Storm Depth Research Articles

Stormwater runoff calculator for evaluation of low impact development practices at ground-mounted solar photovoltaic farms

Estimating runoff at ground-mounted solar photovoltaic (PV) installations is challenging because of the disconnected nature of impervious solar panels and the pervious ground surface underneath and between panel rows. There is a need for improved tools to estimate how low impact development practices at these solar installations affect stormwater runoff. The objective of this study was to develop an innovative spreadsheet-based runoff calculator that rapidly estimates stormwater runoff from ground-mounted solar PV sites. The calculator is built on a 2-D hydrologic model (Hydrus-2D/3D) calibrated and validated using experimental data from five commercial solar farms in Colorado, Georgia, Minnesota, New York, and Oregon. The Hydrus-2D/3D hydrologic model was then used to generate nomographs for stormwater runoff that were incorporated into an easy-to-use Excel-based solar farm runoff calculator. This calculator allows for rapid estimation of NRCS stormwater runoff curve number (CN) values at solar farms by considering several complex factors unique to PV installations including: soil and topographic characteristics, surface cover, disconnected impervious surface factors associated with various solar panel designs, and climatic factors. The solar farm runoff calculator quickly estimates runoff CN for pre- and post-construction scenarios, and can estimate actual depth of runoff based on a user-specified 24-h design storm depth. Factors that have the most significant impact on stormwater runoff include design storm return frequency, soil texture, soil bulk density, and soil depth. Ground surface cover has a moderate impact on stormwater runoff, and factors that have a lesser impact on stormwater runoff include slope and array size, spacing and orientation on the landscape. The runoff calculator allows for accurate estimates of runoff generated by disconnected impervious surfaces and low impact development practices at solar farms as affected by a wide range of site-specific conditions.

Measuring and modeling soil moisture and runoff at solar farms using a disconnected impervious surface approach

AbstractGround‐mounted photovoltaic sites are often treated as impervious surfaces in stormwater permits. This ignores the pervious soils beneath and between solar arrays and leads to an overestimation of runoff. Our objective was to improve solar farm stormwater hydrology models by explicitly considering the disconnected impervious nature of solar design and site characteristics. Experimental sites established on utility scale solar farms in Colorado, Georgia, Minnesota, New York, and Oregon had perennial vegetative plantings with mean precipitation ranging from 40.6 to 124.5 cm, and soil texture ranging from loamy sand to clay. Soil moisture measurements were collected beneath arrays, under drip edges, and in the vegetated area between arrays at each site. Hydrus‐3D models for soil moisture and stormwater hydrology were developed that accounted for precipitation falling on solar panels, drip edge redistribution of rainfall, infiltration, and runoff in the pervious areas between solar arrays and beneath panels. Drip edge runoff averaged 3‐ to 10‐times incident precipitation at the New York and Minnesota sites, respectively. Root mean square error values between measured sub‐hourly soil moisture and predicted moisture for large measured single storm events averaged 0.029 across all five sites. Predicted runoff depths were strongly affected by precipitation depth, soil texture, soil profile depth, and soil bulk density. Runoff depths across the five experimental sites averaged 13%, 25%, and 45% of the 2‐, 10‐, and 100‐year design storm depths, clearly showing that these solar farms do not behave like impervious surfaces, but rather as disconnected impervious surfaces with substantial infiltration of runoff in the vegetated areas between and beneath solar arrays.

Nonstationary recharge responses to a drying climate in the Gnangara Groundwater System, Western Australia

The response of groundwater recharge to climate change needs to be understood to enable sustainable management of groundwater systems today and in the future, yet observations of recharge over long-enough time periods to reveal responses to climate trends are scarce. Here we present a meta-analysis of 60 years of recharge studies over the Gnangara Groundwater System of South-West Western Australia, covering a period of sustained drying consistent with climate change projections. The recharge process in the area is defined by a wet winter during which rain saturates a deep, highly permeable soil profile with very low water storage capacity. Measurements of recharge since the 1960s show near-linear reductions in potential recharge of 50%, in response to a 20% reduction in rainfall. For the best-represented land cover in the dataset (Banksia woodland), the reduction in potential recharge was closer to 70%. A simple analytical model suggests that reductions in the duration of winter, coupled with a decreased frequency of winter storms, were most responsible for these declines, and reveals the potential for nonlinear relationships between the recharge fraction (recharge/precipitation) and climatic variables such as mean storm frequency, mean storm depth, and the length of the winter wet season. Overall, results suggest that recharge declines in drying Mediterranean groundwater systems are likely to outstrip the declines in rainfall, and that leveraging existing observation networks worldwide to characterise recharge responses to changing climate is needed to overcome existing interpretation challenges created by inconsistent sites, methods and durations of recharge estimation.

Urban Ecohydrology: Accounting for Sub‐Grid Lateral Water and Energy Transfers in a Land Surface Model

AbstractAlthough urbanization fundamentally alters water and energy cycles, contemporary land surface models (LSMs) often do not include key urban vegetation processes that serve to transfer water and energy laterally across heterogeneous urban land types. Urban water/energy transfers occur when rainfall landing on rooftops, sidewalks, and driveways is redirected to lawns or pervious pavement and when transpiration occurs from branches overhanging impervious surfaces with the corresponding root water uptake takes place in nearby portions of yards. We introduce Noah‐MP for Heterogenous Urban Environments (Noah‐MP HUE), which adds sub‐grid water transfers to the widely used Noah‐MP LSM. We examine how sub‐grid water transfers change surface water and energy balances by systematically increasing the amount of simulated water transfer for four scenarios: tree canopy expanding over pavement (Urban Tree Expansion), tree canopy shifting over pavement (Urban Tree Shift), and directing impermeable runoff onto surrounding vegetation (Downspout Disconnection) or into an engineered pavement (Permeable Pavement). Even small percentages of sub‐grid water transfer can reduce runoff and enhance evapotranspiration and deep drainage. Event‐scale runoff reduction depends on storm depth, rainfall intensity, and antecedent soil moisture. Sub‐grid water transfers also tend to enhance (reduce) latent (sensible) heat. Results highlight the importance not only of fine‐scale heterogeneity on larger scale surface processes, but also the importance of urban management practices that enhance lateral water transfers and water storage–so‐called green infrastructure–as they change land surface fluxes and, potentially, atmospheric processes. This work opens a pathway to directly integrate those practices in regional climate simulations.

Evaluating the Effectiveness of Best Management Practices in Adapting the Impacts of Climate Change-Induced Urban Flooding

Floods are amongst the most destructive and costly natural disasters impacting communities around the globe. The severity and reoccurrence of flooding events have been more common in recent years as a result of the changing climate and urbanization. Best Management Practices (BMPs) are commonly used flood management techniques that aim to alleviate flooding and its impacts by capturing surface runoff and promoting infiltration. Recent studies have examined the effectiveness of BMPs in countering the effects of flooding; however, the performance of such strategies still needs to be analyzed for possible future climate change. In this context, this research employs climate model-driven datasets from the North American Regional Climate Change Assessment Program to evaluate the effects of climate change on urban hydrology within a study region by calculating historical and projected 6 h 100-year storm depths. Finally, the climate-induced design storms are simulated in the PCSWMM model, and the three BMP options (i.e., porous pavement, infiltration trench, and green roof) are evaluated to alleviate the impact of flooding events. This study quantifies the impact of changing climate on flood severity based on future climate models. The results indicate that peak discharge and peak volume are projected to increase by a range of 5% to 43% and 8% to 94%, respectively. In addition, the results demonstrated that green roofs, Permeable Pavement, and infiltration trenches help to reduce peak discharge by up to 7%, 14%, and 15% and reduce flood volume by up to 19%, 24%, and 29%, respectively, thereby presenting a promising solution to address the challenges posed by climate change-induced flooding events.

Assessing canopy rainfall partitioning by Mediterranean dryland shrubs under extreme rainfall

AbstractIntense rain events have become more frequent in some regions due to climate change, and this trend is particularly concerning in dryland regions where the ecological and geomorphological impacts of rainfall are intimately tied to its intensity. The interception of rainfall by vegetation is a critical process in the water balance of drylands; thus, this study estimated the canopy interception capacity and interception rates as well as stemflow of three typical Mediterranean shrub species (Rosmarinus officinalis, Thymus vulgaris, and Macrochloa tenacissima) of three size classes in Spain under a simulated extreme rainfall rate (~8 mm min−1, historical return period of >100 years). Given that these plants' canopy structures markedly differ from taller woody plants (i.e., trees), a novel method was developed to assess the stemflow fraction. Results showed significant differences in interception amount, rates, and storage capacity among the shrub species, with variations in plant morphology, such as shrub height and canopy diameter, being the key factors determining interception capacity. R. officinalis had the highest interception fraction per unit canopy area, or ‘specific interception,’ (18.4%). In contrast, the lowest specific interception fraction was measured for M. tenacissima (6.5%). Thymus vulgaris was characterized by the highest stemflow yields per unit canopy area (4.85 mm) and fraction (up to 29.6% of rainfall), which was the lowest for M. tenacissima (1.09 mm, ~1%–4%). Strong linear correlations were found between canopy interception and shrub canopy diameter (|r| > −0.51, max = −0.90), when observations were grouped for size class. These linear correlations between shrub morphology and partitioning enabled multiple‐regression linear models to be developed that predicted canopy interception and stemflow with good accuracy (r2 > 0.64, with a maximum of 0.82) from shrub height, canopy diameter, dry biomass, size class, and species. Despite these measurements being conducted under one extreme storm depth and intensity, the results provide: (i) values of rainfall partitioning for important shrub species in Mediterranean dryland environments; and (ii) a simple but reliable model that may be further developed (e.g., embedding variable rainfall values as weather input or incorporating other morphological parameters) and may be integrated into complex hydrological models.

Evaluating the Effectiveness of Low Impact Development Practices against Climate Induced Extreme Floods

Short but extreme flooding events have been frequent and severe globally due to climate change and urbanization in recent years. Similarly, researchers, scientists, and water managers are suggesting the application of sustainable flood management strategies such as Low Impact Development (LID) to mitigate the impacts of such extreme flooding events. However, most of these strategies have primarily been evaluated using historical precipitation events, which may not accurately represent the impact of climate-induced flooding events, which are projected to become more extreme. In this context, this study assesses the effectiveness of LIDs in combating climate change-induced flooding events. The North American Regional Climate Change Assessment Program (NARCCAP) climate model was applied in this study to quantify the magnitude of future projected storm depths, which are expected to increase due to climate change. Similarly, Personal Computer Storm Water Management Model (PCSWMM) was used to develop a rainfall-runoff simulation model and to assess the effectiveness of three LID techniques (Permeable Pavement, Green Roof, and Bio-Retention Cell) in reducing surface runoff under various climate scenarios. The results revealed that under the climate change scenarios the future projected design depths are expected to increase by up to 104%. Similarly, peak discharge, and total flooding volume were found to increase by 37.72% and 88.73%, respectively under the most extreme climate change scenario. Furthermore, the study demonstrated that applying LID strategies decreases peak discharge, offering a viable solution to tackle flooding events induced by climate change. The results illustrated the performance of permeable pavement was superior in reducing the peak discharge by up to 28.57%. Similarly, applying green roofs and bioretention cells reduced the peak discharge by up to 19.93% and 14.25%, respectively.

A Pulsed-Precipitation Model of Dryland Vegetation Pattern Formation

We develop a model for investigating the impact of rainstorm variability on the formation of banded vegetation patterns in dryland ecosystems. Water input, during rare rainstorms, is modeled as an instantaneous kick to the soil water. The redistribution, from surface water to soil moisture, accounts for the impact of vegetation on infiltration rate and downslope overland flow speed. These two positive feedbacks between water and biomass distributions act on the fast timescales of rain storms. During dry periods, a classic reaction-diffusion framework is used for the slow processes associated with soil water and biomass. This pulsed precipitation model predicts that the preferred spacing of the vegetation bands is determined by the characteristic distance that a storm pulse of water travels overland before infiltrating into the soil. In this way, the vegetation pattern is determined by the fast ecohydrological processes and may be attuned with its dryland precipitation pattern. We demonstrate how this modeling framework, suited for stochastic rain inputs, can be used to investigate possible collapse of a dryland pattern-forming ecosystem under different precipitation patterns with identical low annual mean. Model simulations suggest, for instance, that shorter rainy seasons and greater variability in storm depth may both hasten ecosystem collapse.

Investigation on Variability, Broader Types and Patterns of Daily and Sub-daily Rains Using Satellite Sub-Daily Rainfall Records based in Western Indian Province

The present study was conducted during 2021 at middle Gujarat. The objectives of the study remained inclined towards the challenging task, where limited observed rainfall records (daily and sub-daily both) were utilized to project corresponding regional design hyetographs for providing the design hyetographs at similar ungauged sites in the region. This script explores suitability, appropriateness and end efficacies of blended (ground based as well as remotely sensed) precipitation data attained for a data deprived region, which was ultimately utilized to derive location specific rainfall curves adopting satellite based observed rainfall records. A set of representative type curves on rainfall (daily & sub-daily) for study region were conceived, which could be potentially utilized for better rainfall-runoff modelling even under purely ungauged scenarios. Voluminous precipitation data retrieved via GPM (IMREG) satellite precipitation products was endeavoured and used to create values envelope of design curves for use in ungauged situations. 21 years of daily & sub-daily rainfall records (2000–2020) were thoroughly examined and used to retrieve valid understanding towards annual, monthly, daily and even hourly based variability of rainfall across six different stations as adopted in the study. The net magnitude of dispersals in all the derived sets of mass curves/Hyetographs were worked out and suitably compared across 6 specific locations as well as their categorization in terms of storm depths, storm durations and peak rain intensities which could be of high practical importance for water resource planners other relevant research and development.

Effects of Increasing Rainfall Depths and Impervious Areas on the Hydrologic Responses

Hydrologic modeling is a popular tool for estimating the hydrological response of a watershed. However, modeling processes are becoming more complex due to land-use changes such as urbanization, industrialization, and the expansion of agricultural activities. The primary goal of the research was to use the HEC-HMS model to evaluate the impact of impervious soil layers and the increase in rainfall-runoff processes on hydrologic processes. For these purposes, the Watershed Modelling System (WMS) and Hydrologic Engineering Center’s-Hydrologic Modeling System (HEC-HMS) models were used in this study to simulate the rainfall-runoff process. To compute runoff rate, runoff volume, base flow, and flow routing methods SCS curve number, SCS unit hydrograph, recession, and loss routing methods were selected for the research, respectively. To reduce the processing time and computational complexity, a small section of the Pipestem Creek Watershed was selected to understand the methods and concepts associated with the hydrologic simulation model building. A DEM along with other required data such as land use land cover data, soil type data, and meteorological data was utilized to delineate the watershed in WMS. The output of WMS was utilized to run the HEC-HMS model for five different scenario analyses. All the relevant data were plugged in to the model to get the desired map. Subsequently, outlets at appropriate locations were selected for the sub-basin delineation for further analysis. Finally, the model was parametrized to get successful simulation results. Overall, peak discharges and runoff volumes were increased with increasing storm depths and impervious areas. Peak discharges were increased to 36% and 51% when rainfall depths were increased by 10% and 20% from the initial rainfall depth, respectively. Runoff volumes were also increased to 35% and 49% for the same scenarios, respectively. Peak discharges were increased to 12% and 78% with a 10% and 20%, respectively, increase in impervious areas. The runoff volumes were increased by 12% and 76% when impervious areas were increased by 10% and 20%, respectively. The simulation models responded well, and the peak discharges and runoff volumes increased with increasing storm depths and impervious areas.

Modelling Runoff from Permeable Pavements: A Link to the Curve Number Method

Permeable Pavement (PP) models are valuable tools for studying the implementation of PPs in urban environments. However, the runoff simulated by traditional models such as the Curve Number (CN) is different from that created with PP models, as infiltration is computed differently. However, many investigations compare the runoff created by both models to extract broader conclusions without considering how the two models are related. Hence, this research explores the relation between runoff simulated by one general model, selecting the widespread CN model as a baseline, and the PP model provided in the Storm Water Management Model (SWMM). Correlation was set using the hydrograph created with the CN in a single event as a baseline and obtaining the best pavement permeability value from the PP model by calibration. The influence of storm depth, pavement slope, catchment shape, and PP type was also analysed. Calibration was conducted based on the Nash–Sutcliffe coefficient, but peak and volume performances were also studied. The results show that it is possible to link runoff hydrographs computed with the PP model to those created with the CN method, although that relation is not useful for the entire CN range. That relation is practical for CNs higher than 88 and shall be helpful for urban planners and researchers to compare several pervious/impervious scenarios in urban drainage models more robustly. One direct application is to compare the runoff computed by both models without changing the method that simulates runoff. It shall be enough to change a unique parameter that can be linked to a certain imperviousness by the CN.

Real-time variabilities in microplastic abundance and characteristics of urban surface runoff and sewer overflow in wet weather as impacted by land use and storm factors

Urban surface runoff (USR) and drainage system overflows during wet weather (WWF) play a key role in shaping water pollution. Particularly, the impact of large amounts of microplastic pollution on urban water bodies is unclear. We conducted an in-field investigation in six central urban drainage systems along Suzhou Creek in the Shanghai megacity of China and identified the impacts of storm factors and land use on the real-time dynamic changes in microplastic abundance and characteristics in USR and WWF. Microplastic abundances ranged from 228.3 ± 105.4–4969.51 ± 348.8, 309.3 ± 144.3–5195.8 ± 425.5, and 130.0 ± 30.0–8500.0 ± 1241.0 particles/L in the traffic and residential catchment USR, and the WWF, respectively. Under similar storm factor conditions, we observed correlations between environmental factors and microplastic abundance, especially the polymer type, verifying the significant role of land use. The microplastic abundance were 90.2 particles/L higher in the traffic catchment USR than in the residential catchment USR. Notably, we found unique microplastic polymers comprising ethylene vinyl acetate copolymer and thermoplastic elastomers in the residential and traffic catchment USR, respectively. However, land use had a minimum impact on the size and shape of microplastics: small-sized and film microplastics dominated in both USR types. We found statistical evidence of the widespread correlations between microplastic abundance and storm factors (accumulated storm depth and WWF flow) in both USR and WWF. The first flush phenomenon of microplastic dynamics was found in both USR and WWF. Microplastic characteristics also changed dynamically with storm time. With heavy storm factors, polypropylene and small-sized (<1 mm) microplastics in USR events increased and then decreased. This was also true for WWF events in granular and polyethylene terephthalate microplastics. Our results can facilitate the targeted mitigation of emerging pollutants to enhance stormwater management strategies and prevent future contamination.

Quantifying and reducing the uncertainty in multi-source precipitation products using Bayesian total error analysis: A case study in the Danjiangkou Reservoir region in China

Quantifying and reducing the errors and uncertainties in high-resolution gridded precipitation products are essential for hydrological modelling to generate realistic parameter estimates and accurate model predictions. Formal Bayesian inference provides a powerful tool to quantify rainfall uncertainty and other uncertainty factors in hydrological modelling. An efficient way to reduce rainfall uncertainty in hydrological modelling is to merge precipitation estimates from multiple sources prior to their real-world applications. However, it remains a challenging task to optimally determine the weightings of different precipitation estimates based on the predictive ability of a hydrological model, especially from a probabilistic perspective. This study addresses this issue by evaluating and merging six gridded precipitation products using formal Bayesian inference schemes, including the Markov Chain Monte Carlo (MCMC) approach and Bayesian total error analysis (BATEA). MCMC describes the total predictive uncertainty using residual error models. In contrast, BATEA utilizes the storm depth multiplier approach to separately account for rainfall uncertainty in probabilistic uncertainty quantification. A novel signature of model predictability that combines the reliability and precision of probabilistic model predictions is developed to quantify the predictive ability of a hydrological model from precipitation estimates. Using the proposed predictability signature, we examined the benefit of optimally merging multi-source precipitation estimates in improving the accuracy of hydrologic simulations by applying the method to the Danjiangkou Reservoir region (DRR) between 1998 and 2007. The analysis demonstrates that (1) BATEA, compared to MCMC, can provide more realistic estimates of model parameters and tighter predictive intervals of daily streamflow when using rain gauge observations as well as gridded precipitation products; and (2) the proposed predictability signature is efficient for optimally merging precipitation measurements as it can produce tighter uncertainty bands and better streamflow simulations than individual precipitation estimates. This study provides insights into improved quantification and reduction of uncertainty in multi-source precipitation products for hydrological modelling.

Investigation of contaminant profile in highway stormwater runoff and risk assessment by statistical analysis

Highway stormwater runoff pollution has become a severe risk factor for water bodies nowadays. The conventional risk analysis protocols for directly discharging highway runoff are prone to systematic and judgmental errors. Therefore, a numeric and straightforward risk assessment protocol has been developed in this study that minimizes the errors. For this study, three highway segments were selected in the city of Rawalpindi, Pakistan. Event mean concentrations have been used as baseline numeric values for calculating the risk of discharging highway stormwater directly into the water bodies. These values are also correlated with highway characteristics (area, slope, and traffic count) and storm characteristics (storm depth, cumulative runoff volume, antecedent dry days, and cumulative flow). The highway stormwater was monitored for organics, metals, solids, and macro-nutrients at three highway sections. The event mean concentrations of dissolved organic carbon (50–145 mg/L), total suspended solids (1500–3900 mg/L), chromium (0.25–0.45 mg/L), and lead (0.1–0.8 mg/L) are found to be higher than the environmental quality standards. The risk assessment was conducted by the analysis of variance. The analysis showed that the highway characteristics significantly affect contaminant concentrations, but storm characteristics on contaminant concentrations are not found to be significant. Total suspended solids are the most threatening contaminant in highway runoffs. The study concluded that the risk from contaminants in highway stormwater depends particularly on the specific highway sections’ properties. The first flush portion (initial 25% of runoff) of highway runoff poses a higher threat to the receiving environment than the later runoff volumes.

Hydrological effects of the snow fraction and its ecohydrological explication within the Budyko framework

Understanding the effects of climate and catchment characteristics on hydrological behavior is critical in hydrological sciences. Furthermore, improving the clarity associated with the physical meaning of the Budyko hypothesis on how it can be incorporated into ecohydrological processes remains a significant challenge, while the conciseness it affords in addressing hydrological responses has promoted its application. This study explores the hydrological effects of the snow fraction (fs) and assesses the ecohydrological meaning of the catchment parameter (n) within the Budyko framework, employing 1576 globally investigated catchments. Results showed that the Budyko hypothesis decreased systematic errors (NS = 0.76, RMSE = 181.01 mm a−1) when incorporating the fs compared with no snow fraction incorporated it (NS = 0.54, RMSE = 248.85 mm a−1), especially for catchments with higher fs. In these catchments, a higher fs was generally consistent with a higher normalized mean streamflow (Q), while the opposite was true for normalized evaporation (E/P). Under the Budyko framework, n and fs distribution generally exhibited opposite trends. Moreover, higher n was mainly distributed near water limited and the highest energy limited boundaries, while higher fs represented lower (P-Q)/P (i.e., the mean (P-Q)/P was 0.59, 0.57, and 0.38 for catchments with lower (fs < 0.15), medium (0.15 ≤ fs < 0.50), and higher (fs ≥ 0.50) fs). Statistical relationships among ecohydrological processes and catchment parameter suggested that: (i) higher storm depth (α) generally indicated higher normalized mean Q; (ii) a higher plant-available water holding capacity (κ) resulted in a reduction in Q/P, especially when κ was > 0.12; (iii) the effective rooting depth (Ze) exhibited a complex regulatory effect on P partitioning; and (iv) α, Ze, and κ controlled n by 65%, 22%, and 13%, respectively, in all catchments. The ecohydrological processes incorporated into catchment parameters will broaden the ability of the Budyko hypothesis to describe hydrological behavior in a changing world, especially in the ungauged catchment.

Climate change risk and adaptation costs for stormwater management in California coastal parklands

ABSTRACT Extreme precipitation from climate change may strain many existing stormwater systems. While studies have evaluated such effects on stormwater infrastructure, other sources of uncertainty not yet explored should also be considered. This paper presents an analysis of adaptation costs for new stormwater infrastructure to mitigate increases in design storm precipitation depth with climate change, including how economic and managerial uncertainty related to life cycle unit costs and knowledge of existing infrastructure affect costs. For case study areas in California, we quantify adaptation costs for new green infrastructure capacity by evaluating future design storms. Results indicate that design storm depths increase by an average of 28%, but lack of knowledge of the condition of existing infrastructure and life cycle unit costs result in wide cost ranges. The findings illustrate how climate change planning for stormwater should also consider economic and managerial uncertainty when estimating long-term adaptation costs.

Simulation of Vegetation Cover Based on the Theory of Ecohydrological Optimality in the Yongding River Watershed, China

During ecological restoration, it is necessary to comprehensively consider the state of vegetation in climate–soil–vegetation systems. The theory of ecohydrological optimality assumes that this state tends to reach long-term dynamic equilibrium between the available water supply of the system and the water demand of vegetation, which is driven by the maximization of productivity. This study aimed to understand the factors that affect the spatial distribution of vegetation and simulate the ideal vegetation coverage (M0) that a specific climate and soil can maintain under an equilibrium state. The ecohydrological optimality model was applied based on meteorological, soil, and vegetation data during the 2000–2018 growing seasons, and the sensitivity of the simulated results to input data under distinct vegetation and soil conditions was also considered in the Yongding River watershed, China. The results revealed that the average observed vegetation coverage (M) was affected by precipitation characteristic factors, followed by wind speed and relative humidity. The M, as a whole, exhibited horizontal zonal changes from a spatial perspective, with an average value of 0.502, whereas the average M0 was 0.475. The ecohydrological optimality theory ignores the drought resistance measures evolved by vegetation in high vegetation coverage areas and is applicable to simulate the long-term average vegetation coverage that minimizes water stress and maximizes productivity. The differences between M and M0 increased from the northwest to the southeast of this area, with a maximum value exceeding 0.3. Meteorological factors were the most sensitive factors of this model, and the M0 of the steppe was most sensitive to the stem fraction, mean storm depth, and air temperature. Whether soil factors are sensitive depends on soil texture. Overall, the study of the carrying capacity of vegetation in the natural environment contributes to providing new insights into vegetation restoration and the conservation of water resources.

Soil Moisture Responses to Rainfall: Implications for Runoff Generation

AbstractSoil moisture is a key control on runoff generation and biogeochemical processes on hillslopes. Precipitation events can evoke different soil moisture responses with depth through the soil profile, and responses can differ among landscape positions along a hillslope. We sought to elucidate the nature of these responses by estimating changes in water content, response time between peak precipitation and peak soil moisture, and wetting front velocities for 43 storms at 45 locations on three adjacent hillslopes within a headwater catchment of the southern Appalachian Mountains (NC, USA). We used a multivariate modeling approach to quantify the relative influences and the predictability of soil moisture responses by a combination of landscape and storm characteristics. We quantified the lag correlations between hillslope mean soil moisture and catchment runoff to demonstrate how storm properties and hillslope‐scale characteristics may influence runoff at the catchment outlet. Soil moisture responses varied widely, and no consistent patterns were observed among response metrics laterally or vertically along hillslopes. In contrast to other studies, we found that the relative influence of hillslope properties and storm characteristics varied with soil moisture responses and during storms. Antecedent conditions and storm depths influenced the strength of lag correlations between soil moisture and runoff, whereas storm mean intensity was correlated with the lag times. These results highlight the utility of intensive observations for characterizing heterogeneity in soil moisture responses, suggesting, among other things, a need for better representation of the subsurface processes in rainfall‐runoff models. Identifying the relative importance of drivers can be beneficial in building parsimonious hydrological models.

Plant Species Richness in Multiyear Wet and Dry Periods in the Chihuahuan Desert

In drylands, most studies of extreme precipitation events examine effects of individual years or short-term events, yet multiyear periods (&gt;3 y) are expected to have larger impacts on ecosystem dynamics. Our goal was to take advantage of a sequence of multiple long-term (4-y) periods (dry, wet, average) that occurred naturally within a 26-y time frame to examine responses of plant species richness to extreme rainfall in grasslands and shrublands of the Chihuahuan Desert. Our hypothesis was that richness would be related to rainfall amount, and similar in periods with similar amounts of rainfall. Breakpoint analyses of water-year precipitation showed five sequential periods (1993–2018): AVG1 (mean = 22 cm/y), DRY1 (mean = 18 cm/y), WET (mean = 30 cm/y), DRY2 (mean = 18 cm/y), and AVG2 (mean = 24 cm/y). Detailed analyses revealed changes in daily and seasonal metrics of precipitation over the course of the study: the amount of nongrowing season precipitation decreased since 1993, and summer growing season precipitation increased through time with a corresponding increase in frequency of extreme rainfall events. This increase in summer rainfall could explain the general loss in C3 species after the wet period at most locations through time. Total species richness in the wet period was among the highest in the five periods, with the deepest average storm depth in the summer and the fewest long duration (&gt;45 day) dry intervals across all seasons. For other species-ecosystem combinations, two richness patterns were observed. Compared to AVG2, AVG1 had lower water-year precipitation yet more C3 species in upland grasslands, creosotebush, and mesquite shrublands, and more C4 perennial grasses in tarbush shrublands. AVG1 also had larger amounts of rainfall and more large storms in fall and spring with higher mean depths of storm and lower mean dry-day interval compared with AVG2. While DRY1 and DRY2 had the same amount of precipitation, DRY2 had more C4 species than DRY1 in creosote bush shrublands, and DRY1 had more C3 species than DRY2 in upland grasslands. Most differences in rainfall between these periods occurred in the summer. Legacy effects were observed for C3 species in upland grasslands where no significant change in richness occurred from DRY1 to WET compared with a 41% loss of species from the WET to DRY2 period. The opposite asymmetry pattern was found for C4 subdominant species in creosote bush and mesquite shrublands, where an increase in richness occurred from DRY1 to WET followed by no change in richness from WET to DRY2. Our results show that understanding plant biodiversity of Chihuahuan Desert landscapes as precipitation continues to change will require daily and seasonal metrics of rainfall within a wet-dry period paradigm, as well as a consideration of species traits (photosynthetic pathways, lifespan, morphologies). Understanding these relationships can provide insights into predicting species-level dynamics in drylands under a changing climate.

Effectiveness of Retention Ponds for Sustainable Urban Flood Mitigation across Range of Storm Depths in Northern Tehran, Iran

AbstractAlthough there has been a growing interest in the application of stormwater best management practices, many developing countries still rely solely on traditional practices, such as channels...