Ongoing and potential future changes in precipitation will affect water management infrastructure. Urban drainage systems are particularly vulnerable. Design standards for many stormwater practices rely on design storms based on precipitation intensity-duration-frequency (IDF) curves. In many locations, climate projections suggest relatively small changes in total precipitation volume, but increased magnitude of extreme events. We develop an approach for estimating future IDF curves that is efficient, can use widely available downscaled GCM output, and is consistent with published IDF curves for the USA that are used in local stormwater regulations and design guides. The method is GCM-agnostic and provides a relatively simple way to develop scenarios in a format directly useful to assessing risk to stormwater management infrastructure. Model biases are addressed through equidistant quantile mapping, in which the modeled change in both the location and scale of the cumulative distribution of storm events from historical to future conditions is used to adjust the extreme value fit used for IDF curve development. The approach requires only precipitation annual maxima, is readily automated, and hits a mid-point between theoretical rigor and ease of application that will be of practical use for the rapid screening of vulnerabilities across projections. We demonstrate estimation of future IDF curves at locations throughout the USA and link IDF-derived design storms to a rainfall-runoff model to evaluate the potential change in storage volume requirements for capture-based stormwater management practices by 2065.

Ridge–furrow planting is often applied in semi-arid regions to reduce the drought risk on crop yield under rain-fed conditions. Sunflower (Helianthus annuus L.) is widely planted in northern China and how to reduce the drought risk on sunflower production remains a significant issue. A three-year field experiment with seven treatments (a flat plot without mulching, three plastic film-mulching treatments and three non-film-mulching treatments with different ridge–furrow ratios (1.0 m:1.0 m, 1.0 m:0.5 m and 0.5 m:1.0 m)) was conducted to study the effects of the ridge–furrow rainwater harvesting system on the rain-fed sunflower. The results showed that the sunflowers in the film-mulched treatment with the larger ridge–furrow ratio (1.0 m:0.5 m) (M1R2) had greater growth advantage under drought conditions. In the dry year, M1R2 improved the yield and water use efficiency by 11.9%–107.5% and 13.8%–120.6%, respectively, and reduced the blight grain rate by 21.5%–32.5% with less evapotranspiration (ET) compared to other treatments. Based on the historical climatological data, the guarantee rate of sunflower water requirement for M1R2 was about 75%, while the guarantee rates for the other two film-mulched treatments were only about 40% and 50%. Based on the effects of drought resilience and the characteristics of precipitation, M1R2 is recommended to be the relatively optimal treatment for sunflower production in regions with similar climatic conditions to Wuchuan County in northern China.

Over the past several decades there has been an enormous proliferation of sediment models, ranging from empirical to physically-based. Most of these models aim to capture the impacts of three primary sediment processes viz. erosion, deposition, and transport. As a range of process representations exist for simulating these three processes, it is natural to wonder about their influence on estimates of suspended sediment yield from a watershed. While several studies have focused on intercomparison of sediment models, their scopes have generally been restricted to comparing the individual model performances, rather than understanding the role of process representations on sediment model output. Here, six model configurations, which span the different permutations of erosion, deposition and transport process representations being used in extant models, are considered to evaluate the role of process representations on sediment yield estimates. The numerical experiments are designed to evaluate the extent to which the sediment dynamics as modeled by a physically-based model with coupled surface-subsurface hydrologic interactions are captured by simpler models. The presented work delineates the applicability and limitations of widely used representations of sediment processes, and could help users identify the pros and cons of using a sediment model at a given temporal scale.

A warming climate increases thermal inputs to lakes with potential implications for water quality and aquatic ecosystems. In a previous study, we used a dynamic water column temperature and mixing simulation model to simulate chronic (7-day average) maximum temperatures under a range of potential future climate projections at selected sites representative of different U.S. regions. Here, to extend results to lakes where dynamic models have not been developed, we apply a novel machine learning approach that uses Gaussian Process regression to describe the model response surface as a function of simplified lake characteristics (depth, surface area, water clarity) and climate forcing (winter and summer air temperatures and potential evapotranspiration). We use this approach to extrapolate predictions from the simulation model to the statistical sample of U.S. lakes in the National Lakes Assessment (NLA) database. Results provide a national-scale scoping assessment of the potential thermal risk to lake water quality and ecosystems across the U.S. We suggest a small fraction of lakes will experience less risk of summer thermal stress events due to changes in stratification and mixing dynamics, but most will experience increases. The percentage of lakes in the NLA with simulated 7-day average maximum water temperatures in excess of 30°C is projected to increase from less than 2% to approximately 22% by the end of the 21^st century, which could significantly reduce the number of lakes that can support cold water fisheries. Site-specific analysis of the full range of factors that influence thermal profiles in individual lakes is needed to develop appropriate adaptation strategies.