been assessed previously using sharp-interface analytical solutions that consider islands as circular or two-dimensional strips, or the lenses are treated as entirely underlain by seawater. However, many islands do not fit these basic conditions. The current study develops an analytical solution for the freshwater-saltwater interface in elliptical islands where the interface intercepts the aquifer base, and explores (for the first time for island lenses) the applicability of a dispersion correction to the analytical solution. An analysis of the lens response to sea-level rise (SLR) and the accompanying land-surface inundation (LSI) shows that for lenses entirely underlain by seawater, the primary impact of SLR is likely the associated LSI. Counterintuitively, lenses in contact with the aquifer basement may experience an increase in the freshwater volume with SLR, although this requires that LSI is limited (e.g., the shoreline is steep) and the water table is sufficiently deep (below the land surface) to allow it to rise commensurately with SLR. The analysis also shows that as the aspect ratio of the elliptical island increases (i.e., larger values approach strip islands and circular islands have a value of 1), the lens volume decreases (for a given island area). Thus, circular islands have the maximum freshwater storage per island area. Specifically, for typical island aquifer conditions, freshwater storage decreased over 50% as a / b increased from 1 to 10. The results obtained from this study and the new analytical solution are expected to assist in the rapid assessment of the freshwater resources of elliptical-like islands. [ABSTRACT FROM AUTHOR]

• Reducing initial forecasting errors using the EnKF data assimilation technique. • Improving real-time streamflow forecasts by an optimal EnKF implementation. • A seasonal adjustment of the EnKF hyperparameters improves forecast accuracy. • The vadose zone state variable is the most impactful and should be prioritized. • Data assimilation is most sensitive to temperature uncertainty for forecasting. Data Assimilation (DA) is an important step for improving prediction accuracy and real-time correction of hydrological models for operational forecasting purposes. The Ensemble Kalman filter (EnKF) is one of the most popular techniques used to address the issues of updating the model states and parameters by creating a novel set of initial conditions in real-time. This study aims at identifying optimal seasonal EnKF parametrizations to reduce the uncertainty associated with the initial conditions in a semi-distributed hydrological model of a snow-dominated catchment in Canada. Sensitivity analysis is performed to evaluate the effects of the EnKF individual hyperparameters (temperature, precipitation and inflow uncertainties) and the updating of three state variables (vadose zone, saturated zone and snowpack) on the skill of short-term (up to 9 days lead-time) forecasts. Results show that the performance of the forecasts is sensitive to the individual hyperparameters and particularly so to the temperature uncertainty, which varies between seasons. Additionally, the forecast skill is related to the choice of the state variables to be updated depending on the season. The vadose zone state variable displays higher importance and sensitivity than the other states, and the results indicate that all state variables should not be systematically updated on this catchment and with the implemented hydrological model. Finally, combining the best hyperparameter values with the optimal combination of state variables to update, insight is provided on the success of the data assimilation scheme that is evaluated on a rolling horizon using a set of seasonal rules to gain forecast performance over the span of multiple years. [ABSTRACT FROM AUTHOR]

• Pasture growth was simulated using the SWAP model for New Zealand scenarios. • Two root water uptake functions were compared in diverse edaphoclimatic scenarios. • Process-based RWU function shows the highest sensitivity to soil and plant factors. • Compensated RWU affects rainfed and irrigated yields and water productivities. • Irrigation based on relative ET = 0.8 results in significant gains in water productivity. Rootzone hydraulic conditions govern root water uptake and transpiration under drought stress. Process-based approaches to predict the soil water status are advocated for an improved simulation of soil hydrology and crop yield. We investigated the sensitivity to system parameters in root water uptake simulation using a process-based function (MFlux) and an empirical function (Feddes) embedded in the SWAP hydrological model, applied to irrigated pasture scenarios in New Zealand. Data from two locations and three soils were used to simulate 42 growing seasons. The sensitivity analysis of both Feddes and MFlux parameters was performed for a rainfed and two irrigated scenarios, one triggering irrigation based on relative evapotranspiration (I–ETr), the other based on common practice using total available water (I–TAW). Results confirm that some parameters of the MFlux function are more sensitive than those from the Feddes function and both functions support the I–ETr criterion to optimize the water use in grazed pastures in New Zealand. [ABSTRACT FROM AUTHOR]

• An urban hydrological model considering complicated overland flow processes is proposed. • The TVGM-USWM model demonstrated good applicability in the piedmont city. • Disconnected impervious area is an important factor in urban flood simulations. Urbanization has great potential to adversely affect the local hydrological cycle, as well as result in severe ecological and environmental problems. Piedmont cities have a special topography and complicated overland flow processes, thus creating challenges for the simulation of urban flooding/waterlogging processes. The TVGM-USWM model, a new distributed urban hydrological model that considers the flow routing in urban drainage systems and complicated overland flow processes, was developed in this study. We applied this model to the Huangtaiqiao drainage basin of Jinan City, China and set up two simulation scenarios. The spatial heterogeneity of the proportion of directly connected impervious area (DCIA) and disconnected impervious area (DIA) in sub-catchments was considered in scenario B, but not in scenario A. We conducted sensitivity analysis on model parameters by using the Sobol method. Results showed that: (1) model parameters have a certain degree of uncertainty, and the parameter characterizing the proportion of the DCIA and DIA showed a relatively high sensitivity under rainstorms of different levels and different accuracy assessment standards; (2) The simulation of scenario A was poor (with a mean Nash coefficient of 0.59), which ignored the different flow paths followed by runoff from DCIA and DIA; and (3) Considering the spatial differences in the overland flow process, the prediction accuracy of scenario B significantly increased (with a mean Nash coefficient of 0.82). This indicated the importance of the disconnected impervious area and the lateral water exchange across the impervious-pervious interface on the flood simulation in a piedmont city. Compared to most urban hydrological models, the TVGM-USWM model can represent complex rainfall-runoff mechanisms in urban areas with varied terrain. The findings of this study can provide scientific decision-making for the planning and construction of a sponge city in Jinan City. [ABSTRACT FROM AUTHOR]

• Analytical solutions for maximum freshwater extraction in stratified coastal aquifers are developed. • Maximum freshwater extractions under different aquifer domain conceptualizations are quantified and compared. • Total transmissivity and TCE are applicable to characterize the effect of aquifer stratification on maximum freshwater extraction. Existing analytical solutions for defining maximum freshwater extraction in coastal aquifers mostly presume a homogeneous aquifer. In this study, analytical solutions for the pumping-induced interface toe position and the maximum pumping rate of a single well in stratified coastal aquifers are developed, based on the potential theory and the analytical solution proposed by Rathore et al. (2020) , where aquifer stratification is concisely characterized by the total transmissivity and the transmissivity centroid elevation (hereafter "TCE"). Three typical aquifer domain scenarios are examined, including semi-infinite, infinite-strip, and rectangular coastal aquifers. The sensitivity analysis based on the proposed analytical solutions demonstrates that in all aquifer domain scenarios, a lower TCE leads to a more seaward interface toe and a larger maximum pumping rate, while the impact of total transmissivity is complicated, depending on the type of the inland boundary condition. The effect of inland and lateral boundary conditions on the maximum pumping rate is enhanced in the stratified aquifers of lower total transmissivity and TCE. Importantly, the boundary effects can be eliminated by properly defining the size of the stratified aquifer domain, providing valuable guidance for the design of numerical models and laboratory experiments that are often of finite sizes. Our analytical solutions can serve as a simple tool for the first-order assessment of allowable freshwater extraction in stratified coastal aquifers. [ABSTRACT FROM AUTHOR]

[Display omitted] • Design of advanced integrated tool for sewer performance analysis. • Simulator for likelihood functions to select SWMM param. combinations for calibration. • haracterization of effect of sewer fractal dimension layout on flood volume. • Novel tool to optimize and design the sewer layout in urban catchments. • Robust, rapid and accurate method for planning of spatial and sewer network layout. The specific flood volume is an important criterion for assessing the performance of sewage networks. It has been shown that its value is greatly influenced by the layout of the sewers in the catchment area, which is usually expressed by a fractal dimension. Currently, only mechanistic models (such as SWMM) enable the determination of the impact of the layout of the sewers on flooding volume, but they require additional and robust calculations. In the presented study an integrated tool has been proposed that includes: a flooding volume simulator based on rainfall data, catchment and sewage network characteristics, sewers layout expressed by fractal dimension. A logistic model can be applied for fast flooding volume estimation as an alternative approach to SWMM, design and upgrade sewer layout even with limited access to data (spatial planning, architectural concepts, etc.). Using the random forest (RF) method, a likelihood function simulator was developed, which enabled the analysis of interactions and optimal selection of combinations of SWMM model parameters for calibration. It has been shown that the higher the fractal dimension and retention coefficient (the ratio of surface to sewer retention), the greater the influence of SWMM parameters on the specific flood volume. [ABSTRACT FROM AUTHOR]

• Water transport is most sensitive to the unsaturated region of K(h). • K(h) is commonly overestimated when only saturated K is measured. • Simulations without measured unsaturated K are the least accurate. The saturated hydraulic conductivity ( K s ) is considered to be one of the main soil hydraulic properties to simulate water transport in the vadose zone, yet it is notoriously difficult to assess due to its large field-scale variability and sensitivity to preferential flow paths. With the increasing availability of convenient methods to measure the unsaturated hydraulic conductivity ( K u ), the additional value of measuring K s could be questioned. This study investigates the relevance of measuring K s in two ways. Firstly, a sensitivity analysis was performed to evaluate which part of the hydraulic conductivity curve impacts the simulation of unsaturated water transport most. It was found that hydraulic conductivity curve between −10 cm and −1000 cm matric head was affecting the simulation more than at saturation. Secondly, a functional evaluation was performed by simulating water flow in six test fields according to three scenarios, in which the hydraulic properties were determined with the measured K s and/or K u . The simulation of saturation degree at three depths was the least accurate in the scenario where only K s was measured. Remarkably, the most accurate simulation was found in the scenario with only the measured K u . According to these results, measuring K u would be of greater importance than measuring K s for the simulation of water flow in the vadose zone. [ABSTRACT FROM AUTHOR]

• New approach is developed to examine sensitivity of advective travel time in soil. • The approach can be applied to both non-stationary and stationary profiles. • Uncertainty of hydraulic parameters shifts sensitive domain to deep locations. • Travel time is sensitive to layers with small capillary drive and hydraulic conductivity. This study deals with the sensitivity of advective contaminant travel time to soil hydraulic parameters through a heterogeneous soil profile, which is often required in many applications such as designing a monitoring network, developing a remediation strategy for contaminated sites, and determining the associated risks of subsurface contamination. The main objective is to determine relative contributions to the output uncertainty of advective travel time from individual input stochastic hydraulic parameters. A new approach is developed to examine the sensitivity of advective contaminant travel time to saturated hydraulic conductivity and macroscopic capillary drive as a result of infiltration from a wet and contaminated surface source. The approach can be applied to both non-stationary layered formation and stationary soil profile. The results demonstrate that the advective contaminant travel time is sensitive to the layers with small saturated hydraulic conductivity and small macroscopic capillary drive values. Larger uncertainty of saturated hydraulic conductivity and macroscopic capillary drive shifts the sensitive layers to the advective travel time to deeper layers. Correlation range of saturated hydraulic conductivity and macroscopic capillary drive has less significant impact on the sensitivity of advective contaminant travel time than the standard deviation. In the stationary saturated hydraulic conductivity and macroscopic capillary drive profile, although the advective contaminant travel time increase significantly for fine textured soil compared to coarse textured soil, the fractional contribution of spatial locations to the overall uncertainty of total advective travel time is virtually identical for both fine textured and coarse textured soils. [ABSTRACT FROM AUTHOR]

• A zonal current irrigation schedule is generalized with data mining. • Simulation units reflecting the spatial heterogeneity of multi-factors are generated. • Parameters in multi-modules are calibrated based on a global sensitivity analysis. • The distributed model is validated using multisource and multiscale data. • The ET and crop yields of a rotation field at the regional scale are simulated. The agro-hydrological Soil-Water-Atmosphere-Plant-WOrld-FOod-STudy (SWAP-WOFOST) model in a distributed manner represents an important tool for evaluating agro-hydrological cycles and irrigation strategies at different spatiotemporal scales. The reliability of the model simulations and evaluations is dependent on the generation of the distributed simulation units that account for the spatial heterogeneity of various factors, as well as parameter calibration and model validation. In this study, we focused on the most typical overexploited deep groundwater area in the North China Plain (NCP). First, the extended Fourier amplitude sensitivity test (EFAST) was used to conduct global sensitivity analyses for three modules in the SWAP-WOFOST model to identify the parameters that significantly influence the objective variables for calibration at the six experimental stations in the study area. On this basis, the parameters were calibrated and validated in detail using abundant observed data from each station. The normalized root mean square error (NRMSE) values for soil water content, soil salt concentration, winter wheat leaf area index (LAI), winter wheat aboveground biomass, winter wheat yield, summer maize LAI, summer maize aboveground biomass and summer maize yield were 14.93%, 27.20%, 24.60%, 25.27%, 23.12%, 24.55%, 21.33% and 17.94%, respectively, during the calibration period; and the corresponding NRMSE values were 16.26%, 29.36%, 24.20%, 22.75%, 21.77%, 22.03%, 24.38% and 18.62%, respectively, during the validation period. The model provided a good simulation for the soil water contents and summer maize yields and a fair simulation for the other objective variables. Furthermore, a distributed SWAP-WOFOST model was generated by overlaying 12 maps involving meteorology, soil, crops, land use, water resources and administrative divisions, yielding 2809 simulation units. Finally, the simulation accuracy of the distributed model was evaluated under the current irrigation conditions. The results showed that the simulated yields of winter wheat and summer maize were consistent with the statistical values, and the simulated evapotranspiration matched the remote sensing data with acceptable precision. In summary, the distributed SWAP-WOFOST model could be used as an effective tool for simulating crop water productivity in time and space under limited irrigation scenarios and for evaluating the effect of optimized limited irrigation schemes on reducing deep groundwater exploitation in this region. [ABSTRACT FROM AUTHOR]

• Comparison of Global Sensitivity Analysis (GSA) approaches in a large-scale aquifer. • Impacts of uncertain parameters of diverse conceptual models are evaluated via GSA. • Moment-based indices inform how parameters influence statistics of model outputs. We rely on various Global Sensitivity Analysis (GSA) approaches to detect the way uncertain parameters linked to diverse conceptual geological models influence spatial distributions of hydraulic heads in a three-dimensional complex groundwater system. We showcase our analyses by considering a highly heterogeneous, large scale aquifer system located in Northern Italy. Groundwater flow is simulated considering alternative conceptual models employed to reconstruct the spatial arrangement of the geomaterials forming the internal makeup of the domain and characterizing the distribution of hydraulic conductivities. For each conceptual model, uncertain factors include the values of hydraulic conductivity associated with the geomaterials composing the aquifer as well as the system boundary conditions. We explore the relative influence of parametric uncertainties to steady-state hydraulic head distributions across the set of conceptual models considered by way of three GSA methodologies, i.e., (a) a derivative-based approach, which rests on the Morris indices; (b) the classical variance-based approach, grounded on the evaluation of the Sobol' indices; and (c) a moment-based GSA, which takes into account the influence of uncertain parameters on multiple (statistical) moments of a given model output. Due to computational costs, Sobol' and moment-based indices are obtained numerically through the use of a model-order reduction technique based on the polynomial chaos expansion approach. We find that the sensitivity measures considered convey different yet complementary information. The choice of the conceptual model employed to characterize the lithological reconstruction of the aquifer affects the degree of influence that uncertain parameters can have on modeling results. [ABSTRACT FROM AUTHOR]

• Estimation of hydraulic parameters governing the unsaturated flow is a complex task. • Impact of spatial variability in these parameters on transport is poorly investigated. • Parameter variability is quantified based on the analysis of a global soil database. • The behavior of a contaminant plume is simulated using various parameter fields. • Issue of spatial variability in hydraulic parameters is crucial for site remediation. Heterogeneities in textural properties of surficial formations at field scale result in spatial variations in hydraulic parameters governing unsaturated zone flow. This study aims at quantifying the influence of such variations on solute transport resulting from a localized source of radioactive contaminant at ground level. The study focuses on three hydraulic parameters related to the Mualem-van Genuchten formalism, namely the saturated hydraulic conductivity K s , the parameter α inversely proportional to the air-entry value, and the parameter n related to the pore-size distribution. Sets of random fields accounting for spatial variability of these parameters are generated using lognormal distributions with different variances and correlation lengths. These random fields are used as inputs to an unsaturated flow and transport model to simulate radionuclide plume migration. Each simulated plume is characterized by its size (plume surface area), position (location of center of mass) and shape (elongation ratio) within the unsaturated zone. By comparison with the homogeneous medium, K s -, α - and n -random fields generated with the mean variances computed through the analysis of a global soil database respectively result in average in (i) 25 (variable K s ), 20 (variable α) and 65% (variable n) increase in plume size; (ii) 0.8, 1 and 1.8 m horizontal offsets of the plume center; and (iii) 20, 30 and 50% decrease in plume circularity. In addition, changes in the variance values within one order of magnitude appear to have critical consequences only for the n parameter. The issue of spatial variability of hydraulic parameters is thus crucial for characterizing the evolution of pollutant plumes within an unsaturated zone and for developing better remediation strategies for industrial sites. [ABSTRACT FROM AUTHOR]

• Pan coefficients (K p) of China D20 pans ranged from 0.29 to 0.91 across China. • The seasonal relationship of K p was Summer > Annual > Autumn ∼ Spring ∼ Winter. • K p of the China D20 pan was most sensitive to relative humidity. Data of open water evaporation (E ow), such as evaporation of lake and reservoir, have been widely used in hydraulic and hydrological engineering projects, and water resources planning and management in agriculture, forestry and ecology. Because of the low-cost and maneuverability, measuring the evaporation of a pan has been widely regarded as a reliable approach to estimate E ow through multiplying an appropriate pan coefficient (K p). K p is affected by geometry and materials of a pan, and complex surrounding environment variables. However, the relationship between K p and different environment variables is unknown. Thus, this study chose China D20 pan as an example, used meteorological observations from 767 stations and introduced the latest PenPan model to analyze the sensitivity of K p to different environment variables. The results show that, the distribution of annual K p had a strong spatial gradient. For all the stations, annual K p ranged from 0.31 to 0.89, and decreased gradually from southeast to northwest. The sensitivity analysis shows that for China as a whole, K p was most sensitive to relative humidity, followed by air temperature, wind speed and sunshine duration. For 767 stations in China, K p was most sensitive to relative humidity for almost all the stations. For stations north of Yellow River, wind speed and sunshine duration were the next sensitive variables; while for stations south of Yellow River, air temperature was the next sensitive variable. The method introduced in this study could benefit estimating and predicting K p under future changing environment. [ABSTRACT FROM AUTHOR]

• A new framework is developed for detailed flash flood risk mapping in ungauged basin. • Performance of the developed model is assessed with −0.004 m of BIAS for water level. • Flood inundation parameters and socioeconomic features are considered in the model. Flash floods are among the most devastating natural hazards but the analyses of flash floods are hindered by the lack of hydro-meteorological data. In particular, data issues are critical in ungauged basins. This paper presents an investigation of flash flood modeling using coupled hydrological and hydrodynamic models. Specifically, the TOPography-based hydrological MODEL (TOPMODEL) is employed to obtain the flood hydrograph and the coupled 1D-2D hydrodynamic model MIKEFLOOD is used to simulate floodplain inundation. The methods are tested in the Pajiang River, a mountainous basin in South China. The results show that the TOPMODEL effectively simulates the flood hydrograph and the Nash–Sutcliffe coefficients of the flow rate are larger than 0.80 for both calibration and validation. Also, MIKEFLOOD effectively simulates the flood inundation extent and flooding depth and the BIAS for simulated water level is −0.004 m. Further, using socioeconomic and historical flood disaster datasets, areas with high flash flood risk are identified. A flash flood risk assessment shows a clear trend of change in the flood hazard and the spatial heterogeneity of the flood vulnerability is significant. A sensitivity analysis of the flood vulnerability to the socioeconomic indicators indicates that cultivated land is the most sensitive indicators, and the population and the construction areas have similar influences. Overall, the coupled hydrological and hydrodynamic models prove effective and can be used in practical applications for flash flood risk mapping. [ABSTRACT FROM AUTHOR]

Highlights • This work approximates the quasi-exact implicit formulation of Haverkamp model up to three-term expansion. • The accuracy of the proposed approximation is evaluated based on the quasi-exact implicit model. • A sensitivity analysis is conducted to define the infiltration time for which this expansion remains valid. • The proposed approximation is compared with Philip (1957) one- and two-term approximate expansions. Abstract The quasi-exact implicit (QEI) analytical formulation of Haverkamp equation, which might be also known as Parlange model, and its one- and two-term approximate expansions are among the mostly used equations for in situ determination of soil sorptivity, S , and saturated hydraulic conductivity, K s , from unsaturated one-dimensional (1D) cumulative infiltration into soils. However, from practical point of view, the approximate expansions are only valid from short to intermediate times and the QEI analytical formulation has a complex resolution, which makes its use complicated in inverting procedures. Therefore, alternative functions are needed to compute cumulative infiltration for longer times and improve inverting procedures for easier, more robust and accurate predictions of S and K s. In this regard, current work presents and evaluates a new three-term approximation of the QEI analytical formulation. As a first step, we checked the accuracy of the proposed three-term approximate expansion with respect to the QEI formulation for three different soils (sand, loam and silt) in order to define its time domain validity. Next, the accuracy of the three-term approximate expansion to estimate S , K s and the β parameter was compared to those obtained with one- and two-term approximate expansions. Lastly, a large dataset of field experimental data was inverted using the proposed three approximate expansions and the QEI analytical formulation, and goodness of fits predicted and measured cumulative infiltration curves and accuracy of the parameters (S , K s and the β) estimates were compared. The results revealed that the three-term approximate has a larger validity time interval compared to one- and two-term approximate expansions, which allows its use for larger infiltration times durations resulting in more accurate estimates of K s. Its capability of estimating S is also improved. The accurate prediction of β parameter is not still guaranteed for none of the approximate expansions. Compared to the one- and two-term approximate expansions, the experimental infiltration data usage revealed that the three-term approximate expansion resulted in higher performance and better prediction of hydraulic parameters. [ABSTRACT FROM AUTHOR]

Highlights • Joint analysis of catchment metrics, modelled processes and parameter sensitivities. • Spatio-temporal process and parameter dynamic is a fingerprint of catchment behaviour. • Detection of (dis) similarities among four catchments from different landscapes. • Improvement of process understanding in catchment and handling of processes in models. • Applied hydrological model can reproduce dominant processes of contrasting catchments. Abstract The relevance of hydrological processes varies in space and time resulting in typical temporal patterns for catchments. Contrasting catchments moreover differ in their catchment metrics. Hydrological models claim to be able to reproduce typical temporal patterns of dominant processes using site-specific model parameters. Thus, patterns of temporal dynamics in dominant modelled processes and their corresponding dominant parameters are a fingerprint of how a model represents the hydrological behaviour of a catchment and how these process patterns vary between contrasting catchments. In this study, we demonstrate how catchment metrics, modelled processes and parameter dominances can be jointly used to characterise catchments. We assess how catchment characteristics are represented in spatio-temporal process dynamics in models and how to understand the reasons for hydrological (dis)similarity among catchments along a landscape gradient. For this purpose, catchment metrics which characterise contrasting landscapes (lowland, mid-range mountain and alpine catchments) are related to dominant processes and parameters which were provided by a temporally resolved sensitivity analysis (TEDPAS) and simulations of a hydrological model. Our study shows that the applied model is able to represent the different processes and their seasonal variability according to the specific hydrological conditions of the study catchments. By analysing catchment metrics, modelled processes and model parameters jointly, we show that the largest differences are identified for the alpine catchment, whilst similarities are found among the other catchments. Following a landscape gradient, high flow phases are dominated by different flow components. In contrast, the model shows groundwater dominance in low flow phases in non-alpine catchments while in the alpine catchment low flows in winter are mainly controlled by snow processes. The joint analysis of catchment metrics, temporal dynamics of dominant processes and parameters can therefore be used to better disentangle similarities and differences among catchments from different landscapes. [ABSTRACT FROM AUTHOR]

Highlights • Analytical model for describing drawdown induced by constant-head test at partially penetrating well are developed. • The three different outer boundaries of the aquifer system are considered. • The wellbore flow rate and pumping induced leakage rate(volume) solutions are obtained. • Characteristics of the drawdown, wellbore flowrate and leakage rate (volume) are discussed. • Sensitivity analysis for the change of the each of aquifer parameters on drawdown and wellbore flowrate is performed. Abstract An analytical model for describing pumping induced drawdown by constant-head test at a partially penetrating well in a leaky confined aquifer is developed by treating the leakage of aquitard storage as an interface phenomenon rather than an volumetric effect, which is the novel and never developed so far. Three different cases of remote lateral boundaries are considered in the model. The new drawdown solution in Laplace space is obtained and inverted into time-domain solution utilizing the Stehfest method. The (semi-)analytical solutions for well discharge are then obtained under three different cases. Analytical solutions in Laplace and time domains are developed for calculating the (total) leakage rate and volume across the aquitard-aquifer interface due to constant-head test at a partially penetrating well in a leaky confined aquifer, and such solutions are of great use for groundwater resource management. Based on the newly developed solutions, the distribution of wellbore flowrate and aquitard leakage are explored and the sensitivity analysis is conducted to identify the most important controlling factor. The sensitivity analysis shows that the wellbore flowrate and the drawdown in infinite (Case 1), finite (Case 2), and closed (Case 3) leaky confined aquifer systems are both sensitive to the variation of well partial penetration, well radius, and aquifer anisotropic ratio during the whole pumping period and they are most sensitive to the change of outer boundary distance at late time for Case 3. The effect of the outer boundary on the drawdown or wellbore flowrate is more significant for a shorter outer boundary distance. [ABSTRACT FROM AUTHOR]

Highlights • PET showed the most sensitivity to RN in warm season, but to RH in cold season. • The sensitivity of PET to RN decreased as elevation increased when RN was positive. • The sensitivity of PET to T decreased as elevation increased when T was above 0 °C. • The higher the elevation, the greater the sensitivity of PET to both RH and WS. • The topographic shading affected PET and its sensitivity to meteorological factors. Abstract Mountainous regions are important sources of freshwater. Measurement of actual evapotranspiration (ET) is difficult to obtain in high mountain regions because of the harsh natural environment, and potential ET (PET) is therefore a suitable term to describe the atmospheric water demand of land surfaces under given meteorological conditions in those high elevation areas. In situ measured meteorological data were collected in 2015 and 2016 from five meteorological stations at various elevations from 2980 m to 4484 m in the Qilian Mountains, northwestern China, and the meteorological factors changed markedly with elevation. PET calculated with the Penman method showed a significant elevational gradient, and decreased as the elevation increased. The sensitivity analysis indicated that over the whole period, PET in the research region was most sensitive to net radiation (RN), followed by relative humidity (RH), air temperature (T), wind speed (WS) and soil heat flux (G). When RN was positive, the sensitivity of PET to RN decreased as the elevation increased, and when RN was negative, the sensitivity increased as the elevation increased. When T was above 0 °C, the sensitivity of PET to T decreased as the elevation increased, and when T was below 0 °C, the sensitivity increased as the elevation increased. The higher the elevation, the greater the sensitivity of PET to both RH and WS. The topographic shading in mountainous regions affected meteorological factors, PET and its sensitivity to meteorological factors in those high elevation areas. The RN was relatively small at the sites with topographic shading because of the reduction in solar radiation, and resulted in less sensitivity of PET to RN and greater sensitivity of PET to other meteorological factors. This study can help us to understand PET in the Qilian mountains and in other mountain regions from which meteorological data are difficult to obtain and very sensitive to climate change. [ABSTRACT FROM AUTHOR]

Highlights • Fractional derivative models are explored for high and low velocity non-Darcian flow. • All parameters of the fractional derivative models are determined based on test data. • The fractional derivative order is an indicator of non-Darcian flow in porous media. Abstract Fractional Darcian model and fractional Swartzendruber model are proposed for non-Darcian flows with high and low velocity in the paper, respectively. The analytical solutions of the fractional derivative models are given and all parameters are determined based on test data. The dependence of fractional derivative models on parameters such as fractional derivative order and threshold hydraulic gradient are analyzed by sensitivity studies. The fractional derivative models provide better description of non-Darcian flow in porous media under the conditions of high and low velocity. Furthermore, the memory effectiveness of fractional calculus is discussed, showing that the fractional derivative order is an indicator of non-Darcian flow in porous media. [ABSTRACT FROM AUTHOR]

• Deep reinforcement learning (RL) was used for the SWMM model assimilation. • RL manipulated the SWMM parameters by interacting with the observations and outputs. • The runoff and pollutants of three water quality constituents were simulated. • The SWMM-RL primarily outperformed the traditional optimization model. • This framework can be applied as predictive model for urban digital twin. Hydrological models have been used to understand the transportation of water quantity and quality in drainage systems, and the stormwater management model (SWMM) is one of the most widely-used models for runoff quantity and quality simulations in urban areas. Although significant efforts have been made to identify the appropriate input parameters of the SWMM model in various watersheds, it is difficult to reflect the variability of the real environment using a fixed input parameter. Data assimilation (DA) is a compatibility method that improves prediction accuracy by merging observations and simulation outputs. It is useful to reduce the forecast error from temporal transferability with information interaction between the model output and observation data. However, conventional DA approaches cannot completely overcome their unrealistic assumptions such as linearity, normality, and error covariances. To address these challenges, we used deep reinforcement learning (RL) to develop an automatic assimilation model that interactively optimizes the SWMM parameters in a real environment (SWMM-RL). In the SWMM-RL model, the agent is trained by rewarding and/or punishing the action (modulated SWMM input parameter) in real time according to the state changes. The model was constructed to minimize the error of runoff and pollutant load simulation of suspended solids (SS), total nitrogen (TN), and total phosphorus (TP) in each stormwater monitoring event. The results demonstrated that the SWMM-RL model primarily outperformed the fixed input parameter SWMM model (SWMM-PS) in simulating the runoff and the three different pollutant loads (the median value of Nash–Sutcliffe efficiency (NSE) for 10 stormwater events was increased by 0.11, 0.31, 0.36, and 0.07 for runoff, SS, TN, and TP, respectively). The SWMM-PS has disadvantages in simulating low rainfall events because of the high sensitivity of runoff peaks to heavy rainfall conditions. Furthermore, the effects of extreme rainfall events were estimated using the SWMM-RL model. This study showed how SWMM-RL combined with the DA method increased forecast accuracy by providing sensitivity and temporal transitions of input parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • An enhanced degree-day model is developed based on a flexible modeling approach. • Calibration and control were based on local and remotely-sensed snow data, and runoff. • The development resulted in a one-free parameter snow- and ice-accounting routine. • The model is statistically equivalent or better than the alternative models tested. This paper evaluates the degrees of freedom and complexity warranted in temperature-index models to jointly simulate local snow measurements, remotely-sensed snow cover and runoff in mountainous areas. To address this issue, the snow- and ice-accounting routine (SIAR) on top of the GR4J model was developed on a dataset covering 17 mountainous catchments (45 to 3580 km2) in the French Alps and Pyrenees. Model calibration and control were based on streamflow series, fractional snow cover (FSC) computed from MODIS snow products and at least one chronicle of local measurements of snow water equivalent (SWE) acquired in each catchment for the period 2004–2016. SIAR was applied according to an adaptable number of elevation bands and different parametrizations ranging from 11 free parameters (precipitation orographic correction, temperature lapse rate, variation in the temperature lapse rate, snowfall adjustment, rainfall lapse rate, thermal inertia of the snow pack, constant and variable part of the degree-day snow melt factor, degree-day ice melt factor, 2-parameter hysteresis between SWE and FSC), to only fixed parameters. Results showed that the one-free-parameter SIAR is as efficient as more parametrized versions in simulating both local and distributed snow dynamics as well as runoff. Interestingly, using SIAR without any free parameters by fixing the snowfall adjustment to a median value of 60% only led to slight impairment of local SWE dynamics. Certain processes represented in SIAR (glacier-component, sublimation, simple energy balance, snowpack cold-content, variable melt factor) were then alternatively turned off to justify those retained in its final version. The modeling performances were also compared by applying SIAR with different distribution options ranging from full distribution according to 0.25 km2 cells to lumped mode. A number of equal-area elevation bands according to the catchment hypsometry proved to be a good compromise as it allowed snow and runoff simulations of similar accuracy to the full distribution mode, while limiting computational time. Finally, SIAR was compared with the Cemaneige snow routine, which showed its modeling performance was better. These findings suggest that it is possible, and even advisable, to limit the number of free parameters in temperature-index models in order to reduce problems of over-parameterization and equifinality. [ABSTRACT FROM AUTHOR]

• A bioretention model considering nitrogen conversion in submerged zone (SZ) was proposed. • Adding monitoring data of SZ nitrogen in dry periods can reduce prediciton uncertainty. • Outflow NH 4 -N, NO 3 -N, or TN is sensitive to most nitrogen module parameters in SZ. • An increase of SZ depth can improve the annual average nitrogen removal rate. • An increase of SZ depth does not always raise nitrogen removal rate in rainfall events. Bioretention systems are effective stormwater treatment systems. Many studies have been conducted on the nitrogen regulation effects of bioretention systems. However, few studies had investigated the variation of nitrogen concentration in the submerged zone during dry periods and evaluated the effect of the submerged zone on nitrogen removal efficiency under dry-wet alterations. Furthermore, existing models cannot fully consider the impact of the submerged zone on nitrogen removal. This paper presents the validation, prediction uncertainty, parameter sensitivity analysis, and scenario simulations with different submerged zone depths of a proposed model. We conducted 13 consecutive rainfall events for model validation under dry-wet alternations in a mesocosm bioretention system with a submerged zone. The collected data mainly includes outflow rates, outflow nitrogen concentrations (NH 4 -N, NO 3 -N, and TN) in wet periods, and submerged zone nitrogen concentrations (NH 4 -N, NO 3 -N, and TN) in dry periods. The results show that (1) the Nash efficiency coefficients in the wet and dry periods were all above 0.5 during calibration and validation phases; (2) the prediction uncertainty of outflow/submerged zone nitrogen concentration (NH 4 -N, NO 3 -N, and TN) can be significantly reduced by using NSE values of the wet and dry periods as likelihood objective functions than only using NSE values of the wet periods; (3) the sensitivity analysis shows that outflow nitrogen concentrations (NH 4 -N, NO 3 -N, or TN) are sensitive to most of nitrogen module parameters in the submerged zone; (4) in the long-term dry-wet alterations simulation, the increase of submerged zone depth can improve the annual average nitrogen removal rate, while it does not always improve nitrogen removal rates in single rainfall event. If a rainfall event with a short antecedent dry period (<1.5 d) and the inflow nitrogen concentration in the current event is less than that in the previous event, the increase of the submerged zone depth will even reduce the nitrogen removal rate. [ABSTRACT FROM AUTHOR]

• Geometry dominates the sensitivity at both the wetland and the watershed scale. • Hydroconnectivity strongly influences the sensitivity among wetland parameters. • The sensitivity of wetland parameters display a high temporal variability. • Sensitivity analysis can support site selection and timing for field monitoring. Hydrological models offer the opportunity to evaluate wetland conservation networks as nature-based solutions to mitigate hydrological extremes. To optimize the replication of a hydrological model response (i.e., stream flows), the models rely on input data (land cover, hydrographic network, soil types, topography, meteorological data) and parameters. However, the effect of parametric uncertainty on the simulated variables and the spatio-temporal variability of this sensitivity are insufficiently documented. This study presents an application of the novel global sensitivity analysis method, the Variogram Analysis of Response Surface, to the isolated and riparian wetland modules of HYDROTEL, a semi-distributed, process-based, deterministic model, on two watersheds located in the province of Quebec, Canada. The analysis accounted for: (i) the spatial variability by assessing the sensitivity at various locations in the watersheds and by compiling sensitivity indices for wetland networks discretized following the Strahler order classification which quantifies the structure of hydrographic networks and (ii) the temporal variability of the sensitivity using the Generalized Global Sensitivity Matrix. The results indicate that the parameters defining the geometry of wetlands (area, depth) are the most sensitive for both isolated and riparian wetlands, and for all Strahler orders. The temporal assessment of sensitivity highlights the seasonal controlling processes, including a peaking sensitivity of the maximum water depth in wetlands during spring snowmelt, whereas the sensitivity of the evapotranspiration parameter increases during summer, but is null during winter. Results also indicate that the parameters of the isolated modules are more sensitive for wetlands located on lower stream order (upstream/headwaters) while those of the riparian modules display a greater sensitivity when wetlands are located on higher Strahler orders (downstream/main channel). These findings deepen our understanding of the impact of wetland features on stream flows and provide guidelines to plan future data acquisition campaigns in wetlandscapes. [ABSTRACT FROM AUTHOR]

• Tides enhance the variation range of SWI properties in fractured coastal aquifers. • Tides modify the influence pattern of fracture characteristics on SWI. • Horizontal fractures increase tidally driven seawater recirculation, and SGD. • Tides significantly enhance the mixing of fresh-saltwater in fractured aquifer. Fractured aquifers are particularly vulnerable to seawater intrusion (SWI), but the mechanism of SWI in such aquifers remains largely unknown. This study aims to investigate the influence of tides on SWI in fractured aquifers through laboratory experiments and numerical models. Six fractured aquifers and one homogeneous aquifer were employed to conduct laboratory and numerical experiments to preliminary analyze the comprehensive effects of tides and fractures on salinity distributions, flow fields and submarine groundwater discharge (SGD). Sensitivity analyses were performed to further explore the impact of each fracture characteristic (i.e., horizontal position, vertical position, length and orientation) on SWI properties (i.e., saltwater wedge, mixing, saline volume) and concentration distribution under non-tidal and tidal conditions. The results indicate that tides can expand the variation range of most SWI properties in fractured aquifers and alter the influence patterns of fracture characteristics. Fractures tend to converge around freshwater, and their effect on SWI properties depends on their relative position to the saltwater. Toe length (TL) reached its maximum when the fractures had just separated from the saltwater wedge and were fully submerged in freshwater. Longer fractures promoted SWI and relieved the tides' restriction on TL. Moreover, horizontal fractures can significantly amplify high-concentration (50%-90%) saltwater distribution, SGD and upper saline plume (USP) with the help of tides, while vertical fractures only had an impact on the mixing zone. These results have important implications for improving our understanding of comprehensive impact of tides and fractures on SWI in coastal subsurface environments. [ABSTRACT FROM AUTHOR]

Highlights • A systematic framework is presented for parametric uncertainty quantification. • Sensitive parameters were identified with qualitative and quantitative methods. • Sensitive parameters were calibrated with adaptive surrogate-based optimization. Abstract Quantifying and reducing uncertainties in physics-based hydrological model parameters will improve model reliability for hydrological forecasting. We present an uncertainty quantification framework that combines the strengths of stepwise sensitivity analysis and adaptive surrogate-based multi-objective optimization to facilitate practical assessment and reduction of model parametric uncertainties. Framework performance was tested using the distributed hydrological model Coupled Routing and Excess Storage (CREST) for daily streamflow simulation over ten watersheds. By identifying sensitive parameters stepwisely, we reduced the number of parameters requiring calibration from twelve to seven, thus limiting the dimensionality of calibration problem. By updating surrogate models adaptively, we found the optimal sets of sensitive parameters with the surrogate-based multi-objective optimization. The calibrated CREST was able to satisfactorily simulate observed streamflow for all watersheds, improving one minus Nash-Sutcliffe efficiency (1−NSE) by 65–90% and percentage absolute relative bias (|RB|) by 60–95% compared to the default. The validation result demonstrated that the calibrated CREST was also able to reproduce observed streamflow outside the calibration period, improving 1−NSE by 40–85% and |RB| by 35–90% compared to the default. Overall, this uncertainty quantification framework is effective for assessment and reduction of model parametric uncertainties, the results of which improve model simulations and enhance understanding of model behaviors. [ABSTRACT FROM AUTHOR]

Highlights • CRNS soil moisture compares well to total-weighted point soil moisture. • The uncertainty increases for non-weighted volumetric soil moisture estimation. • CRNS is particularly sensitive to four sources of uncertainty (factors). • A new correction approach is proposed to account for the sensitivity to upper soil. • The probabilistic framework could be extended to other sites and factors. Abstract Cosmic-ray neutron sensing (CRNS) is a promising proximal soil sensing technique to estimate soil moisture at intermediate scale and high temporal resolution. However, the signal shows complex and non-unique response to all hydrogen pools near the land surface, providing some challenges for soil moisture estimation in practical applications. Aims of the study were 1) to assess the uncertainty of CRNS as a stand-alone approach to estimate volumetric soil moisture in cropped field 2) to identify the causes of this uncertainty 3) and possible improvements. Two experimental sites in Germany were equipped with a CRNS probe and point-scale soil moisture network. Additional monitoring activities were conducted during the crop growing season to characterize the soil-plant systems. This data is used to identify and quantify the different sources of uncertainty (factors). An uncertainty analysis, based on Monte Carlo approach, is applied to propagate these uncertainties to CRNS soil moisture estimations. In addition, a sensitivity analysis based on the Sobol’ method is performed to identify the most important factors explaining this uncertainty. Results show that CRNS soil moisture compares well to the soil moisture network when these point-scale values are weighted to account for the spatial sensitivity of the signal and other sources of hydrogen (lattice water and organic carbon) are added to the water content. However, the performance decreases when CRNS is considered as a stand-alone method to retrieve the actual (non-weighted) volumetric soil moisture. The support volume (penetration depth and radius) shows also a considerable uncertainty, especially in relatively dry soil moisture conditions. Four of the seven factors analyzed (the vertical soil moisture profile, bulk density, incoming neutron correction and the calibrated parameter N 0) were found to play an important role. Among the possible improvements identified, a simple correction factor based on vertical point-scale soil moisture profiles shows to be a promising approach to account for the sensitivity of the CRNS signal to the upper soil layers. [ABSTRACT FROM AUTHOR]

Highlights • Hydrologic ratios are controlled by climate and catchment variables. • River basins in western part of CONUS exhibit high variability in hydrologic ratios. • Drought indicators are sensitive to the spatial change in hydrologic ratios. • Precipitation has more control on drought indicators compared to PET. Abstract The atmospheric water supply and demand dynamics determine a region’s potential water resources. The hydrologic ratios, such as, aridity index, evaporation ratio and runoff coefficients are useful indicators to quantify the atmospheric water dynamics at watershed to regional scales. In this study, we developed a modeling framework using a machine learning approach to predict hydrologic ratios for watersheds located in contiguous United States (CONUS) by utilizing a set of climate, soil, vegetation, and topographic variables. Overall, the proposed modeling framework is able to simulate the hydrologic ratios at watershed scale with a considerable accuracy. The concept of non-parametric elasticity was applied to study the potential influence of the estimated hydrologic ratios on various drought characteristics (resilience, vulnerability, and exposure) for river basins located in CONUS. Spatial sensitivity of drought indicators to hydrologic ratios suggests that an increase in hydrologic ratios may result in augmentation of magnitude of drought indicators in majority of the river basins. Aridity index seems to have higher influence on drought characteristics in comparison to other hydrologic ratios. It was observed that the machine learning approach based on random forests algorithm can efficiently estimate the spatial distribution of hydrologic ratios provided sufficient data is available. In addition to that, the non-parametric based elasticity approach can identify the potential influence of hydrologic ratios on spatial drought characteristics. [ABSTRACT FROM AUTHOR]

We present artificial neural network (ANN) based software for rapidly predicting the recovery effectiveness (REN) of aquifer storage and recovery (ASR) wells in fresh water aquifers. The REN performance criterion equals the amount of the water injected in an ASR well that is recoverable via the same well during a specified extraction period. The software circumvents the need to prepare and run computationally intensive simulations, by invoking ANN models as surrogate simulators. The paper presents the development of the ANN models and a graphical user interface (GUI) to facilitate using the ANN models and to perform sensitivity analysis. ANN inputs include all factors significantly affecting REN in freshwater aquifers. The software can evaluate REN in any ASR system in confined aquifers whose parameters are within the range of parameters used for training the ANNs in this study. [ABSTRACT FROM AUTHOR]

Increasing demand on groundwater resources motivates understanding of the controls on recharge dynamics so model predictions under current and future climate may improve. Here we address questions about the nonlinear behavior of flux variability in the vadose zone that may explain previously reported teleconnections between global-scale climate variability and fluctuations in groundwater levels. We use hundreds of HYDRUS-1D simulations in a sensitivity analysis approach to evaluate the damping depth of transient recharge over a range of periodic boundary conditions and vadose zone geometries and hydraulic parameters that are representative of aquifer systems of the conterminous United States (U.S). Although the models were parameterized based on U.S. aquifers, findings from this study are applicable elsewhere that have mean recharge rates between 3.65 and 730 mm yr − 1 . We find that mean infiltration flux, period of time varying infiltration, and hydraulic conductivity are statistically significant predictors of damping depth. The resulting framework explains why some periodic infiltration fluxes associated with climate variability dampen with depth in the vadose zone, resulting in steady-state recharge, while other periodic surface fluxes do not dampen with depth, resulting in transient recharge. We find that transient recharge in response to the climate variability patterns could be detected at the depths of water levels in most U.S. aquifers. Our findings indicate that the damping behavior of transient infiltration fluxes is linear across soil layers for a range of texture combinations. The implications are that relatively simple, homogeneous models of the vadose zone may provide reasonable estimates of the damping depth of climate-varying transient recharge in some complex, layered vadose zone profiles. [ABSTRACT FROM AUTHOR]

Many rivers in the Pacific Northwest region of North America are anthropogenically manipulated via dam operations, leading to system-wide impacts on hydrodynamic conditions and aquatic communities. Understanding how dam operations alter abiotic and biotic variables is important for designing management actions. For example, in the Klamath River, dam outflows could be manipulated to alter water age and temperature to reduce risk of parasite infections in salmon by diluting or altering viability of parasite spores. However, sensitivity of water age and temperature to the riverine conditions such as bathymetry can affect outcomes from dam operations. To examine this issue in detail, we conducted a global sensitivity analysis of water age and temperature to a comprehensive set of hydraulics and meteorological parameters in the Klamath River, California, where management of salmonid disease is a high priority. We applied an analysis technique, which combined Latin-hypercube and one-at-a-time sampling methods, and included simulation runs with the hydrodynamic numerical model of the Lower Klamath. We found that flow rate and bottom roughness were the two most important parameters that influence water age. Water temperature was more sensitive to inflow temperature, air temperature, solar radiation, wind speed, flow rate, and wet bulb temperature respectively. Our results are relevant for managers because they provide a framework for predicting how water within ‘high infection risk’ sections of the river will respond to dam water (low infection risk) input. Moreover, these data will be useful for prioritizing the use of water age (dilution) versus temperature (spore viability) under certain contexts when considering flow manipulation as a method to reduce risk of infection and disease in Klamath River salmon. [ABSTRACT FROM AUTHOR]

Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. By using a new variance-based global sensitivity analysis method, this paper identifies important parameters for nitrogen reactive transport with simultaneous consideration of these three uncertainties. A combination of three scenarios of soil temperature and two scenarios of soil moisture creates a total of six scenarios. Four alternative models describing the effect of soil temperature and moisture content are used to evaluate the reduction functions used for calculating actual reaction rates. The results show that for nitrogen reactive transport problem, parameter importance varies substantially among different models and scenarios. Denitrification and nitrification process is sensitive to soil moisture content status rather than to the moisture function parameter. Nitrification process becomes more important at low moisture content and low temperature. However, the changing importance of nitrification activity with respect to temperature change highly relies on the selected model. Model-averaging is suggested to assess the nitrification (or denitrification) contribution by reducing the possible model error. Despite the introduction of biochemical heterogeneity or not, fairly consistent parameter importance rank is obtained in this study: optimal denitrification rate ( K den ) is the most important parameter; reference temperature ( T r ) is more important than temperature coefficient ( Q 10 ); empirical constant in moisture response function ( m ) is the least important one. Vertical distribution of soil moisture but not temperature plays predominant role controlling nitrogen reaction. This study provides insight into the nitrogen reactive transport modeling and demonstrates an effective strategy of selecting the important parameters when future temperature and soil moisture carry uncertainties or when modelers face with multiple ways of establishing nitrogen models. [ABSTRACT FROM AUTHOR]

Despite advancements in developing physics-based formulations to estimate the sheet-flow travel time ( t SHF ), the quantification of the relative impacts of influential parameters on t SHF has not previously been considered. In this study, a brief review of the physics-based formulations to estimate t SHF including kinematic wave (K-W) theory in combination with Manning’s roughness (K-M) and with Darcy-Weisbach friction formula (K-D) over single and multiple planes is provided. Then, the relative significance of input parameters to the developed approaches is quantified by a density-based global sensitivity analysis (GSA). The performance of K-M considering zero-upstream and uniform flow depth (so-called K-M1 and K-M2), and K-D formulae to estimate the t SHF over single plane surface were assessed using several sets of experimental data collected from the previous studies. The compatibility of the developed models to estimate t SHF over multiple planes considering temporal rainfall distributions of Natural Resources Conservation Service, NRCS (I, Ia, II, and III) are scrutinized by several real-world examples. The results obtained demonstrated that the main controlling parameters of t SHF through K-D and K-M formulae are the length of surface plane (mean sensitivity index T ̂ i  = 0.72) and flow resistance (mean T ̂ i  = 0.52), respectively. Conversely, the flow temperature and initial abstraction ratio of rainfall have the lowest influence on t SHF (mean T ̂ i is 0.11 and 0.12, respectively). The significant role of the flow regime on the estimation of t SHF over a single and a cascade of planes are also demonstrated. Results reveal that the K-D formulation provides more precise t SHF over the single plane surface with an average percentage of error, APE equal to 9.23% (the APE for K-M1 and K-M2 formulae were 13.8%, and 36.33%, respectively). The superiority of Manning-jointed formulae in estimation of t SHF is due to the incorporation of effects from different flow regimes as flow moves downgradient that is affected by one or more factors including high excess rainfall intensities, low flow resistance, high degrees of imperviousness, long surfaces, steep slope, and domination of rainfall distribution as NRCS Type I, II, or III. [ABSTRACT FROM AUTHOR]

Retention Soil Filters (RSFs), a form of vertical flow constructed wetlands specifically designed for combined sewer overflow (CSO) treatment, have proven to be an effective tool to mitigate negative impacts of CSOs on receiving water bodies. Long-term hydrologic simulations are used to predict the emissions from urban drainage systems during planning of stormwater management measures. So far no universally accepted model for RSF simulation exists. When simulating hydraulics and water quality in RSFs, an appropriate level of detail must be chosen for reasonable balancing between model complexity and model handling, considering the model input’s level of uncertainty. The most crucial parameters determining the resultant uncertainties of the integrated sewer system and filter bed model were identified by evaluating a virtual drainage system with a Retention Soil Filter for CSO treatment. To determine reasonable parameter ranges for RSF simulations, data of 207 events from six full-scale RSF plants in Germany were analyzed. Data evaluation shows that even though different plants with varying loading and operation modes were examined, a simple model is sufficient to assess relevant suspended solids (SS), chemical oxygen demand (COD) and NH 4 emissions from RSFs. Two conceptual RSF models with different degrees of complexity were assessed. These models were developed based on evaluation of data from full scale RSF plants and column experiments. Incorporated model processes are ammonium adsorption in the filter layer and degradation during subsequent dry weather period, filtration of SS and particulate COD (X COD ) to a constant background concentration and removal of solute COD (S COD ) by a constant removal rate during filter passage as well as sedimentation of SS and X COD in the filter overflow. X COD , SS and ammonium loads as well as ammonium concentration peaks are discharged primarily via RSF overflow not passing through the filter bed. Uncertainties of the integrated simulation of the sewer system and RSF model mainly originate from the model parameters of the hydrologic sewer system model. [ABSTRACT FROM AUTHOR]

Climate change impact assessment on meteorological variables involves large uncertainties as a result of incomplete knowledge on the future greenhouse gas concentrations and climate model physics, next to the inherent internal variability of the climate system. Given that the alteration in greenhouse gas concentrations is the driver for the change, one expects the impacts to be highly dependent on the considered greenhouse gas scenario (GHS). In this study, we denote this behavior as GHS sensitivity. Due to the climate model related uncertainties, this sensitivity is, at local scale, not always that strong as expected. This paper aims to study the GHS sensitivity and its contributing role to climate scenarios for a case study in Belgium. An ensemble of 160 CMIP5 climate model runs is considered and climate change signals are studied for precipitation accumulation, daily precipitation intensities and wet day frequencies. This was done for the different seasons of the year and the scenario periods 2011–2040, 2031–2060, 2051–2081 and 2071–2100. By means of variance decomposition, the total variance in the climate change signals was separated in the contribution of the differences in GHSs and the other model-related uncertainty sources. These contributions were found dependent on the variable and season. Following the time of emergence concept, the GHS uncertainty contribution is found dependent on the time horizon and increases over time. For the most distinct time horizon (2071–2100), the climate model uncertainty accounts for the largest uncertainty contribution. The GHS differences explain up to 18% of the total variance in the climate change signals. The results point further at the importance of the climate model ensemble design, specifically the ensemble size and the combination of climate models, whereupon climate scenarios are based. The numerical noise, introduced at scales smaller than the skillful scale, e.g. at local scale, was not considered in this study. [ABSTRACT FROM AUTHOR]

Aquifer salinization has recently increased significantly due to human activity and has caused irreparable environmental and economic effects. In this research, a new method is proposed for modeling the vulnerability to salinity for the Ghaemshahr-juybar aquifer. Specifically, the GALDIT (Sea water intrusion) and TAWLBIC (Saltwater up-coning) indices were combined to produce a map of vulnerability (Comprehensive Salinity Index or CSI) to seawater intrusion of a region near the coast and saltwater up-coning away from the coast, respectively. Single parameter and removal layer sensitivity analysis were performed in order to identify the sensitive parameters and achieve optimal weights (through the single-parameter method) of contributing factors in all three methods. The three optimized methods produced were GALDIT-Opt, TAWLBIC-Opt and CSI-Opt. To assess the accuracy of the original maps and optimal ones, the Pearson correlation was used. Results indicated that the Pearson correlation of the optimized GALDIT, TAWLBIC and CSI model was better than GALDIT, TAWLBIC and CSI. The results show that the increase in correlation between EC (Electrical Conductivity), TDS (Total Dissolved Solids) and SAR (Sodium Adsorption Ratio) from the GALDIT model to the CSI-Opt model from values of 0.64, 0.56 and 0.68 has improved to values of 0.81, 0.88 and 0.91, respectively. The highest concentration of EC, with a value of 7050 µs/cm, is sampled in the areas of the east and northwest of the Ghaemshahr-juybar aquifer, which are classified in the CSI-Opt model as high and very high vulnerability levels. The highest concentration of TDS and SAR has been found in the east, northwest and northeast of the Ghaemshahr-juybar aquifer with a value of 4724 ppm for TDS and 14 mg/l for SAR that have been modeled in the CSI-Opt index as highly vulnerable areas. Eventually, CSI mapping can be used as an efficient tool in prioritizing in terms of the vulnerability to aquifer salinity, carrying out adjustments, recharge, and adaptation policies for this issue. [ABSTRACT FROM AUTHOR]