Antecedent Moisture Conditions Research Articles

Estimation of water inflows to reservoirs of irrigation dams in the Guinea Savannah ecological zone of Ghana

ABSTRACT The study was carried out in the northern Ghana to estimate water inflows to nine reservoirs of irrigation dams for a period of 20 years (1999–2018) using the USDA Natural Resources Conservation Service Curve Number model. The key input parameters of the model were hydrologic soil group, rainfall amount, landuse/landcover, weighted curve number, antecedent moisture condition, and potential maximum soil moisture retention. The annual rainfall within the reservoir catchments was found to vary between 617.20 and 1,382.30 mm. The estimated annual runoff depths ranged from 54.10 to 125.55 mm. A high degree of positive correlation was found between rainfall and runoff depths with a coefficient of determination (R2) ranging from 79.80 to 90.70%. Hydrologic soil groups accounted for about 67.2 and 62.5% of the variation in runoff depth and percentage runoff, respectively. The estimated annual water inflows to the reservoirs ranged from 145,206 ± 125,814 m3/y at Gambibgo to 47,074,634 ± 4,395,860 m3/y at Tono. The variation in the annual water inflows and annual storage capacity recharge among the various reservoirs was noted to be influenced by reservoir size, catchment size, rainfall, and soil characteristics in the reservoir catchments.

How Much Does Land-Atmosphere Coupling Influence Summertime Temperature Variability in the Western United States?

Abstract Interannual fluctuations in average summertime temperatures across the western United States are captured by a leading EOF that explains over 50% of the total observed variance. In this paper, we explain the origins of this pattern of interannual temperature variability by examining soil moisture-temperature coupling that acts across seasons in observations and climate models. We find that a characteristic pattern of coupled temperature-soil moisture climate variability accounts for 34% of the total observed variance in summertime temperature across the region. This pattern is reproduced in state-of-the-art global climate models, where experiments that eliminate soil moisture variability reduce summertime average temperature variance by a factor of three on average. We use an idealized model of the coupled atmospheric boundary layer and underlying land surface to demonstrate that feedbacks between soil moisture, boundary layer relative humidity, and precipitation can explain the observed relations between springtime soil moisture and summertime temperature. Our results suggest that antecedent soil moisture conditions and subsequent land-atmosphere interactions play an important role in interannual summertime temperature variability in the western U.S.; soil moisture variations cause distal temperature anomalies and impart predictability at timescales longer than one season. Our results indicate that 40% of the observed warming trend across the western U.S. since 1981 has been driven by wintertime precipitation trends in the U.S. southwest.

Amplified Extreme Floods and Shifting Flood Mechanisms in the Delaware River Basin in Future Climates

AbstractHistorical 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.

Response times as explanatory variable for export of dissolved organic carbon (DOC) from small forested catchments

Rising trends in the concentrations of dissolved organic carbon (DOC) are observed in many inland waters, including the headwater catchment of the Große Ohe river in the Bavarian Forest National Park (Germany). During flood events, DOC is mobilized via different hydrological pathways, affecting the hydrochemistry of aquatic ecosystems and the viability of drinking water supply. In our field experiments we observed different phases of DOC mobilization during six intensively studied rainfall-runoff events with contrasting antecedent wetness conditions. We propose response time diagrams to link the different phases of DOC mobilization to different response times along a hillslope-riparian-zone-transect. Depending on the antecedent wetness conditions, the hillslope and riparian zone participated differently to phases of the runoff event and shaped the flow hydrograph and DOC export at the catchment outlet. The hillslope always responded with little time delay to precipitation events regardless of the antecedent wetness condition. In contrast, response times in the riparian zone varied. For wet antecedent conditions we observed little delay between the hillslope- and riparian zone peak response, which caused high peak discharge, fast DOC mobilization and high DOC export from the catchment. In contrast, for dry antecedent conditions, the riparian zone response was significantly lower and much delayed. This led not only to attenuated peak discharge but also to larger time lags between the flow hydrograph- and the DOC concentration peak. The combination of low runoff rates and delayed DOC concentration peaks resulted into lower DOC export from the catchment. Thus, the antecedent wetness condition and response times of the hillslope and riparian zone are important indicators for DOC export in the catchment.

Baseflow significantly contributes to river floods in Peninsular India

Extreme rainfall prior to a flood event is often a necessary condition for its occurrence; however, rainfall alone is not always an indicator of flood severity. Antecedent wetness condition of a catchment is another important factor which strongly influences the flood magnitudes. The key role of soil moisture in driving floods is widely recognized; however, antecedent conditions of deeper saturated zone may contribute to river floods. Here, we assess how closely the flood magnitudes are associated to extreme rainfall, soil moisture and baseflow in 70 catchments of Peninsular India for the period 1979–2018. Annual flood magnitudes have declined across most of the catchments. Effect of flow regulations is also assessed to understand the impact of human interventions on flood characteristics. Reservoir regulation has positive effect by reducing the flood peak and volume, whereas the duration of flood events has increased after the construction of dams. Baseflow exhibits similar patterns of trends as floods, whereas trends in rainfall and soil moisture extremes are weakly correlated with trends in flood magnitudes. Baseflow is found to be more strongly influencing the flood magnitudes than soil moisture at various time lags. Further analysis with event coincidence analysis confirms that baseflow has stronger triggering effect on river floods in Peninsular India.

Drivers of widespread floods in Indian river basins

Abstract Widespread floods affecting multiple subbasins in a river basin have implications for infrastructure, agriculture, environment, and groundwater recharge. However, the crucial linkage between widespread floods and their drivers remains unexplored for Indian sub-continental river basins. Here, we examine the occurrence and drivers of widespread flooding in seven Indian sub-continental river basins during the observed climate (1959-2020). The peninsular river basins have a high probability of widespread flooding, compared to the transboundary basins of Ganga and Brahmaputra. Favorable antecedent baseflow and soil moisture conditions, uniform precipitation distribution, and precipitation seasonality determine the probability of widespread floods in Indian river basins. The widespread floods are associated with large atmospheric circulations that cause precipitation in a large part of a river basin. Our findings highlight the prominent drivers and mechanisms of widespread floods with implications for flood mitigation in India.

Pluvial Flood Risk Assessment in Urban Areas: A Case Study for the Archaeological Site of the Roman Agora, Athens

Ancient monuments located in urbanized areas are subject to numerous short- and long-term environmental hazards with flooding being among the most critical ones. Flood hazards in the complex urban environment are subject to large spatial and temporal variability and, thus, require location-specific risk assessment and mitigation. We devise a methodological scheme for assessing flood hazard in urban areas, at the monument’s scale, by directly routing rainfall events over a fine-resolution digital terrain model at the region of interest. This is achieved using an open-source 2D hydraulic modelling software under unsteady flow conditions, employing a scheme known as ‘direct rainfall modelling’ or ‘rain-on-grid’. The method allows for the realistic representation of buildings and, thus, is appropriate for detailed storm-induced (pluvial) flood modelling in urbanized regions, within which a major stream is usually not present and conventional hydrological methodologies do not apply. As a case study, we perform a pilot assessment of the flood hazard in the Roman Agora, a major archaeological site of Greece located in the center of Athens. The scheme is incorporated within an intelligent decision-support system for the protection of monumental structures (ARCHYTAS), allowing for a fast and informative assessment of the flood risk within the monument’s region for different scenarios that account for rainfall’s return period and duration as well as uncertainty in antecedent wetness conditions.

An empirical rainfall threshold approach for the civil protection flood warning system on the Milan urban area

Non-structural risk mitigation tools such as civil protection alerts for citizens proves highly beneficial in minimizing the impacts linked to floods. Flood forecasting represents a challenge due to complex and non-linear hydrological processes involved, especially in highly urbanized areas. In this study, a Flood Warning System (FWS) based on the development of catchment-specific empirical Rainfall Thresholds (RTs) is proposed.Seven river catchments in the “Hydraulic node of Milan,” northern Italy, were analyzed using a dataset of 25 years (1998–2022) of hourly rainfall and discharge data.An empirical methodology, based only on historical rainfall-runoff data and applicable to any river catchment, is proposed with the aim to validate and improve the existing Rainfall Threshold (RT) defined on the same area by the Lombardy Region civil protection.The RTs obtained using the proposed method showed improvements with respect to the existing civil protection RTs, because it allows to derive time-continuous and catchment-specific RTs. Additionally, accounting for the Antecedent Moisture Conditions (AMC) with the proposed “equivalent rainfall” approach results in more accurate RTs, suggesting its consideration for issuing civil protection alerts. The accuracy and uncertainty of the RTs were analyzed by means of binary classification measures coupled with bootstrap resampling.The proposed procedure for constructing RTs, which is applicable to any river catchment having sufficiently long time series of rainfall and runoff data, and not necessarily on urban areas only, indicates potential for being an additional and simple FWS to mitigate flood risks for civil protection purposes.

A time-varying Copula-based approach to quantify the effects of antecedent drought on hot extremes

Hot extremes may adversely impact human health and agricultural production. Owing to anthropogenic and climate changes, the close and dynamic interaction between drought and hot extremes in most areas of China need to be revisited from the perspective of nonstationarity. This study therefore proposes a time-varying Copula-based model to describe the nonstationary dependence structure of summer extreme temperature (SET) and antecedent soil moisture condition to quantify the dynamic risk of hot extremes conditioned on dry/wet condition. A general statistical inspection procedure which was composed of maximum likelihood (ML)-based estimation, nonstationary Goodness-of-fit (GOF) tests, the log likelihood ratio (LR) tests and minimum corrected Akaike information criterion-based selection was promoted to select the best-fitted nonstationary models efficiently. This study proposed a new approach to identify the soil moisture driving law over extreme temperature from the point view of tail monotonicity and nonstationary risk assessment. Owing to the LTI-RTD (left tail increasing and right tail decreasing) tail monotonicity for dependence structure of these two extremes derived from most areas, two kinds of the driving laws of soil moisture over SET were detected. Because of the spatiotemporal divergence of sensitivity index derived from tail monotonicity (SITM), we can conclude that the spatial and temporal heterogeneity of response degree of ET over the variations of antecedent dry/wet conditions is evident. Incorporation of nonstationarity and tail monotonicity helps identify the changes of driving mechanism (laws) between soil moisture and hot extremes.

The effects of multilayer blue-green roof on the runoff water quality

In the context of climate changes, characterized by an increase of short but intense rainfall events and rise of the average temperature, the fast population growth and consequent urbanization require the implementation of innovative solutions to mitigate pluvial floods and, at the same time, reduce the water demand. Among the different nature-based solutions, multilayer blue-green roofs have been widely recognized for their high capacity of reducing runoff generation from rooftops, and their additional storage layer enables to collect water, which could be reused for different purposes. However, the quality of the collected water in a multilayer blue-green roof and the influence that the additional storage layer has on it have not been analysed yet. Following this knowledge gap, we investigated the potential benefits of a multilayer blue-green roof installed in Cagliari, with respect to a traditional roof. The outflow triggered by artificial irrigation and natural rainfall events was analysed, both from a quantitative and qualitative perspective. Results confirm the high contribution of multilayer blue-green roofs in mitigating runoff generation, which is however influenced by antecedent soil moisture and water level conditions. The outflow from the multilayer blue-green roof presents lower suspended solids and heavy metals concentrations than from a traditional roof. On the other hand, Carbon Oxigen Demand (COD) concentrations in the multilayer blue-green roof outflow exceed the limits defined by the Italian regulations (125 mg/l) for water discharge or reuse, partially due to the high residence time in the storage layer. Specific treatments could be planned to reuse the collected water for urban purposes.

A drought monitor for Australia

The UniSQ Drought Monitor and Australian Combined Drought Indicator (CDI) are introduced. The objective of these products is to use a multi-index approach to fully capture the long- and short-term consequences of drought as an information service for the public. Since drought is a complex phenomenon relying on multiple variables such as rainfall evaporative demand, and antecedent soil moisture conditions, monitoring drought using a single index or indicator is not always appropriate.The Drought Monitor was developed using a normalized linear combination with weighting determined by PCA. It was evaluated against observed wheat yield as well as total pasture growth simulated by the AussieGRASS model. The results indicate that there was a significant positive correlation with both. The Drought Monitor was also well-received by survey respondents and has the potential to become a valuable drought monitoring tool for identifying drought impacts and related risks.

Local‐ and network‐scale influence of peatlands on boreal catchment response to rainfall events

AbstractBoreal catchments are composed of different land covers, such as forests, peatlands and lakes, which differ in their runoff response to rainfall events. Understanding the individual and combined responses to rainfall events of these different land cover types is crucial for predicting potential impacts of future climate conditions on boreal water cycling. A common assumption is that peatlands attenuate peak flows, which is used as a motivation to restore drained boreal wetlands. However, it remains unclear how and to what extent peatlands can affect peak flow response. Only a few previous studies have looked at the hydrologic dynamics of peatlands in response to specific rainfall events across a wide range of nested sub‐catchments with varying peatland cover. In this study, we use nine years of hourly hydrometric data from 14 catchments within the Krycklan Catchment Study in northern Sweden to examine how peatlands contribute to flood attenuation at both local and stream network scales. Our analysis at the local scale demonstrated that during large events with low antecedent wetness conditions, peatland‐dominated catchment exhibited more muted responses compared to the similar‐sized forest‐dominated catchment. However, during events with high antecedent wetness conditions, the peatland‐dominated catchment exhibited flood magnitudes similar to the forest‐dominated catchment, although the elevated flow condition at the peatland‐dominated catchment persisted for longer periods. Finally, our analysis revealed no significant influence of peatlands on the attenuation or amplification of floods at the stream network scale.

Quantifying the relative contributions of different flood generating mechanisms to floods across CONUS

The occurrence of flooding in a catchment is attributable to various mechanisms. Several researchers have used linear models to quantify the relative contributions of different flood generating mechanisms. However, the use of linear models for this quantification may not be suitable given the complex and nonlinear processes from climate to floods. In this study, we used nonlinear machine learning models with interpretability methods to quantify the contributions of multiple flood generating mechanisms to floods across the continental United States (CONUS). Four flood generating mechanisms were considered comprehensively, which varied in terms of the influence of antecedent soil moisture condition or snowmelt on flood generation. These mechanisms involved three common mechanisms (rainfall, rainfall excess, and snowmelt/rain on snow) and one complex mechanism (effective precipitation) that accounts for snowmelt/rain on snow lost to unsaturated soil zone. Consistent with previous studies, we observed that rainfall alone (i.e., without considering other hydrometeorological processes) is seldom the dominant mechanism for flood generation. Floods in most catchments are largely attributable to rainfall excess and snowmelt. Unlike previous studies, we noted that in the midwest and northeastern CONUS, snowmelt/rain on snow upon saturated soil was the greatest contributor to local flood generation. This finding highlights that both snowmelt and soil moisture condition affect flood generation in the midwest and northeastern CONUS, instead of only snowmelt as has been reported previously. Overall, this study provides new insights into flood generating mechanisms in CONUS and demonstrates the potential of applying interpretable machine learning to examine flood generating mechanisms.

The effect of antecedent soil moisture conditions on soil nitrous oxide and dinitrogen dynamics after wetting: An intact soil core study

Ruminant excreta urinary‑nitrogen (N) deposited onto pasture soil generates inorganic-N concentrations in excess of the pasture's immediate requirements. Consequently, nitrous oxide (N2O) and dinitrogen (N2) may be emitted from the urine-affected soil via denitrification. Soil moisture plays a key role in determining the end-product of denitrification. Although the effects of soil moisture and drying-wetting cycles on soil N2O production have been widely studied using sieved soils in a controlled environment, these experiments may not reflect the actual conditions in the field due to the lack of plant cover and the disturbed soil structure. To address this an experiment was performed using intact soil cores (7.5 cm depth), with pasture plants present, under controlled environmental conditions (20 °C, 8 h of light per day). The 15N flux method was used to investigate how antecedent soil moisture affected soil N2O production and consumption in a pasture soil after wetting with a nitrate (NO3−) solution. The results showed that the antecedent dry condition (25 % WFPS, Dp/Do = 0.1783) increased the magnitude of the N2O flux peak after the soil was wetted (75 % WFPS, Dp/Do = 0.006) when compared to antecedent wet conditions (50 % WFPS, Dp/Do = 0.0503). There were no differences in the denitrification and nitrification related functional gene abundances between the two antecedent conditions post-wetting, indicating that the effect was not regulated by microbial changes but by the intensity of the “Birch effect” induced by soil wetting. The N2O reduction related gene (nosZII) abundance decreased after wetting with the NO3− solution, indicating a suppression effect on N2O consumption from the NO3− applied that overrode the potential “Birch effect” on N2 emission. The results of the study indicate that rewetting of antecedent dry soil increase N2O emissions but not N2 emissions when the soil NO3− concentration is high.

Consideration of vegetation interception of rainfall within the SCS-CN model: Application to the west bank of Dianchi Lake

Study regionThe west bank of Dianchi Lake, China. Study focusThe Soil Conservation Service Curve Number (SCS-CN) model is widely used to simulate rainfall runoff of mountain floods. The determination of the CN parameter within the SCS-CN model is key to accurately simulating runoff. CN is closely related to regional land use, soil type, antecedent moisture conditions and slope. Therefore, this study established an optimized SCS-CN model by comprehensively considering the above factors, particularly the interception of rainfall by vegetation. New hydrological insights for the region(1) The original CN value resulted in underestimation of surface runoff in the study area [Nash-Sutcliffe Efficiency Coefficient (NSE) = 0.2260], indicating that regional differences reduce the applicability of the original CN value; (2) the optimized model considering the slope, antecedent rainfall, and other single or combined factors generated simulations of runoff that were closer to observations (NSE = 0.6811); the accuracy of the CN value can be effectively improved by combining various factors; however, since the CN had not yet reached the optimal value, simulations remained inaccurate; (3) consideration of interception of rainfall by vegetation in the model greatly improved the accuracy of simulated runoff [NSE = 0.8620]; indicating that the impact of vegetation interception of rainfall on surface runoff cannot be ignored. The results demonstrated that the SCS-CN model can provide more accurate simulations of mountain flood runoff in the study area after considering multiple factors, such as vegetation interception of rainfall.

On the role of climate change in the 2018 flooding event in Kerala

The extreme precipitation during August 2018 in Kerala, India was catastrophic, triggering one of the worst floods in history. There is growing evidence of human-induced climate change in driving hydroclimatic extremes across the globe. However, whether and to what degree the 2018 flooding event in Kerala was influenced by climate change has yet to be fully understood. To this end, we present the first formal attribution analysis of the event, using the probabilistic event attribution (PEA) framework. Three methods using (i) Historical and HistoricalNat runs from CMIP6 (general circulation models-based method), (ii) observed records from 1901–2018 for two periods, split at 1950 (time-slice method) and (iii) observations that are scaled to 1901 and 2018 climates (scaling method), are considered for quantifying the risk ratio (RR) of the event. Using an objective approach, the 2018 precipitation event is defined by the return period of the 4 day cumulative precipitation over the Periyar River Basin (PRB), during 15–18 August, 2018. The subsequent flood event is characterized by the return period of the 1 day maximum streamflow at one of the outlets of the PRB, where maximum impact during the event was reported. The results from multiple methods are consistent, suggesting that the event is exceptionally less likely to have been caused by anthropogenic climate change, with RR for the precipitation and flood events ranging from 0.31 to 0.82 and 0.55 to 0.8, respectively. The role of wet antecedent soil moisture conditions, which is found to be the primary driving factor of floods in the PRB, is also found to be unchanged between simulations with and without climate change. Our results highlight the challenges in unequivocal discerning of the climate change signal on regional hydrological events and emphasize the importance of better consideration of local confounding interventions in PEA studies.

Warm Season Extreme Flood Events in the Midwestern US—Sources of Moisture and Physical Mechanisms

AbstractA comprehensive picture of the development of warm season extreme floods in the Midwestern US is presented. We first identify the climatological moisture sources for precipitation in the Midwest using the two‐layer dynamic recycling model (2L‐DRM) with ECMWF Reanalysis v5 (ERA5) data. Terrestrial sources supply most of the moisture for Midwestern US precipitation during the warm season, while oceanic sources dominate during the cold season. The Empirical Orthogonal Function (EOF) analysis is used to select extreme flood events characterized by both positive soil moisture and precipitation anomalies. During the warm season flood events, moisture coming from oceanic sources increases by more than 45% compared to the climatology. In addition, our results show that moisture can come from remote regions due to sustained anomalous circulation. Low‐level circulation anomalies associated with wave trains that traverse the continent enhance moisture contributions from terrestrial sources along narrow paths. However, moisture budget analysis reveals that the primary flood‐producing mechanism is the convergence of moisture due to intense circulation anomalies. Moisture advection and thermodynamic terms are responsible for flood termination. Our results suggest that knowledge of antecedent wet soil moisture conditions is unlikely to improve the predictability of flood‐producing storms.

River Geomorphology Affects Biogeochemical Responses to Hydrologic Events in a Large River Ecosystem

AbstractShifts in the frequency and intensity of high discharge events due to climate change may have important consequences for the hydrology and biogeochemistry of rivers. However, our understanding of event‐scale biogeochemical dynamics in large rivers lags that of small streams. To fill this gap, we used high‐frequency sensor data collected during four consecutive summers from a main channel and backwater site of the Upper Mississippi River. We identified high discharge events and calculated event concentration‐discharge responses for both physical‐chemical (nitrate, turbidity, and fluorescent dissolved organic matter) and biological (chlorophyll‐a and cyanobacteria) constituents using metrics of hysteresis and slope. We found a range of responses across events, particularly for nitrate. Although fluorescent dissolved organic matter (FDOM) and turbidity exhibited more consistent responses across events, contrasting hysteresis metrics indicated that FDOM was flushed to the river from more distant sources than turbidity. Biological responses (chlorophyll a and cyanobacteria) differed more between sites than physical and chemical constituents. Lastly, we found that the event characteristics best explaining concentration responses differed between sites, with event magnitude more frequently related to responses in the main channel, and antecedent wetness conditions associated with response variation in the backwater. Our results indicate that event responses in large rivers are distinct across the diverse habitats and biogeochemical components of a large floodplain river, which has implications for local and downstream ecosystems as the climate shifts.

Exploring extreme rainfall-triggered landslides using 3D unsaturated flow, antecedent moisture and spatially distributed soil depth

The susceptibility to shallow landslides is affected by several factors. Rainfall intensity, soil thickness distribution, and antecedent moisture conditions are components that influence the spatio-temporal prediction of landslides at the watershed scale. Yet, the combined impact of these factors on the susceptibility to landslides has been overlooked. The purpose of this paper is to investigate landslides triggered by extreme precipitation in the Tijuca Massif, Rio de Janeiro, southeastern Brazil. Our approach couples a 3D variably saturated flow solver with the infinite slope stability method to calculate the statistical distributions of the safety factor and the pore pressure at the soil–bedrock interface. Numerical simulations were performed for 6 scenarios considering 1-year spin-up time for soil moisture and different spatially distributed soil depths. The results show that during extreme precipitation events, soil depth and initial moisture conditions may not have much influence on the safety factor, as the intensity and duration of rainfall will be the triggering agents. Comparison of simulated unstable zones and field data from mapped landslide scars further supported this conclusion. We conclude that simple soil depth models are feasible options for regional studies of landslide susceptibility. Our findings are relevant to understanding shallow landslides induced by extreme rainfall in Rio de Janeiro and other regions with similar geological and climate settings.

Flood generating mechanisms investigation and rainfall threshold identification for regional flood early warning

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).