Votre recherche

Abstract Floods are the most common and threatening natural risk for many countries in the world. Flood risk mapping is therefore of great importance for managing socio-economic and environmental impacts. Several researchers have proposed low-complexity and cost-effective flood mapping solutions that are useful for data scarce environments or at large-scale. Among these approaches, a line of recent research focuses on hydrogeomorphic methods that, due to digital elevation models (DEMs), exploit the causality between past flood events and the hydraulic geometry of floodplains. This study aims to compare the use of freely-available DEMs to support an advanced hydrogeomorphic method, Geomorphic Flood Index (GFI), to map flood-prone areas of the Basento River basin (Italy). The five selected DEMs are obtained from different sources, are characterized by different resolutions, spatial coverage, acquisition process, processing and validation, etc., and include: (i) HydroSHEDS v.1.1 (resolution 3 arc-seconds), hydrologically conditioned, derived primarily from STRM (NASA) and characterized by global coverage; (ii) ASTER GDEM v.3 with a res. of around 30 m (source: METI and NASA) and global coverage; (iii) EU-DEM v. 1.1 (res. 1 arc-second), Pan-European and combining SRTM and ASTER GDEM, customized to obtain a consistency with the EU-Hydro and screened to remove artefacts (source: Copernicus Land Monitoring Service); (iv) TinItaly DEM v. 1.1, (res. 10 m-cell size grid) and produced and distributed by INGV with coverage of the entire Italian territory; (v) Laser Scanner DEM with high resolution (5 m cell size grid) produced on the basis of Ground e Model Keypoint and available as part of the RSDI geoportal of the Basilicata Region with coverage at the regional administrative level. The effects of DEMs on the performance of the GFI calibration on the main reach of the Basento River, and its validation on one of its mountain tributaries (Gallitello Creek), were evaluated with widely accepted statistical metrics, i.e., the Area Under the Receiver Operating Characteristics (ROC) curve (AUC), Accuracy, Sensitivity and Specificity. Results confirmed the merits of the GFI in flood mapping using simple watershed characteristics and showed high Accuracy (AUC reached a value over 0.9 in all simulations) and low dependency on changes in the adopted DEMs and standard flood maps (1D and 2D hydraulic models or three return periods). The EU-DEM was identified as the most suitable data source for supporting GFI mapping with an AUC > 0.97 in the calibration phase for the main river reach. This may be due in part to its appropriate resolution for hydrological application but was also due to its customized pre-processing that supported an optimal description of the river network morphology. Indeed, EU-DEM obtained the highest performances (e.g., Accuracy around 98%) even in the validation phase where better results were expected from the high-resolution DEM (due to the very small size of Gallitello Creek cross-sections). For other DEMs, GFI generally showed an increase in metrics performance when, in the calibration phase, it neglected the floodplains of the river delta, where the standard flood map is produced using a 2D hydraulic model. However, if the DEMs were hydrologically conditioned with a relatively simple algorithm that forced the stream flow in the main river network, the GFI could be applied to the whole Basento watershed, including the delta, with a similar performance.

Abstract A quantitative and qualitative understanding of the anticipated climate-change-driven multi-scale spatio-temporal shifts in precipitation and attendant river flows is crucial to the development of water resources management approaches capable of sustaining and even improving the ecological and socioeconomic viability of rain-fed agricultural regions. A set of homogeneity tests for change point detection, non-parametric trend tests, and the Sen’s slope estimator were applied to long-term gridded rainfall records of 27 newly formed districts in Chhattisgarh State, India. Illustrating the impacts of climate change, an analysis of spatial variability, multi-temporal (monthly, seasonal, annual) trends and inter-annual variations in rainfall over the last 115 years (1901–2015 mean 1360 mm·y −1 ) showed an overall decline in rainfall, with 1961 being a change point year (i.e., shift from rising to declining trend) for most districts in Chhattisgarh. Spatio-temporal variations in rainfall within the state of Chhattisgarh showed a coefficient of variation of 19.77%. Strong inter-annual and seasonal variability in regional rainfall were noted. These rainfall trend analyses may help predict future climate scenarios and thereby allow planning of effective and sustainable water resources management for the region.

Mapping the delineation of areas that are flooded due to water control infrastructure failure is a critical issue. Practical difficulties often present challenges to the accurate and effective analysis of dam-break hazard areas. Such studies are expensive, lengthy, and require large volumes of incoming data and refined technical skills. The creation of cost-efficient geospatial tools provides rapid and inexpensive estimates of instantaneous dam-break (due to structural failure) flooded areas that complement, but do not replace, the results of hydrodynamic simulations. The current study implements a Geographic Information System (GIS) based method that can provide useful information regarding the delineation of dam-break flood-prone areas in both data-scarce environments and transboundary regions, in the absence of detailed studies. Moreover, the proposed tool enables, without advanced technical skills, the analysis of a wide number of case studies that support the prioritization of interventions, or, in emergency situations, the simulation of numerous initial hypotheses (e.g., the modification of initial water level/volume in the case of limited dam functionality), without incurring high computational time. The proposed model is based on the commonly available data for masonry dams, i.e., dam geometry (e.g., reservoir capacity, dam height, and crest length), and a Digital Elevation Model. The model allows for rapid and cost-effective dam-break hazard mapping by evaluating three components: (i) the dam-failure discharge hydrograph, (ii) the propagation of the flood, and (iii) the delineation of flood-prone areas. The tool exhibited high accuracy and reliability in the identification of hypothetical dam-break flood-prone areas when compared to the results of traditional hydrodynamic approaches, as applied to a dam in Basilicata (Southern Italy). In particular, the over- and under-estimation rates of the proposed tool, for the San Giuliano dam, Basilicata, were evaluated by comparing its outputs with flood inundation maps that were obtained by two traditional methods whil using a one-dimensional and a two-dimensional propagation model, resulting in a specificity value of roughly 90%. These results confirm that most parts of the flood map were correctly classified as flooded by the proposed GIS model. A sensitivity value of over 75% confirms that several zones were also correctly identified as non-flooded. Moreover, the overall effectiveness and reliability of the proposed model were evaluated, for the Gleno Dam (located in the Central Italian Alps), by comparing the results of literature studies concerning the application of monodimensional numerical models and the extent of the flooded area reconstructed by the available historical information, obtaining an accuracy of around 94%. Finally, the computational efficiency of the proposed tool was tested on a demonstrative application of 250 Italian arch and gravity dams. The results, when carried out using a PC, Pentium Intel Core i5 Processor CPU 3.2 GHz, 8 GB RAM, required about 73 min, showing the potential of such a tool applied to dam-break flood mapping for a large number of dams.

Abstract Analyzing intra-annual stream flow can reveal the main causes for runoff changes and the contributions of climate variability and human activities. For this purpose, the Mann–Kendall and cumulative rank difference (CRD) tests, and the double mass curve method, were applied to a time series of hydro-meteorological variables from 1971 to 2010 in the Tajan River basin in Iran. Results indicated that runoff changes in the wet and dry seasons after 1999 had significant respective decreasing and increasing trends, at the 0.01 confidence level, due to dam construction. In the pre-dam period (1991–1998), the results of the double mass curve method showed that climate variability and human activities contributed 57.76% and 42.24%, respectively, to the runoff decrease during the wet season. For the post-dam period (1999–2010), climate variability and anthropogenic activities contributed 24.68% and 75.32%, respectively, to the wet season runoff decrease of 116.55 mm. On the other hand, in the same period during the dry season, climate variability contributed −30.68% and human activities contributed 130.68% to the runoff increase of 41.45 mm. It is evident that runoff changes in both wet and dry seasons were mainly due to human activities associated with dam construction to meet water supply demands for agriculture.

Abstract Groundwater quality modelling plays an important role in water resources management decision making processes. Accordingly, models must be developed to account for the uncertainty inherent in the modelling process, from the sample measurement stage through to the data interpretation stages. Artificial intelligence models, particularly fuzzy inference systems (FIS), have been shown to be effective in groundwater quality evaluation for complex aquifers. In the current study, fuzzy set theory is applied to groundwater-quality related decision-making in an agricultural production context; the Mamdani, Sugeno, and Larsen fuzzy logic-based models (MFL, SFL, and LFL, respectively) are used to develop a series of new, generalized, rule-based fuzzy models for water quality evaluation using widely accepted irrigation indices and hydrological data from the Sarab Plain, Iran. Rather than drawing upon physiochemical groundwater quality parameters, the present research employs widely accepted agricultural indices (e.g., irrigation criteria) when developing the MFL, SFL and LFL groundwater quality models. These newly-developed models, generated significantly more consistent results than the United States Soil Laboratory (USSL) diagram, addressed the inherent uncertainty in threshold data, and were effective in assessing groundwater quality for agricultural uses. The SFL model is recommended as it outperforms both MFL and LFL in terms of accuracy when assessing groundwater quality using irrigation indices.

Abstract The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO 3 ) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin‐Shahr plain, Iran. Twenty‐two groundwater samples were collected from wells in the study area, and their respective NO 3 concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO 3 concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon–GA–DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R 2 of 0.22, the Wilcoxon–GA–DRASTIC obtained a maximum R 2 value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R 2 value to the highest degree. , Article impact statement : The results showed that both linear and nonlinear methods are useful in modifying the ratings and weights of the DRASTIC method for assessing the groundwater vulnerability.

Soil erosion is a significant threat to the environment and long-term land management around the world. Accelerated soil erosion by human activities inflicts extreme changes in terrestrial and aquatic ecosystems, which is not fully surveyed/predicted for the present and probable future at field-scales (30-m). Here, we estimate/predict soil erosion rates by water erosion, (sheet and rill erosion), using three alternative (2.6, 4.5, and 8.5) Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios across the contiguous United States. Field Scale Soil Erosion Model (FSSLM) estimations rely on a high resolution (30-m) G2 erosion model integrated by satellite- and imagery-based estimations of land use and land cover (LULC), gauge observations of long-term precipitation, and scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The baseline model (2020) estimates soil erosion rates of 2.32 Mg ha 1 yr 1 with current agricultural conservation practices (CPs). Future scenarios with current CPs indicate an increase between 8% to 21% under different combinations of SSP-RCP scenarios of climate and LULC changes. The soil erosion forecast for 2050 suggests that all the climate and LULC scenarios indicate either an increase in extreme events or a change in the spatial location of extremes largely from the southern to the eastern and northeastern regions of the United States.