Hydrological Hazards Research Articles

Explaining Factors Affecting Flood Evacuation From the Point of View of Stakeholders: A Content Analysis Study

Background: Floods are a natural hydrological hazard that causes human and environmental damage. In the response phase, evacuation is necessary to reduce deterioration. This study explains the influential factors on flood evacuation based on the opinion of the stakeholders. Materials and Methods: This is a conventional content analysis study conducted in 2021. A total of 27 participants were selected using the purposive sampling method. Data collection was performed using semi-structured interviews. Interviews continued until information saturation was reached. The recorded interviews were handwritten word by word and then analyzed. The criteria for entering the study included flood experience, the ability to communicate, and willingness to participate. The period of the interview was 40 to 70 min. Written consent was obtained from the interviewees for audio recording. The Granheim and Lundman method was used for data analysis. This study’s reliability was based on the Goba and Lincoln method. Results: Data analysis extracted four main categories with ten sub-categories, including human factors (a subcategory of individual characteristics and risk perception), social factors (a sub-category of cultural conditions and financial status), geographical factors (a sub-category of urban location and climate conditions), and infrastructure factors (a sub-category of facilities related to transportation tools and routes, urban constructions, communication infrastructure, and meteorological facilities). Conclusion: Reaching a successful evacuation requires determining the relevant factors. These factors include human, social, geographic, and infrastructure factors. The findings of this study can help managers in flood management.

Accidents With Oil Spills Triggered by Natural Hazards

In addition to direct social and economic damage, natural hazards also have a devastating impact on technological facilities, which may trigger accidents at these infrastructures. Especially dangerous consequences for the population and the environment are caused by such natural hazard triggered technological accidents (Natechs), which may result in the release of various hazardous chemical substances including mineral oil. This paper analyzes Natech accidents involving spills of oil and oil products. The materials of the author’s database of technological and natural hazard triggered technological accidents occurred in Russia since 1991 are used as initial information for the analyses. The types of natural hazards and adverse natural phenomena that caused accidents with oil spills on the territory of Russia in 1991-2021 are identified. Natechs make up about 6% of all accidents with oil spills. The main triggers of these accidents are hydrological and meteorological hazards such as strong winds, storms, floods, melt waters, rainfalls, lightning, snow loads, and heat wave, as well as landslides and other ground movements. Earthquakes caused the largest damage, but they are less frequent events in Russia. More often, mineral oil leaks occur as a result of ship accidentsor the destruction of oil storage facilities and pipelines. The regions of the greatest risk of accident occurrences with oil spills in Russia are identified.

Spatiotemporal Changes and Hazard Assessment of Hydrological Drought in China Using Big Data

The intensification of the regional water cycle resulting from climate change, coupled with the influence of human activities, has brought about alterations in the frequency, scale, and intensity of droughts. In this study, based on hydrological big data and the standardized runoff drought index (SRI), the multi-scale spatiotemporal evolution of hydrological drought in China from 1948 to 2014 was analyzed using the run-length theory and gravity center model. Meanwhile, the hydrological drought hazard index was constructed to analyze the distribution of the hazard levels of drought in China. The results showed that, during 1948~2014, there was an opposite spatial distribution between the average intensity and the average coverage–duration–frequency of drought in the Yellow River Basin, Haihe River Basin and southeastern river basins. The drought situation in most river basins in China has shown an aggravating trend, among which the southeastern river basins, Haihe River Basin, Songliao River Basin and Pearl River Basin have generally shown an aggravating trend. The drought situation in China was severe in the 1950s and 1960s, gradually reduced in the 1970s, 1980s, and 1990s, and the drought situation was the mildest in the 1990s. After entering the 21st century, the drought situation began to worsen sharply. Meanwhile, from 1948 to 2014, the hazard level of drought generally presented a pattern of high in the west and north, and low in the east and south. The hazard levels of drought in the northwest and northeast were generally higher, and those in the southwest and southeast regions were generally lower. In general, the hazard levels of drought were relatively high in most areas of China.

Characterization of flood and drought hazards on the Gereb-Geba water supply dam in the semi-arid northern Ethiopian highlands

Evaluating the flood and drought hazards provides vital information for sustainable water resources management, particularly in semi-arid, water-deficit environments. Most prior studies were limited in exploring the flood and drought hazards, which are important for early warning systems and preparedness. This study characterized the hydrological extreme hazards on the Gereb-Geba reservoir, namely the Suluh, Genfel, and Agula rivers. Flood frequency analysis was performed using the fitted flood frequency distribution in MATLAB. The 2D hydrodynamic model HEC-RAS was implemented to produce a flood-inundation map. Meteorological, agricultural, and hydrological droughts were analyzed using the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), and Streamflow Drought Index (SDI), respectively. Using the Generalized Extreme Value (GEV), the estimated flood magnitude showed an increasing tendency in all the rivers across all the return periods (2-, 5-, 10-, 20-, 50-, and 100-years). The reservoir inundated an area of 12.8 km2 at an elevation of 1830 m.a.s.l. with a water depth of 80 m at the outlet. Suluh experienced more severe to extreme hydrological drought episodes than the Agula and Genfel rivers. Severe to extreme meteorological droughts were also observed in the respective catchments. Moreover, severe agricultural drought prevalence was also detected across all the river catchments. This study provides vital and comprehensive flood and drought information for water resources planning, management, and development.

Geomorphologic risk zoning to anticipate tailings dams' hazards: A study in the Brumadinho's mining area, Minas Gerais, Brazil

The use of tailings dams in the mining industry is recurrent and a matter of concern given the risk of collapse. The planning of tailings dam's emplacement usually attends construction design criteria and site geotechnical properties, but often neglects the risk of installing the depositional facilities in potentially unstable landscapes, namely those characterized by steep slopes and(or) high drainage densities. In order to help bridging this gap, the present study developed a framework model whereby geomorphologic vulnerability is assessed by a set of morphometric parameters (e.g., drainage density; relief ratio; roughness coefficient). Using the Ribeirão Ferro-Carvão micro-basin (3265.16 ha) as test site, where six dams currently receive tailings from the mining of iron-ore deposits in the Brumadinho region (Minas Gerais, Brazil) and one has collapsed in 25 January 2019 (the B1 dam of Córrego do Feijão mine of Vale, S.A.), the risk of dam instability derived from geomorphologic vulnerability was assessed and alternative suitable locations were highlighted when applicable. The results made evident the location of five dams (including the collapsed B1) in high-risk regions and two in low-risk regions, which is preoccupying. The alternative locations represent 58 % of Ribeirão Ferro-Carvão micro-basin, which is a reasonable and workable share. Overall, the study exposed the fragility related with tailings dams' geography, which is not restricted to the studied micro-basin, because dozens of active tailings dams exist in the parent basin (the Paraopeba River basin) that can also be vulnerable to geomorphologically-dependent hydrologic hazards such as intensive erosion, valley incision or flash floods. Attention to this issue is therefore urgent to prevent future tragedies related with tailings dams' breaks, in the Paraopeba River basin or elsewhere, using the proposed framework model as guide.

Atmospheric River Sequences as Indicators of Hydrologic Hazard in Historical Reanalysis and GFDL SPEAR Future Climate Projections

AbstractWhen multiple atmospheric rivers (ARs) occur in rapid succession, the combined effect on the hydrologic system can lead to more flooding and damage than would be expected from the individual events. This temporally compounding risk is a source of growing concern for water managers in California. We present a novel moving average‐based definition of AR “sequences” that identifies the time periods of elevated hydrologic hazard that occur during and after consecutive AR events. This marks the first quantitative evaluation of when temporal compounding is contributing to AR flood risk. We also assess projected changes in sequence frequency, intensity, and duration in California using the Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and EArth system Research (GFDL SPEAR) global coupled model. Sequence frequency increases over time and is fairly uniform across the state under both intermediate (SSP2–4.5) and very high (SSP5–8.5) emissions scenarios, with the largest changes occurring by the end of the century (+0.72 sequences/year in SSP2–4.5, +1.13 sequences/year in SSP5–8.5). Sequence intensity and duration both see increases in the medians and extreme values of their respective distributions relative to the historical baselines. In particular, “super‐sequence” events longer than 60 days are projected to occur 2–3x more frequently and to emerge in places that have never seen them in the historical record. In a world where California precipitation is becoming more variable, our definition of sequences will help identify when and where hydrologic impacts will be most extreme, which can in turn support better management of the state's highly variable water resources and inform future flood mitigation strategies.

Integrated framework for hydrologic modelling in data-sparse watersheds and climate change impact on projected green and blue water sustainability

Climate and hydrologic hazards pose a threat to the distribution of watersheds’ water resources in time and space, necessitating planning for sustainable resilience and adaptation. Hydrologic modelling has emerged as a potential solution for understanding watershed responses to projected climate change, and a prediction model that can deliver actionable information is necessary, although it requires basin-scale observations to calibrate the model to reliably predict basin-scale water resources hazards. Such luxury is not always tenable in watersheds with inadequate ground-based observation. However, satellite-based evapotranspiration (ET) data coupled with a machine learning feature selection as a data refinement process has made integrated water balance modelling widely regarded as a viable alternative for improving the capability of watershed modelling processes in data-sparse regions. This study developed a convincing hydrologic model framework to sufficiently calibrate and provide accurate behavioural solutions for all model responses. The framework was applied to four sub-basins that form the larger Lake Chad basin. The model results were applied to assess the dynamic changes in projected blue and green water resource sustainability in response to climate change in one of the sub-basins. Study findings indicate that hydrologic fluxes can be simulated accurately with varying degrees of acceptability, with R2 and NSE values in the range of 0.69–0.88 and 0.45–0.77 for calibration and 0.69–0.79 and 0.34–0.63 for validation, respectively, and captured within a satisfactory uncertainty range of P-factor and R-factor values of 0.68–0.93 and 0.73–1.31, respectively, in 83%, 67%, 85.7%, and 81.3% of the sub-watersheds based on multi-site simulation despite distinct watershed morphology, although there are significant trade-offs in parameter sensitivity. Whilst green water is the dominant freshwater component across the basin relative to blue water, climate change may be a significant factor influencing changes in the projected green water sustainability status, and the combination of socioeconomic drivers and climate change may significantly impact the projected blue water sustainability status across the basin. Projected changes in the green and blue water sustainability status have shown that more than 50% of the watershed will become ecologically fragile. In addition, the identified freshwater geographic sustainability hotspots may be beyond restoration without adequate long-term river basin water resource plans.

Characterizing CONUS-wide spatio-temporal changes in daily precipitation, flow, and variability of extremes

The hydrological cycle strongly relies on the spatial and temporal variability of precipitation, which has been considerably affected by climate change. Focusing on CONUS, this research extended the analysis by evaluating the variability of daily concentrations of various components of the hydrological cycle in terms of the Gini index and its potential effects on floods and droughts by integrating historical data with an existing database of extreme events. Differences were found in the geographical location of significant correlations among the hydrological variables investigated, implying that the impacts of variability are neither spatially nor temporally uniform. The work also showed the importance of selecting thresholds of precipitation and flow to define “extreme” events. We identified a precipitation deficit threshold at the 5th percentile and an extreme precipitation threshold at the 95th percentile as the most representative. Moreover, for flood, the 95th percentile and for hydrological drought, the 5th percentile were the most representative. Spatially the study showed that, the concentration of precipitation deficit and evapotranspiration had a major impact on the occurrence of hydrological hazards, such as droughts and floods.

Macroscale composite principal-monotonicity distributed projection of climate-induced hydrologic changes: Tempo-spatial variabilities, and variation decompositions

A macroscale composite principal-monotonicity distributed hydro-projection system is developed through hybridizing advanced statistical hydroclimatic systems analysis methods of verified superiorities. The system can enhance accuracies and feasibilities of climate-informed hydrologic projections, quantitatively reveal climate-induced hydro-magnitude and -variability changes, and decompose variations of the changes with factors such as time, space, scales and greenhouse gas (GHG) emissions. The system of high adaptability for worldwide watersheds is applied to a representative large cold-region watershed, Athabasca River Basin in Canada. Incremental ratios of river discharges with climate change are projected to enlarge at fine timescales, e.g., from 3.4% (octo-decadal) to 7.0% (monthly). According to mixed-level multifactorial analyses, variations of the ratios are dominated by catchments (59.2%) and GHG emissions (19.6%) at annual and longer timescales, and the dominance increases with timescales. At intra-annual timescales, the dominator becomes interactive tempo-spatial variations (62.6%) of the ratios. GHG emissions and their interaction with catchments pose significant or even leading effects on trends of discharge changes. Ratios of changes (i.e., decreases mostly) in tempo-spatial variabilities of discharges are generally the most significant for interannual variabilities, in warm seasons, at downstream catchments, under high GHG emissions, and by the late 21st century. Their variation is dominated by individual factorial effects (78.0%), e.g., catchments (50.3%) and GHG emissions (16.2%); the dominance declines from interannual (89.0%), temporal (79.4%), intra-annual (75.6%) to spatial (67.9%) variabilities. Interactions of the factors in explaining variations of spatial and interannual variabilities are non-negligible in warm and cool seasons, respectively. These findings can help advance macroscale distributed climate-informed hydrologic projections and, accordingly, scientize assessment, adaptation and mitigation of hydrologic hazards over climate-sensitive cold regions.

Spatial Modeling through GIS Analysis of Flood Risk and Related Financial Vulnerability: Case Study: Turcu River, Romania

The present study is part of the context in which Romania adopted the European Parliament Directive 2007/60/EC on flood risk assessment and management. Therefore, the aim of this research is to assess the risk induced by a hydrological hazard, expressed by a financial value estimation, for the Turcu River in the northern sector of the Bran–Dragoslavele transcarpathian corridor (Romania), an important tourist axis where the pressure on land has increased considerably. As a result, the intra-village areas of Moieciu de Sus, Cheia, Moieciu de Jos, Bran and Tohanu Nou have also expanded into areas vulnerable to flooding. There are currently no studies on the areas potentially affected as well as the extent of the possible damage. For this reason, we proceeded to model the water level corresponding to the maximum flow value with a probability exceeding 1%, using HEC-RAS and ArcGIS software. The results of the implementation of the spatial analysis model resulted in the delineation of the floodplain and the assessment of the potential financial loss related to the minimum market value of the land with the related real estate infrastructures. The research reveals that in the 1% band area (78.7841 ha) with water depth > 0.5 m, more than 433 infrastructures are at high risk of flooding, most of them with high real estate value, i.e., 5.61 km of roads for which a cost of EUR 3,402,666.90 was calculated for restoration. A knowledge of financial vulnerability to flooding becomes important for the community; local authorities involved in making decisions for insuring real estate at risk and planning/managing investments work to prevent/combat the effects of flooding.

Evaluating the representation of disaster hazards in SNOMED CT: gaps and opportunities.

Climate change, an underlying risk driver of natural disasters, threatens the environmental sustainability, planetary health, and sustainable development goals. Incorporating disaster-related health impacts into electronic health records helps to comprehend their impact on populations, clinicians, and healthcare systems. This study aims to: (1) map the United Nations Office for Disaster Risk Reduction and International Science Council (UNDRR-ISC) Hazard Information Profiles to SNOMED CT International, a clinical terminology used by clinicians, to manage patients and provide healthcare services; and (2) to determine the extent of clinical terminologies available to capture disaster-related events. Concepts related to disasters were extracted from the UNDRR-ISC's Hazard Information Profiles and mapped to a health terminology using a procedural framework for standardized clinical terminology mapping. The mapping process involved evaluating candidate matches and creating a final list of matches to determine concept coverage. A total of 226 disaster hazard concepts were identified to adversely impact human health. Chemical and biological disaster hazard concepts had better representation than meteorological, hydrological, extraterrestrial, geohazards, environmental, technical, and societal hazard concepts in SNOMED CT. Heatwave, drought, and geographically unique disaster hazards were not found in SNOMED CT. To enhance clinical reporting of disaster hazards and climate-sensitive health outcomes, the poorly represented and missing concepts in SNOMED CT must be included. Documenting the impacts of climate change on public health using standardized clinical terminology provides the necessary real time data to capture climate-sensitive outcomes. These data are crucial for building climate-resilient healthcare systems, enhanced public health disaster responses and workflows, tracking individual health outcomes, supporting disaster risk reduction modeling, and aiding in disaster preparedness, response, and recovery efforts.

Learning from history of natural disasters in the Sahel: a comprehensive analysis and lessons for future resilience.

One of the first environmental crises to attract interest in development initiatives and aid was the great drought of the 1970s in the Sahel. This study investigates the extent of damage caused by natural disasters from one of the most widely used databases-EM-DAT-with a sample size of 16 Sahelian countries over the period 1960-2020. These countries have been divided into three regions: Western Africa Sahel (WAS), Central Africa Sahel (CAS), and Eastern Africa Sahel (EAS). The analyses encompass four categories of natural hazards, namely, biological, climatological, hydrological, and meteorological. We used descriptive and test statistics to summarize the natural disaster records. Through this approach, we explore tendencies to identify the most frequently reported natural hazards; we examine their spatial distribution and evaluate their impacts in terms of socioeconomic damage and causalities. During the study period, a total of 1000 events were recorded in the database. The Western Africa Sahel (WAS) region had the highest number of disasters, with 476 events, followed by the Eastern Africa Sahel (EAS) region with 369 events. The most common hazards in the Sahel were hydrological (41.8%), mainly floods, and biological (39.5%) hazards. Approximately 300 million people in the Sahel were affected by natural hazards, with 59.17% in EAS, 36.48% in WAS, and 4.35% in CAS. Although droughts occurred less frequently (14%), they had a significant impact on the population, affecting 84% of those affected by natural hazards. In general, EAS experiences a higher impact from natural hazards, potentially influenced by the pastoral lifestyle of its population. However, WAS is also very vulnerable to natural hazards especially epidemics and nowadays floods. The uncontrolled urbanization in the area may contribute to this vulnerability.

Evaluating Hydrological Drought Risk in Lithuania

Hydrological drought poses a major global challenge, exacerbated by climate change and increasing water demand, leading to water scarcity, environmental degradation, and socioeconomic impacts. Thereby, there is a need for comprehensive methods to assess and predict hydrological droughts. The methodology part was based on the calculation of hydrological drought risk components—hazard and vulnerability—according to the equal weight scale of each variable. The spatial distribution of point values was performed by the inverse distance weighting interpolation method. To calculate indices, the spatial layer overlapping of variables was performed using the Raster Calculator tool. Statistical tools were used to estimate drought risk in river catchments. As a result, three main maps were prepared: The hydrological drought hazard index, the hydrological drought vulnerability index, and the hydrological drought risk. These maps highlight regional variations in drought hazards, vulnerability, and risk. Hazard and risk index values are higher in the northern part of Lithuania and lower in the south. The central region exhibits the highest percentage of areas at high and very high risk; the western region shows less risk due to a maritime climate; and the Southeastern region demonstrates the lowest susceptibility to hydrological drought due to physical-geographical factors.

Simulating Changes in Hydrological Extremes—Future Scenarios for Morocco

This paper presents a comprehensive river discharge analysis to estimate past and future hydrological extremes across Morocco. Hydrological simulations with historical forcing and climate change scenario inputs have been performed to better understand the change in magnitude and frequency of extreme discharge events that cause flooding. Simulations are applied to all major rivers of Morocco, including a total of 16 basins that cover the majority of the country. An ensemble of temperature and precipitation input parameter sets was generated to analyze input uncertainty, an approach that can be extended to other regions of the world, including data-sparse regions. Parameter uncertainty was also included in the analyses. Historical simulations comprise the period 1979–2021, while future simulations (2015–2100) were performed under the Shared Socioeconomic Pathway (SSP) 2–4.5 and SSP5–8.5. Clear patterns of changing flood extremes are projected; these changes are significant when considered as a proportion of the land area of the country. Two types of basins have been identified, based on their different behavior in climate change scenarios. In the Northern/Mediterranean basins we observe a decrease in the frequency and intensity of events by 2050 under both SSPs, whereas for the remaining catchments higher and more frequent high-flow events in the form of flash floods are detected. Our analysis revealed that this is a consequence of the reduction in rainfall accumulation and intensity in both SSPs for the first type of basins, while the opposite applies to the other type. More generally, we propose a methodology that does not rely on observed time series of discharge, so especially for regions where those do not exist or are not available, and that can be applied to undertake future flood projections in the most data-scarce regions. This method allows future hydrological hazards to be estimated for essentially any region of the world.

VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments

Meteorological drought is a common hydrological hazard that affects human life. It is one of the significant factors leading to water and food scarcity. Early detection of drought events is necessary for sustainable agricultural and water resources management. For the catchments with scarce meteorological observatory stations, the lack of observed data is the main leading cause of unfeasible sustainable watershed management plans. However, various earth science and environmental databases are available that can be used for hydrological studies, even at a catchment scale. In this study, the Global Drought Monitoring (GDM) data repository that provides real-time monthly Standardized Precipitation and Evapotranspiration Index (SPEI) across the globe was used to develop a new explicit evolutionary model for SPEI prediction at ungauged catchments. The proposed model, called VMD-GP, uses an inverse distance weighting technique to transfer the GDM data to the desired area. Then, the variational mode decomposition (VMD), in conjunction with state-of-the-art genetic programming, is implemented to map the intrinsic mode functions of the GMD series to the subsequent SPEI values in the study area. The suggested model was applied for the month-ahead prediction of the SPEI series at Erbil, Iraq. The results showed a significant improvement in the prediction accuracy over the classic GP and gene expression programming models developed as the benchmarks.

Historical information sheds new light on the intensification of flooding in the Central Mediterranean

Hydrological disasters, such as floods, can have dire consequences for human societies. Historical information plays a key role in detecting whether particular types of hydrological disasters have increased in frequency and/or magnitude and, if so, they are more likely attributable to natural or human-induced climatic and other environmental changes. The identification of regions with similar flood conditions is essential for the analysis of regional flooding regimes. To this end, we here present the longest existing flood reconstruction for the Eastern Liguria Area (ELA) in northwestern Italy, covering 1582 to 2022 CE, which offers a case study representative of the central Mediterranean region. An Annual Flood Intensification Index was developed to transform the historical data into a continuous annual hydrological time-series contained by a homogeneous data structure for the study-area. We found two change-points (trend breaks) in the reconstructed time-series, in 1787 and 1967, with only occasional heavy floods comparable to present-day disasters occurring before the first change-point, and an increasing intensification of floods after the second change-point up to the present day. The recent intensification of flooding in the ELA, associated with changes in land use and land cover, also appears to coincide with phases in which hydrological hazards have become more changeable and extreme in disaster-affected areas. This is evidenced by river basin responses to human-induced disturbances.

Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models.

Land subsidence (LS) as a major geological and hydrological hazard poses a major threat to safety and security. The various triggers of LS include intense extraction of aquifer bodies. In this study, we present an LS inventory map of the Daumeghan plain of Iran using 123 LS and 123 non-LS locations which were identified through field survey. Fourteen LS causative factors related to topography, geology, hydrology, and anthropogenic characteristics were selected based on multi-collinearity test. Based on the results, five susceptibility maps were generated employing models and input data. The LS susceptibility models were evaluated and validated using the receiver operating characteristic (ROC) curve and statistical indices. The results indicate that the LS susceptibility maps produced have good accuracy in predicting the spatial distribution of LS in the study area. The result showed that the optimization models BA and GWO were better than the other machine learning algorithm (MLA). In addition, The BA model has 96.6% area under of ROC (AUROC) followed by GWO (95.8%), BART (94.5%), BRT (93.1%), and SVR (92.7%). The LS susceptibility maps formulated in our study can serve as a useful tool for formulating mitigation strategies and for better land-use planning.

Guidance for parameterizing post‐fire hydrologic models with in situ infiltration measurements

AbstractWildfire can alter soil‐hydraulic properties, often resulting in an increased prevalence of infiltration‐excess overland flow and greater potential for debris‐flow hazards. Mini disk tension infiltrometers (MDIs) can be used to estimate soil hydraulic properties, such as field‐saturated hydraulic conductivity (Kfs) and wetting front potential (Hf), and their spatial variability following wildfire. However, the small (point‐scale) footprint of MDI measurements makes it challenging to use these data to parameterize hydrologic models at the hillslope and watershed scales where hydrologic hazards, such as debris flows, initiate. Here, we designed numerical experiments to estimate spatially constant or watershed‐scale effective hydrologic parameters (EHPs) that approximate the response of spatially variable hydrologic parameters with distributions derived from MDI measurements at five sites in the southwestern United States. We found that it is possible to define EHPs for both Kfs and Hf based on the MDI measurements that lead to reasonable approximations of run‐off hydrographs at the outlets of small watersheds (<1 km2). We found that watershed EHPs are functions of rainfall characteristics, although they are most sensitive to rainfall intensity and relatively less sensitive to the temporal distribution of rainfall. EHPs are lower than the arithmetic mean of the MDI measurements and are better approximated by the median or geometric mean of the MDI measurements, particularly for storms with recurrence intervals of approximately 1 year or less that commonly initiate post‐fire debris flows. This work demonstrated that using the proposed upscaling method to estimate watershed‐scale EHPs, as opposed to approximating EHPs based on the arithmetic mean of the MDI measurements, improved the ability of a hydrologic model to identify storms that are likely to produce debris flows. Results improved our ability to link point‐scale MDI measurements and watershed‐scale EHPs in post‐fire settings and helped guide our ability to use MDI data to parameterize post‐fire hydrologic models.

Improving global hydrological simulations through bias-correction and multi-model blending

There is an immediate need to develop accurate and reliable global hydrological forecasts in light of the future vulnerability to hydrological hazards and water scarcity under a changing climate. As a part of the World Meteorological Organization’s (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we investigated different approaches to blending multi-model simulations for developing holistic operational global forecasts. The ULYSSES (mULti-model hYdrological SeaSonal prEdictionS system) dataset, to be published as “Global seasonal forecasts and reforecasts of river discharge and related hydrological variables ensemble from four state-of-the-art land surface and hydrological models” is used in this study. The first step for improving these forecasts is to investigate ways to improve the model simulations, as global models are not calibrated for local conditions. The analysis was performed over 119 different catchments worldwide for the baseline period of 1981–2019 for three variables: evapotranspiration, surface soil moisture and streamflow. This study evaluated blending approaches with a performance metric based (weighted) averaging of the multi-model simulations, using the catchment’s Kling-Gupta Efficiency (KGE) for the variable to define the weight. Hydrological model simulations were also bias-corrected to improve the multi-model blending output. Weighted blending in conjunction with bias-correction provided the best improvement in performance for the catchments investigated. Applying modelled weights during blending original simulations improved performance over ungauged catchments. The results indicate that there is potential to successfully and easily implement the bias-corrected weighted blending approach to improve operational forecasts globally. This work can be used to improve water resources management and hydrological hazard mitigation, especially in data-sparse regions.

Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework

Study regionA total of 11,913 sub-catchments in Peru and transboundary catchments with neighboring countries in South America. Study focusThis paper aims to develop a national hydrological model using physiographic and climatic characteristics to identify donor and receptor sub-catchments (sub-zones). Therefore, we use the hydrometeorological PISCO dataset (0.1º x 0.1º) to drive a sub-catchment conceptual rainfall-runoff (ARNO/VIC) model and a river-routing (RAPID) model in thousands of river reaches. We identify 43 hydrological zones (with 122 sub-zones) to run the hybrid hydrological modeling framework (ARNO/VIC+RAPID) with previously calibrated and validated parameters with 43 fluviometric stations for 1981–2020. Simulated flow series show a higher performance at daily scale (KGE ≥ 0.75, NSEsqrt ≥ 0.65, MARE ≤ 1, and −25% ≤ PBIAS ≤ 25%) for catchments located at the Pacific coast and the Andes-Amazon transition, and good representation (R≥0.75) of seasonal and interannual variability. New hydrological insights for the regionIncreasing hydrological hazards such as floods highlight the importance of a systematic hydrological analysis and modeling at national level in gauged and ungauged catchments in Peru. This study introduces a new hydrological dataset of simulated daily flow series. The results are helpful for short-term flood risk scenario simulations and long-term water resource planning as the outcomes can provide valuable information for hydrologists and water resource managers in Peruvian regions with limited or no access to in-situ networks.