Hydrology, Volume 9, Issue 2 (February 2022) – 25 articles

We compared five regression approaches, namely, ordinary least squares, major axis, reduced major axis, robust, and Prais–Winsten to estimate δ¹⁸O–δ²H relationships in four water types (precipitation, surface water, groundwater collected in wells from lowlands, and groundwater from low-yield springs) from the northern Italian Apennines. Differences in terms of slopes and intercepts of the different regressions were quantified and investigated by means of univariate, bivariate, and multivariate statistical analyses. We found that magnitudes of such differences were significant for water types surface water and groundwater (both in the case of wells and springs), and were related to robustness of regressions (i.e., standard deviations of the estimates and sensitiveness to outliers). With reference to surface water, we found the young water fraction was significant in inducing changes of slopes and intercepts, leading us to suppose a certain role of kinetic fractionation processes as well (i.e., modification of former water isotopes from both snow cover in the upper part of the catchments and precipitation linked to pre-infiltrative evaporation and evapotranspiration processes). As final remarks, due to the usefulness of δ¹⁸O–δ²H relationships in hydrological and hydrogeological studies, we provide some recommendations that should be followed when assessing the abovementioned water types from the northern Italian Apennines. Full article

The DRAINMOD model is a superior tool used to predict the changes in farmland water balance under different agricultural drainage layouts, fields, weather conditions, and management practices. In the present study, we assessed the sensitivity of the DRAINMOD predictions in farmland water balance to the time step (hourly or daily) in daily evapotranspiration (ET₀) computations for 12-hectares of farmland located at the lower reaches of the Yangtze River basin. The model was calibrated and validated and then was applied twice under two sets of daily ET₀ values, computed using the standardized ASCE Penman–Monteith model (one using the hourly time step (HTS) and the other using the daily time step (DTS)). Regarding daily computed ET₀ values, results show that abrupt diurnal changes in the weather always result in significant differences between daily ET₀ values when computed based on DTS and HTS. DRAINMOD simulations show that such differences between daily computed ET₀ values affected the model’s predictions of the “water fate” in the study area; e.g., adopting HTS rather than DTS resulted in a 4.8% increase, and a 3.1% and 1% decrease in the models’ cumulative predictions of runoff, drainage, and infiltration, respectively. Therefore, for a particular study area, it is critical to pay attention when deciding the best time step in ET₀ computations to ensure accurate DRAINMOD simulations, thereby ensuring better utilization of agricultural water alongside high agricultural productivity. Full article

The application of numerical models to understand the behavioural pattern of a flood is widely found in the literature. However, the selection of an appropriate hydraulic model is highly essential to conduct reliable predictions. Predicting flood discharges and inundation extents are the two most important outcomes of flood simulations to stakeholders. Precise topographical data and channel geometries along a suitable hydraulic model are required to accurately predict floods. One-dimensional (1D) hydraulic models are now replaced by two-dimensional (2D) or combined 1D/2D models for higher performances. The Hydraulic Engineering Centre’s River Analysis System (HEC-RAS) has been widely used in all three forms for predicting flood characteristics. However, comparison studies among the 1D, 2D to 1D/2D models are limited in the literature to identify the better/best approach. Therefore, this research was carried out to identify the better approach using an example case study of the Kelani River basin in Sri Lanka. Two flood events (in 2016 and 2018) were separately simulated and tested for their accuracy using observed inundations and satellite-based inundations. It was found that the combined 1D/2D HEC-RAS hydraulic model outperforms other models for the prediction of flows and inundation for both flood events. Therefore, the combined model can be concluded as the better hydraulic model to predict flood characteristics of the Kelani River basin in Sri Lanka. With more flood studies, the conclusions can be more generalized. Full article

Studying the changes in extreme river runoff induced by climate change is of utmost importance, as the variability of floods directly affects life and human activities. This study examines the fluctuations and persistence of winter floods in 14 catchments in the Rika River Basin (Ukraine) and ten catchments in the Steyr River Basin (Austria). The catchments represent typical hydrological regimes in the Danube River region. The fluctuations and persistence of floods are analyzed by the hydro-genetic method and a seasonality analysis for the period 1951–2015. The results show a much more pronounced fluctuation pattern in the upper Rika catchments than in the upper Steyr catchments. This pattern indicates an increase in winter flood magnitudes between the mid-1960s and the 1990s, followed by a decrease until recently. The flood seasonality shows a large inter-annual variability in both regions. The most significant winter floods tend to occur in November and December. The winter flood fluctuations are compared with changes in associated climate characteristics, i.e., seven-day maximum precipitation, a melt index, and annual maximum snow depth. The seasonality of these characteristics has a strong inter-annual variability and only partly explains the winter flood fluctuations. Full article

The main objective of this study is to evaluate the performance of the integrated hydrological model, MIKE SHE in a small watershed to analyze the effect of two different precipitation sources on model outputs (groundwater elevation and surface water flows). The model was calibrated and validated with observed groundwater elevations and surface water flows measured at the United States Geological Survey (USGS) gage stations in the basin. The model calibration performance for surface water flows (R = 0.80, MAE= 0.20 m³/s, BIAS = −0.14 m³/s, NSE = 0.59) and groundwater elevations (R = 0.74, MAE = 0.45 m, BIAS = 0.08 m, NSE = 0.35) showed that the model was able to predict hydrological processes based on forcing variables in a small watershed. The analysis did not show the model with precipitation at the nearer (NOAA-Edwardsville) gauge station has better performance than the farther gauge station (NOAA-St. Louis). The quantitative analyses for the most sensitive model output variable suggested that precipitation uncertainties had noticeable impacts on surface water flows (0.81% to 11.19%), than groundwater elevations (0.06% to 0.07%), with an average of 6.71% and 0.66%, respectively. Our results showed noticeable differences in simulated surface water flows in spring (12.9%) and winter (36%) seasons compared to summer (11.4%) and fall (4.6%) as a result of difference (6% to 18%) in precipitation, which indicated that uncertainties in precipitation impact simulated surface water flows in a small watershed vary with different seasons. Our analyses have shown that precipitation affects the simulated hydrological processes and care should be taken while selecting input datasets (i.e., precipitation) for better hydrological model performance, specifically for surface water flows. Full article

Sediment load in fluvial systems is one of the critical factors shaping the river geomorphological and hydraulic characteristics. A detailed understanding of the total sediment load (TSL) is required for the protection of physical, environmental, and ecological functions of rivers. This study develops a robust methodological approach based on multiple linear regression (MLR) and support vector regression (SVR) models modified by principal component analysis (PCA) to predict the TSL in rivers. A database of sediment measurement from large-scale physical modelling tests with 4759 datapoints were used to develop the predictive model. A dimensional analysis was performed based on the literature, and ten dimensionless parameters were identified as the key drivers of the TSL in rivers. These drivers were converted to uncorrelated principal components to feed the MLR and SVR models (PCA-based MLR and PCA-based SVR models) developed within this study. A stepwise PCA-based MLR and a 10-fold PCA-based SVR model with different kernel-type functions were tuned to derive an accurate TSL predictive model. Our findings suggest that the PCA-based SVR model with the kernel-type radial basis function has the best predictive performance in terms of statistical error measures including the root-mean-square error normalized with the standard deviation (RMSE/StD) and the Nash–Sutcliffe coefficient of efficiency (NSE), for the estimation of the TSL in rivers. The PCA-based MLR and PCA-based SVR models, with an overall RMSE/StD of 0.45 and 0.35, respectively, outperform the existing well-established empirical formulae for TSL estimation. The analysis of the results confirms the robustness of the proposed PCA-based SVR model for prediction of the cases with high concentration of sediments (NSE = 0.68), where the existing sediment estimation models usually have poor performance. Full article

Kalman filter (KF) and its variants and extensions are wildly used for hydrologic prediction in environmental science and engineering. In many data assimilation applications of Kalman filter (KF) and its variants and extensions, accurate estimation of extreme states is often of great importance. When the observations used are uncertain, however, KF suffers from conditional bias (CB) which results in consistent under- and overestimation of extremes in the right and left tails, respectively. Recently, CB-penalized KF, or CBPKF, has been developed to address CB. In this paper, we present an alternative formulation based on variance-inflated KF to reduce computation and algorithmic complexity, and describe adaptive implementation to improve unconditional performance. For theoretical basis and context, we also provide a complete self-contained description of CB-penalized Fisher-like estimation and CBPKF. The results from one-dimensional synthetic experiments for a linear system with varying degrees of nonstationarity show that adaptive CBPKF reduces the root-mean-square error at the extreme tail ends by 20 to 30% over KF while performing comparably to KF in the unconditional sense. The alternative formulation is found to approximate the original formulation very closely while reducing computing time to 1.5 to 3.5 times of that for KF depending on the dimensionality of the problem. Hence, adaptive CBPKF offers a significant addition to the dynamic filtering methods for general application in data assimilation when the accurate estimation of extremes is of importance. Full article

Seventy-seven percent of all agricultural land is related to livestock, meat and dairy, including grazing land and arable fields used for animal feed production. The effect of livestock on the natural environment is well documented. Many types of research describe these effects on biodiversity. The surface runoff and soil erosion on grasslands and pastures are investigated with smaller intensity since grasslands are one of the two major land uses that are considered as natural or at least semi-natural lands. Still, mainly due to overuse, grazing on sloping pasture lands can cause severe soil damage, the trampling can cause compaction, compaction decrease infiltration and thus increase runoff and, consequently, soil loss. There are several consequences of the grazing pressure that cause water erosion and surface runoff above the acceptable limit, such as a dramatic decrease in grass densities and/or above-ground bio-mass, compaction, animal tracks, etc. Related research started as early as 1911 and continues until today. There are several methods to analyse the consequences of grazing pressure, e.g., in situ rainfall simulations, infiltration and soil resilience measurements, modelling of runoff, soil loss and infiltration, calculation of ecological costs, etc. Furthermore, most importantly, scientists are investigating the possibilities for improvement of the achieved unstable grazing system due to bad management. Numerous publications have been publishing results on positive changes with the removal of grazing livestock from the grasslands. However, since the socio-economic situation is changing on Earth, more people requiring the products of the pastures, an optimal grazing solution is greatly needed. One of the solutions can be the planning of the optimal animal unit per area, based on the expected grass yields. However, due to the big differences in yields, caused by the greatly unreliable weather, the solution for the future must be a multifunctional agriculture and a flexible land use. Full article

Regional estimations of Potential Evapotranspiration (PET) are of key interest for a number of geosciences, particularly those that are water-related (hydrology, agrometeorology). Therefore, several models have been developed for the consistent quantification of different time scales (hourly, daily, monthly, annual). During the last few decades, remote sensing techniques have continued to grow rapidly with the simultaneous development of new local and regional evapotranspiration datasets. Here, we develop a novel set T maps over the globe, namely RASPOTION, for the period 2003 to 2016, by integrating: (a) mean climatic data at 4088 stations, extracted by the FAO-CLIMWAT database; (b) mean monthly PET estimates by the Penman–Monteith method, at the aforementioned locations; (c) mean monthly PET estimates by a recently proposed parametric model, calibrated against local Penman–Monteith data; (d) spatially interpolated parameters of the Parametric PET model over the globe, using the Inverse Distance Weighting technique; and (e) remote sensing mean monthly air temperature data. The RASPOTION dataset was validated with in situ samples (USA, Germany, Spain, Ireland, Greece, Australia, China) and by using a spatial Penman–Monteith estimates in England. The results in both cases are satisfactory. The main objective is to demonstrate the practical usefulness of these PET map products across different research disciplines and spatiotemporal scales, towards assisting decision making for both short- and long-term hydro-climatic policy actions. Full article

India’s water resources are under tremendous pressure due to elevated demand for various purposes. The over-exploitation of these valuable resources has resulted in an imbalance in the watershed ecology. The application of spatial analysis tools in studying the morphological behaviour of watersheds has increased in recent decades worldwide due to the accessibility of the geospatial database. A morphometric analysis of a river basin is vital to determine the hydrological behaviour to develop effective management. Under the current study, morphological behaviour of Nand Samand catchment in the hard rock region was evaluated employing remote sensing (RS) and geographical information system (GIS) tools. The Nand Samand catchment (Rajasthan State, India) has an area of 865.18 km² with the highest and lowest elevations of 1318 m and 570 m above mean sea level, respectively. This study utilises a 30 m high-spatial-resolution ASTER imagery digital elevation model for delineating the catchment. The drainage network is assessed using a GIS method, and morphometric parameters like linear, areal, and relief aspects were calculated. Results were obtained for parameters viz., basin length of 82.66 km, constant channel maintenance equal to 0.68 km, stream frequency of 2.11 km⁻², drainage density of 1.48 km⁻¹, and length overflow of 0.34 km. Form factor of 0.13, and the circulatory ratio of 0.28 showed that an elongated shape characterises the study area. The results would help understand the relationship between hydrological variables and geomorphological parameters for better decision-making. The techniques used could effectively help to perform better drainage basin and channel network morphometric analyses. The found morphometric characteristics will be helpful in understanding the Nand Samand catchment and similar areas in India in order to better guide the decision-makers in providing adequate policy to the development of the region. Full article

A phenomenon causing instability of soil structure and associated hydraulic properties in recently tilled soils is aggregate fragmentation induced by wetting and drying cycles. We analyzed data from three experiments in Puerto Rico, the UK and China measuring fragmentation and resulting evolution of aggregate size distributions during successive wetting and drying cycles in heavy textured soils. Aggregate distributions were represented as the cumulative fraction F of aggregates passing through successively larger sieve sizes X. To a good approximation, all distributions exhibited similarity in that the aggregate diameter X(F) corresponding to F in a given test distribution was always a characteristic multiple $\overline{\alpha }$ of X(F) in a fixed reference distribution, where $\overline{\alpha }$ for a distribution was calculated as its mean weight aggregate diameter (MWD) divided by the MWD of the reference distribution. In most cases, $\overline{\alpha }$ for a given soil varied inversely with the square of the number of wetting and drying cycles. For different soils of similar initial aggregate sizes, $\overline{\alpha }$ for a given wet–dry cycle decreased with increasing activity coefficient, reflecting the enhancing effect of soil shrink–swell potential on fragmentation. Results highlight usefulness of the van Bavel mean weight diameter as a natural scaling parameter for characterizing aggregate distributions. Full article

This paper presents a system for supporting hydropower production on mountainous areas. The system couples the outputs of a numerical weather prediction model and a snow melting and accumulation temperature-based model. Several procedures are presented for interpolating meteorological variables and calibrating and validating model parameters that can be generalized to any other mountainous area where the estimation of current and forecasted snow water equivalent and melting amount is required. The system reliability has been assessed through the validation of three components: spatial interpolation of meteorological data, mathematical modeling, and quantitative meteorological forecast. The results show that good accuracy of meteorological data spatial interpolation can be achieved when the data from snow gauges are used for assessing the precipitation lapse rate at higher altitudes, and the temperature lapse rate is computed from data at each time step. The temperature-based hydrological model proved to be effective in simulating lake inflow water volume and energy production. No clear result has been found for snow melt forecast due to the difficulties in providing reliable quantitative weather forecast in complex alpine area. Full article

The Okama Crater Lake is located in the highly active Zao Volcano on the boundary of Miyagi and Yamagata Prefectures, Japan. At present, the lake stays relatively calm with neither bubbling, steaming nor gas smell at a pH of 3.2–3.4, though the lake did change color with steaming from the water surface in 1939 as the result of one of Zao’s volcanic activities. In order to clarify the geothermal effect on Okama and the groundwater flow system below or around Okama, field observations were performed in 2019 and 2020. Groundwater inflow and outflow in Okama were separately evaluated by estimating the hydrological and chemical budgets of the lake, based on the hydrometeorology, water temperature and river inflow measured in the field. The average groundwater inflow and outflow were estimated at 0.012 m³/s and 0.039 m³/s during the non-rainfall periods of 2020, respectively. A surplus of groundwater outflow makes the lake level consistently decrease during non-rainfall periods or the completely ice-covered season. In the completely ice-covered periods, the water temperature consistently increased at 0–15 m above the lake bottom, which is probably due to thermal leakage from a hydrothermal reservoir below the lake bottom. The heat fluxes averaged over December 2019–April 2020 and December 2020–March 2021 were calculated at 2.5 and 2.9 W/m², respectively. A coupling between the estimated groundwater inflow and the calculated geothermal heat flux was used to evaluate the temperature of inflowing groundwater. Full article

One of the primary steps in managing the flow in an open channel is determining its properties. Empirical equations are developed to provide further information regarding the flow in open channels. Obtaining such experimental equations is expensive and time consuming; therefore, alternative solutions have been sought. Over the last century, the Parshall flume, a static measuring device with no moving parts, has played a significant role in measuring the flow in open channels. Many researchers have focused their interest on studying the application of Parshall flumes in various fields like irrigation and wastewater management. Although various scholars used experimental results to enhance the rating equation of the Parshall flume, others used an alternative source of data to recalibrate the height–discharge relation equation using numerical simulation. Computational Fluid Dynamic (CFD) software is becoming popular nowadays as computing hardware has advanced significantly within the last few decades, making it possible to go beyond the limited resolution that was experienced in the past. Multiple CFD models, depending on their availability, either open-source or commercially licensed, have been used to perform numerical simulations on different configurations of flumes, especially Parshall flumes, to produce water level results. Regarding various CFD tools that have been used, i.e., FLOW-3D, Ansys Fluent, or OpenFOAM, after precise calibration with experimental data, it has been determined that the output is reliable and can be implemented to the actual scenarios. The benefit of using this technique to produce results is the ability of the CFD approach to adjust the initial conditions, like flow velocity or structural geometry, where necessary. With respect to channel size and the condition of the site where the flume is located, the choices are narrowed to the specific Parshall flume suitable to the situation. It is not always possible to select the standard Parshall flume; therefore, engineers provide some modification to the closest flume size and provide a new rating curve to produce accurate flowrates. This review has been performed on the works of a number of scholars who targeted the application of numerical simulation and physical experimental data in Parshall flumes to either enhance the existing rating equation or propose further modification to the structure’s geometry. Full article

On the one hand, Sinkhole Flooding (SF) is an essential hydrological process to recharge karst aquifer in arid to dry sub-humid regions. On the other hand, the increase of rain extremes is one of the major consequences of global warming, together with the expansion of drylands. Thus, appropriate runoff regulation in endorheic karst basins in order to reduce the risk of flooding and improve the quantity and quality of the water drained by sinkholes will be more and more crucial. With these premises, a systematic review of SF cases study was performed by using Web of Science (WoS) engine to infer the hydrological properties for modeling the water management in regions actually or potentially affected by water scarcity. Hydrological models are essential to manage the consequences of climate change on karst water resource, however the review shows that providing the tools necessary for reliable modeling is still challenging. Finally, due to the intrinsic vulnerability of the karst aquifers, pollution reduction and wastewater recycling policy will play a key role in the next decades. Full article

Major ions, stable isotopes, and trace elements, including rare earth elements (REEs), are used as natural tracers in the qualitative assessment of potential water sources in lakes and rivers of the upper Yana River basin, between Verkhoyansk and Chersky Ranges, during the late summer period. Three distinct regions were sampled, and a dominant water source in each region was qualitatively inferred from water chemistry data. The REE distribution pattern was found to be highly regional and controlled by pH and carbonate contents. Mountain headwater stream at the Verkhoyansk Range north slope, the Dulgalakh River, shows an input from a mixture of shallow groundwater and icing meltwater, with a depleted isotopic signature (δ¹⁸O below –21‰), d-excess (d_ex = δ2H − 8·δ18O) above 18, enrichment in Mg and Sr, and depletion in heavy REEs. The Derbeke Depression lakes and streams are fed by rainfall having ultra-low total dissolved solids (TDS) content, below 25 mg/L, and a convex-up REE pattern. In a medium mountainous river at the Chersky Range flank, the Dogdo River, leaching through fissured Jurassic carbonates is a dominant runoff pathway. Riverine water is heavily depleted in light REEs, but enriched in Mo, Rb, Sb, W and U. In the Dulgalakh River water, high positive Sm and Gd anomalies were observed, attributed either to local geology (greenshists), historical mining legacy, or contemporary winter road operations. Full article

Determination of groundwater recharge is a major challenge in areas where rainfall is generally abundant. Variability and uncertainty are inherent in the estimation of recharge, and several methods are therefore recommended for its estimation at a regional level. In this study, we evaluated several methods for estimating recharge: the web GIS-based automated hydrological analysis tool (WHAT), water table fluctuation (WTF), hydrograph analyses, a recession curve displacement method, graphical separation, and empirical formulas. The annual recharge estimated by combining direct recharge and base-flow varied from 84 mm in 2019 to 66.4 mm in 2020. The mean direct recharge was about 44 mm in 2018 and 57.3 mm in 2019, representing about 4% and 5% of the respective rainfall. In 2020, this direct recharge was 43 mm, or about 6% of rainfall, around 25% lower than in 2019. Base-flow separation methods and recession curve displacement generally gave low results, whereas modified empirical formulas gave results close to those of the WTF method and were considered more consistent and reasonable. The regression curve displacement method implemented in the United States geologic survey (USGS) RORA program was found to be unsuitable for the study area. However, the other methods presented more reasonable results and could be used to estimate groundwater recharge in the study area. Full article

Several studies have analyzed the changes in individual soil properties and covers and quantified the hydrological response of burned forest soils (with or without post-fire treatment). Less research exists on the influence of these changes on runoff and erosion rates immediately after a prescribed fire and post-fire treatment. Moreover, hydrological modeling of burned areas is based on complex models rather than relying on simple regression equations. This study carries out a combined analysis of the hydrological response of soil and its driving factors in three forests (pine, oak, and chestnut) of Southern Italy that were subjected to prescribed fire and post-fire treatment with mulching. Moreover, simple regression models based on a limited set of soil properties/covers are proposed to predict runoff and erosion. The Principal Component Analysis has shown that the runoff coefficients increase when the water infiltration rate and litter cover decrease and repellency, ash cover, organic carbon content, and bare soil area increase. All the analyzed variables play a secondary role in influencing the sediment concentration. Due to these properties, clear differences in soil properties and covers have been found between unburned and burned soils. The distinctions between the burned soils (mulched or not) are much lower. The proposed regression models use a very low number of soil covers and two dummy variables as input parameters. These models are very accurate in simulating the surface runoff and soil erosion in all soil conditions in the short term. Full article

People in the southwestern (SW) coastal part of Bangladesh are suffering from a severe freshwater crisis due to saline groundwater at a shallow depth. Fresh groundwater below a 200 m depth is an option, but it is costly to construct deep tubewells for the local inhabitants. The processes of salinization and freshening were previously identified using conventional methods. In this study, we brought new insight into these processes by analyzing existing datasets using multivariate statistics to identify the factors affecting groundwater chemistry. Cluster analysis (CA) revealed three major clusters. Cluster A corresponded to saline (NaCl-type) water. Cluster B was also saline (NaCl-type) water but showed mixing effects. Cluster C was fresh groundwater (NaHCO₃-type) and isolated. The hydrochemical characteristics of clusters A, B and C compared remarkably well with the groundwaters from the upper shallow aquifer (USA), lower shallow aquifer (LSA) and deep aquifer (DA), respectively. Factor analysis (FA) showed that 75% of the total variance was influenced by evaporate dissolution, carbonate dissolution/precipitation, cation exchange and anthropogenic pollution to some extent. Therefore, the integrated approach showed the validity of applying multivariate statistical techniques to infer the dominant hydrochemistry and to characterize and understand a complicated hydrogeological system. Full article

There is a growing global awareness about the impacts of climate change on cultural and natural heritage sites. In Greece—a homeland of important historical and cultural resources—archaeological sites are vulnerable to climate change-related flood events. In order to investigate the flood risk of the archaeological site of Ancient Messene under different climate change projections, a physically-based hydrological model was implemented and six climate change scenarios were examined. Additionally, the effectiveness of a soft structural nature-based solution adaption plan was evaluated. Based on the results, the archaeological site of Ancient Messene is shielded against small or larger flood events and only in case of extreme precipitation events is the area likely to be at risk. This flood risk can be further eliminated after implementing the soft structural nature-based solution adaptation plan proposed. Nature-based solutions provide a cost-effective alternative approach for flood risk reduction and climate change adaptation, with minimum possible disturbance, while hydrological modeling, even in case of data scarcity, constitute a valuable tool for flood risk estimation and adaptation plan management. Nature-based solutions seems to be most effective against small or medium precipitation events, and to limit the damages of extreme events. Their benefits for flood adaptation should not be underestimated. Full article

Nowadays, the Mediterranean region is generally recognized as a climate change hot spot given its strong response to global warming, with relevant impacts on rainfall amount and distribution. Within this context, in this work the temporal variability of rainfall at annual, seasonal and monthly scale was analyzed in Italy using rainfall data extracted from the reanalysis dataset ERA5-Land during the period 1950–2020. In particular, rainfall trend magnitude and significance have been estimated by means of non-parametric tests applied to 3215 grid points falling within the Italian territory. The main results of this analysis evidenced only a few relevant trends at the annual scale, mostly involving northern Italy (positive trend) and the Sardinia region (negative trend). At seasonal scale, the results showed a marked negative trend in winter, characterizing almost all the Italian territory, while in the other seasons a positive trend was identified in the majority of grid points, especially in the Alps. Finally, at the monthly scale, September was identified as the month of the year with the highest percentage of grid points with positive trends mainly located in central, southern, and north-eastern Italy. Full article

The Nilwala river basin is prone to frequent flooding during the southwest monsoon and second intermonsoon periods. Several studies have recommended coupling 1D and 2D models for flood modelling as they provide sufficient descriptive information of floodplains with greater computational efficiency. This study aims to couple a 1D hydrological model (HEC-HMS) with a 2D hydraulic model (iRIC) to simulate flooding in the Nilwala river basin. Hourly rainfall and streamflow data of three flood events were used for calibration and validation of HEC-HMS. The model performed exceptionally well considering the Nash–Sutcliffe coefficient, percent bias, and root mean square error. The flood event of May 2017 was simulated on iRIC using the streamflow hydrographs modelled by HEC-HMS. An overall accuracy of 81.5% was attained when the simulated extent was compared with the surveyed flood extent. The accuracy of the simulated flood depth was assessed using the observed water level at Tudawa gauging station, which yielded an NSE of 0.94, PBIAS of −4.28, RMSE of 0.18 and R² of 0.95. Thus, the coupled model provided an accurate estimate of the flood extent and depth and can be further developed for hydrological flood forecasting on a regional scale. Full article