In this article, extension and application to variably-saturated wetland conditions of a process-based wetland model, namely WetQual is demonstrated. The new model described in this article is an improved version of an earlier model, which was only capable of capturing nutrient dynamics in continuously ponded wetlands. The upgraded model is capable of simulating nutrient cycling and biogeochemical reactions in both ponded and unsaturated zones of the wetland. To accomplish this goal, a comprehensive module for tracking water content in wetland soil was implemented in the model, and biogeochemical relationships were added to explain cycling of nitrogen (N) and carbon (C) in variably saturated zones of wetlands. The developed model was applied to a small, restored wetland receiving agricultural runoff, located on Kent Island, Maryland. On average, during the two year study period, the ponded compartment of the study wetland covered 65% of the total 1.2 ha area. Through mass balance analysis, it was revealed that the mass of nitrogen lost to denitrification at the variably saturated compartment of the study wetland was about 3 times higher than that of the ponded compartment (32.7 ± 29.3 kg vs. 9.5 ± 5.5 kg) whereas ammonia volatilization at the variably saturated compartment was a fraction of that of ponded compartment (1.2 ± 1.9 kg vs. 11.3 ± 11.8 kg). Sensitivity analysis showed that cycling of carbon related constituents in variably saturated compartment had high sensitivity to temperature and available soil moisture.
Headwater wetlands provide many benefits such as water quality improvement, water storage, and providing habitat. These wetlands are characterized by water levels near the surface and respond rapidly to rainfall events. Driven by both groundwater and surface water inputs, water levels (WLs) can be above or below the ground at any given time depending on the season and climatic conditions. Therefore, WL predictions in headwater wetlands is a complex problem. In this study a hybrid modeling approach was developed for improved WL predictions in wetlands, by coupling a watershed model with artificial neural networks (ANNs). In this approach, baseflow and stormflow estimates from the watershed draining to a wetland are first estimated using an uncalibrated Soil and Water Assessment Tool (SWAT). These estimates are then combined with meteorological variables and are utilized as inputs to an ANN model for predicting daily WLs in wetlands. The hybrid model was used to successfully predict WLs in a headwater wetland in coastal Alabama, USA. The model was then used to predict the WLs at the study wetland from 1951 to 2005 to explore the possible teleconnections between the El Niño Southern Oscillation (ENSO) and WLs. Results show that both precipitation and the variations in WLs are partially affected by ENSO in the study area. A correlation analysis between seasonal precipitation and the Nino 3.4 Index suggests that winters are wetter during El Niño in Coastal Alabama. Analysis also revealed a significant negative correlation between WLs and the Nino 3.4 Index during the El Niño phase for spring. The findings of this study and the developed methodology/tools are useful to predict long-term WLs in wetlands and construct more accurate restoration plans under a variable climate.
Rainfall kinetic energy (RKE) constitutes one of the most critical factors that drive rainfall erosivity on surface soil. Direct measurements of RKE are limited, relying instead on the empirical relations between kinetic energy and rainfall intensity (KE-I relation), which have not been well regionalized for data-scarce regions. Here, we present the first global rainfall microphysics-based RKE (RKEMPH) flux retrieved from radar reflectivity at different frequencies. The results suggest that RKEMPH flux outperforms the RKE estimates derived from a widely used empirical KE-I relation (RKEKE-I) validated using ground disdrometers. We found a potentially widespread underestimation of RKEKE-I, which is especially prominent in some low-income countries with ~20% underestimation of RKE and the resultant rainfall erosivity. Given the evidence that these countries are subject to greater rainfall-induced soil erosion, these underestimations would mislead conservation practices for sustainable development of terrestrial ecosystems.
Abstract Manning's equation is used widely to predict stream discharge ( Q ) from hydraulic variables when logistics constrain empirical measurements of in‐bank flow events. Uncertainty in Manning's roughness ( n M ) is the major source of error in natural channels, and sand‐bed streams pose difficulties because flow resistance is affected by flow‐dependent bed configuration. Our study was designed to develop and validate models for estimating Q from channel geometry easily derived from cross‐sectional surveys and available GIS data. A database was compiled consisting of 484 Q measurements from 75 sand‐bed streams in Alabama, Georgia, South Carolina, North Carolina (Southeastern Plains), and Florida (Southern Coastal Plain), with six New Zealand streams included to develop statistical models to predict Q from hydraulic variables. Model error characteristics were estimated with leave‐one‐site‐out jackknifing. Independent data of 317 Q measurements from 55 Southeastern Plains streams indicated the model ( Q = A c R H 0.6906 S 0.1216 ; where A c is the channel area, R H is the hydraulic radius, and S is the bed slope) best predicted Q , based on Akaike's information criterion and root mean square error. Models also were developed from smaller Q range subsets to explore if subsets increased predictive ability, but error fit statistics suggested that these were not reasonable alternatives to the above equation. Thus, we recommend the above equation for predicting in‐bank Q of unbraided, sandy streams of the Southeastern Plains.
Lower Apalachicola-Chattahoochee-Flint (ACF) River Basin of southeastern United States The threats of climate change on the surface- and groundwater resources of the lower ACF River Basin of southeastern U.S. is an important concern for the long-term ecological as well as agricultural sustainability. This study developed a coupled SWAT-MODFLOW for the study region and evaluated the impacts of climate change projected under RCP4.5 and RCP8.5 emissions scenarios. Evaluation of simulated streamflow and groundwater levels showed that SWAT-MODFLOW can adequately replicate the hydrology of a karstic watershed such as that present in the study domain even without the incorporation of conduit flows/karst features. Comparison to baseline conditions indicates a shift in the monthly streamflow pattern in the region with a reduction from April to June and increases in the rest of the year under future climate. The region will also likely see an increase in low-flow as well as high-flow events, thus increasing streamflow variability in the region. More frequent low flow conditions in the future can lead to increased drying of ephemeral streams threatening the ecological sustainability of the region due to habitat loss. This, along with the projected reduction in groundwater levels can lead to increased stress on water resources of the region for irrigation, thus threatening the agriculture sustainability and increasing water conflict between the neighboring states.
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