Yu Zhang

In southeast coastal Texas, expansive shrink–swell vertisols limit the effectiveness of infiltration-based green infrastructures (GIs) for stormwater management. Both pervious concrete (PC) and bioretention (BR) were conjunctively deployed at Lamar University (LU) and Montrose Park (MP) to evaluate their performance. The field measurements of drawdown rates indicated that post-event evaporation enhanced the system’s effectiveness by draining the media pore spaces of PC-BR. A SWMM model was developed at LU to verify soil infiltration behavior. A total of 66 water samples have been collected in the PC-BR systems to measure levels of NH₄⁺-N, NO₂⁻-N, NO₃⁻-N, TN, PO₄³⁻, and COD. The results showed that no elevated pollution was observed, and the measurements were compliant with the local river water criteria. Both systems were operated under local weather conditions, without irrigation or other human interference, and are still functioning well after 3 years. Two identical laboratory systems were fabricated to replicate the field system as zero-tension lysimeters. Laboratory testing demonstrated effective removal of nutrients, organic matter, and E. coli, with removal efficiencies generally exceeding 80% for PO₄³⁻ and NH₄⁺-N and reaching up to 95% for E. coli. These findings demonstrate that the PC-BR systems are an effective and sustainable solution for managing stormwater runoff and mitigating nutrient and bacterial pollution, particularly in regions with expansive shrink–swell vertisols and vulnerable to localized nuisance flooding.

This study examines the impact of human-induced changes to the lower Brazos-San Bernard delta on sedimentation rates and sediment sources filling the estuarine lakes within its western inland area. The delta began forming in 1929 when the Brazos River mouth was relocated 10 km west of its original position, placing it 5 km from the San Bernard River's mouth. Over time, the wave-dominated Brazos Delta expanded westward, closing the San Bernard River mouth and extending beyond it. In 1949, the Gulf Coast Intercoastal Waterway (GCIWW) canal was completed, bisecting the Cedar Lakes, a series of five brackish lakes, and enabling sediment transport to these lakes. The San Bernard River drainage basin, situated between the larger Brazos and Colorado Rivers, occasionally receives floodwaters from the Colorado River during high-discharge events like Hurricane Harvey (2017). The geological differences between the Brazos and Colorado Rivers facilitated the development of distinct sediment "fingerprints" using X-ray fluorescence, color spectrometry, imaging, and grain size analysis. Eighteen vibracores collected across the study area, combined with 137Cs dating, enabled the identification of deposits formed before and after the GCIWW's creation. Findings revealed that prior to the GCIWW and the Brazos River mouth's relocation, Colorado River-derived deposits dominated the region. Following these alterations, the western Cedar Lakes recorded Gulf of Mexico overwash sands and occasional Brazos River flood deposits. The GCIWW acts as both a conduit for Brazos River sediment and a barrier to Colorado River sediment. North of the GCIWW, in lakes isolated from the canal, sediment records show a mixture of Brazos and Colorado River deposits. East of the San Bernard River, deposits include Colorado River material, Gulf of Mexico overwash, or layered deposits from both the Brazos and Colorado Rivers, reflecting simultaneous flooding events. Most Cedar Lake cores exhibit Pleistocene deposits at their base, overlaid by 50–90 cm of pre-GCIWW sediments and 90–130 cm of post-GCIWW deposits. This suggests a significant increase in sedimentation rates after the canal's construction. In summary, the GCIWW's creation and the Brazos River mouth relocation have significantly altered sediment sources and deposition rates in the Cedar Lakes and adjacent brackish lakes of the Brazos-San Bernard delta. The results of this study are being used by various stakeholders to develop a better management plan for this region.

Study Region -This study is conducted for a cluster of watersheds within the Matagorda Basin along the south Texas coast in the United States. Study Focus - Retrospective streamflow simulations of two hydrologic models of contrasting formulations and complexity, namely the TxRR, a simple analog model, and the National Water Model (NWM), a land surface-hydrologic model that explicitly accounts for surface energy balance in calculating water budget, and whose output. The comparison was motivated by a) the need for improving the modeling of runoff dynamics for watersheds along central Texas coast through the introduction of NWM to inform freshwater inflow monitoring and flood planning, and b) the need to better understand the potential ability of the NWM to address shortcomings of TxRR related to the latter’s mechanistic deficiencies in a region known for hydroclimatic extremes. The study focuses on the temporal scale and climate dependence in the relative performance of the two models, the behaviors of models during extreme floods and droughts, and the relative efficacy of parameter transfer schemes. It further seeks to relate the performance differentials to model physics and calibration approaches. New Hydrologic Insights – NWM, with sophisticated representations of energe closure, is advantageous to TxRR in capturing low-frequency (interannual) variations in streamflow modulated by the surface energy balance, as evidenced by the superior correlation of its streamflow simulations at the interannual range. Yet, its overall performance trails behind TxRR at daily scale, and it tends to underpredict runoff volumes during two major floods. These features underscore the tendency of the model to suppress soil moisture at the onset of these events. On the other hand, TxRR outperforms in reproducing the volumes for the flooding events, but it overpredicts runoff during the extreme drought episode of 2011, likely an outcome of inadequate representation of the impacts of root zone soil moisture in regulating runoff. TxRR’s parameteralization scheme proves more effective for adaptation across watersheds due to the presence of steep gradation in soil properties .

The community Noah land surface model with multiparameterization options (Noah‐MP) provides a plethora of model configurations with varying complexity for land surface modeling. The practical application of this model requires a basic understanding of the relative abilities of its various parameterization configurations in representing spatiotemporal variability and hydrologic connectivity. We designed an ensemble of 288 experiments from multiparameterization schemes of six physical processes to assess and reduce the structural uncertainty for land surface modeling over 10 hydrologic regions in China for the period 2001–2010. The observed latent heat (LH) was well reproduced by the ensemble. Meanwhile, most experiments underestimated sensible heat (SH) throughout the year and overestimated the cold season but underestimated the warm season terrestrial water storage anomaly (TWSA). The sensitive processes and best‐performing schemes varied not only with regions but also among variables. The LH and SH were most sensitive to runoff‐groundwater (RUN), surface heat exchange coefficient (SFC), and radiation transfer (RAD). The TWSA was dominated by RUN and RAD while largely influenced by soil moisture factor for stomatal resistance (BTR) and frozen soil permeability (INF) over some limited regions. By contrast, supercooled liquid water (FRZ) had little influence on all variables. Our optimization for individual variables produced high mean Taylor skill scores that ranged from 0.95–0.99 for LH, 0.82–0.99 for SH, and 0.63–0.95 for TWSA depending on regions. The simultaneous optimization made trade‐off among the three variables, which improved TWSA performance at the cost of reducing the skill for LH and SH over a few regions.

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a five-year period between 2003–2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS (National Weather Service) operations) over two regions: (I) Inside radar effective coverage and (II) immediately outside radar coverage. The outcomes of the evaluation indicate (a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and (b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a 5-year period between 2003-2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS operations) over two regions: I) inside radar effective coverage and II) immediately outside radar coverage. The outcomes of the evaluation indicate a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.