Estimating Groundwater Pumping for Irrigation: A Method Comparison.

Effective groundwater management is critical to future environmental, ecological, and social sustainability and requires accurate estimates of groundwater withdrawals. Unfortunately, these estimates are not readily available in most areas due to physical, regulatory, and social challenges. Here, we compare four different approaches for estimating groundwater withdrawals for agricultural irrigation. We apply these methods in a groundwater-irrigated region in the state of Kansas, USA, where high-quality groundwater withdrawal data are available for evaluation. The four methods represent a broad spectrum of approaches: (1) the hydrologically-based Water Table Fluctuation method (WTFM); (2) the demand-based SALUS crop model; (3) estimates based on satellite-derived evapotranspiration (ET) data from OpenET; and (4) a landscape hydrology model which integrates hydrologic- and demand-based approaches. The applicability of each approach varies based on data availability, spatial and temporal resolution, and accuracy of predictions. In general, our results indicate that all approaches reasonably estimate groundwater withdrawals in our region, however, the type and amount of data required for accurate estimates and the computational requirements vary among approaches. For example, WTFM requires accurate groundwater levels, specific yield, and recharge data, whereas the SALUS crop model requires adequate information about crop type, land use, and weather. This variability highlights the difficulty in identifying what data, and how much, are necessary for a reasonable groundwater withdrawal estimate, and suggests that data availability should drive the choice of approach. Overall, our findings will help practitioners evaluate the strengths and weaknesses of different approaches and select the appropriate approach for their application.

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.

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.

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.

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.

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.

Outsized nutrient contributions from small tributaries to a Great Lake.

Excessive nitrogen (N) and phosphorus (P) loading is one of the greatest threats to aquatic ecosystems in the Anthropocene, causing eutrophication of rivers, lakes, and marine coastlines worldwide. For lakes across the United States, eutrophication is driven largely by nonpoint nutrient sources from tributaries that drain surrounding watersheds. Decades of monitoring and regulatory efforts have paid little attention to small tributaries of large water bodies, despite their ubiquity and potential local importance. We used a snapshot of nutrient inputs from nearly all tributaries of Lake Michigan-the world's fifth largest freshwater lake by volume-to determine how land cover and dams alter nutrient inputs across watershed sizes. Loads, concentrations, stoichiometry (N:P), and bioavailability (percentage dissolved inorganic nutrients) varied by orders of magnitude among tributaries, creating a mosaic of coastal nutrient inputs. The 6 largest of 235 tributaries accounted for ∼70% of the daily N and P delivered to Lake Michigan. However, small tributaries exhibited nutrient loads that were high for their size and biased toward dissolved inorganic forms. Higher bioavailability of nutrients from small watersheds suggests greater potential to fuel algal blooms in coastal areas, especially given the likelihood that their plumes become trapped and then overlap in the nearshore zone. Our findings reveal an underappreciated role that small streams may play in driving coastal eutrophication in large water bodies. Although they represent only a modest proportion of lake-wide loads, expanding nutrient management efforts to address smaller watersheds could reduce the ecological impacts of nutrient loading on valuable nearshore ecosystems.

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.

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.

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.

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.

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.

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.

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.