Significance: Economists have long argued, with recent acceptance from the science and policy community, that natural resources are capital assets. Pricing of natural capital has remained elusive, with the result that its value is often ignored, and expenditures on conservation are treated as costs rather than investments. This neglect stems from a lack of a valuation framework to enable apples to apples comparisons with traditional forms of capital. We develop such an approach and demonstrate it on Kansas’ groundwater stock. Between 1996 and 2005, groundwater withdrawal reduced Kansas’ wealth approximately $110 million per year. Wealth lost through groundwater depletion in Kansas is large, but in a range where offsetting investments may be feasible.

During early 2019, a series of events set the stage for devastating floods in eastern Nebraska, western Iowa, and southeastern South Dakota. When the floodwaters hit, dams and levees failed, cutting off towns while destroying roads, bridges, and rail lines, further exacerbating the crisis. Lives were lost and thousands of cattle were stranded. Estimates indicate that the cost of the flooding has topped $3 billion as of August 2019, with this number expected to rise. After a warm and wet start to winter, eastern Nebraska, western Iowa, and southeastern South Dakota endured anomalously low temperatures and record-breaking snowfall. By March 2019, rivers were frozen, frost depths were 60–90 cm, and the water equivalent of the snowpack was 30–100 mm. With these conditions in place, a record-breaking surface cyclone rapidly developed in Colorado and moved eastward, producing heavy rain toward the east and blizzard conditions toward the west. In areas of eastern Nebraska, western Iowa, and southeastern South Dakota, rapid melting of the snowpack due to this rain-on-snow event quickly led to excessive runoff that overwhelmed rivers and streams. These conditions brought the region to a standstill. In this paper, we provide an analysis of the antecedent conditions in eastern Nebraska, western Iowa, and southeastern South Dakota and the development of the surface cyclone that triggered the historic flooding, along with a look into the forecast and communication of flood impacts prior to the flood. The study used multiple datasets, including in situ observations and reanalysis data. Understanding the events that led to the flooding could aid in future forecasting efforts.

This study combines empirical predictive and economics models to estimate the cost of remediation for domestic wells exceeding suggested treatment thresholds for nitrates. A multiple logistic regression model predicted the probability of well contamination by nitrate, and a life cycle costing methodology was used to estimate costs of nitrate contamination in groundwater in two areas of Nebraska. In south-central Nebraska, 37% of wells were estimated to be at risk of exceeding a threshold of 7.5 mg/L as N, and 17% were at risk of exceeding 10 mg/L as N, the legal limit for human consumption in the United States. In an area in northeastern Nebraska, 82% of wells were at risk of exceeding the 10 mg/L as N legal threshold. Reverse osmosis Point-of-Use (POU) treatment was the option with the lowest costs for a household (3–4 individuals), with an average of $4–$164 total regional cost per household per year depending on the threshold for treatment. Ion exchange and distillation were the next most cost-effective options. At the community level (~10,000 individuals), a reverse osmosis Point-of-Entry (POE) treatment system was the most expensive option for a community due to high initial costs and ongoing operation and maintenance costs, whereas the biological denitrification system was least expensive due to economies of scale. This study demonstrates integrated modeling methods to assess water treatment costs over time associated with groundwater nitrate contamination, including quantification of at-risk wells, and identifies suitable options for treatment systems for rural households and communities based on their cost.

Groundwater levels across parts of western Kansas have been declining at unsustainable rates due to pumping for agricultural irrigation despite water‐saving efforts. Accelerating this decline is the complex agricultural landscape, consisting of both categorical (e.g., management boundaries) and numerical (e.g., crop prices) factors that drive irrigation decisions, making integrated water budget management a challenge. Furthermore, these factors frequently change through time, rendering management strategies outdated within relatively short time scales. This study uses boosted regression trees to simultaneously analyze categorical and numerical data against annual irrigation pumping to determine the relative influence of each factor on groundwater pumping across both space and time. In all, 45 key water use variables covering approximately 19,000 groundwater wells were tested against irrigation pumping from 2006 to 2016 across five categories: (1) management/policy, (2) hydrology, (3) weather, (4) land/agriculture, and (5) economics. Study results showed that variables from all five categories were included among the top 10 drivers to irrigation, and the greatest influence came from variables such as irrigated area per well, saturated thickness, soil permeability, summer precipitation, and pumping costs (depth to water table). Variables that had little influence included regional management boundaries and irrigation technology. The results of this study are further used to target the factors that statistically lead to the greatest volumes of groundwater pumping to help develop robust management strategy suggestions and achieve water management goals of the region.