Ty Paul Andrew Ferre

Film-mulched drip irrigation (FMDI) is used increasingly for cotton (Gossypium hirsutum L.) production in arid regions. However, salts often accumulate in the soil, eventually leading to soil salinization and crop failure when using FMDI with brackish water inappropriately. Evaluation of the leaching requirement (LR) for cotton under FMDI with brackish water that comprehensively considers cotton yield, water saving, soil conditions, and economic benefits needs to be investigated more completely. The present study compared the cotton growth for different leaching fractions (LF) under FMDI with brackish water and provides comprehensive analysis of LR for cotton and its relationships with soil conditions. A higher LF was related to a lower cotton yield when the LF was larger than 0.15. Moreover, a larger LF led to a lower ratio of reproductive growth and irrigation water productivity when the LF was larger than 0.2. A high soil water content (SWC) strip was observed in the 40–60 cm soil layers for all scenarios. Moreover, a higher SWC proportion in the deeper soil layers as for LF_0.15 or LF_0.2 may also be beneficial to cotton growth. Soil salinity decreased with decreases in irrigation water quantity when the LF was lower than 0.2, but increased when the LF was higher than 0.2. Either too much or too little irrigation water was not beneficial from an economic perspective. Our study indicated that the LR values between 0.05 and 0.15 were recommended for FMDI when the total dissolved solids for brackish water is within 1.61–3.21 g L⁻¹. Integrated strategies, including optimized irrigation-fertilizer management, groundwater depth monitoring, and halophyte intercropping, are required to sustain production while mitigating secondary salinization and groundwater pollution under FMDI with brackish water.

This study leverages a Random Forest model to predict flood hazard in Arizona, New Mexico, Colorado, and Utah, focusing on enhancing sustainability in flood management. Utilizing the National Flood Hazard Layer (NFHL), an intricate flood map of Arizona was generated, with the Random Forest Classification algorithm assessing flood hazard for each grid cell. Weather variable predictions from TerraClimate were integrated with NFHL classifications and Digital Elevation Model (DEM) analyses, providing a comprehensive understanding of flood dynamics. The research highlights the model’s capability to predict flood hazard in areas lacking NFHL classifications, thereby supporting sustainable flood management by elucidating weather’s influence on flood hazard. This approach aligns with sustainable development goals by aiding in resilient infrastructure design and informed urban planning, reducing the impact of floods on communities. Despite recognizing constraints such as input data precision and the model’s potential limitations in capturing complex variable interactions, the methodology offers a robust framework for flood hazard evaluation in other regions. Integrating diverse data sources, this study presents a valuable tool for decision-makers, supporting sustainable practices, and enhancing the resilience of vulnerable regions against flood hazards. This integrated approach underscores the potential of advanced modeling techniques in promoting sustainability in environmental hazard management.

Temporal drain flow dynamics and understanding of their underlying controlling factors are important for water resource management in tile-drained agricultural areas. The use of physics-based water flow models to understand tile drained systems is common. These models are complex, with large parameter sets and require high computational effort. The primary goal of this study was to examine whether simpler, more efficient machine learning (ML) models can provide acceptable solutions. The specific aim of our study was to assess the potential of ML tools for predicting drain flow time series in multiple catchments subject to a range of climatic and landscape conditions. The investigation is based on unique data containing time series of daily drain flow in multiple field scale drain sites in Denmark. The data include: climate (precipitation, potential evapotranspiration, temperature); geological properties (clay fraction, first sand layer thickness, first clay layer thickness); and topographical indexes (curvature, Topographical wetness indexes, Topographical position index, elevation). Both static and dynamic variables are used in the prediction of drain flows. The ML algorithm extreme gradient boosting (XGBoost) and convolutional neural network (CNN) were examined, and the results were compared with a physics-based distributed model (MIKE-SHE). The results show that XGBoost performs similarly to the physics-based MIKE-SHE models, and both outperform CNN. Both ML models required significantly less effort to build, train, and run than MIKE-SHE. In addition, the ML models support efficient feature importance analysis. This showed that climatic variables were important for CNN models and XGBoost. The results support the use of ML models for hydrologic applications with sufficient data for training. Further, the insights offered by the feature importance analysis may support further data collection and developments of physics-based models when existing data are insufficient to support ML approaches.

The freeze–thaw process controls several hydrologic processes, including infiltration, runoff, and soil erosion. Simulating this process is important, particularly in cold and mountainous regions. The Soil and Cold Regions Model (SCRM) was used to simulate, study, and understand the behavior of twelve homogenous soils subject to a freeze–thaw process, based on meteorological data at a snow-dominated forest site in Laramie, WY, USA, from 2010 and 2012. The relationships of soil pore size, soil particle contact, and meteorological data were varied. Our analysis of the model compared simulations using metrics such as soil frost depth, days with ice, and maximum ice content. The model showed that the freeze–thaw process was strongest in the period with a shallow snowpack, with particle packing within the soil profile being an important factor in this process; that soil texture and water content control soil thermal properties; and that water movement towards the freezing front was especially important in fine-textured soils, where water and ice were concentrated in the upper layers. Based on these results, future research that combines a broader set of soil conditions with an extended set of field meteorology and real soil data could elucidate the influence of soil texture on the thermal properties related to soil frost.

Anisotropic fast-marching algorithms are computationally efficient tools for generating realistic maps of karst conduit networks, constrained by both the spatial extent and the orientation of karstifiable geologic units. Existing models to generate conduit network maps are limited either by high computational requirements (for chemistry-based models) or by their inability to incorporate the effects of elevation and orientation gradients (for isotropic fast-marching models). The new anisotropic fast-marching approach described here provides a significant improvement, though it imitates rather than reproduces actual speleogenetic processes. It can rapidly generate a stochastic ensemble of plausible networks from basic geologic information, which can also be used as input to karst-appropriate flow models. This paper introduces an open-source, easy-to-use implementation through the Python package pyKasso, then describes its application to a well-mapped geologically complex long-term study site: the Gottesacker alpine karst system (Germany/Austria). Groundwater flow in this system is exceptionally well understood from speleological investigations and tracer tests. Conduit formation primarily occurs at the base of the karst aquifer, following plunging synclines. Although previous attempts to reproduce the conduit network at this site yielded implausible network maps, pyKasso quickly generated networks faithful to the known conduit system. However, the model was only able to generate these realistic networks when the inlet-outlet connections of the system were correctly assigned, highlighting the importance of pairing modeling efforts with field tracer tests. Therefore, a model ensemble method is also presented, to optimize field efforts by identifying the most informative tracer tests to perform.

We examine the ability of machine learning (ML) and deep learning (DL) algorithms to infer surface/ground exchange flux based on subsurface temperature observations. The observations and fluxes are produced from a high-resolution numerical model representing conditions in the Columbia River near the Department of Energy Hanford site located in southeastern Washington State. Random measurement error, of varying magnitude, is added to the synthetic temperature observations. The results indicate that both ML and DL methods can be used to infer the surface/ground exchange flux. DL methods, especially convolutional neural networks, outperform the ML methods when used to interpret noisy temperature data with a smoothing filter applied. However, the ML methods also performed well and they are can better identify a reduced number of important observations, which could be useful for measurement network optimization. Surprisingly, the ML and DL methods better inferred upward flux than downward flux. This is in direct contrast to previous findings using numerical models to infer flux from temperature observations and it may suggest that combined use of ML or DL inference with numerical inference could improve flux estimation beneath river systems.

Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.