Important Role of Overland Flows and Tile Field Pathways in Nutrient Transport.

Nitrogen and phosphorus pollution is of great concern to aquatic life and human well-being. While most of these nutrients are applied to the landscape, little is known about the complex interplay among nutrient applications, transport attenuation processes, and coastal loads. Here, we enhance and apply the Spatially Explicit Nutrient Source Estimate and Flux model (SENSEflux) to simulate the total annual nitrogen and phosphorus loads from the US Great Lakes Basin to the coastline, identify nutrient delivery hotspots, and estimate the relative contributions of different sources and pathways at a high resolution (120 m). In addition to in-stream uptake, the main novelty of this model is that SENSEflux explicitly describes nutrient attenuation through four distinct pathways that are seldom described jointly in other models: runoff from tile-drained agricultural fields, overland runoff, groundwater flow, and septic plumes within groundwater. Our analysis shows that agricultural sources are dominant for both total nitrogen (TN) (58%) and total phosphorus (TP) (46%) deliveries to the Great Lakes. In addition, this study reveals that the surface pathways (sum of overland flow and tile field drainage) dominate nutrient delivery, transporting 66% of the TN and 76% of the TP loads to the US Great Lakes coastline. Importantly, this study provides the first basin-wide estimates of both nonseptic groundwater (TN: 26%; TP: 5%) and septic-plume groundwater (TN: 4%; TP: 2%) deliveries of nutrients to the lakes. This work provides valuable information for environmental managers to target efforts to reduce nutrient loads to the Great Lakes, which could be transferred to other regions worldwide that are facing similar nutrient management challenges.

Sustainable hydropower in the 21st century.

Hydropower has been the leading source of renewable energy across the world, accounting for up to 71% of this supply as of 2016. This capacity was built up in North America and Europe between 1920 and 1970 when thousands of dams were built. Big dams stopped being built in developed nations, because the best sites for dams were already developed and environmental and social concerns made the costs unacceptable. Nowadays, more dams are being removed in North America and Europe than are being built. The hydropower industry moved to building dams in the developing world and since the 1970s, began to build even larger hydropower dams along the Mekong River Basin, the Amazon River Basin, and the Congo River Basin. The same problems are being repeated: disrupting river ecology, deforestation, losing aquatic and terrestrial biodiversity, releasing substantial greenhouse gases, displacing thousands of people, and altering people's livelihoods plus affecting the food systems, water quality, and agriculture near them. This paper studies the proliferation of large dams in developing countries and the importance of incorporating climate change into considerations of whether to build a dam along with some of the governance and compensation challenges. We also examine the overestimation of benefits and underestimation of costs along with changes that are needed to address the legitimate social and environmental concerns of people living in areas where dams are planned. Finally, we propose innovative solutions that can move hydropower toward sustainable practices together with solar, wind, and other renewable sources.

Trends in Water Use, Energy Consumption, and Carbon Emissions from Irrigation: Role of Shifting Technologies and Energy Sources.

Novel low-pressure irrigation technologies have been widely adopted by farmers, allowing both reduced water and energy use. However, little is known about how the transition from legacy technologies affected water and energy use at the aquifer scale. Here, we examine the widespread adoption of low-energy precision application (LEPA) and related technologies across the Kansas High Plains Aquifer. We combine direct energy consumption and carbon emission estimates with life cycle assessment to calculate the energy and greenhouse gas (GHG) footprints of irrigation. We integrate detailed water use, irrigation type, and pump energy source data with aquifer water level and groundwater chemistry information to produce annual estimates of energy use and carbon emissions from 1994 to 2016. The rapid adoption of LEPA technologies did not slow pumping, but it reduced energy use by 19.2% and GHG emissions by 15.2%. Nevertheless, water level declines have offset energy efficiency gains because of LEPA adoption. Deeper water tables quadrupled the proportion of GHG emissions resulting from direct carbon emissions, offsetting the decarbonization of the regional electrical grid. We show that low-pressure irrigation technology adoption, absent policies that incentivize or mandate reduced water use, ultimately increases the energy and carbon footprints of irrigated agriculture.

Linking fecal bacteria in rivers to landscape, geochemical, and hydrologic factors and sources at the basin scale.

Linking fecal indicator bacteria concentrations in large mixed-use watersheds back to diffuse human sources, such as septic systems, has met limited success. In this study, 64 rivers that drain 84% of Michigan's Lower Peninsula were sampled under baseflow conditions for Escherichia coli, Bacteroides thetaiotaomicron (a human source-tracking marker), landscape characteristics, and geochemical and hydrologic variables. E. coli and B. thetaiotaomicron were routinely detected in sampled rivers and an E. coli reference level was defined (1.4 log10 most probable number⋅100 mL(-1)). Using classification and regression tree analysis and demographic estimates of wastewater treatments per watershed, septic systems seem to be the primary driver of fecal bacteria levels. In particular, watersheds with more than 1,621 septic systems exhibited significantly higher concentrations of B. thetaiotaomicron. This information is vital for evaluating water quality and health implications, determining the impacts of septic systems on watersheds, and improving management decisions for locating, constructing, and maintaining on-site wastewater treatment systems.

Regional Variations of Bovine and Porcine Fecal Pollution as a Function of Landscape, Nutrient, and Hydrological Factors.

The effects of manure application in agriculture on surface water quality has become a local to global problem because of the adverse consequences on public health and food security. This study evaluated (i) the spatial distribution of bovine (cow) and porcine (pig) genetic fecal markers, (ii) how hydrologic factors influenced these genetic markers, and (iii) their variations as a function of land use, nutrients, and other physiochemical factors. We collected 189 samples from 63 watersheds in Michigan's Lower Peninsula during baseflow, spring melt, and summer rain conditions. For each sample, we quantified the concentrations of bovine and porcine genetic markers by digital droplet polymerase chain reaction and measured , dissolved oxygen, pH, temperature, total phosphorus, total nitrogen, nitrate-nitrite (NO), ammonia (NH), soluble reactive phosphorus, streamflow, and watershed specific precipitation. Bovine and porcine manure markers were ubiquitous in rivers that drain agricultural and natural fields across the study region. This study provides baseline conditions on the state of watershed impairment, which can be used to develop best management practices that could improve water quality. Similar studies should be performed with higher spatial sampling density to elucidate detailed factors that influence the transport of manure constituents.

Agricultural implications of providing soil-based constraints on urban expansion: Land use forecasts to 2050.

Urbanization onto adjacent farmlands directly reduces the agricultural area available to meet the resource needs of a growing society. Soil conservation is a common objective in urban planning, but little focus has been placed on targeting soil value as a metric for conservation. This study assigns commodity and water storage values to the agricultural soils across all of the watersheds in Michigan's Lower Peninsula to evaluate how cities might respond to a soil conservation-based urbanization strategy. Land Transformation Model (LTM) simulations representing both traditional and soil conservation-based urbanization, are used to forecast urban area growth from 2010 to 2050 at five year intervals. The expansion of urban areas onto adjacent farmland is then evaluated to quantify the conservation effects of soil-based development. Results indicate that a soil-based protection strategy significantly conserves total farmland, especially more fertile soils within each soil type. In terms of revenue, ∼$88 million (in current dollars) would be conserved in 2050 using soil-based constraints, with the projected savings from 2011 to 2050 totaling more than $1.5 billion. Soil-based urbanization also increased urban density for each major metropolitan area. For example, there were 94,640 more acres directly adjacent to urban land by 2050 under traditional development compared to the soil-based urbanization strategy, indicating that urban sprawl was more tightly contained when including soil value as a metric to guide development. This study indicates that implementing a soil-based urbanization strategy would better satisfy future agricultural resource needs than traditional urban planning.

Mapping agricultural tile drainage in the US Midwest using explainable random forest machine learning and satellite imagery.

There has been an increase in tile drained area across the US Midwest and other regions worldwide due to agricultural expansion, intensification, and climate variability. Despite this growth, spatially explicit tile drainage maps remain scarce, which limits the accuracy of hydrologic modeling and implementation of nutrient reduction strategies. Here, we developed a machine-learning model to provide a Spatially Explicit Estimate of Tile Drainage (SEETileDrain) across the US Midwest in 2017 at a 30-m resolution. This model used 31 satellite-derived and environmental features after removing less important and highly correlated features. It was trained with 60,938 tile and non-tile ground truth points within the Google Earth Engine cloud-computing platform. We also used multiple feature importance metrics and Accumulated Local Effects to interpret the machine learning model. The results show that our model achieved good accuracy, with 96 % of points classified correctly and an F1 score of 0.90. When tile drainage area is aggregated to the county scale, it agreed well (r2 = 0.69) with the reported area from the Ag Census. We found that Land Surface Temperature (LST) along with climate- and soil-related features were the most important factors for classification. The top-ranked feature is the median summer nighttime LST, followed by median summer soil moisture percent. This study demonstrates the potential of applying satellite remote sensing to map spatially explicit agricultural tile drainage across large regions. The results should be useful for land use change monitoring and hydrologic and nutrient models, including those designed to achieve cost-effective agricultural water and nutrient management strategies. The algorithms developed here should also be applicable for other remote sensing mapping applications.

Hydraulic Conductivity Fields: Gaussian or Not?

Hydraulic conductivity (K) fields are used to parameterize groundwater flow and transport models. Numerical simulations require a detailed representation of the K field, synthesized to interpolate between available data. Several recent studies introduced high resolution K data (HRK) at the Macro Dispersion Experiment (MADE) site, and used ground-penetrating radar (GPR) to delineate the main structural features of the aquifer. This paper describes a statistical analysis of these data, and the implications for K field modeling in alluvial aquifers. Two striking observations have emerged from this analysis. The first is that a simple fractional difference filter can have a profound effect on data histograms, organizing non-Gaussian ln K data into a coherent distribution. The second is that using GPR facies allows us to reproduce the significantly non-Gaussian shape seen in real HRK data profiles, using a simulated Gaussian ln K field in each facies. This illuminates a current controversy in the literature, between those who favor Gaussian ln K models, and those who observe non-Gaussian ln K fields. Both camps are correct, but at different scales.

Solar array placement, electricity generation, and cropland displacement across California's Central Valley.

Understanding agriculturally co-located solar photovoltaic (PV) installation capacity, practices, and preferences is imperative to foster a future where solar power and agriculture co-exist with limited impacts on food production. Crops and PV panels are often co-located as they have similar ideal conditions for maximum yield. The recent boom in solar photovoltaics is displacing a significant amount of cropland. The literature on agriculturally co-located PV array installations lacks important spatiotemporal details that could help inform future array installations and improve associated policies and incentive programs. This study used imagery from the National Agriculture Imagery Program for object-based analysis (within eCognition Developer), and from Landsat 5 TM, 7 ETM+ and 8 OLI for temporal analysis (using LandTrendr) to identify and characterize non-residential ground-mounted PV arrays in California's Central Valley installed between 2008 and 2018. This dataset includes over 210,000 individually identified panels grouped by mount and installation year into 1006 PV arrays (69% are agriculturally co-located). The most common type of mounting system is fixed-axis, and individual co-located systems tend to be small (0.34 MW). There were fewer single-axis tracking arrays, although the average capacity per system is nearly four times higher (1.20 MW). In total, the mapped arrays accounted for 3.6 GW of capacity and generated a cumulative of 32,700 GWh within the Central Valley during the study period. For the 694 identified agriculturally co-located arrays (2.1 GW), significantly sub-optimal installation practices were observed in the spacing and spatial field placement of the arrays. In terms of crop conversion preferences, commodity crops (pastureland) dominated the total cumulative area converted although specialty crops (orchards) also contributed to a large number of solar installations on cropland. These results provide important details of current PV placement practices; understanding these can help to inform future practices and guide future regulations that might promote solar installations while preserving agricultural production.

Detangling Seasonal Relationships of Fecal Contamination Sources and Correlates with Indicators in Michigan Watersheds.

Despite the widely acknowledged public health impacts of surface water fecal contamination, there is limited understanding of seasonal effects on (i) fate and transport processes and (ii) the mechanisms by which they contribute to water quality impairment. Quantifying relationships between land use, chemical parameters, and fecal bacterial concentrations in watersheds can help guide the monitoring and control of microbial water quality and explain seasonal differences. The goals of this study were to (i) identify seasonal differences in Escherichia coli and Bacteroides thetaiotaomicron concentrations, (ii) evaluate environmental drivers influencing microbial contamination during baseflow, snowmelt, and summer rain seasons, and (iii) relate seasonal changes in B. thetaiotaomicron to anticipated gastrointestinal infection risks. Water chemistry data collected during three hydroclimatic seasons from 64 Michigan watersheds were analyzed using seasonal linear regression models with candidate variables including crop and land use proportions, prior precipitation, chemical parameters, and variables related to both wastewater treatment and septic usage. Adaptive least absolute shrinkage and selection operator (LASSO) linear regression with bootstrapping was used to select explanatory variables and estimate coefficients. Regardless of season, wastewater treatment plant and septic system usage were consistently selected in all primary models for B. thetaiotaomicron and E. coli. Chemistry and precipitation-related variable selection depended upon season and organism. These results suggest a link between human pollution (e.g., septic systems) and microbial water quality that is dependent on flow regime. IMPORTANCE In this study, a data set of 64 Michigan watersheds was utilized to gain insights into fecal contamination sources, drivers, and chemical correlates across seasons for general E. coli and human-specific fecal indicators. Results reaffirmed a link between human-specific sources (e.g., septic systems) and microbial water quality. While the importance of human sources of fecal contamination and fate and transport variables (e.g., precipitation) remain important across seasons, this study provides evidence that fate and transport mechanisms vary with seasonal hydrologic condition and microorganism source. This study contributes to a body of research that informs prioritization of fecal contamination source control and surveillance strategy development to reduce the public health burden of surface water fecal contamination.

Snowpacks decrease and streamflows shift across the eastern US as winters warm.

Climate change is increasing winter temperatures across the planet, altering snowmelt hydrology. This study addresses a gap in snow research in non-alpine areas by examining changes to snow and winter and spring streamflow across most of the eastern US using daily observations from weather stations and stream gages from water years 1960-2019. These daily data were aggregated across drainage basins and classified winters with similar temperatures; differences between winters and both seasonal and annual trends were statistically quantified. Winters were classified as "warm" or "cool" in each drainage basin relative to the 60-year mean; analysis of the data indicates that warm winters occur more frequently in recent decades from an average of 0.39 to 3.96 warm winters/decade from the 1960's to the 2010's respectively. Those classifications were then used to examine changes in snowpack over the same period, which shows that warmer winters have on average 50.1 cm less annual snowfall, a reduced maximum snowpack depth by 14.4 cm, and 34 more bare ground days. These changes correlate with shifts to higher winter streamflows as well as peak basin yields that are 0.02 cm lower and occur three days earlier in warm winters. In addition to altered soil moisture and stream ecosystem dynamics, these snow and streamflow changes may have negative infrastructure and economic implications including impacts to winter tourism and agriculture.

Modeling phosphorus sources and transport in a headwater catchment with rapid agricultural expansion.

Increasing riverine phosphorus (P) levels in headwaters due to expanded and intensified human activities are worldwide concerns, because P is a well-known limiting nutrient for freshwater eutrophication. Here we adopt the conceptual framework of the SPAtially Referenced Regressions On Watershed attributes (SPARROW) model to describe total phosphorus (TP) sources and transport in a headwater watershed undergoing rapid agricultural expansion in the upper Taihu Lake Basin, China. Our models, which include variables for land cover, river length, runoff depth, and pond density, explain 94% of the spatio-temporal variability in TP loads. Agricultural lands contribute the largest percentage (61%) of the TP loads delivered downstream, followed by forestland (21%) and urban land (18%). Future agricultural expansion to 15% of the total basin area is possible, which could lead to a 50% increase in TP loads. According to our analysis, an average of 24% of the total P export from the watershed landscape was intercepted in ponds. The exported amount was subsequently retained by tributaries and along the mainstem river, accounting for 14% and 43% of their inflowing loads, respectively. The remaining ∼6 tons yr-1 of TP was eventually transported into Tianmu Lake, in Southeastern China. The model identified several sub-catchments as hotspots of TP loss and thus logical sites for targeted management. Our study underscores the significance of agricultural expansion as a factor that can exacerbate headwater TP pollution, highlighting the importance of landscapes to buffer TP losses from sensitive hilly catchments. This also points to a need for an integrated management strategy that considers the spatial-varying P sources and associated transport of TP in precious headwater resources.

Nitrogen transport and retention in a headwater catchment with dense distributions of lowland ponds.

The existence of lowland ponds alter watershed nitrogen (N) cycles via combined changes in runoff and N processing potential, which can significantly buffer watershed N transport. Here, we adopt the conceptual framework of the SPAtially Referenced Regressions On Watershed attributes (SPARROW) model to describe N transport and explore the buffering roles of lowland ponds in a small headwater watershed of Taihu Lake Basin, China. Our model, which included variables for nutrient sources, riverine length, precipitation and pond density, explained 95% of the spatio-temporal variability in total N loads. Results indicated that the northern parts of this watershed were hotspot regions, which contributed relatively large N yields. While their contributions have high temporal variations, they depend upon local precipitation rates. The model results also revealed important processes of landscape N retention. On average, approximately 87% of terrestrial N inputs were removed via denitrification, plant uptake, and other processes or retained in the subsurface during land-to-water delivery. This amount can be further differentiated into 12% retained by lowland ponds and the remaining 75% associated with other landscapes including nutrient storage in soils and groundwater, as a legacy of historical inputs. By contrast, in-stream retention processes only removed 3% of the total terrestrial N inputs. In the future, riverine N pollution will likely be exacerbated by releases from legacy storage and intensified human activities, especially as climate change is expected to enhance extreme rainfall conditions. An integrated N management strategy that appropriately considers the buffering roles of lowland ponds and other landscapes, is required to optimize N fertilizer inputs and protect precious headwater resources.

The land-use legacy effect: Towards a mechanistic understanding of time-lagged water quality responses to land use/cover.

Numerous studies have linked land use/land cover (LULC) to aquatic ecosystem responses, however only a few have included the dynamics of changing LULC in their analysis. In this study, we explicitly recognize changing LULC by linking mechanistic groundwater flow and travel time models to a historical time series of LULC, creating a land-use legacy map. We then illustrate the utility of legacy maps to explore relationships between dynamic LULC and lake water chemistry. We tested two main concepts about mechanisms linking LULC and lake water chemistry: groundwater pathways are an important mechanism driving legacy effects; and, LULC over multiple spatial scales is more closely related to lake chemistry than LULC over a single spatial scale. We applied statistical models to twelve water chemistry variables, ranging from nutrients to relatively conservative ions, to better understand the roles of biogeochemical reactivity and solubility on connections between LULC and aquatic ecosystem response. Our study illustrates how different areas can have long groundwater pathways that represent different LULC than what can be seen on the landscape today. These groundwater pathways delay the arrival of nutrients and other water quality constituents, thus creating a legacy of historic land uses that eventually reaches surface water. We find that: 1) several water chemistry variables are best fit by legacy LULC while others have a stronger link to current LULC, and 2) single spatial scales of LULC analysis performed worse for most variables. Our novel combination of temporal and spatial scales was the best overall model fit for most variables, including SRP where this model explained 54% of the variation. We show that it is important to explicitly account for temporal and spatial context when linking LULC to ecosystem response.

Water Level Declines in the High Plains Aquifer: Predevelopment to Resource Senescence.

A large imbalance between recharge and water withdrawal has caused vital regions of the High Plains Aquifer (HPA) to experience significant declines in storage. A new predevelopment map coupled with a synthesis of annual water levels demonstrates that aquifer storage has declined by approximately 410 km(3) since the 1930s, a 15% larger decline than previous estimates. If current rates of decline continue, much of the Southern High Plains and parts of the Central High Plains will have insufficient water for irrigation within the next 20 to 30 years, whereas most of the Northern High Plains will experience little change in storage. In the western parts of the Central and northern part of the Southern High Plains, saturated thickness has locally declined by more than 50%, and is currently declining at rates of 10% to 20% of initial thickness per decade. The most agriculturally productive portions of the High Plains will not support irrigated production within a matter of decades without significant changes in management.

Complex water management in modern agriculture: Trends in the water-energy-food nexus over the High Plains Aquifer.

In modern agriculture, the interplay between complex physical, agricultural, and socioeconomic water use drivers must be fully understood to successfully manage water supplies on extended timescales. This is particularly evident across large portions of the High Plains Aquifer where groundwater levels have declined at unsustainable rates despite improvements in both the efficiency of water use and water productivity in agricultural practices. Improved technology and land use practices have not mitigated groundwater level declines, thus water management strategies must adapt accordingly or risk further resource loss. In this study, we analyze the water-energy-food nexus over the High Plains Aquifer as a framework to isolate the major drivers that have shaped the history, and will direct the future, of water use in modern agriculture. Based on this analysis, we conclude that future water management strategies can benefit from: (1) prioritizing farmer profit to encourage decision-making that aligns with strategic objectives, (2) management of water as both an input into the water-energy-food nexus and a key incentive for farmers, (3) adaptive frameworks that allow for short-term objectives within long-term goals, (4) innovative strategies that fit within restrictive political frameworks, (5) reduced production risks to aid farmer decision-making, and (6) increasing the political desire to conserve valuable water resources. This research sets the foundation to address water management as a function of complex decision-making trends linked to the water-energy-food nexus. Water management strategy recommendations are made based on the objective of balancing farmer profit and conserving water resources to ensure future agricultural production.

Heterogeneity of chlorinated hydrocarbon sorption properties in a sandy aquifer.

Hydraulic conductivity and sorption coefficients for chlorinated hydrocarbons (chloroform, carbon tetrachloride and tetrachloroethylene) were evaluated for 216 sediment samples collected across a 15 m transect and a 21 m depth interval in a contaminated aquifer near Schoolcraft, Michigan. Relationships between hydraulic conductivity, linear sorption partition coefficients, grain size classes, and spatial location were investigated using linear regression analysis and geostatistical techniques. Clear evidence of layering was found in sorption properties, hydraulic conductivity and grain sizes. Conductivity correlated well with grain size, as expected, but sorption varied inversely with grain size, contrary to some previous reports. No significant correlation was found between sorption properties and hydraulic conductivity. This is likely due to the unexpected presence of small amounts of highly sorptive coal-like solids, which dominate the sorption behavior but have little effect on conductivity. The results demonstrate that recent findings regarding the high sorption capacity of coal materials found in soils can exert a controlling influence on contaminant transport. Designers of in situ remediation systems should be cautioned that 1) it is not reasonable to assume that sorption capacity and hydraulic conductivity are related, 2) sorption capacity and hydraulic conductivity are critical measurements for contaminant site characterization and subsequent transport modeling, 3) estimating sorption capacity from organic carbon measurement may lead to greater errors than performing sorption isotherms, and 4) it is more important to characterize vertical heterogeneity rather than horizontal heterogeneity because both sorption and hydraulic conductivity are correlated across longer distances in the horizontal plane.

Analysis of recharge-induced geochemical change in a contaminated aquifer.

Recharge events that deliver electron acceptors such as O2, NO3, SO4, and Fe3+ to anaerobic, contaminated aquifers are likely important for natural attenuation processes. However, the specific influence of recharge on (bio)geochemical processes in ground water systems is not well understood. The impact of a moderate-sized recharge event on ground water chemistry was evaluated at a shallow, sandy aquifer contaminated with waste fuels and chlorinated solvents. Multivariate statistical analyses coupled with three-dimensional visualization were used to analyze ground water chemistry data (including redox indicators, major ions, and physical parameters) to reveal associations between chemical parameters and to infer processes within the ground water plume. Factor analysis indicated that dominant chemical associations and their interpreted processes (anaerobic and aerobic microbial processes, mineral precipitation/dissolution, and temperature effects) did not change significantly after the spring recharge event of 2000. However, the relative importance of each of these processes within the plume changed. After the recharge event, the overall importance of aerobic processes increased from the fourth to the second most important factor, representing the variability within the data set. The anaerobic signatures became more complex, suggesting that zones with multiple terminal electron-accepting processes (TEAPs) likely occur in the same water mass. Three-dimensional visualization of well clusters showed that water samples with similar chemical associations occurred in distinct water masses within the aquifer. Water mass distinctions were not based on dominant TEAPs, suggesting that the recharge effects on TEAPs occurred primarily at the interface between infiltrating recharge water and the aquifer.

Can Impacts of Climate Change and Agricultural Adaptation Strategies Be Accurately Quantified if Crop Models Are Annually Re-Initialized?

Estimates of climate change impacts on global food production are generally based on statistical or process-based models. Process-based models can provide robust predictions of agricultural yield responses to changing climate and management. However, applications of these models often suffer from bias due to the common practice of re-initializing soil conditions to the same state for each year of the forecast period. If simulations neglect to include year-to-year changes in initial soil conditions and water content related to agronomic management, adaptation and mitigation strategies designed to maintain stable yields under climate change cannot be properly evaluated. We apply a process-based crop system model that avoids re-initialization bias to demonstrate the importance of simulating both year-to-year and cumulative changes in pre-season soil carbon, nutrient, and water availability. Results are contrasted with simulations using annual re-initialization, and differences are striking. We then demonstrate the potential for the most likely adaptation strategy to offset climate change impacts on yields using continuous simulations through the end of the 21st century. Simulations that annually re-initialize pre-season soil carbon and water contents introduce an inappropriate yield bias that obscures the potential for agricultural management to ameliorate the deleterious effects of rising temperatures and greater rainfall variability.

Identifying relationships between baseflow geochemistry and land use with synoptic sampling and R-mode factor analysis.

The relationship between land use and stream chemistry is often explored through synoptic sampling of rivers at baseflow conditions. However, baseflow chemistry is likely to vary temporally and spatially with land use. The purpose of our study is to examine the usefulness of the synoptic sampling approach for identifying the relationship between complex land use configurations and stream water quality. This study compares biogeochemical data from three synoptic sampling events representing the temporal variability of baseflow chemistry and land use using R-mode factor analysis. Separate R-mode factor analyses of the data from individual sampling events yielded only two consistent factors. Agricultural activity was associated with elevated levels of Ca2+, Mg2+, alkalinity, and frequently K+, SO4(2-), and NO3-. Urban areas were associated with higher concentrations of Na+, K+, and Cl-. Other retained factors were not consistent among sampling events, and some factors were difficult to interpret in the context of biogeochemical sources and processes. When all data were combined, further associations were revealed such as an inverse relationship between the proportion of wetlands and stream nitrate concentrations. We also found that barren lands were associated with elevated sulfate levels. This research suggests that an individual sampling event is unlikely to characterize adequately the complex processes controlling interactions between land use and stream chemistry. Combining data collected over two years during three synoptic sampling events appears to enhance our ability to understand processes linking stream chemistry and land use.