Hydraulic modeling of the Periyar River using HEC-RAS: unraveling flow dynamics.

The Periyar River, a vital component of Kerala's ecosystem in India, serves as a lifeline supporting agriculture, hydropower generation, and ecological equilibrium. This study adopts a multifaceted approach to address critical challenges in the Periyar basin, with a primary focus on flood mitigation due to the region's susceptibility to devastating floods. Covering a length of 67.85 km, the study intricately segments the Periyar River into distinct reaches for a comprehensive steady flow analysis, considering factors such as seasonal monsoon fluctuations, diverse catchment topography, and human-induced alterations. Utilizing advanced modeling techniques, particularly HEC-RAS software, the study effectively predicts and simulates shifts in hydraulic behavior. The results, including velocity plots and cross-sectional maps, offer accurate insights into critical parameters, enabling the identification of areas with high velocity occurrence. This information proves instrumental in making informed decisions for the construction of river restoration structures, crucial for mitigating the impact of floods. The study's findings contribute valuable tools for future forecasting and sustainable management of the Periyar River, addressing the complex interplay of natural and anthropogenic factors.

Determining the most appropriate stormwater management strategies within the development of urban landscape infrastructure using the TOPSIS method: applications to Rize city.

In this study, the application of multi-criteria decision-making (MCDM) methods in determining the most appropriate stormwater management strategy is examined using different areas in Rize. The determination of the most appropriate stormwater management practices for the Rize coastal park and Güneysu-Rize connection highway with TOPSIS is presented in detail within this study. In this context, commonly used applications suitable for urban areas are discussed. The criteria and their weights used for the evaluation of the selected applications were determined by consulting expert opinions from leading researchers. The most suitable applications in different scenarios such as changes in the cost or the amount of precipitation for Rize coastal park and Güneysu-Rize connection road were determined by the TOPSIS method. The TOPSIS analyses' ranking of the ideal solutions matches the results of the SWMM simulations one to one. SWMM results confirm that the outcomes of TOPSIS are the alternatives that provide maximum decrease in surface runoff.

Effects of water flow and ocean acidification on oxygen and pH gradients in coral boundary layer.

Reef-building corals live in highly hydrodynamic environments, where water flow largely controls the complex chemical microenvironments surrounding them-the concentration boundary layer (CBL). The CBL may be key to alleviate ocean acidification (OA) effects on coral colonies by partially isolating them. However, OA effects on coral CBL remain poorly understood, particularly under different flow velocities. Here, we investigated these effects on the reef-building corals Acropora cytherea, Pocillopora verrucosa, and Porites cylindrica. We preconditioned corals to a control (pH 8.0) and OA (pH 7.8) treatment for four months and tested how low flow (2 cm s-1) and moderate flow (6 cm s-1) affected O2 and H+ CBL traits (thickness, surface concentrations, and flux) inside a unidirectional-flow chamber. We found that CBL traits differed between species and flow velocities. Under OA, traits remained generally stable across flows, except surface pH. In all species, the H+ CBL was thin and led to lower surface pH. Still, low flow thickened H+ CBLs and increased light elevation of surface pH. In general, our findings reveal a weak to null OA modulation of the CBL. Moreover, the OA-buffering capacity by the H+ CBL may be limited in coral species, though low flow could enhance CBL sheltering.

The impact of aragonite saturation variability on shelled pteropods: An attribution study in the California Current System.

Observations from the California Current System (CalCS) indicate that the long-term trend in ocean acidification (OA) and the naturally occurring corrosive conditions for the CaCO3 mineral aragonite (saturation state Ω < 1) have a damaging effect on shelled pteropods, a keystone group of calcifying organisms in the CalCS. Concern is heightened by recent findings suggesting that shell formation and developmental progress are already impacted when Ω falls below 1.5. Here, we quantify the impact of low Ω conditions on pteropods using an individual-based model (IBM) with life-stage-specific mortality, growth, and behavior in a high-resolution regional hindcast simulation of the CalCS between 1984 and 2019. Special attention is paid to attributing this impact to different processes that lead to such low Ω conditions, namely natural variability, long-term trend, and extreme events. We find that much of the observed damage in the CalCS, and specifically >70% of the shell CaCO3 loss, is due to the pteropods' exposure to naturally occurring low Ω conditions as a result of their diel vertical migration (DVM). Over the hindcast period, their exposure to damaging waters (Ω < 1.5) increases from 9% to 49%, doubling their shell CaCO3 loss, and increasing their mortality by ~40%. Most of this increased exposure is due to the shoaling of low Ω waters driven by the long-term trend in OA. Extreme OA events amplify this increase by ~40%. Our approach can quantify the health of pteropod populations under shifting environmental conditions, and attribute changes in fitness or population structure to changes in the stressor landscape across hierarchical time scales.

Groundwater recharge estimation using WetSpass-M and MTBS leveraging from HydroOffice and WHAT tools for baseflow in Weyib watershed, Ethiopia.

WetSpass-M model and multi-technique baseflow separation (MTBS) were applied to estimate spatio-temporal groundwater recharge (GWR) to be used to comprehend and enhance sustainable water resource development in the data-scarce region. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) techniques outperform the existing 13 MTBS techniques to separate baseflow depending on the correlation matrix; mean baseflow was 5.128 m3/s. The WetSpass-M model performance evaluated by Nash-Sutcliff Efficiency (NSE) was 0.95 and 0.89; R2 was 0.90 and 0.85 in comparison to observed and simulated mean monthly baseflow and runoff (m3/s), respectively. The estimated mean annual water balance was 608.2 mm for actual evapotranspiration, 221.42 mm for the surface runoff, 87.42 mm for interception rate, and 177.66 mm for GWR, with an error of - 3.29 mm/year. The highest annual actual evapotranspiration was depicted in areas covered by vegetation, whereas lower in the settlement. The peak annual interception rates have been noticed in areas covered with forests and shrublands, whereas the lowest in settlement and bare land. The maximum annual runoff was depicted in settlement and bare land, while the lowest was in forest-covered areas. The annual recharge rates were low in bare land due to high runoff and maximum in forest-covered areas due to low surface runoff. The watershed's downstream areas receive scanty annual rainfall, which causes low recharge and drought. The findings point the way ahead in terms of selecting the best approach across multi-technique baseflow separations.

Threshold dynamics of a reaction-advection-diffusion schistosomiasis epidemic model with seasonality and spatial heterogeneity.

Most water-borne disease models ignore the advection of water flows in order to simplify the mathematical analysis and numerical computation. However, advection can play an important role in determining the disease transmission dynamics. In this paper, we investigate the long-term dynamics of a periodic reaction-advection-diffusion schistosomiasis model and explore the joint impact of advection, seasonality and spatial heterogeneity on the transmission of the disease. We derive the basic reproduction number R 0 and show that the disease-free periodic solution is globally attractive when R 0 < 1 whereas there is a positive endemic periodic solution and the system is uniformly persistent in a special case when R 0 > 1 . Moreover, we find that R 0 is a decreasing function of the advection coefficients which offers insights into why schistosomiasis is more serious in regions with slow water flows.

The loss of dissolved organic matter from biological soil crust at various successional stages under rainfall of different intensities: Insights into the changes of molecular components at different rainfall stages.

Biological soil crusts (BSCs) are typical covers in arid and semiarid regions. The dissolved organic matter (DOM) of BSCs can be transported to various aquatic ecosystems by rainfall-runoff processes. However, the spatiotemporal variation in quality and quantity of DOM in runoff remains unclear. Herein, four kinds of runoff plots covered by four successional stages of BSCs were set up on slopes, including bare runoff plot (BR), cyanobacteria crust covered runoff plot (CR), mixed crust covered runoff plot (MIR), and moss crust covered runoff plot (MOR). The quantity and quality of DOM in runoff during rainfall was investigated based on the stimulated rainfall experiments combined with optical spectroscopy and ultra-high resolution mass spectrometry analyses. The results showed that the DOM concentrations (i.e., 0.30 to 45.25 mg L-1) in runoff followed the pattern of MOR>MIR>CR>BR, and they were exponentially decreased with rainfall duration. The DOM loss rate of BR (8.26 to 11.64 %) was significantly greater than those of CR, MIR, and MOR (0.84 to 3.22 %). Highly unsaturated compounds (HUCs), unsaturated aliphatic compounds (UACs), saturated compounds (SCs), and peptide-like compounds (PLCs) were the dominated compounds of the water extractable DOM from the original soils. Thereinto, PLCs and UACs were more easily leached into runoff during rainfall. The relatively intensity of HUCs in runoff generally decreased with rainfall duration, while the relatively intensities of UACs, PLCs, and SCs slightly increased with rainfall duration. These findings suggested that the DOM loss rate was effectively decreased with the successional of BSCs during rainfall; meanwhile, some labile compounds (e.g., PLCs and UACs) were transported into various aquatic ecosystems by rainfall-runoff processes.

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.

Comparative analysis of tracer studies in the determination of longitudinal dispersion coefficient.

The research presented in this paper is to determine the best tracer studies that will give acceptable estimates of longitudinal dispersion coefficient for Orashi river using rhodamine WT dye and sodium chloride as water tracer. Estimated results obtained for longitudinal dispersion coefficient for the case of rhodamine WT experiment ranges between 71 and 104.4 m2s-1 while that of sodium chloride experiment ranges between 20.1 and 34.71 m2s-1. These results revealed lower dispersion coefficient using sodium chloride as water tracer (WT) indicating that for larger rivers, sodium chloride should not be used as water tracer. The usage of sodium chloride as water tracer in the estimation of longitudinal dispersion coefficient is recommended in smaller streams as NaCl is relatively conservative. The established equations for both cases of investigation are proving satisfactory upon validation as degree of accuracy of 100.0% was obtained using discrepancy ratio (Dr). Standard error (SE), normal mean error (NME) and mean multiplication error (MME) of the developed equations is better when compared with other existing equations. However, Equation (17) is satisfactorily recommended.

Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction.

In this paper, we address the critical task of 24-h streamflow forecasting using advanced deep-learning models, with a primary focus on the transformer architecture which has seen limited application in this specific task. We compare the performance of five different models, including persistence, long short-term memory (LSTM), Seq2Seq, GRU, and transformer, across four distinct regions. The evaluation is based on three performance metrics: Nash-Sutcliffe Efficiency (NSE), Pearson's r, and normalized root mean square error (NRMSE). Additionally, we investigate the impact of two data extension methods: zero-padding and persistence, on the model's predictive capabilities. Our findings highlight the transformer's superiority in capturing complex temporal dependencies and patterns in the streamflow data, outperforming all other models in terms of both accuracy and reliability. Specifically, the transformer model demonstrated a substantial improvement in NSE scores by up to 20% compared to other models. The study's insights emphasize the significance of leveraging advanced deep learning techniques, such as the transformer, in hydrological modeling and streamflow forecasting for effective water resource management and flood prediction.

Application of multi-criteria decision-making on low-impact development practice selection for the Kinyerezi River sub-catchments in Dar es Salaam, Tanzania.

The impermeable areas in catchments are proportional to peak flows that result in floods in river reaches where the flow-carrying capacity is inadequate. The high rate of urbanization witnessed in the Kinyerezi River catchment in Dar es Salaam city has been noted to contribute to floods and siltation in the Msimbazi River. The Low-Impact Development (LID) practices that includes bio-retention (BR) ponds, rain barrels (RBs), green roofs (GRs), etc. can be utilized to mitigate portion of the surface runoff. This study aims to propose suitable LID practices and their sizes for mitigating runoff floods in the Kinyerezi River catchment using the Multi-Criteria Decision-Making (MCDM) approach. The results indicated that the BR and RBs were ranked high in capturing the surface runoff while the sediment control fences were observed to be the best in reducing sediments flowing into the BR. The proposed BR ponds were greater than 800 m2 with 1.2 m depth while RB sizes for Kinyerezi and Kisungu secondary schools and Kinyerezi and Kifuru primary schools were 2,730; 2,748; 1,385; and 1,020 m3, respectively. The BR ponds and RBs are capable of promoting water-demanding economic activities such as horticulture, gardening, car washing while reducing the school expenses and runoff generation.

Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso.

With the widespread application of machine learning in various fields, enhancing its accuracy in hydrological forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on daily-scale streamflow and explores the application of the Lasso feature selection method alongside three machine learning models (long short-term memory, LSTM; transformer for time series, TTS; random forest, RF) in short-term streamflow prediction. Through comparative experiments, we found that the Lasso method significantly enhances the model's performance, with a respective increase in the generalization capabilities of the three models by 21, 12, and 14%. Among the selected features, lagged streamflow and precipitation play dominant roles, with streamflow closest to the prediction date consistently being the most crucial feature. In comparison to the TTS and RF models, the LSTM model demonstrates superior performance and generalization capabilities in streamflow prediction for 1-7 days, making it more suitable for practical applications in hydrological forecasting in the Haihe River Basin and similar regions. Overall, this study deepens our understanding of feature selection and machine learning models in hydrology, providing valuable insights for hydrological simulations under the influence of complex human activities.

A dynamic one-dimensional model for simulating unsteady air-water stratified flow in sewer pipes.

Ventilation is paramount in sanitary and stormwater sewer systems to mitigate odor problems and avert pressure surges. Existing numerical models have constraints in practical applications in actual sewer systems due to insufficient airflow modeling or suitability only for steady-state conditions. This research endeavors to formulate a mathematical model capable of accurately simulating various operational conditions of sewer systems under the natural ventilation condition. The dynamic water flow is modeled using a shock-capturing MacCormack scheme. The dynamic airflow model amalgamates energy and momentum equations, circumventing laborious pressure iteration computations. This model utilizes friction coefficients at interfaces to enhance the description of the momentum exchange in the airflow and provide a logical explanation for air pressure. A systematic analysis indicates that this model can be easily adapted to include complex boundary conditions, facilitating its use for modeling airflow in real sewer networks. Furthermore, this research uncovers a direct correlation between the air-to-water flow rate ratio and the filling ratio under natural ventilation conditions, and an empirical formula encapsulating this relationship is derived. This finding offers insights for practical engineering applications.

Impact of the comprehensive remediation project on hydrological conditions in the lower reaches of the Ganjiang River.

This study undertakes a systematic analysis of the hydrological changes before and after the implementation of the Comprehensive Remediation Project in the lower reaches of the Ganjiang River. It focuses on changes in downstream inflow, ratios of flow distribution, and water levels, as well as water velocity near the gates. The results indicate a significant improvement in the spatial distribution of water resources in the lower reaches of the Ganjiang River. The project enhances the inflow from the northern and southern branches, positively influencing downstream water usage and the ecological environment. Building upon these findings, the study proposes operational recommendations tailored to different hydrological years, such as timely adjustments to the southern branch's water inflow and optimizing flow distribution ratios. This research provides a scientific basis for the implementation and dispatch of comprehensive remediation projects and offers insights into water resource management in similar regions.

Individual and combined impacts of carbon dioxide enrichment, heatwaves, flow velocity variability, and fine sediment deposition on stream invertebrate communities.

Climate change and land-use change are widely altering freshwater ecosystem functioning and there is an urgent need to understand how these broad stressor categories may interact in future. While much research has focused on mean temperature increases, climate change also involves increasing variability of both water temperature and flow regimes and increasing concentrations of atmospheric CO2, all with potential to alter stream invertebrate communities. Deposited fine sediment is a pervasive land-use stressor with widespread impacts on stream invertebrates. Sedimentation may be managed at the catchment scale; thus, uncovering interactions with these three key climate stressors may assist mitigation of future threats. This is the first experiment to investigate the individual and combined effects of enriched CO2, heatwaves, flow velocity variability, and fine sediment on realistic stream invertebrate communities. Using 128 mesocosms simulating small stony-bottomed streams in a 7-week experiment, we manipulated dissolved CO2 (ambient; enriched), fine sediment (no sediment; 300 g dry sediment), temperature (ambient; two 7-day heatwaves), and flow velocity (constant; variable). All treatments changed community composition. CO2 enrichment reduced abundances of Orthocladiinae and Chironominae and increased Copepoda abundance. Variable flow velocity had only positive effects on invertebrate abundances (7 of 13 common taxa and total abundance), in contrast to previous experiments showing negative impacts of reduced velocity. CO2 was implicated in most stressor interactions found, with CO2 × sediment interactions being most common. Communities forming under enriched CO2 conditions in sediment-impacted mesocosms had ~20% fewer total invertebrates than those with either treatment alone. Copepoda abundances doubled in CO2-enriched mesocosms without sediment, whereas no CO2 effect occurred in mesocosms with sediment. Our findings provide new insights into potential future impacts of climate change and land use in running freshwaters, in particular highlighting the potential for elevated CO2 to interact with fine sediment deposition in unpredictable ways.

Numerical investigation on characteristics of vortex dissipation in multi-horizontal submerged jets stilling basin.

Multi-horizontal submerged jets stilling basins have been utilized in large-scale water conservancy and hydropower projects due to its stable flow pattern, high energy dissipation rate and less atomization. This study employs vorticity criterion, Q criterion, λ2 criterion and Ω criterion to investigate the characteristics of vortex formation and turbulent dissipation in multi-horizontal submerged jets stilling basins with various configurations, including crest overflowing orifice alone (COO), combination of crest overflowing orifice and mid-discharge orifice (COO-MO) and mid-discharge orifice alone (MO). The results indicate that the Q criterion and λ2 criterion are effective in identifying vortex structure within multi-horizontal submerged jets stilling basin. Specifically, the stronger intensity of vortex structure and vortex dissipation are mainly distributed in the vicinity of the vertical drop, which gradually weakens for the increasing distance to the vertical drop. Furthermore, the intensity and number of vortexes with COO-MO are the largest. This conclusion can provide guidance for energy dissipation and bottom protection of stilling pool.

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data.

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098-0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Integrated monitoring and modeling to disentangle the complex spatio-temporal dynamics of urbanized streams under drought stress.

We have a poor understanding of how urban drainage and other engineered components interact with more natural hydrological processes in green and blue spaces to generate stream flow. This limits the scientific evidence base for predicting and mitigating the effects of future development of the built environment and climate change on urban water resources and their ecosystem services. Here, we synthesize > 20 years of environmental monitoring data to better understand the hydrological function of the 109-km2 Wuhle catchment, an important tributary of the river Spree in Berlin, Germany. More than half (56%) of the catchment is urbanized, leading to substantial flow path alterations. Young water from storm runoff and rapid subsurface flow provided around 20% of stream flow. However, most of it was generated by older groundwater (several years old), mainly recharged through the rural headwaters and non-urban green spaces. Recent drought years since 2018 showed that this base flow component has reduced in response to decreased recharge, causing deterioration in water quality and sections of the stream network to dry out. Attempts to integrate the understanding of engineered and natural processes in a traditional rainfall-runoff model were only partly successful due to uncertainties over the catchment area, effects of sustainable urban drainage, adjacent groundwater pumping, and limited conceptualization of groundwater storage dynamics. The study highlights the need for more extensive and coordinated monitoring and data collection in complex urban catchments and the use of these data in more advanced models of urban hydrology to enhance management.

Streamflow seasonality in a snow-dwindling world.

Climate warming induces shifts from snow to rain in cold regions1, altering snowpack dynamics with consequent impacts on streamflow that raise challenges to many aspects of ecosystem services2-4. A straightforward conceptual model states that as the fraction of precipitation falling as snow (snowfall fraction) declines, less solid water is stored over the winter and both snowmelt and streamflow shift earlier in season. Yet the responses of streamflow patterns to shifts in snowfall fraction remain uncertain5-9. Here we show that as snowfall fraction declines, the timing of the centre of streamflow mass may be advanced or delayed. Our results, based on analysis of 1950-2020 streamflow measurements across 3,049 snow-affected catchments over the Northern Hemisphere, show that mean snowfall fraction modulates the seasonal response to reductions in snowfall fraction. Specifically, temporal changes in streamflow timing with declining snowfall fraction reveal a gradient from earlier streamflow in snow-rich catchments to delayed streamflow in less snowy catchments. Furthermore, interannual variability of streamflow timing and seasonal variation increase as snowfall fraction decreases across both space and time. Our findings revise the 'less snow equals earlier streamflow' heuristic and instead point towards a complex evolution of seasonal streamflow regimes in a snow-dwindling world.

Improving stormwater infiltration and retention in compacted urban soils at impervious/pervious surface disconnections with biochar.

Urban development often results in compacted soils, impairing soil structure and reducing the infiltration and retention of stormwater runoff from impervious features. Biochar is a promising organic soil amendment to improve infiltration and retention of stormwater runoff. Soil at the disconnection between impervious and pervious surfaces represents a critical biochar application point for stormwater management from urban impervious features. This study tested the hypothesis that biochar would significantly improve water retention and transmission at four sites, where varying percentages (0%, 2%, and 4% w/w) of biochar were amended to soils between impervious pavement, and pervious grassed slopes. Field-saturated hydraulic conductivity (Ksat) and easily drainable water storage capacity were monitored at these sites for five months (two sites) and 15 months (two sites). At the end of the monitoring periods, the physical, chemical, and biological properties of each site's soil were assessed to understand the impact of biochar on soil aggregation, which is critical for improved soil structure and water infiltration. Results indicated that the field Ksat, drainable water storage capacity, and plant available water content (AWC) were 7.1 ± 3.6 SE, 2.0 ± 0.3 SE, and 2.1 ± 0.3 SE times higher in soils amended with 4% biochar, respectively, compared to the undisturbed soil. Factor analysis elucidated that biochar amendment increased the organic matter content, aggregate mean weight diameter, organo-mineral content, and fungal hyphal length while decreasing the bulk density. Across the 12 biochar/soil combinations, the multiple linear regression models derived from factor analysis described the changes in Ksat and AWC reasonably well with R2 values of 0.51 and 0.71, respectively. Using soil and biochar properties measured before biochar addition, two recent models, developed from laboratory investigations, were found helpful as screening tools to predict biochar's effect on Ksat and AWC at the four field sites. Overall, the findings illustrate that biochar amendment to compacted urban soils can significantly improve soil structure and hydraulic function at impervious/pervious surface disconnections, and screening models help to predict biochar's effectiveness in this context.