Catchment Characteristics Research Articles

Assessment of different interpolation algorithms for daily rainfall spatial distribution in the Var catchment, France

AbstractTo have effective water resource management, the distributed hydrological models are commonly applied for supporting the decision‐making processes. Among different inputs, the spatial distributed rainfall plays significant role in those model simulations. Many interpolation methods have been developed for generating distributed rainfall based on measurement samples. However, depending on the catchment characteristics and data availability, the suitable interpolation algorithm is case‐dependent. This paper presents one operational approach for determining the resonable interpolation algorithm in a complex large catchment (Var catchment, France). Based on the daily rainfall data (2008–2014) collected from 16 stations in the Var catchment, six different interpolation approaches including: inverse distance weight (IDW), spline, kriging with linear and spherical semi‐variogram models and geographically weighted regression considering elevation effects and the combined impacts of elevation and distance to the sea were tested. Integrated the results of statistical and modeling assessments, the 400 m resolution distributed rainfall generated by IDW algorithm shows high preference in generating distributed rainfall in the Var catchment. Moreover, the strategy described in the article also shows promising acceptability for other catchments.

Assessment of the hydro-geochemical characteristics of a Tropical River catchment in the southern Western Ghats, India

Assessment of the hydro-geochemical characteristics of a Tropical River catchment in the southern Western Ghats, India

Impacts of changing weather patterns on the dynamics of water pollutants in agricultural catchments: Insights from 11-year high temporal resolution data analysis

Widespread and long-term shifts in weather patterns are contributing to further degradation of surface water quality. This challenge caused by the increasing frequency of extreme weather events requires appropriate adaptation of current mitigation strategies. But to confirm the need to redesign such strategies, an understanding of the impacts of increasing weather extremes on pollutant losses in different catchment types is required. With this in view, this study investigated the impact of changing weather patterns on the inter-seasonal and inter-annual dynamics of nutrient losses in six agricultural catchments in Ireland over 11 years. The high temporal resolution data (10-min) from these intensively managed catchments represented different characteristics and management practices. Mann-Kendall Trend Analysis and Generalised Additive Models were used to study nutrient concentration trends, and to investigate the significance of water discharge, precipitation, potential evapotranspiration, soil moisture deficit, air temperature, and soil temperature on the losses of nutrients, respectively. The analysis of historical data revealed changes in the trends of daily average nitrate (NO3-N), phosphorus (P), and suspended sediment (SS) concentrations in association with significant increasing trends in air temperature, soil temperature, and precipitation across the same month over 11 years of monitoring. While discharge was significantly contributing to the concentrations of NO3-N, P, and SS across different catchments, air and soil temperature were significantly correlated to NO3-N losses, and precipitation was the major contributor to regulating P (total P and total reactive P) concentrations. In short, air temperature, soil temperature, soil moisture deficit, and precipitation were the main climatic drivers regulating the nutrient concentrations while the soil chemistry and drainage status were the non-climatically related drivers. The results revealed that the extent of the impact of climatic drivers depends on catchment characteristics. Therefore, expanding the application of this type of study would facilitate better understanding of current and future challenges to water management and provision of climate-resilient mitigation strategies for different catchment typologies.

Validating the Effect of Topography and Geology on Rainfall–Runoff in Mountainous Catchments Using the Improved HYdrologic CYcle Model

ABSTRACTRainfall–runoff characteristics of mountainous catchments are affected by many factors, such as topography and geology. Traditionally, the effects of geology on rainfall–runoff characteristics have been explained using geology, but the differences in runoff characteristics within the same geological settings have not been examined. These differences can be expressed as differences between the hydrological model parameters. However, the effects of geology on the model calculations have not yet been clarified. Thus, this study aims to clarify the effects of topography and geology on model calculations using an improved HYdrologic CYcle (HYCY) model that considers bedrock infiltration. Runoff observations were conducted for approximately 3 years in 19 catchments at 2 sites located in sedimentary rock and granite mountains. Rainfall was recorded at each site. The observed hydrographs were used to optimise the parameters for each catchment using the least‐squares method. The relationship between parameter m and the soil layer storage was calculated using the optimised parameters, representing the percentage of the area contributing to runoff. Furthermore, these results were compared with observational analysis results. The improved HYCY model accurately represented all 19 runoffs. When the total precipitation in 1 event exceeded 200 mm, parameter m became ~1 and ~0.3–0.4 in sedimentary rock and granitic catchments, respectively, which shows the effect of geology. The effects of topography on the parameters were exhibited in Kc and Kb, which calculated the storm flow from the channels and baseflow hydrographs, respectively. However, the parameter distributions exhibited geological differences, namely in parameter Kh, Kb and m. The parameter Kh calculates the overland flow hydrograph. This implies that geological differences affect the probability of the overland flow generation rate and the recession hydrographs of the overland flow and baseflow.

Operational sensitivity analysis of flooding volume in urban areas

We focus on a probability-based approach to analyze the flooding volume during extreme rainfall events in urban areas. Our approach considers uncertainty in both non-operational and operational variables. The former are quantities that are tied to the characterization of rainfall events and key catchment features, whose uncertainties stem from incomplete characterization. The latter comprise elements of the system on which actions can be taken within a range of design values. As the non-deterministic nature of these two types of quantities differs at a fundamental level, we rely on an operational Global Sensitivity Analysis theoretical framework that explicitly recognizes this distinction. As a test bed to showcase our approach, we consider an urban catchment in Northern Italy. We assess the sensitivity of the flooding volume to a set of operational variables, such as diameter and roughness of a set of conduits of the sewer network as well as potential improvement of the infiltration capacity of the urban catchment. We show that our approach can identify the operational configuration with the highest effectiveness in mitigating instances of flooding, taking into account the uncertainties in the non-operational quantities that drive the system behavior.

Runoff Change Characteristics and Response to Climate Variability and Human Activities Under a Typical Basin of Natural Tropical Rainforest Converted to Monoculture Rubber Plantations

Climate variability and human activities are major influences on the hydrological cycle. However, the driving characteristics of hydrological cycle changes and the potential impact on runoff in areas where natural forests have been converted to rubber plantations on a long-term scale remain unclear. Based on this, the Mann–Kendall (MK) and Pettitt breakpoint tests and the Double Mass Curve method were employed to identify the variation characteristics and breakpoints of precipitation (P), potential evapotranspiration (ET0), and runoff depth (R) in the Wanquan River Basin (WQRB) during the 1970–2016 period. The changes in runoff attributed to P, ET0, and the catchment characteristics parameter (n) were quantified using the elastic coefficient method based on the Budyko hypothesis. The results revealed that the P and R in the WQRB exhibited statistically insignificant decreasing trends, while ET0 displayed a significant increasing trend (p < 0.05). The breakpoint of runoff changes in the Jiabao and the Jiaji stations occurred in 1991 and 1983, respectively. The runoff changes show a negative correlation with both the n and ET0, while exhibiting a positive correlation with P. Moreover, it is observed that P and ET0 display higher sensitivity towards runoff changes compared to n. The decomposition analysis reveals that in the Dingan River Basin (DARB), human activities account for 53.54% of the runoff changes, while climate variability contributes to 46.46%. In the Main Wanquan River Basin (MWQRB), human activities contribute to 46.11%, whereas climate variability accounts for 53.89%. The research findings suggest that runoff is directly reduced by climate variability (due to decreased P and increased ET0), while human activities indirectly contribute to changes in runoff through n, exacerbating its effects. Rubber forest stands as the prevailing artificial vegetation community within the WQRB. The transformation of natural forests into rubber plantations constitutes the primary catalyst for the alteration of n in the WQRB. The research findings provide important reference for quantifying the driving force of hydrological changes caused by deforestation, which is of great significance for sustainable management of forests and water resources.

Pattern-based assessment of the influence of catchment characteristics on urban stormwater quality

ABSTRACT Use of traditional catchment characteristics and lack of effective separation of catchment characteristics from rainfall characteristics limits the adequate understanding of the actual influences of catchment characteristics on stormwater quality. In this context, this study adopted a pattern-based separation approach to identifying common events from three rainfall clusters of study catchments, where rainfall characteristics for identified common 8, 9 and 36 events are similar, but catchment characteristics are different. This study identified that the locations of pervious surfaces and urban forms are also important catchment characteristics. The contribution of the pervious area to runoff is significant for long durational (cluster 1) and high-intensity (cluster 3) rainfall events associated with low antecedent dry days and initiated at around 35 mm rainfall depth. A catchment having a large impervious area and pervious area located at far distances from the drainage system can contribute more pollutant load at first 10% runoff volume. High socio-economic developed urban form can contribute a high fraction wash-off corresponding to 10–70% runoff volume due to traffic-related behaviour and various anthropogenic activities. The developed knowledge will overcome the shortcomings of lumped characteristics-based treatment design and improve the efficiency of current stormwater treatment system design practices.

Linking curve number with environmental flows: a novel approach.

Change in flow regime is one of the major reasons which influence the services offered by rivers and integrity of their aquatic ecosystems requiring certain amount of flow in the river known as environmental flow. In this study, the environmental flows described by Tennant's method are correlated with a very versatile index defined in terms of Curve Number (CN) that incorporates hydrometeorological and geomorphological characteristics of catchment. Parameter CN is commonly known to be closely related with catchment characteristics (viz., land use, soil type, etc.). The rainfall-runoff data of 17 catchments from five major Indian river basins (i.e., Brahmani-Baitarani, Godavari, Mahanadi, Mahi, and Narmada) of low flow season (October-June) are used to derive the relationship between percentage of average annual flow (%AAF) and CN. The %AAF is seen to increase linearly with increasing CN, and vice versa, for all catchments and the correlation (R) is found to be greater than 0.7 for 16 (out of 17) catchments. The correlation between %AAF and CN is maximum for Dhariawad (R2 = 0.899) and Baronda (R2 = 0.814) catchments, indicating excellent relationship between %AAF and CN, which can be useful for the environmental flow prediction based on catchment characteristics. The existence of such a relationship is further strengthened by the relation established between the standard normal deviate of CN and the Standard Flow Index (SQI), a variation of %AAF, using the same field data.

Trends and Drivers of Water Temperature Extremes in Mountain Rivers

AbstractWater temperature extremes can pose serious threats to the aquatic ecosystems of mountain rivers. These rivers are influenced by snow and glaciermelt, which change with climate. As a result, the frequency and severity of water temperature extremes may change. While previous studies have documented changes in non‐extreme water temperature, it is yet unclear how extreme water temperatures change in a warming climate and how their hydro‐meteorological drivers differ from those of non‐extremes. This study aims to assess temporal changes and spatial variability in water temperature extremes and enhance our understanding of the driving processes across European mountain rivers in the current climate, at both a regional and continental scale. First, we describe the characteristics of extreme events and explore their relationships with catchment characteristics. Second, we assess trends in water temperature extremes and compare them with trends in mean water temperature. Third, we use random forest models to identify the main driving processes of water temperature extremes. Last, we conduct a co‐occurrence analysis to examine the relationship between water temperature extremes and hydro‐climatic extremes. Our results show that mean water temperature has increased by °C per decade, leading to more extreme events at high elevations in spring and summer. While non‐extreme water temperatures are mainly driven by air temperature, water temperature extremes are also importantly influenced by soil moisture, baseflow, and meltwater. Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes.

Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain

Accurate rainfall-runoff modeling is crucial for effective water resources management and planning, especially in flash catchments prone to rapid floods. This study investigates the performance of ensemble learning methods applied to regionally optimized deep learning models, specifically long short-term memory (LSTM) networks, for enhanced hydrological prediction. Three ensemble approaches were developed based on optimized regional hyperparameter settings: catchment-wise, top-10 regional, and K-means clustering selected configurations. These networks were trained, and the median of their simulations on the test set was considered the final prediction for each ensemble. The final predictions were then evaluated against observed data. Our findings show that ensemble learning methods consistently outperform conventional single-configuration approach of selecting the best regional setting in all locations, especially in catchments with prediction complexity or anthropogenic footprints. The catchment-wise ensemble demonstrated the highest prediction accuracy and robustness, highlighting the importance of tailoring network configurations to the unique characteristics of individual catchments. The findings highlight the potential of ensemble learning to significantly improve hydrological forecasts and inform better decision-making in water resources management. Specifically, this research demonstrates how ensemble learning of catchment-wise configurations can overcome limitations in regional hydrological predictions by deep learning models, addressing the “uniqueness of the place” paradigm.

Environmental surveillance for Salmonella Typhi in rivers and wastewater from an informal sewage network in Blantyre, Malawi.

Environmental surveillance for Salmonella Typhi may provide information on the community-level dynamics of typhoid fever in resource poor regions experiencing high disease burden. Many knowledge gaps concerning the feasibility of ES remain, especially in areas lacking formal sewage systems. We implemented protocols for S. Typhi ES, including site selection and catchment population estimation, sample concentration and testing using qPCR for S. Typhi specific gene targets. Between May 2021 and May 2022, we collected grab samples and Moore swabs from 43 sites in Blantyre, Malawi. Catchment characteristics, water quality, and human faecal contamination (qPCR for Bacteroides HF183) were also recorded. Their association with S. Typhi detection was investigated using a logistic mixed-effects regression analysis. Prevalence of S. Typhi in ES samples was 2.1% (1.1-4.0%) and 3.9% (1.9-7.9%) for grab and Moore swab samples, respectively. HF183 was associated S. Typhi positivity, with a unit increase in log genome copies/microlitre increasing the odds of detection of S. Typhi by 1.56 (95% CI: 1.29-1.89) and 1.33 (1.10-1.61) in Moore swabs and grab samples, respectively. The location and timing of S. Typhi detection through ES was not associated with the incidence of typhoid fever reported in associated catchment populations. During this period of relatively low typhoid fever incidence, wastewater surveillance continued to detect S. Typhi in human sewage and wastewater suggesting that ES using natural river systems can be a sensitive indicator of transmission.

The impacts of climate change, early agriculture and internal fluvial dynamics on paleo-flooding episodes in Central China

Floods clustered in episodes are the most prevalent natural disaster worldwide, causing substantial economic and human losses. Although these events are often linked to time-periods of extreme rainstorms and unique atmospheric circulation patterns, the river basin characteristics affected by anthropogenic land use changes could exert a strong influence. However, the way and extent of how land use changes across different time scales affect flooding periods are still unclear, especially considering the historical land use changes. This study uses the Landlab landscape evolution model, coupled with an evapotranspiration model, to investigate the forcing factors for the paleo-flooding trends in the Wei River catchment over the last 5000 years. The results indicate that the flooding period from 4400 to 4000 BP was caused by an increase of 28 % in antecedent moisture content as well as a decrease of 28 % in its spatial variability, which are primarily due to climate change, and that the contribution of land-use change is less than 5 %. The increases of about 14 % and 8 % in main channel sedimentation rate play a leading role in flood generation during the time periods from 3400 to 2800 BP and 2000–1400 BP, respectively. These two periods of increased flooding are primarily caused by the erosional effects of increasing anthropogenic land use, whose contributions range from 33 % to 64 %. Furthermore, based on our modelling results, we suggest that the downstream propagation of the main flooding locations, from the Wei River to the lower reaches of the Yellow River, can be explained by the downstream migrating sediment wave. In conclusion, our simulation results give new insights into the causes of Holocene flooding periods in the middle Yellow River from the perspective of dynamic changes in catchment characteristics, which is helpful to improve regional flood risk management under future climate change and anthropogenic activities.

Analysis of flow regime classification in the Omo-Gibe River Basin: insights into fluid dynamics in Ethiopia

ABSTRACT The study investigates flow regime in the Omo-Gibe River Basin to address hydrological complexity caused by precipitation and catchment features. Despite employing various methodologies, daily flow data highlight the need for a more comprehensive understanding of flow variability. The study aims to scrutinize flow regime classification, emphasizing the challenges posed by the basin's unique hydrological dynamics, with the ultimate goal of improving water management practices in the region. Using XLSTAT (Excel statistics software), the average base flow index (60.66%), zero flow index (0.25%), coefficient of variation (1.56%), and flashiness index (0.276%) were determined to be the primary hydrological indices that contributed to streamflow characterization. Finally, flow regime classification was described as non-perennial (13%) or perennial (87%) using the shape of the flow duration curve and this hydrological index. However, the magnitude of extreme flow events was judged depending on flow duration curve and calibrated by the flashiness index computed in the study. The study's findings serve as an input for streamflow regionalization and the foundation for future research on the ecology and hydrology of Ethiopia's river basins as well as the management of the water resources throughout the Omo-Gibe River Basin.

Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany

Abstract. Climatic variability can considerably affect catchment-scale root zone storage capacity (Sumax), which is a critical factor regulating latent heat fluxes and thus the moisture exchange between land and atmosphere as well as the hydrological response and biogeochemical processes in terrestrial hydrological systems. However, direct quantification of changes in Sumax over long time periods and the mechanistic drivers thereof at the catchment scale are missing so far. As a consequence, it remains unclear how climatic variability, such as precipitation regime or canopy water demand, affects Sumax and how fluctuations in Sumax may influence the partitioning of water fluxes and therefore also affect the hydrological response at the catchment scale. Based on long-term daily hydrological records (1953–2022) in the upper Neckar River basin in Germany, we found that variability in hydro-climatic conditions, with an aridity index IA (i.e. EP/P) ranging between ∼ 0.9 and 1.1 over multiple consecutive 20-year periods, was accompanied by deviations ΔIE between −0.02 and 0.01 from the expected IE inferred from the long-term parametric Budyko curve. Similarly, fluctuations in Sumax, ranging between ∼ 95 and 115 mm or ∼ 20 %, were observed over the same time period. While uncorrelated with long-term mean precipitation and potential evaporation, it was shown that the magnitude of Sumax is controlled by the ratio of winter precipitation to summer precipitation (p < 0.05). In other words, Sumax in the study region does not depend on the overall wetness condition as for example expressed by IA, but rather on how water supply by precipitation is distributed over the year. However, fluctuations in Sumax were found to be uncorrelated with observed changes in ΔIE. Consequently, replacing a long-term average, time-invariant estimate of Sumax with a time-variable, dynamically changing formulation of that parameter in a hydrological model did not result in an improved representation of the long-term partitioning of water fluxes, as expressed by IE (and fluctuations ΔIE thereof), or in an improved representation of the shorter-term response dynamics. Overall, this study provides quantitative mechanistic evidence that Sumax changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water (and thus latent heat) fluxes as expressed by deviations ΔIE from the parametric Budyko curve over multiple time periods with different climatic conditions. Similarly, it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment. This further suggests that accounting for the temporal evolution of Sumax with a time-variable formulation of that parameter in a hydrological model does not improve its ability to reproduce the hydrological response and may therefore be of minor importance for predicting the effects of a changing climate on the hydrological response in the study region over the next decades to come.

Spatiotemporal variability of streamflow under current and projected climate scenarios of Andit Tid watershed, central highland of Ethiopia

This study examined the impact of climate change on streamflow in the Andit Tid watershed using climate models of dynamically downscaled Ethiopia’s CORDEX. The Arc SWAT and ArcGIS 10.5 software assessed the spatial and temporal distribution of streamflow, incorporating geospatial data like land use maps, digital elevation models, soil maps, and climate data. The SWAT model was calibrated and validated using SWAT-CUP with the SUFI-2 algorithm. The Canadian Centre for Climate Modeling and Analysis, Canada (CCCma (RCA4) model was selected for future projections after validation. From 1991 to 2021, the average streamflow rate was 0.0374 m3/s (247 mm), with R2 values of 0.83 for calibration and 0.72 for validation. Hotspots with active gullies and slopes over 20% were identified mainly in cultivated lands. Future projections indicated a comparable streamflow rate to current conditions at 0.0322 m3/s (212.6 mm). A decline in streamflow is projected: 7.2% and 30.2% decreases in the near and far future under RCP 4.5, and 32.3% decreases and 5% increases under RCP 8.5 scenarios. These variations were attributed to differences in catchment characteristics and climate variability. Further research is needed to validate these findings by incorporating additional biophysical variables. This study provides insights into hydrological planning and management in the Andit Tid watershed and similar regions facing climate variability.

Spatial and environmental drivers of temperate estuarine archaeal communities

Archaea play a crucial role in the global biogeochemical cycling of elements and nutrients, helping to maintain the functional stability of estuarine systems. This study characterised the abundance and diversity of archaeal communities and identified the environmental conditions shaping these microbial communities within six temperate estuaries along approximately 500 km of the New South Wales coastline, Australia. Estuarine sediments were found to exhibit significantly higher species richness than planktonic communities, with representative sequences from the Crenarchaeota phylum characterising each environment. Ordinate analyses revealed catchment characteristics as the strongest drivers of community variability. Our results also provide evidence supporting distance-decay patterns of archaeal biogeography across intermediate scales within and between temperate estuaries, contributing to a growing body of evidence revealing the extent spatial scales play in shaping microbial communities. This study expands our understanding of microbial diversity in temperate estuaries, with a specific focus on archaeal community structure and their role in maintaining ecosystem stability.

Monthly new water fractions and their relationships with climate and catchment properties across Alpine rivers

Abstract. The Alps are a key water resource for central Europe, providing water for drinking, agriculture, and hydropower production. Thus, understanding runoff generation processes of Alpine streams is important for sustainable water management. It is currently unclear how much streamflow is derived from old water stored in the subsurface and how much stems from more recent precipitation that reaches the stream via near-surface quick flow processes. It is also unclear how this partitioning varies across different Alpine catchments in response to hydroclimatic forcing and catchment characteristics. Here, we use stable water isotope time series in precipitation and streamflow to quantify the young water fractions (Fyw; i.e., the fraction of water younger than approximately 2–3 months) and new water fractions (Fnew; here, the fraction of water younger than 1 month) in streamflow from 32 Alpine catchments. We contrast these measures of water age between summer and winter and between wet and dry periods and then correlate them with hydroclimatic variables and physical catchment properties. New water fractions varied between 3.5 % and 9.6 %, with values of 9.2 % in rainfall-dominated catchments, 9.6 % in hybrid catchments, and 3.5 % in snow-dominated catchments (mean across all catchments of 7.1 %). Young water fractions were approximately twice as large (reflecting their longer timescale) and ranged between 10.1 % and 17.6 %, with values of 17.6 % in rainfall-dominated catchments, 16.6 % in hybrid catchments, and 10.1 % in snow-dominated catchments (mean across all catchments of 14.3 %). New water fractions were negatively correlated with catchment size (Spearman rank correlation, rS, of −0.38), q95 baseflow (rS=-0.36), catchment elevation (rS=-0.37), total catchment relief (rS=-0.59), and the fraction of slopes steeper than 40° (rS=-0.48). Large new water fractions, implying faster transmission of precipitation to streamflow, are more prevalent in small catchments, at low elevations, with small elevation differences, and with large fractions of forest cover (rS=0.36). New water fractions averaged 3.3 % following dry antecedent conditions, compared with 9.3 % after wet antecedent conditions. Our results quantify how hydroclimatic and physical drivers shape the partitioning of old and new waters across the Alps, thus indicating which landscapes transmit recent precipitation more readily to streamflow and which landscapes tend to retain water over longer periods. Our results further illustrate how new water fractions may find relationships that remained invisible with young water fractions.

Changed Seasonality and Forcings of Peak Annual Flows in Ephemeral Channels at Flagstaff, Northern Arizona, USA

Flood variability associated with urbanization, ecological change, and climatic change is of increasing economic and social concern in and around Flagstaff, Arizona, where flood hydrology is influenced by a biannual precipitation regime and the relatively unique geologic setting at the edge of the San Francisco Volcanic Field on the southern edge of the Colorado Plateau. There has been limited long-term gauging of the ephemeral channels draining the developed lands and dry coniferous forests of the region, resulting in a spaciotemporal gap in observation-based assessments of large-scale flooding patterns. We present new data from over 10 years of flood monitoring using a crest stage gauge network, combined with other channel monitoring records from multiple agency sources, to assess inter-decadal patterns of flood change in the area, with a specific emphasis on examining how various controls and disturbances have altered the character and seasonality of peak annual flows. Methods of analysis included the following: using Fisher’s Exact Test to compare the seasonality of flooding between historic data spanning the 1970s and contemporary data obtained since 2010; summarizing GIS-based spatial data and meteorological timeseries to characterize study catchment conditions and changes between flood study periods; and relating spatiotemporal patterns of flood seasonality and occurrences of notably large floods with catchment characteristics and environmental changes. Our results show systematic patterns and changes in Flagstaff-area flood regimes that relate to geologic and topographic controls of the varied catchment systems, and in response to records of climate variations and local catchment disturbances, including urbanization and, especially, high-severity wildfire. For most catchments there has been a shift from predominantly late winter to spring snowmelt floods, or mixed seasonal flood regimes, towards monsoon-dominated flooding, patterns which may relate to observed local warming and precipitation changes. Post-wildfire flooding has produced extreme flood discharges which have likely exceeded historical estimates of flood magnitude over decade-long monitoring periods by one to two orders of magnitude. We advocate for continued monitoring and the expansion of local stream gauge networks to enable seasonal, magnitude-frequency trend analyses, improved climate and environmental change attribution, and to better inform the many planned and ongoing flood mitigation projects being undertaken in the increasingly developed Flagstaff region.

Evolution of evapotranspiration in the context of land cover/climate change in the Han River catchment of China

AbstractEvapotranspiration (ET) stands as a pivotal element in the terrestrial‐atmospheric energy interchange, modulated by a complex array of factors including land use dynamics and climate change. The elucidation of regional and temporal patterns, alongside the mechanisms underpinning ET and its components, amidst environmental shifts, has emerged as a focal point in contemporary hydrological discourse. The Han River catchment, under the influence of the subtropical monsoon, presents an exemplary case study for hydrological inquiry due to its distinct catchment characteristics. This research probes the evolution and influencing mechanisms of ET within the catchment from 2000 to 2018, employing the improved Shuttleworth–Wallace model (i.e., SWH model), multivariate statistical techniques and additional methodologies. Findings reveal that (1) the annual mean ET, evaporation (E) and vegetation transpiration (T) within the Han River catchment from 2000 to 2018 were quantified at 1156.77, 784.21 and 372.56 mm, respectively. The overall spatial pattern showed a gradual decrease from the Chaoshan Plain area identified as having higher values compared to other regions, which may be attributed to the weakened vegetation cooling effect and the indirect effect of the heat island effect brought about by construction land expansion. (2) The significant decrease of E may be attributed to the optimization of vegetation growth conditions in the catchment, resulting in more solar radiation intercepted by the vegetation canopy. (3) Climatic alterations exerted a notable influence on ET, E and T than land use changes. Temperature, Normalized Difference Vegetation Index (NDVI), net radiation and wind speed were identified as the most consequential factors affecting ET. This study lays a scientific groundwork for subsequent exploration into the spatio‐temporal dynamics and mechanisms influencing evapotranspiration and its elements in the Han River catchment, contributing to a broader understanding of hydrological cycling.

Assessment of hydrological model performance in Morocco in relation to model structure and catchment characteristics

Study region30 catchments in Morocco. Study focusWe assessed the KGE performance of eight monthly lumped hydrological models forced by ground-based rainfall observations. We then examined how the performance relates to model complexity and structure, applied exploratory correlation analysis to identify the catchment features (over 200 features were considered) most significantly related to model performance, and investigated how these models respond to three rainfall forcings (ERA5, CHIRPS, and PERSIANN-CDR). New hydrological insights for the regionThe findings indicate that no hydrological model outperformed (or underperformed) consistently across all the catchments and that model performance depends more on model structure and hydro-climatic characteristics, particularly those related to calibration and calibration-validation data difference, than on model complexity and non-hydro-climatic features. The linearity between rainfall and runoff was the primary feature influencing model performance. Additionally, besides the expected improvement of model performance when forced with richer rainfall and runoff calibration data in terms of wet and dry years, our results show that this holds true even if the calibration data is only relatively richer than the validation data and that dry periods are more beneficial to model performance than wet periods. Lastly, all the models responded similarly to the different rainfall inputs; each model performed better when using ERA5 than when using CHIRPS and underperformed when using PERSIANN-CDR. The metric that best explained this similarity was the Pearson correlation coefficient between the precipitation products and observed runoff.