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The impact of saltwater intrusion on coastal forests and farmland is typically understood as sea-level-driven inundation of a static terrestrial landscape, where ecosystems neither adapt to nor influence saltwater intrusion. Yet recent observations of tree mortality and reduced crop yields have inspired new process-based research into the hydrologic, geomorphic, biotic, and anthropogenic mechanisms involved. We review several negative feedbacks that help stabilize ecosystems in the early stages of salinity stress (e.g., reduced water use and resource competition in surviving trees, soil accretion, and farmland management). However, processes that reduce salinity are often accompanied by increases in hypoxia and other changes that may amplify saltwater intrusion and vegetation shifts after a threshold is exceeded (e.g., subsidence following tree root mortality). This conceptual framework helps explain observed rates of vegetation change that are less than predicted for a static landscape while recognizing the inevitability of large-scale change. 

Saltwater intrusion on coastal farmlands can render productive land unsuitable for agricultural activities. While the visible extent of salt-impacted land provides a useful saltwater intrusion proxy, it is challenging to identify in early stages. Moreover, associated ecological and economic impacts are often underestimated as reduced crop yields in farmlands surrounding salt patches are difficult to quantify. Here we develop a high-resolution (1 m) dataset showing salt patches on farm fringes and quantify the extent of salt-impacted lands across the Delmarva Peninsula, United States. Our method is transferable to other regions across and beyond the mid-Atlantic with similar saltwater intrusion issues, such as Georgia and the Carolinas. Our results show that between 2011 and 2017, visible salt patches almost doubled and 8,096 ha of farmlands converted to marsh—another saltwater intrusion consequence. Field-based electrical conductivity measurements show elevated salinity values hundreds of metres from visible salt patches, indicating the broader extent of at-risk farmlands. More farmland areas were within 200 m of a visible salt patch in 2017 compared to 2011, a rise ranging between 68% in Delaware and 93% in Maryland. On the basis of assumed 100% profit loss in at-risk farmlands within a 200 m buffer around salt patches in 2016–2017, the range of economic losses was estimated between US$39.4 million and US$107.5 million annually, under 100% soy or corn counterfactuals, respectively. 

Abstract Interpreting time domain reflectometry (TDR) waveforms obtained in soils with non‐uniform water content is an open question. We design a new TDR waveform interpretation model based on convolutional neural networks (CNNs) that can reveal the spatial variations of soil relative permittivity and water content along a TDR sensor. The proposed model, namely TDR‐CNN, is constructed with three modules. First, the geometrical features of the TDR waveforms are extracted with a simplified version of VGG16 network. Second, the reflection positions in a TDR waveform are traced using a 1D version of the region proposal network. Finally, the soil relative permittivity values are estimated via a CNN regression network. The three modules are developed in Python using Google TensorFlow and Keras API, and then stacked together to formulate the TDR‐CNN architecture. Each module is trained separately, and data transfer among the modules can be facilitated automatically. TDR‐CNN is evaluated using simulated TDR waveforms with varying relative permittivity but under a relatively stable soil electrical conductivity, and the accuracy and stability of the TDR‐CNN are shown. TDR measurements from a water infiltration study provide an application for TDR‐CNN and a comparison between TDR‐CNN and an inverse model. The proposed TDR‐CNN model is simple to implement, and modules in TDR‐CNN can be updated or fine‐tuned individually with new data sets. In conclusion, TDR‐CNN presents a model architecture that can be used to interpret TDR waveforms obtained in soil with a heterogeneous water content distribution. 

Abstract Fertilized temperate croplands export large amounts of reactive nitrogen (N), which degrades water and air quality and contributes to climate change. Fertilizer use is poised to increase in the tropics, where widespread food insecurity persists and increased agricultural productivity will be needed, but much less is known about the potential consequences of increased tropical N fertilizer application. We conducted a meta‐analysis of tropical field studies of nitrate leaching, nitrous oxide emissions, nitric oxide emissions, and ammonia volatilization totaling more than 1,000 observations. We found that the relationship between N inputs and losses differed little between temperate and tropical croplands, although total nitric oxide losses were higher in the tropics. Among the potential drivers we studied, the N input rate controlled all N losses, but soil texture and water inputs also controlled hydrological N losses. Irrigated systems had significantly higher losses of ammonia, and pasture agroecosystems had higher nitric oxide losses. Tripling of fertilizer N inputs to tropical croplands from 50 to 150 kg N ha⁻¹ year⁻¹would have substantial environmental implications and would lead to increases in nitrate leaching (+30%), nitrous oxide emissions (+30%), nitric oxide (+66%) emissions, and ammonia volatilization (+74%), bringing tropical agricultural nitrate, nitrous oxide, and ammonia losses in line with temperate losses and raising nitric oxide losses above them.