Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m3). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

Modeled forest conversion influences humid tropical watershed hydrology more than projected climate change

In the humid tropics, forest conversion and climate change threaten the hydrological function and stationarity of watersheds, particularly in steep terrain. As climate change intensifies, shifting precipitation patterns and expanding agricultural and pastoral land use may effectively reduce the resilience of headwater catchments. Compounding this problem is the limited long-term monitoring in developing countries for planning in an uncertain future. In this paper, we asked which change, climate or land use, more greatly affects stream discharge in humid tropical mountain watersheds? To answer this question, we used the process-based, spatially distributed Soil Moisture Routing model. After first evaluating model performance (Ns = 0.73), we conducted a global sensitivity analysis to identify the model parameters that most strongly influence simulated watershed discharge. In particular, peak flows are most influenced by input model parameters that represent baseflow and shallow subsurface soil pathways while low flows are most sensitive to antecedent moisture, macropore hydraulic conductivity, soil depth and porosity parameters. We then simulated a range of land use and climate scenarios in three mountain watersheds of central Costa Rica. Our results show that deforestation influences streamflow more than altered precipitation and temperature patterns through changes in first-order hydrologic hillslope processes. However, forest conversion coupled with intensifying precipitation events amplifies hydrological extremes, reducing the hydrological resilience to predicted climate shifts in mountain watersheds of the humid tropics. This finding suggests that reforestation can help mitigate the effects of climate change on streamflow dynamics in the tropics including impacts to water availability, flood pulses, channel geomorphology and aquatic habitat associated with altered flow regimes.

Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

The Soil Conservation Service Curve Number Model is a conceptual model intended for estimating effective rainfall (ER). This model is grounded in a parameter – referred to as Curve Number (CN), which is determined from information on the characteristics of the watershed. The Standard Method (M1) for determining the CN is based on soil and land-use tables; however, some authors have proposed alternative methodologies for defining the CN value from monitored rainfall-runoff events, such as those described by Hawkins (1993) (M2), Soulis and Valiantzas (2012) (M3), and Soulis and Valiantzas (2013) (M4). The objective of this study was to evaluate the impact of using these methods for determination of the CN parameter on the estimation of ER, taking as reference forty rainfall-runoff events monitored between 2015 and 2018 in the Cadeia River Watershed, which has characteristics of the Pampa biome. The different methods assessed for definition of the CN parameter resulted in contrasting performances with respect to the estimation of ER for CRW, as the following findings: i) M1 gave ER values with little reliability, mainly due to the classification of antecedent moisture content classes; ii) M3 provided the best results in determining ER, followed by M2; and iii) the ER values estimated according to M4 differed from those observed, mainly for events with lower rainfall depths.

The importance of antecedent vegetation and drought conditions as global drivers of burnt area

The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥ 1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (∼ 4 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.

Hydrological Analysis of Extreme Rain Events in a Medium-Sized Basin

The hydrological response of a medium-sized watershed with both rural and urban characteristics was investigated through event-based modeling. Different meteorological event conditions were examined, such as events of high precipitation intensity, double hydrological peak, and mainly normal to wet antecedent moisture conditions. Analysis of the hydrometric features of the precipitation events was conducted by comparing the different rainfall time intervals, the total volume of water, and the precedent soil moisture. Parameter model calibration and validation were performed for rainfall events under similar conditions, examined in pairs, in order to verify two hydrological models, the lumped HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System model) and the semi-distributed HBV-light (a recent version of Hydrologiska Byråns Vattenbalansavdelning model), at the exit of six individual gauged sub-basins. Model verification was achieved by using the Nash–Sutcliffe efficiency and volume error index. Different time of concentration (Tc) formulas are better applied to the sub-watersheds with respect to the dominant land uses, classifying the Tc among the most sensitive parameters that influence the time of appearance and the magnitude of the peak modeled flow through the HEC-HMS model. The maximum water content of the soil box (FC) affects most the peak flow via the HBV-light model, whereas the MAXBAS parameter has the greatest effect on the displayed time of peak discharge. The modeling results show that the HBV-light performed better in the events that had less precipitation volume compared to their pairs. The event with the higher total precipitated water produced better results with the HEC-HMS model, whereas the rest of the two high precipitation events performed satisfactorily with both models. April to July is a flood hazard period that will be worsened with the effect of climate change. The suggested calibrated parameters for severe precipitation events can be used for the prediction of future events with similar features. The above results can be used in the water resources management of the basin.

Influence of infiltration on soil erosion in green infrastructures

Rainwater-induced erosion in green geotechnical infrastructures such as a multilayered landfill cover system (MLCS) is a severe concern in the current era. Although vegetation is a proven measure to control erosion in the MLCS, there are other factors such as infiltration rate which influence the control of the phenomenon. Most of the existing studies are limited to understand influence of vegetation on erosion control or infiltration rate alone. In this study, an attempt is made to incorporate infiltration measurements alongside vegetation cover to understand erosion in surface layer of the MLCS. For this purpose, a pilot MLCS was constructed, and erosion of its surface soil was temporally evaluated through soil loss depth of eroded cover surface under the influence of natural as well as simulated rainfall conditions. Alongside erosion, the amount of vegetated cover was evaluated through photographic image analyses and infiltration rate was measured by mini disk infiltrometer. From the observed results, it is understood that soil erosion and infiltration rate depict a contrasting behaviour with growing vegetation. Antecedent moisture contents were observed to show greater influence on such erosion behaviour which was observed during the testing period. Such studies may be helpful to researchers and practicing engineers for understanding performance of various green geotechnical infrastructures and scheduling the maintenance services to increase the longevity of their layered soil systems.

Capturing the transient hydrological response in sandy soils during a rare cloudburst associated with shallow slope failures—a case study in the Atlantic Highlands, New Jersey, USA.

A cloudburst on August 7, 2018, in the coastal bluffs of the Atlantic Highlands, New Jersey induced flooding, erosion, and multiple shallow slope failures that adversely impacted the surrounding hillside residential area. Historically, short-duration deluges are rare in the New York Bay region, with only eight cloudbursts of greater magnitude documented since 1948. The coastal bluffs consist of a variably thick, sandy surficial material overlying flat lying, mostly non-indurated Cretaceous and Tertiary sediments, including some low permeability glauconitic units. The bluffs have been impacted by both historical deep-seated and shallow landslide movement, the latter typically related to heavy, relatively long-duration rainfall associated with tropical cyclones and nor'easters. The shallow hydrological response during the rare cloudburst was captured at two hydrological monitoring sites and yielded insights into rapidly changing moisture conditions resulting in slope failure. Additional information is provided on historical cloudbursts that have affected the region, antecedent moisture conditions, and documented landslide types and processes.Supplementary material: A USGS ScienceBase data release of the time-series monitoring data accompanies the publication of this paper https://doi.org/10.5066/P9A601HC.

Modeling the Physiological Responses of a Desert Shrub to Rainfall Pulses in an Arid Environment

<p>A physically-based model for soil-plant-atmosphere continuum (SPAC) is parameterized and evaluated against field-measured physiological responses of a desert shrub, Haloxylon Ammodendron (HA), to rainfall pulses in a desert environment in northwestern China. Despite its simplicity, the model was successfully employed to assess the complexity and uncertainty involved in the physiological responses of HA following pulsed rainfall events. Through modelling efforts, we report a systematic evaluation of the non-linear relationship between the physiological responses of HA and pulse magnitude or antecedent moisture. The results show that following the rainfall pulses, the modeled daily transpiration and assimilation rates either stayed the same or decreased monotonically with water stress. However, the stomatal conductance (g<sub>s</sub>) and photosynthetic rate (A<sub>n</sub>) responses were relatively weaker when compared to the increase in water potential. We found that rainfall events with <5 mm cannot induce any substantial response of A<sub>n</sub> (Δ<4μmol m<sup>-2</sup>s<sup>-1</sup>), and at least 13 mm of rain is required to increase A<sub>n</sub> by 10 μmol m<sup>-2</sup>s<sup>-1</sup>. Significant responses of water use efficiency (WUE) were not even discernible from viewing the simulation. Our analysis reproduced the judgements with a certain uncertainty that HA is basically a kind of drought resistant species, it tends to have a more conservative water-use strategy and thus a safer photosynthetic behavior. The inverse–texture hypothesis is much more clearly supported by the modeling experiments, suggesting that soil texture drives differences in the effects of pulses on the magnitude and sensitivity of the physiological responses of plants, and the interaction between rainfall and soil texture may lead to the preferred acquisition and use of pulsed precipitation by HA. The modelling work and findings in this study is likely to shed light on the quantitative understanding of the physiological behavior of other plants in water-limited environments.</p>

Flood and center timing in a changing world

<p>Climate change is expected to change the pattern of rainfall resulting in changed flood magnitude. However, in large part due to interannual variability, identifying a climate change signal in flood magnitude remains difficult. As an alternative to investigating trends in flood magnitude, it has been suggested that trends in flood timing, that is, the day of annual streamflow maxima, may be a detectable trend due climate change.</p><p>Here, using high-quality data from around the world, trends in flood and center timing are investigated. We begin by standardizing the data on a local definition of water year. We find an interesting property, that after standardization, the flood and centre timing of streamflow can be approximated by a normal distribution. Moreover, we find that without the standardization on local water year the calculated trend can reverse. We proceed by analyzing trends in centre and flood timing globally using linear regression.</p><p>Results are commensurable with large-scale climatic change. But, unlike changes in extreme rainfall, trends are not spatially consistent. Flood timing is shifting to earlier in the year in the tropics, and later in the year in the extra-tropics, consistent with changes in mean rainfall and flood magnitude. There is evidence of a reversal of trends post-drought, suggesting that the mechanisms controlling flooding at a catchment scale are changing as a result of climate change. It is concluded that trends in flood timing are related to flood generating mechanisms, and largely modulated by changing antecedent moisture conditions.</p>