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The Soil Conservation Service Curve Number (SCS-CN) method is popular for predicting surface runoff due to its simplicity, ease of application, and widespread acceptance. However, it has limitations, such as the neglect of storm duration, a lack of guidance on antecedent moisture conditions, and the assumption of a constant initial abstraction coefficient (λ = 0.2), leading to uncertainty. Its reliance on static land use classifications and empirical assumptions limits its accuracy across diverse geographic regions and complex hydrological scenarios, particularly under extreme weather conditions. Furthermore, selecting the most suitable watershed CN values remains a subject of global debate. Moreover, the model is widely applied beyond its originally intended purpose. Its basic assumptions, flexibility in dealing with different hydrological conditions, and susceptibility to variables including soil type, land use, and antecedent moisture conditions have all drawn criticism for the method. To overcome the original curve number method limitations, many studies have been made on improving the SCS-CN method. Despite these advancements, significant gaps remain, particularly in the method's applicability across diverse geographic regions and its accuracy in extreme weather events. This paper revisits the popular SCS-CN method, its history, development of methodology, limitations, and refinements that occurred to the original method with the progress of science and technology. It also explores the need for further research to improve its applicability, highlighting opportunities for more robust, flexible runoff estimation models.

Rainfed agriculture in Ethiopia has failed to produce enough food, to achieve the increasing demand for food. Pinpointing the appropriate site for rainwater harvesting (RWH) have a substantial contribution to increasing the available water and enhancing agricultural productivity. The current study related to the identification of the potential RWH sites was conducted at the Chacha watershed central highlands of Ethiopia which is endowed with rugged topography. The Geographic Information System with Analytical Hierarchy Process was used to generate the different maps for identifying appropriate sites for RWH. In this study, 11 factors that determine the RWH locations including slope, soil texture, runoff depth, land cover type, annual average rainfall, drainage density, lineament intensity, hydrologic soil group, antecedent moisture content, and distance to the roads were considered. The overall analyzed result shows that 10.50%, 71.10%, 17.90%, and 0.50% of the areas were found under highly, moderately, marginally suitable, and unsuitable areas for RWH, respectively. The RWH site selection was found highly dependent on a slope, soil texture, and runoff depth; moderately dependent on drainage density, annual average rainfall, and land use land cover; but less dependent on the other factors. The highly suitable areas for rainwater harvesting expansion are lands having a flat topography with a soil textural class of high-water holding capacity that can produce high runoff depth. The application of this study could be a baseline for planners and decision-makers and support any strategy adoption for appropriate RWH site selection.

This paper introduces a graphical user interface (GUI) for the R software that allows the rainfall-runoff relationship to be calculated, using the curve number method. This GUI is a raster-tool whose outputs are runoff estimates calculated using land use/land cover and hydrologic soil group maps. The package allows the user to select among three different antecedent moisture conditions and includes modifications about the initial abstraction parameter. We tested this GUI with data derived from two watersheds in Mexico and the outputs were compared with those produced using a well-established GIS tool in a vector environment. The results produced by these two approaches were practically the same. The main advantages of our package are: (1) ‘Sara4r’ is faster than previous vector based tools; (2) it is easy to use, even for people with no previous experience using R; (3) the modular design allows the integration of new routines; and (4) it is free and open source.

Runoff generation process in any watershed is mainly affected by precipitation, land use and land cover, existing soil moisture conditions and losses. Shallow groundwater table conditions that occur in many regions are known to affect the soil moisture retention capacity, infiltration and ultimately the runoff. A methodology that links soil moisture capacity to the shallow groundwater table or High-Water Table (HWT) using a nonlinear functional relationship within a curve number (CN)-based runoff estimation method, is proposed and investigated using single and continuous event simulation models in this study. The relationship is used to obtain an adjusted CN that incorporates the effect of change in soil moisture conditions due to HWT. The CN defined for average conditions is replaced by this adjusted CN and is used for runoff estimation. A single event model that uses Soil Conservation Service (SCS) CN approach is used for evaluation of variations in runoff depths and peak discharges based on different HWT conditions. A real-life case study from central Florida region in the USA was adopted for application and evaluation of the proposed methodology. Results from the case study application of the models indicate that HWT conditions significantly influence the magnitudes of peak discharge by as much as 43% and runoff depth by 48% as the water table height reaches the land surface. The magnitudes of increases in peak discharges are specific to case study region and are dependent on the functional form of the relationship linking HWT and soil storage capacity. Also, for specific values of HWT, an equivalency between HWT-based CN and wet antecedent moisture condition (AMC)-based CN can be established.

The aim of the present study was to investigate stream turbidity and water chemical parameters under varying environmental conditions. We analyzed a three-year-long (2021-2023) daily and bi-weekly dataset collected at six points (P1-P6) along a small stream. We measured stream water turbidity (FNU), total dissolved inorganic nitrogen (TDIN) content, water pH, and specific conductivity (SPC). Meteorological data were collected at the catchment outlet. Daily data showed a moderate positive correlation between FNU and precipitation (r=0.42, <0.001), while weak negative connections were observed between SPC and FNU values (r=-0.14, =0.011, n=349). The FNU values at the groundwater spring-fed sampling point (P3) were significantly different from the other sampling points on most parameters ( <0.05). The results of the cluster analysis revealed three main clusters based on daily turbidity data. These groups of daily precipitation totals were i) below 4.8 mm, ii) averaging 6.3 mm, and iii) averaging 23.7 mm. The clusters were most significantly separated along precipitation and FNU values. Turbidity values were strongly correlated with precipitation events for two days, after which stream water quality returned to baseline. Stream water quality was not significantly influenced by soil management or antecedent moisture content but rather by water origin (i.e., precipitation, groundwater).

A cost effective and easily applied methodological approach for the identification of the main factors involved in flood generation mechanisms and the development of rainfall threshold for incorporation in flood early warning systems at regional scale is proposed. The methodology was tested at the Pinios upstream flood-prone area in Greece. High frequency monitoring rainfall and water level/discharge time-series were investigated statistically. Based on the results, the study area is impacted by “long-rain floods” triggered by several days long and low-intensity precipitation events in the mountainous areas, that saturate the catchment and cause high flow conditions. Time lag between the peaks of rainfall and water level was 17–25 h. The relationship between cumulative rainfall Rsum on the mountainous areas and maximum water level MaxWL of the river at the particular river site can be expressed as: MaxWL = 1.55ln(Rsum) − 3.70 and the rainfall threshold estimated for the mountainous stations can be expressed as: Rsum = 20.4*D0.3, where D is the duration of the event. The effect of antecedent moisture conditions prior each event was limited to the decrease of the time lag between rainfall and water level response. The limitations of the specific methodological approach are related to the uncertainties that arise due to the other variables contributing to the complex flood generating mechanisms not considered (e.g., the effect of snowmelt and air temperature, soil characteristics, the contribution of tributaries, or the inadequate maintenance of river network that may cause debris accumulation and river bank failure).

Historical records in the Delaware River Basin reveal complex and spatially diverse flood generating mechanisms influenced by the region's mountains‐to‐plains gradients. This study focuses on predicting future flood hazards and understanding the underlying drivers of changes across the region. Using a process‐based hydrological model, we analyzed the hydrometeorological condition of each historical and future flood event. For each event, at the subbasin scale, we identified the dominant flood generating mechanism, including snowmelt, rain‐on‐snow, short‐duration rain, and long‐duration rain. The rain‐induced floods are further categorized based on the soil's Antecedent Moisture Condition (AMC) before the event, whether dry, normal, or wet. Our historical analysis suggests that rain‐on‐snow is the primary flood mechanism of the Upper Basin. Although most frequent, the magnitude of rain‐on‐snow floods is often less severe than short rain floods. In contrast, historical floods in the Lower Basin are primarily caused by short rain under normal AMC. Given the uncertainties in climate projections, we used an ensemble of future climate scenarios for flood projections. Despite variations in regional climate projections, coherent perspectives emerge: the region will shift toward a warmer, wetter climate, with a projected intensification of extreme floods. The Upper Basin is projected to experience a marked decrease in rain‐on‐snow floods, but a substantial increase in short rain floods with wet AMC. The largest increase in flood magnitude will be driven by short rains with wet AMC in the Upper Basin and by short rains with normal AMC in the Lower Basin. Plain Language Summary The Delaware River Basin spans a diverse landscape, from mountains to coastal plains. This geographic diversity makes the region susceptible to various flood events with distinct causes and timing. For example, in spring 2005, a rain‐on‐snow event accelerated the melting of an existing snowpack, leading to significant flooding. Then, in the summer of 2011, heavy rainfall from Hurricane Irene caused widespread inundation in the basin. This study focuses on predicting future flood hazards and understanding the underlying drivers of changes across the region. We found that, historically, the Upper Basin has mostly experienced floods caused by rain‐on‐snow, while the Lower Basin floods have been primarily driven by short, intense rainfalls. Intriguingly, even though rain‐on‐snow floods are most frequent in the Upper Basin, they are often less intense than floods caused by short rain, resulting in high‐frequency, low‐magnitude flood events. As climate models predict a shift toward a warmer and wetter regional climate, simulations suggest that the Upper Basin is likely to experience fewer rain‐on‐snow floods and more short rain‐driven floods intensified by wetter pre‐flood soil conditions. Despite variability in regional climate projections, a prevailing consensus suggests that the region's future floods will be more severe, predominantly driven by short, intense rainfalls. Key Points Historical floods in the Delaware River Basin are primarily from rain‐on‐snow at the Upper Basin and short, intense rain at the Lower Basin Future climate shifts the Upper Basin's primary flood mechanism from rain‐on‐snow to short, intense rain amplified by wetter pre‐flood soils Despite climate projection uncertainties, consensus is future extreme floods, particularly from short rains, will intensify regionally

Soil moisture is the major factor influencing microbial properties and nitrous oxide (N2O) production. Agricultural soils can be probed under wetting, wet/dry alternating, and constant moisture conditions to evaluate the combined effects of early (previous) and immediate (current) moisture on N2O emission and nitrification/denitrification. In view of the water history of upland black soil, five moisture regimes comprising different antecedent and present water holding capacity (WHC) levels were set up in the microcosm study. The 20% WHC was adopted as the initial legacy moisture, while three immediate water statuses include constant WHC, dry-wet cycle, and incremental moisture. Quantitative PCR and 16S rRNA amplicon sequencing were used to assess the impact of current and previous moisture on the bacterial community composition and abundance of nitrification/denitrification genes (amoA, nirS, and nosZ); the soil physicochemical properties, and N2O emission were monitored. The N2O production and nitrifying-denitrifying microbial communities were influenced by the antecedent moisture and pattern of the dry-wet cycle. The nitrifying-denitrifying microbial communities, especially members of β-/γ-Proteobacteria, Bacteroidetes and Gemmatimonadetes, in black soil were important in explaining the variation of N2O production. The key taxonomic groups in response to the moisture alteration, e.g., Acidobacteria, Sphingobacteriia, Deltaproteobacteria, Methylobacterium, Gemmatimonas and Pseudarthrobacter, etc., were also highlighted. The soil nitrate, ammonium nitrogen, N2O emission, nitrification/denitrification and mineralization were profoundly impacted by water regimes and showed statistically significant correlation with specific bacterial genera; the nitrite/nitrate reduction to ammonium could be boosted by high moisture. Both nitrifier denitrification and heterotrophic denitrification could be enhanced substantially when the black soil moisture was increased to above 60% WHC. These findings help evaluate the effects of the water mode on the N2O emission from black soil, as well as the associated impacts on both soil fertility and the global environment.

Event-based rainfall–runoff models are useful tools for hydrologic design. Of the many loss models, the ‘initial loss-continuing loss’ model is widely adopted in practice. Some of the key limitations with these types of loss models include the arbitrary selection of initial moisture (IM) conditions and lack of physically meaningful parameters. This paper investigates the applicability of a physically based soil water balance model (SWMOD) with distributed IM conditions for flood modelling. Four catchments from the east coast of New South Wales, Australia, are modelled. The IM content in SWMOD represents the antecedent moisture condition. A quasi-Monte Carlo simulation framework is adopted, where the IM is stochastically varied according to a lognormal probability distribution. In calibration, it is found that the adopted modelling framework is able to simulate the majority of the observed flood hydrographs with a higher degree of accuracy; however, in a design context, when compared to the results of conventional flood frequency analysis, discrepancies are noted for a range of annual exceedance probabilities. The quasi-Monte Carlo simulation framework proved to be useful in assessing the effect of the IM content on design flood estimates.

Flash droughts have drawn increasing attention due to their rapid onset and severe impacts. However, the physical factors controlling onset speed remain poorly understood across climate regimes. This study presents a global assessment of flash drought onset speed using satellite‐based evaporative stress estimates and Shapley Additive Explanation interpretation to identify dominant hydrometeorological factors that govern drought acceleration. We find that while humid regions experience flash droughts more frequently, events in drylands intensify more rapidly. This contrast reflects differences in energy and water constraints: net radiation plays a greater role in humid regions, whereas surface drying dominates in drylands. Moreover, short‐term antecedent moisture recovery followed by rapid drying accelerates the onset, with soil moisture depths and timescales exerting region‐specific influences. These results reveal climate‐dependent mechanisms underlying flash drought intensification and highlight the need for tailored monitoring strategies in diverse hydroclimatic contexts.