Soil data recency: The foundation for harmonizing soil data across time.

Sustainable soil resource management depends on reliable soil information, often derived from 'legacy soil data' or a combination of old and new soil data. However, the task of harmonizing soil data collected at different times remains a largely unexplored in the literature. Addressing this challenge requires incorporating the temporal dimension into mathematical and statistical models for spatio-temporal soil studies. This study aimed to create a comprehensive framework for harmonizing soil data across various time. We assessed the integration of historical and recent soil data, ranging from 4 to 48 years old data, using soil data recency analysis. To achieve this, we introduced an 'age of data' attribute, calculating the time difference between soil survey years and the present (e.g., 2022). We applied three machine learning models - Decision Trees (DT), Random Forest (RF), Gradient Boosting (GBM) - to a dataset containing 6339 sites and 28,149 depth-harmonized layers. The results consistently demonstrated robust performance across models, RF outperforming with an R-squared value of 0.99, RMSE of 1.41, and a concordance of 0.97. Similarly, DT and GBM also showed strong predictive power. Terrain-derived environmental covariates played a more important role than land use and land cover (LULC) change in predicting soil data recency. While LULC change showed soil organic carbon concentration variability across the different depths, it was a less important factor. Anthropogenic factors, such as LULC change and normalized difference vegetation index (NDVI), were not primary determinants of soil data recency. Variations in soil depth had no impact on predicting soil data recency. This study validated that terrain-derived covariates, especially elevation factors, effectively explain the quality of older soil data when predicting current soil attributes using the soil data recency concept. This approach has the potential to enhance real-time estimates, such as carbon budgets, and we emphasize its importance in global earth system models.

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E).

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation.

Identifying hotspots and representative monitoring locations of field scale N2O emissions from agricultural soils: A time stability analysis.

Greenhouse gas sampling from agricultural fields is laborious and time-consuming. Soil and topographical heterogeneity cause spatiotemporal variations, making nitrous oxide (N2O) estimation and management a challenge. Identification of representative monitoring locations, hotspots, and coldspots could facilitate the mitigation of agricultural N2O emissions. The objective of this study was to identify and characterize representative monitoring locations, hotspots, and coldspots of N2O emissions in agricultural fields (Baggs farm; BF and Research North farm; RN) in Cambridge, Ontario, Canada, under humid continental climate. Soil in both fields was classified as Orthic Melanic Brunisol, with some areas categorized as Gleyed Brunisolic Gray Brown Luvisol and Orthic Humic Gleysol. In total, 28 sampling points were selected following conditional Latin hypercube design using topographical parameters (digital elevation, slope, topographical wetness index, and Pennock landform classification). Gas samples were collected over a two-year crop rotation with corn (2019) and soybean (2020). Additional sampling was conducted at BF at spring thaw (2020). Time stability analysis using mean relative difference (MRD) and standard deviation of mean relative difference (SDRD) was performed to test the hypothesis that "simultaneous analysis of spatiotemporal variations in N2O emissions could help to identify and characterize representative monitoring locations, hotspots, coldspots and areas with few hot and cold moments. Most of the hotspots were located at shoulder positions, coldspots, and cold moments at backslope, and representative monitoring points were located at leveled positions or localized depressions. Time stability analysis coupled with multivariate groping analysis supported our hypothesis and helped successfully identify hotspots, coldspots, and representative locations based on landform classification with few exceptions. However, inclusion of additional topographical (curvature, contributing area, aspect) and morphological parameters (texture, thickness of soil horizon, depth to bedrock, and water table) are suggested for consideration in future research to manage variable-rate fertilizer application and mitigate N2O hotspots at landscape level.

A comparative evaluation of the continuous and event-based modelling approaches for identifying critical source areas for sediment and phosphorus losses.

Proper identification of critical source areas (CSAs) is important for economic viability of any best management practices (BMPs) aimed at reducing sediment and phosphorus loads to receiving water bodies. Both continuous and event-based hydrologic and water quality models are widely used to identify and assess CSAs, however, their comparative assessment is lacking. In this study, we have used continuous Soil and Water Assessment Tool (SWAT) and event-based Agriculture Non-Point Source (AGNPS) pollution models to identify CSAs for sediment and phosphorus in a watershed in Ontario, Canada. Along with their original version, both models were re-conceptualized to incorporate saturation excess mechanism of runoff generation, which is also refereed as variable source area (VSA) integration. The models were set-up using high resolution spatial, crop- and land-management, and meteorological dataset; and calibrated with reasonable accuracy against streamflow, sediment and phosphorus concentration data at multiple locations. Threshold value (t-value) approach was used to identify CSA areas in the watershed. Results showed that both models were in agreement (up to 96% of fields) that summer season did not constitute hot-moments (<6% of the watershed area as CSAs) for both sediment and phosphorus. SWAT models identified winter (~50% of watershed area as CSA) and AGNPS models identified early spring (~50% of watershed areas as CSAs) season as the hot-moment for both sediment and phosphorus. Contrasting result, as indicated by low (1%) matching in field CSA potential, was observed in autumn season. In the same season, VSA integrated SWAT and AGNPS models showed better matching (43% for sediment and 31% for phosphorus), highlighting the importance of VSA integration in the models. Qualitative validation of model-based CSA potential with oblique aerial-photograph-based CSA potential in two soil moisture conditions (wetter and drier) indicated slightly better performance of the SWAT models, and over-prediction of the AGNPS models. However, a more comprehensive analysis based on more detailed field observations is needed to further confirm the results.

Projecting the effects of agricultural conservation practices on stream fish communities in a changing climate.

How anticipated climate change might affect long-term outcomes of present-day agricultural conservation practices remains a key uncertainty that could benefit water quality and biodiversity conservation planning. To explore this issue, we forecasted how the stream fish communities in the Western Lake Erie Basin (WLEB) would respond to increasing amounts of agricultural conservation practice (ACP) implementation under two IPCC future greenhouse gas emission scenarios (RCP4.5: moderate reductions; RCP8.5: business-as-usual conditions) during 2020-2065. We used output from 19 General Circulation Models to drive linked agricultural land use (APEX), watershed hydrology (SWAT), and stream fish distribution (boosted regression tree) models, subsequently analyzing how projected changes in habitat would influence fish community composition and functional trait diversity. Our models predicted both positive and negative effects of climate change and ACP implementation on WLEB stream fishes. For most species, climate and ACPs influenced species in the same direction, with climate effects outweighing those of ACP implementation. Functional trait analysis helped clarify the varied responses among species, indicating that more extreme climate change would reduce available habitat for large-bodied, cool-water species with equilibrium life-histories, many of which also are of importance to recreational fishing (e.g., northern pike, smallmouth bass). By contrast, available habitat for warm-water, benthic species with more periodic or opportunistic life-histories (e.g., northern hogsucker, greater redhorse, greenside darter) was predicted to increase. Further, ACP implementation was projected to hasten these shifts, suggesting that efforts to improve water quality could come with costs to other ecosystem services (e.g., recreational fishing opportunities). Collectively, our findings demonstrate the need to consider biological outcomes when developing strategies to mitigate water quality impairment and highlight the value of physical-biological modeling approaches to agricultural and biological conservation planning in a changing climate.

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners.

While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., Ca 2 + , Mg 2 + , Na + , K + , HCO 3 - , CO 3 2 - , SO 4 2 - , and Cl - concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na+ and Ca2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and -0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.

Integrating multimedia models to assess nitrogen losses from the Mississippi River basin to the Gulf of Mexico.

This study describes and implements an integrated, multimedia, process-based system-level approach to estimating nitrogen (N) fate and transport in large river basins. The modeling system includes the following components: (1) Community Multiscale Air Quality (CMAQ),(2) Weather Research and Forecasting Model (WRF), (3) Environmental Policy Integrated Climate (EPIC), and (4) Soil and Water Assessment Tool (SWAT). The previously developed Fertilizer Emission Scenario Tool for CMAQ (FEST-C), an advanced user interface, integrated EPIC with the WRF model and CMAQ. The FEST-C system, driven by process-based WRF weather simulations, includes atmospheric N additions to agricultural cropland and agricultural cropland contributions to ammonia emissions. This study focuses on integrating the watershed hydrology and water quality model with FEST-C system so that a full multimedia assessment on water quality in large river basins to address impacts of fertilization, meteorology, and atmospheric N deposition on water quality can be achieved. Objectives of this paper are to describe how to expand the previous effort by integrating the SWAT model with the FEST-C (CMAQ/WRF/EPIC) modeling system, as well as to demonstrate application of the Integrated Modeling System (IMS) to the Mississippi River basin (MRB) to simulate streamflow and dissolved N loadings to the Gulf of Mexico (GOM). IMS simulation results generally agree with US Geological Survey (USGS) observations/estimations; the annual simulated streamflow is 218.9 mm and USGS observation is 211.1 mm and the annual simulated dissolved N is 2.1 kg ha-1 and the USGS estimation is 2.8 kg ha-1. Integrating SWAT with the CMAQ/WRF/EPIC modeling system allows for its use within large river basins without losing EPIC's more detailed biogeochemistry processes, which will strengthen the assessment of impacts of future climate scenarios, regulatory and voluntary programs for N oxide air emissions, and land use and land management on N transport and transformation in large river basins.

Spatial and temporal patterns of precipitation and stream flow variations in Tigris-Euphrates river basin.

Tigris and Euphrates river basin (TERB) is one of the largest river basins in the Middle East, and the precipitation (in the form of snowfall) is a major source of streamflow. This study investigates the spatial and temporal variability of precipitation and streamflow in TERB to better understand the hydroclimatic variables and how they varied over time. The precipitation shows a decreasing trend with 1980s being wetter and 2000s being drier. A total of 55 and 40% reduction in high flows in Tigris and Euphrates rivers at T20 and E3 was seen in post-reservoir period. A lag time of 3 to 4 and 5 to 6 months was estimated between peak snowfall and runoff time periods. Decreasing precipitation and streamflow along with several planned dams could hamper the sustainability of several Mesopotamian marshlands that completely depend on the water from the Tigris and Euphrates rivers.

Reconstructing the historical water regime of the contributing basins to the Hawizeh marsh: Implications of water control structures.

The Hawizeh marsh, a unique wetland which is part of the Mesopotamian marshes, is recognized as a wetland of international importance. The marsh has been shrinking and there has been little research into its degradation. This study aims to reconstruct historical water regimes in the contributing basins (Tigris and Karkheh river Basins, TKRB) to investigate factors that have affected the wellbeing of the marsh. The Soil and Water Assessment Tool (SWAT) was used for this study. The model was calibrated and validated using nine river gauging stations. Results indicated that inflows to the marsh decreased by 65% and 80% in the '90s and 2000s, respectively, compared to the '80s. The reductions in streamflow were caused by decrease in precipitation and water abstraction. The annual precipitation decreased by 14% and 38% in the '90s and 2000s, respectively, compared to the '80s. Highest water abstraction was seen in Karkheh dam which caused a reduction of 45% in the annual streamflows. Average annual evaporative losses from Tharthar lake (2700km2) were very high (2260hm3 [cubic hectometer]). Although the Hawizeh marsh has been shrinking for the last three decades, recent satellite images (2013) have shown that the marsh has been reviving, mainly due to increased precipitation from 2011 to 2013. The revival of the marsh is promising; however, if the planned dams on TKRB are implemented, the future of the marsh remains uncertain. The sustainability of the Hawizeh marsh will require integrated water resources management among the riparian countries to rehabilitate and maintain this unique wetland.

Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios.

Complex watershed simulation models are powerful tools that can help scientists and policy-makers address challenging topics, such as land use management and water security. In the Western Lake Erie Basin (WLEB), complex hydrological models have been applied at various scales to help describe relationships between land use and water, nutrient, and sediment dynamics. This manuscript evaluated the capacity of the current Soil and Water Assessment Tool (SWAT) to predict hydrological and water quality processes within WLEB at the finest resolution watershed boundary unit (NHDPlus) along with the current conditions and conservation scenarios. The process based SWAT model was capable of the fine-scale computation and complex routing used in this project, as indicated by measured data at five gaging stations. The level of detail required for fine-scale spatial simulation made the use of both hard and soft data necessary in model calibration, alongside other model adaptations. Limitations to the model's predictive capacity were due to a paucity of data in the region at the NHDPlus scale rather than due to SWAT functionality. Results of treatment scenarios demonstrate variable effects of structural practices and nutrient management on sediment and nutrient loss dynamics. Targeting treatment to acres with critical outstanding conservation needs provides the largest return on investment in terms of nutrient loss reduction per dollar spent, relative to treating acres with lower inherent nutrient loss vulnerabilities. Importantly, this research raises considerations about use of models to guide land management decisions at very fine spatial scales. Decision makers using these results should be aware of data limitations that hinder fine-scale model interpretation.

Assessing the implications of water harvesting intensification on upstream-downstream ecosystem services: A case study in the Lake Tana basin.

Water harvesting systems have improved productivity in various regions in sub-Saharan Africa. Similarly, they can help retain water in landscapes, build resilience against droughts and dry spells, and thereby contribute to sustainable agricultural intensification. However, there is no strong empirical evidence that shows the effects of intensification of water harvesting on upstream-downstream social-ecological systems at a landscape scale. In this paper we develop a decision support system (DSS) for locating and sizing water harvesting ponds in a hydrological model, which enables assessments of water harvesting intensification on upstream-downstream ecosystem services in meso-scale watersheds. The DSS was used with the Soil and Water Assessment Tool (SWAT) for a case-study area located in the Lake Tana basin, Ethiopia. We found that supplementary irrigation in combination with nutrient application increased simulated teff (Eragrostis tef, staple crop in Ethiopia) production up to three times, compared to the current practice. Moreover, after supplemental irrigation of teff, the excess water was used for dry season onion production of 7.66 t/ha (median). Water harvesting, therefore, can play an important role in increasing local- to regional-scale food security through increased and more stable food production and generation of extra income from the sale of cash crops. The annual total irrigation water consumption was ~4%-30% of the annual water yield from the entire watershed. In general, water harvesting resulted in a reduction in peak flows and an increase in low flows. Water harvesting substantially reduced sediment yield leaving the watershed. The beneficiaries of water harvesting ponds may benefit from increases in agricultural production. The downstream social-ecological systems may benefit from reduced food prices, reduced flooding damages, and reduced sediment influxes, as well as enhancements in low flows and water quality. The benefits of water harvesting warrant economic feasibility studies and detailed analyses of its ecological impacts.

Pasture BMP effectiveness using an HRU-based subarea approach in SWAT.

Many conservation programs have been established to motivate producers to adopt best management practices (BMP) to minimize pasture runoff and nutrient loads, but a process is needed to assess BMP effectiveness to help target implementation efforts. A study was conducted to develop and demonstrate a method to evaluate water-quality impacts and the effectiveness of two widely used BMPs on a livestock pasture: off-stream watering site and stream fencing. The Soil and Water Assessment Tool (SWAT) model was built for the Pottawatomie Creek Watershed in eastern Kansas, independently calibrated at the watershed outlet for streamflow and at a pasture site for nutrients and sediment runoff, and also employed to simulate pollutant loads in a synthetic pasture. The pasture was divided into several subareas including stream, riparian zone, and two grazing zones. Five scenarios applied to both a synthetic pasture and a whole watershed were simulated to assess various combinations of widely used pasture BMPs: (1) baseline conditions with an open stream access, (2) an off-stream watering site installed in individual subareas in the pasture, and (3) stream or riparian zone fencing with an off-stream watering site. Results indicated that pollutant loads increase with increasing stocking rates whereas off-stream watering site and/or stream fencing reduce time cattle spend in the stream and nutrient loads. These two BMPs lowered organic P and N loads by more than 59% and nitrate loads by 19%, but TSS and sediment-attached P loads remained practically unchanged. An effectiveness index (EI) quantified impacts from the various combinations of off-stream watering sites and fencing in all scenarios. Stream bank contribution to pollutant loads was not accounted in the methodology due to limitations of the SWAT model, but can be incorporated in the approach if an amount of bank soil loss is known for various stocking rates. The proposed methodology provides an adaptable framework for pasture BMP assessment and was utilized to represent a consistent, defensible process to quantify the effectiveness of BMP proposals in a BMP auction in eastern Kansas.