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HighlightsAgES and PRMS accurately simulated the dramatic change in streamflow response to precipitation after fire.Measured annual streamflow increased by 170% in 4-year postfire period with similar precipitation.Postfire AgES parameters were drastically reduced for snow and rain interception, canopy density, and ET.Decreased soil depression storage and field capacity also contributed to increased streamflow in AgES.AgES shows promise for simulating hydrologic impacts of sub-alpine forest resource management and fire response.Abstract. Forest fires alter hydrologic responses to precipitation, and streamflow changes can be challenging to simulate. Streamflow data before and after wildfire in a 14 km2 mixed-conifer, sub-alpine watershed in south-central New Mexico were used to calibrate a watershed model (Agricultural Ecosystems Services, AgES) using four years of prefire and postfire conditions. Streamflow results and parameter values from AgES and a prior Precipitation Runoff Modeling System (PRMS) modeling study in the same watershed were compared. Both AgES and PRMS simulated the dramatic change in streamflow response to precipitation after a fire, including a smaller precipitation threshold to induce streamflow. Still, AgES had substantially less bias and better performance when comparing monthly metrics. Only AgES was assessed at the daily scale. AgES simulated daily streamflow accurately throughout the study period, with slight overestimation in the prefire period (Oct 2007 to Oct 2011, 1.2% bias, 0.90 Nash-Sutcliffe efficiency [NSE]) and in the postfire period (Oct 2013 to Oct 2017, 4.4% bias, 0.54 NSE). Although each 4-year model period had nearly identical cumulative precipitation, annual streamflow increased by 170% in the postfire period compared to the prefire period. Appropriate soil and vegetation parameters were modified by the stepwise calibration process to represent the effects of fire in AgES: interception storage was decreased for rain (-99.8%) and snow (-26%), two factors influencing ET were decreased, soil depression storage was reduced (-97%), and soil field capacity was reduced (-50%). AgES demonstrated an improved streamflow simulation compared with PRMS, calibrated parameter shifts that were more consistent to interpret, and particular skill in simulating daily streamflow response to fire. Knowledge gained from this study will guide future simulations of hydrologic responses to fires in the western USA using AgES. Keywords: Agricultural Ecosystems Services model, Forest, Landscape change, Precipitation-Runoff Modeling System, Streamflow change, Surface hydrology, Wildfire.

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential (“risky” stomatal regulation), high xylem hydraulic conductivity, and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance, and low stomatal conductance at low leaf water potential (“conservative” stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits and their interactions (i.e., networks), has potential to improve crop growth predictions in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.Summary statementOur process-based model uncovered two beneficial but contrasting trait networks for maize which can be understood by their integrated effect on water use/conservation. Modification of multiple, physiologically aligned, traits were required to bring about meaningful improvements in NPP and yield.

Ecosystem services provided by depressional wetlands on the coastal plain of the Chesapeake Bay watershed (CBW) have been widely recognized and studied. However, wetland–groundwater interactions remain largely unknown in the CBW. The objective of this study was to examine the vertical interactions of depressional wetlands and groundwater with respect to different subsurface soil characteristics. This study examined two depressional wetlands with a low‐permeability and high‐permeability soil layer on the coastal plain of the CBW. The surface water level (SWL) and groundwater level (GWL) were monitored over 1 year from a well and piezometer at each site, respectively, and those data were used to examine the impacts of subsurface soil characteristics on wetland–groundwater interactions. A large difference between the SWL and GWL was observed at the wetland with a low‐permeability soil layer, although there was strong similarity between the SWL and GWL at the wetland with a high‐permeability soil layer. Our observations also identified a strong vertical hydraulic gradient between the SWL and GWL at the wetland with a high‐permeability soil layer relative to one with a low‐permeability soil layer. The hydroperiod (i.e., the total time of surface water inundation or saturation) of the wetland with a low‐permeability soil layer appeared to rely on groundwater less than the wetland with a high‐permeability soil layer. The findings showed that vertical wetland–groundwater interactions varied with subsurface soil characteristics on the coastal plain of the CBW. Therefore, subsurface soil characteristics should be carefully considered to anticipate the hydrologic behavior of wetlands in this region.

We investigated the fluvial geochemistry of two catchments at different stages in the forest recovery process which have been a focus of an Environmental Services Payment (ESP) program in Brazil. The Posses (PS) and Salto de Cima (SC) catchments (1200 ha and 1500 ha, respectively) are situated in the municipality of Extrema, Minas Gerais state. Their streams flow into the Jaguari River that supplies part of the water demand of the São Paulo metropolitan area. Samples were collected for chemical analysis and physical-chemical field measures every 2 weeks from January to December 2017. An important pollution point source was discovered in the PS stream related to bovine urine and feces, as well another unidentified source that can be related to a small food processing industry and/or a small fish farm. At the SC stream, on the other hand, there was clear evidence of domestic sewage input. This preliminary study confirmed a limited improvement of the stream water quality in response to recovery of the forest vegetation. Therefore, we recommend that in addition to enhanced monitoring to help distinguish biogeochemical sources and the benefits of land conservation practices, the ESP program should consider controlling point source pollution to accomplish its purpose. Made available in DSpace on 2020-12-12T09:02:19Z (GMT). No. of bitstreams: 1 Figueiredo-Hydrobiogeochemistry-Catchments-2020.pdf: 1803241 bytes, checksum: 739bf4b8390c0a9ec968700725c9b2c1 (MD5) Previous issue date: 2021

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

Environmental models are often essential to implement projects in planning, consulting and regulatory institutions. Research models are often poorly suited to such applications due to their complexity, data requirements, operational boundaries, and factors such as institutional capacities. This contribution enhances a modeling framework to help mitigate research model complexity, streamline data and parameter setup, reduce runtime, and improve model infrastructure efficiency. Using a surrogate modeling approach, we capture the intrinsic knowledge of a conceptual or process-based model into an ensemble of artificial neural networks. The enhanced modeling framework interacts with machine learning libraries to derive surrogate models for each model service. This process is secured using blockchain technology. After describing the methods and implementation, we present an example wherein hydrologic peak discharge provided by the curve number model is emulated with a surrogate model ensemble. The ensemble median values outperformed any individual surrogate model fit to the curve number model.

Many applications require soil moisture estimates over large spatial extents (30–300 km) and at fine-resolutions (10–30 m). Remote-sensing methods can provide soil moisture estimates over very large spatial extents (continental to global) at coarse resolutions (10–40 km), but their output must be downscaled to reach fine resolutions. When large spatial extents are considered, the downscaling procedure must consider multiple coarse-resolution grid cells, yet little attention has been given to the treatment of multiple grid cells. The objective of this paper is to compare the performance of different methods for addressing multiple coarse grid cells. To accomplish this goal, the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT + VS) downscaling model is generalized to accept multiple coarse grid cells, and two methods for their treatment are implemented and compared. The first method (fixed window) is a direct extension of the original EMT + VS model and downscales each coarse grid cell independently. The second method (shifting window) replaces the coarse grid cell values with values that are calculated from windows that are centered on each fine grid cell. The window values are weighted averages of the coarse grid values within the window extent, and three weighting methods are considered (box, disk, and Gaussian). The methods are applied to three small catchments with detailed soil moisture observations and one large region. The fixed window typically provides more accurate estimates of soil moisture than the shifting window, but it produces abrupt changes in soil moisture at the coarse grid boundaries, which may be problematic for some applications. The three weighting methods produce similar results.

Southeastern Brazil has recently experienced drought conditions that have impacted watershed conservation and the management of water quality and quantity for agricultural and urban demands. The Ribeirão das Posses watershed is being monitored as a headwater of the Jaguarí River, which is one of the contributing rivers of the Cantareira Reservoir Complex in the state of São Paulo. The landscape has changed over the last century from native forests to more homogeneous vegetation for pastures, crops and some forest plantations of eucalyptus, which have cumulative impacts on water yield and quality. Currently, the Projeto Conservador das Águas (Water Conservationist Project) has planted small areas with native species vegetation in order to recover degraded areas. The objective of this study was to evaluate the quantity of water in the Ribeirão das Posses Basin by both measurements and by simulating hydrological responses. The Agricultural Ecosystem Services (AgES) watershed model was applied to simulate water movement and storage among land areas. The simulation period was from 2009 to 2014, because the daily streamflow and meteorological data were available for model calibration and testing. We discuss data input requirements, model calibration to fit measured streamflow, and sensitivity to spatially variable rainfall inputs. The calibrated model may be used to estimate streamflow during periods of missing data, and in the future to estimate impacts of land use changes on stream water quantity and quality. Such information can be used in programs of payments for ecosystem services. Resumo A recente estiagem no Sudeste do Brasil tem impactado a conservação de bacias hidrográficas e a gestão da qualidade e quantidade da água para as demandas agrícolas e urbanas. A Bacia do Ribeirão das Posses está sendo monitorada por ser uma bacia de cabeceira da Bacia do Rio Jaguari, importante contribuinte do Sistema Cantareira no estado de São Paulo. A paisagem foi alterada no século passado, a partir de florestas nativas para uma vegetação mais homogênea como pastos, campos agrícolas e florestas de eucalipto, que geram impactos cumulativos sobre a quantidade e qualidade da água. Atualmente, o Projeto Conservador das Águas tem restaurado pequenas áreas com vegetação nativa para a recuperação de áreas degradadas. O objetivo dessa pesquisa foi avaliar a quantidade da água na Bacia do Ribeirão das Posses usando dados observados e dados da simulação de respostas hidrológicas. O modelo de bacia “Serviço Agroecossistêmico” (AgES) é aplicado para simular a variação no armazenamento e movimento da água nas áreas terrestres. O período de simulação (2009-2014) foi definido em função da disponibilidade dos dados de vazão e meteorológicos para calibrar e testar o modelo. Dados de entrada requisitados, calibração do modelo para ajuste à vazão medida e a sensibilidade do modelo aos dados de chuva são discutidos neste artigo. O modelo calibrado pode ser utilizado para estimar vazões durante períodos com dados ausentes e, no futuro, para analisar os impactos de mudança de uso da terra sobre a quantidade e qualidade da água. Essas informações podem ser usadas em programas de pagamentos por serviços ambientais.

Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing, but that resolution is poorly suited for many applications. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution soil moisture using fine-resolution topographic, vegetation, and soil data to produce fine-resolution (10-30 m) estimates of soil moisture. The EMT+VS model performs well at catchments with low topographic relief (≤124 m), but it has not been applied to regions with larger ranges of elevation. Large relief can produce substantial variations in precipitation and potential evapotranspiration (PET), which might affect the fine-resolution patterns of soil moisture. In this research, simple methods to downscale temporal average precipitation and PET are developed and included in the EMT+VS model, and the effects of spatial variations in these variables on the surface soil moisture estimates are investigated. The methods are tested against ground truth data at the 239 km2 Reynolds Creek watershed in southern Idaho, which has 1145 m of relief. The precipitation and PET downscaling methods are able to capture the main features in the spatial patterns of both variables. The space-time Nash-Sutcliffe coefficients of efficiency of the fine-resolution soil moisture estimates improve from 0.33 to 0.36 and 0.41 when the precipitation and PET downscaling methods are included, respectively. PET downscaling provides a larger improvement in the soil moisture estimates than precipitation downscaling likely because the PET pattern is more persistent through time, and thus more predictable, than the precipitation pattern. This article is protected by copyright. All rights reserved.

Numerous applications require fine-resolution (10–30 m) soil moisture patterns, but most satellite remote sensing and land-surface models provide coarse-resolution (9–60 km) soil moisture e...

Imparting knowledge of the physical processes of a system to a model and determining a set of parameter values for a hydrologic or water quality model application (i.e., parameterization) are important and difficult tasks. An exponential increase in the literature has been devoted to the use and development of these models over the years. Few articles, however, have been devoted to developing general parameterization guidelines to assist in hydrologic model application, which is the main objective of this article along with discussing a few important parameters and extracting several case studies from the literature. The following guidelines were extracted from reviewing a special collection of 22 articles along with other relevant literature: (1) use site-specific measured or estimated parameter values where possible, (2) focus on the most uncertain and sensitive parameters, (3) minimize the number of optimized parameters, (4) constrain parameter values to within justified ranges, (5) use multiple criteria to help optimize parameter values, (6) use “soft” data to optimize parameters, and (7) use a warm-up period to reduce model dependence on initial condition state variables. A few soil and hydrology related parameters common to many models are briefly described along with a discussion of measurement and estimation methods and parameter sensitivity (curve number, Manning‘s “n”, soil bulk density and porosity, soil hydraulic conductivity, soil field capacity and wilting point, and leaf area index). Weather and management inputs are also discussed, as they are critical hydrologic system information that must be imparted to the model. Several case studies from previously reported research illustrate implementation of the parameterization guidelines. This research will help improve model parameterization, resulting in more consistency, better representation of the field or watershed, and a reduced range of parameter value sets resulting in acceptable model simulations.

Many applications require fine-resolution soil-moisture maps that exhibit realistic statistical properties (e.g., spatial variance and correlation). Existing downscaling models can estimate soil moisture based on its dependence on topography, vegetation, and soil characteristics. However, observed soil-moisture patterns also contain stochastic variations around such estimates. The objectives of this research are to perform a geostatistical analysis of the stochastic variations in soil moisture and to develop a downscaling model that reproduces the observed statistical features while including the dependence on topography, vegetation, and soil properties. Extensive soil-moisture observations from two catchments (8.0 and 10.5 ha) are used for the geostatistical analysis and model development, and two other catchments (6.0 and 60 ha) are used for model evaluation. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT + VS) model is used to estimate soil moisture, and the difference between the point measurements and the EMT + VS estimates are considered to be the stochastic variations. The stochastic variations contain a temporally stable pattern along with temporally unstable patterns. All of these patterns include spatially correlated and uncorrelated variations. Moreover, the spatial variance of the stochastic patterns increases in absolute terms with the spatial-average moisture content. The EMT + VS model can reproduce the observed statistical features if it is generalized to include stochastic deviations from equilibrium soil moisture, variations in porosity, and measurement errors.

The United Arab Emirates (UAE) recently witnessed algal/phytoplankton blooms attributed to the high concentrations of Chlorophyll-a associated with the spread and accumulation of a wide range of organisms with toxic effects that influence ecological and fishing economic activities and water desalination along coastal areas. This research explores the UAE coasts as a case study for the framework presented here. In this research, we argue that advances in satellite remote sensing and imaging of spatial and temporal data offer sufficient information to find the best-fit regression method and relationship between Chlorophyll-a concentration and a set of climatic and biological explanatory variables over time. Three functional forms of regression models were tested and analysed to reveal that the Log-Linear Model found to be the best fit providing the most statistically robust model compared to the Linear and the Generalised Least Square models. Besides, it is useful to identify the factors Sea Surface temperature, Calcite Concentration, Instantaneous Photosynthetically Available Radiation, Normalized Fluorescence Line Height, and Wind Speed that significantly influence Chlorophyll-a concentration. Research results can be beneficial to aid decision-makers in building a best-fit statistical system and models of algal blooms in the study area. The study found results to be sensitive to the study’s temporal time-period length and the explanatory variables selected for the analysis.

Soil moisture can be estimated over large regions with spatial resolutions greater than 500 m, but many applications require finer resolutions (10–100 m). Several methods use topographic data to downscale, but vegetation and soil patterns can also be important. In this paper, a downscaling model that uses fine-resolution topographic, vegetation, and soil data is presented. The method is tested at the Cache la Poudre catchment where detailed vegetation and soil data were collected. Additional testing is performed at the Tarrawarra and Nerrigundah catchments where limited soil data are available. Downscaled soil moisture patterns at Cache la Poudre improve when vegetation and soil data are used, and model performance is similar to an EOF method. Using interpolated soil data at Tarrawarra and Nerrigundah decreases model performance and results in worse performance than an EOF method, suggesting that soil data needs greater spatial detail and accuracy to be useful for downscaling.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonate (CaCO(3)) profiles. The objective is to describe a simple conceptual model and detailed protocol for repeatable field and laboratory measurements of these quantities. Here, accurate elevation is measured using a ground-based differential global positioning system (GPS); other data acquisition methods could be applied to the same basic method. Soil samples are collected from prescribed depth intervals and analyzed in the lab using an efficient and precise modified pressure-calcimeter method for quantitative analysis of inorganic carbon concentration. Standard statistical methods are applied to point data, and representative results show significant correlations between changes in soil surface layer CaCO(3 )and changes in elevation consistent with the conceptual model; CaCO(3 )generally decreased in depositional areas and increased in erosional areas. Maps are derived from point measurements of elevation and soil CaCO(3) to aid analyses. A map of erosional and depositional patterns at the study site, a rain-fed winter wheat field cropped in alternating wheat-fallow strips, shows the interacting effects of water and wind erosion affected by management and topography. Alternative sampling methods and depth intervals are discussed and recommended for future work relating soil erosion and deposition to soil CaCO(3).

The "Corn Belt" is a commonly used term, but often referenced as a vaguely defined region in the Midwest USA. A few key studies have delineated synoptic maps of the Corn Belt boundaries going back to the early 20th century, but a modern flexible and accessible framework for mapping the Corn Belt in space and time is needed. New tools provide reference maps for the Corn Belt in the 21st century and the ability to quantify space-time changes in corn cropping patterns. The Landuse and Agricultural Management Practices web-Service (LAMPS) was used to estimate the average corn (maize, Zea mays L.) area in each county of the contiguous 48 USA states for the years 2010-2016. LAMPS provides a modified areal Fraction of corn (Fc) used to map the Corn Belt at three intensity levels, for example. The resulting patterns illustrate a mostly contiguous Midwest Corn Belt surrounded by more scattered regions, including southern and eastern regions. We also mapped irrigated areas and temporal changes in Fc. Mapped patterns have the potential to help researchers study issues related to food, feed, biofuel, and water security.

Greenhouses have been used in the United Arab Emirates (UAE) to produce vegetables that contribute toward UAE food security, including offering fresh vegetable produce in the off-season. However, to manage such greenhouses, farmers face both technical and environmental limitations (i.e., high water scarcity), as well as vegetable market price instability. The objective of this study is to explore tradeoffs between returns (i.e., gross margin) of selected vegetables (tomato, pepper, and cucumber), risk (deviation from gross margin means), and an environmental constraint (water salinity) using a unique target MOTAD (minimization of total absolute deviations) approach to support UAE farmer decision-making processes. The optimal target MOTAD solution included all three vegetables and no corner solution. The results showed tradeoffs between returns and risks, and confirmed that product diversification reduces overall risk. The analysis was consistent with farmer perceptions based on a survey of 78 producers in the region. The search for the optimal mix of vegetable production under UAE greenhouse conditions revealed that reduction in tomato production should be offset by an increase in cucumber production while maintaining a constant level of pepper production. In other words, risk is reduced as cucumber production increases due to the high level of tomato and lettuce price volatility as the alternative to cucumber. The results also demonstrated the importance of the water salinity environmental constraint, as it was found to have a positive marginal value in the optimal vegetable mix solution (i.e., important factor). Thus the optimal solution was highly sensitive to changes in the crop water salinity constraint. The study results also demonstrate that the target MOTAD approach is a suitable optimization methodology. As a practical approach, a decision-maker in the UAE can consider gross margin (total revenue-variable costs) maximization with risk and water quality constraints to find the optimal vegetable product mix under greenhouse conditions.

Intensive agricultural activities in the Hetao irrigation district have severely degraded local aquatic ecosystems and water quality, and Ulansuhai Lake is now the most rapidly degrading eutrophic lake in China. A better understanding of the hydro-agronomic and pollutant transport processes in the area is thus urgently needed. This study simulated monthly streamflow, total nitrogen (TN) and total phosphorus (TP) for the Hetao irrigation district using the Soil and Water Assessment Tool (SWAT) to evaluate the nutrient load, source areas, and hydrological pathways. The Nash-Sutcliffe efficiency (NSE) values obtained for the streamflow simulations were 0.75 and 0.78 for the calibration and evaluation periods, respectively. The SWAT model captured the temporal variation in streamflow (R2 > 0.8) for two periods; the NSE values for the TN and TP loads were 0.63 and 0.64 for the calibration period and 0.48 and 0.42 for the evaluation period, respectively. The predicted monthly TN load was correlated with irrigation (r = 0.61) and the monthly TP load with precipitation (r = 0.89), indicating that nitrogen transport is primarily associated with soil leaching and groundwater flow, and phosphorus is primarily transported by sediments caused by rainfall erosion. A case study of split nitrogen fertilizer applications demonstrated reduced annual TN load by as much as 13% in one year. Fertilization timing also affects the load in different pathways especially in lateral subsurface flow and shallow groundwater. Better agricultural management could thus reduce nitrogen losses, and buffer strips could minimize phosphorus transport.

Soil erosion and deposition impact the sustainability of agricultural lands within the semiarid Great Plains in the United States. Temporal differences between high-resolution digital elevation maps provide physical estimates of spatial erosion or deposition, and the depth to a calcic horizon is a chemical indicator. We hypothesized that soil surface layer CaCO₃ concentration is inversely correlated with the change in surface elevation (Δz). We studied a 109-ha field in northeastern Colorado under winter wheat (Triticum aestivum L.)–fallow rotation in alternating strips perpendicular to the prevailing wind. Soil samples (top 30 cm) collected from 185 landscape positions in 2001 and 2012 were analyzed for CaCO₃ using a modified pressure-calcimeter method. The change in CaCO₃ (ΔC) was significantly correlated with large-scale erosional and depositional areas (west and east blocks, respectively) and with soil units, whereas Δz was correlated with management strips and blocks. The west block had an average ΔC of 3.2 g kg⁻¹ with 2.0 cm of erosion, whereas the east block decreased by 4.4 g kg⁻¹ with 4.2 cm of deposition. Summit positions had the highest CaCO₃, and toeslope positions had the lowest. We found inverse relationships between Δz and ΔC in summit and toeslope positions at both erosional (Δz 5 cm) areas, but Δz was not correlated significantly with ΔC overall. High values of CaCO₃ (>100 g kg⁻¹) decreased with time. A high-resolution map of Δz showed complex spatial patterns across scales, which inferred water and wind erosion and deposition affected by terrain and management.

Vector network analyzers measure both reflection (S11) and transmission (S21) functions, but S21 has not been used to estimate soil dielectric permittivity independently. The objectives of this study were to: (1) derive the mathematical model for S21 of a multisection transmission line, and (2) test this model and demonstrate the method. The mathematical model for S21 integrates multiple transmissions across a section as a feedback subsystem. Two experiments were conducted using oven-dried sand and air to examine the S21 model. In the first experiment, a waveguide was filled with one section of sand to form a three-section transmission line; in the second experiment, two sections of sand formed a five-section transmission line. The S11 and S21 functions were measured with a vector network analyzer, and the complex dielectric permittivity values were calculated. Results of Debye model fitting and sensitivity analysis demonstrated that the complex dielectric permittivity of low-loss sand estimated from the measured S21 were less variable and followed the Debye model more closely than those from the measured S11 in the frequency range from 45.0 MHz to 3.0 GHz. Therefore, the complex dielectric permittivity of low-loss materials may be estimated more precisely using measurements of S21 with the derived feedback model than with the widely used method based on S11. These general behaviors are also expected for more lossy media within a limited frequency range, but the present theory and testing methods should be used to evaluate permittivity measurements under field conditions in moist heterogeneous soils. Page 2 of 32

Accurate moisture sensing is an important need for many research programs as well as in control of industrial processes. This paper describes the development of a high accuracy frequency domain sensing probe for use in obtaining dielectric measurements of materials suitable for work ranging from 300 MHz to 1 GHz. The probe was developed to accommodate a wide range of permittivity’s ranging from er = 2.5 to elevated permittivity’s as high as er = 40. The design provides a well-matched interface between the soil and the interconnecting cables. A key advantage of the frequency domain approach is that a change of salt concentration has a significantly reduced effect on e′, versus the traditional time-domain reflectometry, TDR, measured apparent permittivity, Ka.

The Drought Calculator (DC), a spreadsheet-based decision support tool, was developed to help ranchers and range managers predict reductions in forage production due to drought. Forage growth potential (FGP), the fraction of historical average production, is predicted as a weighted average of monthly precipitation from January through June. We calibrated and evaluated the DC tool in the Great Plains of the United States, using FGP and precipitation data from Colorado (CO), North Dakota (ND), and Wyoming (WY). In CO, FGP was most sensitive to precipitation in April and May, in ND to precipitation in April and June, and in WY to precipitation in April, May, and June. Weights in these months ranged from 0.16 to 0.52. Prediction was better for CO and WY than for ND. When January–June precipitation was used, the tool correctly predicted 83% of the years with FGP reduced by drought for CO, 82% for WY, and only 67% for ND. Positive values of the True Skill Statistic (0.53 for CO, 0.42 for WY, and 0.17 for ND) indicate that FGP was classified as above or below average better than random selection. Predicting FGP earlier than April in CO and WY will require accurate forecasts of April–June precipitation. Use of the DC is most limited by insufficient forage data to determine the site-specific relationships between FGP and monthly precipitation. Even so, the decision tool is useful for discriminating drought effects on FGP classification being above or below the long-term average, and it provides a quantitative prediction to producers for their destocking decisions in drought years.

The environmental modeling community has historically been concerned with the proliferation of models and the effort associated with collective model development tasks (e.g., code generation, data transformation, etc.). Environmental modeling frameworks (EMFs) have been developed to address this problem, but much work remains before EMFs are adopted as mainstream modeling tools. Environmental model development requires both scientific understanding of environmental phenomena and software developer proficiency. EMFs support the modeling process through streamlining model code development, allowing seamless access to data, and supporting data analysis and visualization. EMFs also support aggregation of model components into functional units, component interaction and communication, temporal-spatial stepping, scaling of spatial data, multi-threading/multi-processor support, and cross-language interoperability. Some EMFs additionally focus on high-performance computing and are tailored for particular modeling domains such as ecosystem, socio-economic, or climate change research. The Object Modeling System Version 3 (OMS3) EMF employs new advances in software framework design to better support the environmental model development process. This paper discusses key EMF design goals/constraints and addresses software engineering aspects that have made OMS3 framework development efficacious and its application practical, as demonstrated by leveraging software engineering efforts outside of the modeling community and lessons learned from over a decade of EMF development. Software engineering approaches employed in OMS3 are highlighted including a non-invasive lightweight framework design supporting component-based model development, use of implicit parallelism in system design, use of domain specific language design patterns, and cloud-based support for computational scalability. The key advancements in EMF design presented herein may be applicable and beneficial for other EMF developers seeking to better support environmental model development through improved framework design.

The theory of travel time and residence time distributions is reworked from the point of view of the hydrological storages and fluxes involved. The forward and backward travel time distribution functions are defined in terms of conditional probabilities. Previous approaches that used fixed travel time distributions are not consistent with our new derivation. We explain Niemi's formula and show how it can be interpreted as an expression of the Bayes theorem. Some connections between this theory and population theory are identified by introducing an expression which connects life expectancy with travel times. The theory can be applied to conservative solutes, including a method of estimating the storage selection functions. An example, based on the Nash hydrograph, illustrates some key aspects of the theory. Generalization to an arbitrary number of reservoirs is presented.

Projected global change includes groundwater systems, which are linked with changes in climate over space and time. Consequently, global change affects key aspects of subsurface hydrology (including soil water, deeper vadose zone water, and unconfined and confined aquifer waters), surface-groundwater interactions, and water quality. Research and publications addressing projected climate effects on subsurface water are catching up with surface water studies. Even so, technological advances, new insights and understanding are needed regarding terrestrial-subsurface systems, biophysical process interactions, and feedbacks to atmospheric processes. Importantly, groundwater resources need to be assessed in the context of atmospheric CO2 enrichment, warming trends and associated changes in intensities and frequencies of wet and dry periods, even though projections in space and time are uncertain. Potential feedbacks of groundwater on the global climate system are largely unknown, but may be stronger than previously assumed. Groundwater has been depleted in many regions, but management of subsurface storage remains an important option to meet the combined demands of agriculture, industry (particularly the energy sector), municipal and domestic water supply, and ecosystems. In many regions, groundwater is central to the water-food-energy-climate nexus. Strategic adaptation to global change must include flexible, integrated groundwater management over many decades. Adaptation itself must be adaptive over time. Further research is needed to improve our understanding of climate and groundwater interactions and to guide integrated groundwater management.

Controlled subsurface drainage can reduce nitrate loss to tile flow, but the effects may vary with different N application rates and weather conditions. Interactions between these factors can be understood better via combinations of field experiments and modeling. Using an automated parameter estimation method (PEST), the Root Zone Water Quality Model (RZWQM2) was calibrated with measured monthly tile flow, N loss and flow weighted nitrate-N concentration (FWNC) from 2006 to 2008 in a corn and soybean rotation system with free drainage (FD) management. Similar data from 2006 to 2008 with controlled drainage (CD) management were used to evaluate the model. Changing from FD to CD reduced the annual N loss in tile flow by 22 and 32% based on measured and RZWQM2 simulated results, respectively. The model over-predicted the CD effect possibly because of the slope of the field, which reduces the effect of CD but is not simulated by the model. Long-term RZWQM2 simulations (1996–2008) suggest that N loss can be reduced by about 40% in both FD and CD by decreasing N rate from 245 to 140 kg N ha −1 with little effect on corn yield. A further reduction in N loss of 39% (9.3 kg N ha −1 ) was simulated by implementing CD at the reduced N rate, and the reduced N loss to tile flow was mainly associated with increased N loss to seepage (lateral flow) and crop N uptake. The percent of N loss reduction using CD relative to FD was magnified with increased rainfall (from approximately 20 to 50% with annual rainfall ranging from 600 to 1100 mm), but the reduction varied only between 38 and 40% under different N rates (0–250 kg N ha −1 ). The results indicate that RZWQM2 accurately responded to CD compared to field measurements, and CD management in combination with reduced N application rates can substantially reduce N loss to the environment with little negative effect on corn yield.