Description: Evapotranspiration (ET) is a poorly constrained flux in the North American monsoon (NAM) region, leading to potential errors in land-atmosphere feedbacks. We quantified the spatio-temporal variations of ET using the Variable Infiltration Capacity (VIC) model, modified to account for soil evaporation (E soil ), irrigated agriculture, and the variability of land surface properties derived from the Moderate Resolution Imaging Spectroradiometer during 2000-2012. Simulated ET patterns were compared to field observations at fifty-nine eddy covariance towers, water balance estimates in nine basins, and six available gridded ET products. The modified VIC model performed well at eddy covariance towers representing the natural and agricultural land covers in the region. Simulations revealed that major sources of ET were forested mountain areas during the summer season and irrigated croplands at peak times of growth in the winter and summer, accounting for 22% and 9% of the annual ET, respectively. Over the NAM region, E soil was the largest component (60%) of annual ET, followed by plant transpiration (T, 32%) and evaporation of canopy interception (8%). E soil and T displayed different relationships with P in natural land covers, with E soil tending to peak earlier than T by up to one month, while only a weak correlation between ET and P was found in irrigated croplands. Based on the model performance, the VIC-based estimates are the most realistic to date for this region. Furthermore, spatio-temporal patterns reveal new information on the magnitudes, locations and timing of ET in the North American monsoon region with implications on land-atmosphere feedbacks. This article is protected by copyright. All rights reserved.

Description: We describe a new approach that couples hydrograph separation with high-frequency nitrate data to quantify time-variable groundwater and runoff loading of nitrate to streams, and the net in-stream fate of nitrate at the watershed-scale. The approach was applied at three sites spanning gradients in watershed size and land use in the Chesapeake Bay watershed. Results indicate that 58-73% of the annual nitrate load to the streams was groundwater-discharged nitrate. Average annual first order nitrate loss rate constants (k) were similar to those reported in both modelling and in-stream process-based studies, and were greater at the small streams (0.06 and 0.22 d −1 ) than at the large river (0.05 d −1 ), but 11% of the annual loads were retained/lost in the small streams, compared with 23% in the large river. Larger streambed area to water volume ratios in small streams result in greater loss rates, but shorter residence times in small streams result in a smaller fraction of nitrate loads being removed than in larger streams. A seasonal evaluation of k values suggests that nitrate was retained/lost at varying rates during the growing season. Consistent with previous studies, streamflow and nitrate concentration were inversely related to k. This new approach for interpreting high-frequency nitrate data and the associated findings furthers our ability to understand, predict, and mitigate nitrate impacts on streams and receiving waters by providing insights into temporal nitrate dynamics that would be difficult to obtain using traditional field-based studies. This article is protected by copyright. All rights reserved.

Description: Peatland drainage has been an important component of forestry management in the boreal zone and the resulting ditch networks are maintained regularly to sustain forest productivity. In Finland, this is recognized as the most detrimental forestry practice increasing diffuse loads of suspended solids. Alongside forestry management on peatlands, interest in peatland restoration has grown lately. Distributed hydrological modeling has the potential to address these matters by recognizing relevant physical mechanisms and identifying most suitable strategies for mitigating undesired outcomes. This study investigates the utility of such a modeling approach in a drained peatland forest environment. To provide a suitable tool for this purpose, we coupled channel network flow to the three-dimensional distributed hydrological model FLUSH. The resulting model was applied to a 5.2 ha drained peatland forest catchment in Eastern Finland. The model was calibrated and validated using field measurements obtained over frost-free periods of five months. The application showed that distributed modeling can disentangle the importance of spatial factors on local soil moisture conditions, which is significant as peatland drainage aims to control these conditions. In our application, we limited the spatial aspect to the topography and the drainage network, and found that the drainage configuration had a clear effect on the spatial soil moisture patterns but that the effect was less pronounced during the wetter summer. Future applications of distributed modeling in this field comprises investigating the impacts of other spatial factors, modeling channel erosion and solid transport to address strategies for their mitigation, and evaluating restoration schemes. This article is protected by copyright. All rights reserved.

Description: Topological persistence is a powerful and general technique for characterising the geometry and topology of data. Its theoretical foundations are over fifteen years old and efficient computational algorithms are now available for the analysis of large digital images. We explain here how quantities derived from topological persistence relate to other measurements on porous materials such as grain and pore-size distributions, connectivity numbers, and the critical radius of a percolating sphere. The connections between percolation and topological persistence are explored in detail using data obtained from micro-CT images of spherical bead packings, unconsolidated sand packing, a variety of sandstones, and a limestone. We demonstrate how persistence information can be used to estimate the percolating sphere radius and to characterise the connectivity of the percolating cluster. This article is protected by copyright. All rights reserved.

Description: The flow of water between soil and plants follows the gradient in water potential and depends on the hydraulic properties of the soil and the root. In models for root water uptake (RWU), it is usually assumed that the hydraulic properties near the plant root (i.e. in the rhizosphere) and in the bulk soil are identical. Yet, a growing body of evidence has shown that the hydraulic properties of the rhizosphere are affected by root exudates (specifically, mucilage) and markedly differ from those of the bulk soil. In this work, we couple a 3D detailed description of RWU with a model that accounts for the rhizosphere specific properties (i.e. rhizosphere hydraulic properties and a non-equilibrium relation between water content and matric head). We show that as the soil dries out (due to water uptake), the higher water holding capacity of the rhizosphere results in a delay of the stress onset. During rewetting, non-equilibrium results in a slower increase of the rhizosphere water content. Furthermore, the inverse relation between water content and relaxation time implies that the drier is the rhizosphere the longer it takes to rewet. Another outcome of non-equilibrium is the small fluctuation of the rhizosphere water content compared to the bulk soil. Overall, our numerical results are in agreement with recent experimental data and provide a tool to further examine the impact of various rhizosphere processes on RWU and water dynamics. This article is protected by copyright. All rights reserved.

Description: Modeling to accurately predict river phytoplankton distribution and abundance is important in water quality and resource management. Nevertheless, the complex nature of eutrophication processes in highly connected river systems makes the task challenging. To model dynamics of river phytoplankton, represented by chlorophyll a (Chla) concentration, we propose a Bayesian hierarchical model that explicitly accommodates seasonality and upstream-downstream spatial gradient in the structure. The utility of our model is demonstrated with an application to the Nakdong River (South Korea), which is a eutrophic, intensively regulated river, but functions as an irreplaceable water source for more than 13 million people. Chla is modeled with two manageable factors, river flow and total phosphorus (TP) concentration. Our model results highlight the importance of taking seasonal and spatial context into account when describing flow regimes and phosphorus delivery in rivers. A contrasting positive Chla-flow relationship across stations vs. negative Chla-flow slopes that arose when Chla was modeled on a station-month basis is an illustration of Simpson's paradox, which necessitates modeling Chla-flow relationships decomposed into seasonal and spatial components. Similar Chla-TP slopes among stations and months suggest that, with the flow effect removed, positive TP effects on Chla are uniform regardless of the season and station in the river. Our model prediction successfully captured the shift in the spatial and monthly patterns of Chla. This article is protected by copyright. All rights reserved.

Description: Low water levels in the Great Lakes have recently had significant financial impacts on the region's commercial shipping, which transports hundreds of millions of dollars' worth of bulk goods each year. Cargo capacity is a function of a ship's draft, the distance between water level and the ship's bottom, and lower water levels force ships to reduce cargo loads to prevent running aground in shallow harbors and locks. Financial risk transfer instruments, such as index-based insurance contracts, may provide an adaptable method for managing these financial risks. In this work, a relationship between water levels and shipping revenues is developed and used in an actuarial analysis of the frequency and magnitude of revenue losses. This analysis is used to develop a standardized suite of binary financial contracts, which are indexed to water levels and priced according to predefined thresholds. These contracts are then combined to form hedging portfolios with different objectives for the shippers. Results suggest that binary contracts could substantially reduce the risk of financial losses during low lake level periods and at a relatively low cost of only one to three percent of total revenues, depending on coverage level. This article is protected by copyright. All rights reserved.

Description: To improve the level skill of climate models (CMs) in reproducing the statistics of daily rainfall at a basin level, two types of statistical approaches have been suggested. One is statistical correction of CM rainfall outputs based on historical series of precipitation. The other, usually referred to as statistical rainfall downscaling, is the use of stochastic models to conditionally simulate rainfall series, based on large-scale atmospheric forcing from CMs. While promising, the latter approach attracted reduced attention in recent years, since the developed downscaling schemes involved complex weather identification procedures, while demonstrating limited success in reproducing several statistical features of rainfall. In a recent effort, Langousis and Kaleris (2014) developed a statistical framework for simulation of daily rainfall intensities conditional on upper-air variables, which is simpler to implement and more accurately reproduces several statistical properties of actual rainfall records. Here, we study the relative performance of: a) direct statistical correction of CM rainfall outputs using non-parametric distribution mapping, and b) the statistical downscaling scheme of Langousis and Kaleris (2014), in reproducing the historical rainfall statistics, including rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e. the Flumendosa catchment, using rainfall and atmospheric data from 4 CMs of the ENSEMBLES project. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the CM used and the characteristics of the calibration period. This is particularly the case for yearly rainfall maxima. This article is protected by copyright. All rights reserved.

Description: Parameter specification is an important source of uncertainty in large, complex geophysical models. These models generally have multiple model outputs that require multi-objective optimization algorithms. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this paper, a multi-objective adaptive surrogate modeling-based optimization (MO-ASMO) algorithm is introduced that aims to reduce computational cost while maintaining optimization effectiveness. Geophysical dynamic models usually have a prior parameterization scheme derived from the physical processes involved, and our goal is to improve all of the objectives by parameter calibration. In this study, we developed a method for directing the search processes towards the region that can improve all of the objectives simultaneously. We tested the MO-ASMO algorithm against NSGA-II and SUMO with 13 test functions and a land surface model - the Common Land Model (CoLM). The results demonstrated the effectiveness and efficiency of MO-ASMO. This article is protected by copyright. All rights reserved.

Description: Knowledge of porosity- and saturation-dependent thermal conductivities is necessary to investigate heat and water transfer in natural porous media such as rocks and soils. Thermal conductivity in a porous medium is affected by the complicated relationship between the topology and geometry of the pore space and the solid matrix. However, as water content increases from completely dry to fully saturated, the effect of the liquid phase on thermal conductivity may increase substantially. Although various methods have been proposed to model the porosity and saturation dependence of thermal conductivity, most are empirical or quasi-physical. In this study, we present a theoretical upscaling framework from percolation theory and the effective-medium approximation, which is called percolation-based effective-medium approximation (P-EMA). The proposed model predicts the thermal conductivity in porous media from endmember properties (e.g., air, solid matrix, and saturating fluid thermal conductivities), a scaling exponent, and a percolation threshold. In order to evaluate our porosity- and saturation-dependent models, we compare our theory with 193 porosity-dependent thermal conductivity measurements and 25 saturation-dependent thermal conductivity datasets and find excellent match. We also find values for the scaling exponent different than the universal value of 2, in insulator-conductor systems, and also different from 0.76, the exponent in conductor-superconductor mixtures, in three dimensions. These results indicate that the thermal conductivity under fully and partially saturated conditions conforms to nonuniversal behavior. This means the value of the scaling exponent changes from medium to medium and depends not only on structural and geometrical properties of the medium but also characteristics (e.g., wetting or nonwetting) of the saturating fluid. This article is protected by copyright. All rights reserved.