EISSN : 23065338
Current Publisher: MDPI (10.3390)
Total articles ≅ 329
Latest articles in this journal
Hydrology, Volume 7; doi:10.3390/hydrology7030037
Forward logistic regression and conditional analysis have been compared to assess landslide susceptibility across the whole territory of the Sicilian region (about 25,000 km2) using previously existing data and a nested tiered approach. These approaches were aimed at singling out a statistical correlation between the spatial distribution of landslides that have affected the Sicilian region in the past, and a set of controlling factors: outcropping lithology, rainfall, landform classification, soil use, and steepness. The landslide inventory used the proposal of building the models like the official one obtained in the PAI (hydro geologic asset plan) project, amounting to more than 33,000 events. The 11 types featured in PAI were grouped into 4 macro-typologies, depending on the inherent conditions believed to generate various kinds of failures and their kinematic evolution. The study has confirmed that it is possible to carry out a regional landslide susceptibility assessment based solely on existing data (i.e., factor maps and the landslide archive), saving a considerable amount of time and money. For scarp landslides, where the selected factors (steepness, landform classification, and lithology) are more discriminate, models show excellent performance: areas under receiver operating characteristic (ROC) (AUCs) average > 0.9, while hillslope landslide results are highly satisfactory (average AUCs of about 0.8). The stochastic approach makes it possible to classify the Sicilian territory depending on its propensity to landslides in order to identify those municipalities which are most susceptible at this level of study, and are potentially worthy of more specific studies, as required by European-level protocols.
Hydrology, Volume 7; doi:10.3390/hydrology7020036
The aim of this study was to estimate evapotranspiration (ET) using remote sensing and the Surface Energy Balance Algorithm for Land (SEBAL) in the Ilam province, Iran. Landsat 8 satellite images were used to calculate ET during the cultivation and harvesting of wheat crops. The evaluation using SEBAL, along with the FAO-Penman–Monteith method, showed that SEBAL has a sufficient accuracy for estimating ET. The values of the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), and correlation coefficient were 0.466, 2.9%, 0.222 mm/day, and 0.97, respectively. Satellite images showed that rainfall, except for the last month of cultivation, provided the necessary water requirements and there was no requirement for the use of other water resources for irrigation, with the exception of late May and early June. The maximum ET on the Ein Khosh Plain occurred in March. The irrigation requirements showed that the Ein Khosh Plain in March, which witnessed the highest ET, did not experience any deficiency of rainfall that month. However, during April and May, with maxima of 50 and 70 mm, respectively, water was needed for irrigation. During the plant growth periods, the greatest and least amount of water required were 231.23 and 19.47 mm/hr, respectively.
Hydrology, Volume 7; doi:10.3390/hydrology7020035
The lack and inefficiency of urban drainage systems, as well as extreme precipitation, can lead to system overloading and, therefore, an urban pluvial flood. The study brings insights into this phenomenon from the perspective of the statistical relationship between precipitation and flooding parameters. The paper investigates the possibility of predicting sewer overloading based on the characteristics of the upcoming rain event using the Storm Water Management Model (SWMM) and statistical methods. Additionally, it examines the influence of precipitation resolution on the model sensitivity regarding floods. The study is set in a small urban catchment in Dresden (Germany) with a separated stormwater sewer system (SWSS). The flood-event-based calibrated model runs with observed and designed heavy rain events of various sums, durations, and intensities. Afterward, the analysis focuses on precipitation and model overloading parameters (total flood volume, maximum flooding time and flow rate, and maximum nodal water depth) with pairwise correlation and multi-linear regression (MLR). The results indicate that it is possible to define a certain threshold (or range) for a few precipitation characteristics, which could lead to an urban flood, and fitting MLR can noticeably improve the predictability of the SWSS overloading parameters. The study concludes that design and observed rain events should be considered separately and that the resolution of the precipitation data (1/5/10 min) does not play a significant role in SWSS overloading.
Hydrology, Volume 7; doi:10.3390/hydrology7020034
Soil information is critical in watershed-scale hydrological modelling; however, it is still debated which level of complexity the soil data should contain. In the present study, we have compared the effect of two levels of soil data on the hydrologic simulation of a mesoscale, urbanised watershed (630 km2) in central South Africa. The first level of soil data, land type (LT) data, is currently the best, readily available soil information that covers the whole of South Africa. In the LT database, the entire study area is covered by only two soil types. The second level of soil data (DSM) was created by means of digital soil mapping based on hydropedological principles. It resulted in six different soil types with different hydrological behaviour (e.g., interflow, recharge, responsive). The two levels of soil data were each included in the revised version of the Soil and Water Assessment Tool (SWAT+). To compare the effects of different complexity of soil information on the simulated water balance, the outputs of the uncalibrated models were compared to the three nested gauging stations of the watershed. For the LT scenario, the simulation efficiencies calculated with the Kling–Gupta efficiency (KGE) for the three nested gauging stations (640 km2, 550 km2, 54 km2) of 0, 0.33 and −0.23 were achieved, respectively. Under the DSM scenario, KGE increased to 0.28, 0.44 and 0.43 indicating an immediate improvement of the simulation by integrating soil data with detailed information on hydrological behaviour. In the LT scenario, actual evapotranspiration (aET) was clearly underestimated compared to MODIS-derived aET, while surface runoff was overestimated. The DSM scenario resulted in higher simulated aET compared to LT and lower surface runoff. The higher simulation efficiency of DSM in the smaller headwater catchments can be attributed to the inclusion of the interflow soil type, which covers the governing runoff generation process better than the LT scenario. Our results indicate that simulations benefit from more detailed soil information, especially in smaller areas where fewer runoff generation processes dominate.
Hydrology, Volume 7; doi:10.3390/hydrology7020033
Regional assessments of droughts are limited, and meticulous assessments over larger spatial scales are generally not substantial. Understanding drought variability on a regional scale is crucial for enhancing the resiliency and adaptive ability of water supply and distribution systems. Moreover, it can be essential for appraising the dynamics and projection of droughts based on regional climate across various spatial and temporal scales. This work focuses on drought analysis using a high-resolution dataset for three drought-prone regions of India between 1950 and 2016. This study also uses monthly values of the self-calibrating Palmer Drought Severity Index (scPDSI), incorporating Penman–Monteith approximation, which is physically based on potential evapotranspiration. Climate data are statistically downscaled and formulated to form a timeline for characterizing major drought events. The downscaled climate data hold a good statistical agreement with station data with correlation coefficients (R) ranging from 0.91 to 0.96. Drought analysis indicates and identifies several major incidences over the analysis time period considered in this work, which truly adheres to the droughts recorded in reports of various literatures for those regions.
Hydrology, Volume 7; doi:10.3390/hydrology7020032
The aim of this paper is the application of temporal analysis of daily and 10 min of rainfall data from Poprad station, located in Eastern Slovakia. There are two types of data used in the analysis, firstly, a daily time step data, manually collected between the years 1951 and 2018 and secondly, 10 min of data, automatically collected between the years 2000 and 2018. For proper comparability, the automatically collected data has been recalculated to the daily form. After a comparison of the sets of data, manually collected daily data has been used in further analysis. The main analysis can be divided into two sections. The first section consists of basic statistics (mean, standard deviation, etc.) and the second section of descriptive statistics, where the subjects of examination were trend, stationarity, homogeneity, periodicity and noise. The results of the basic statistics outlined trend behavior in the data meaning that the annual total rainfall for the period 1951–2018 is slightly increasing but the further investigation supported by the methods of descriptive statistics refuted this thesis. The number of rainy days is decreasing but maximum rainfall intensity is increasing year by year, indicating that total rainfall is happening in lesser and lesser days, with an increase in the number of 0 rainfall days. The results demonstrated no presence of the trend or only a weak trend in daily time step, but a significant increasing trend in annual rainfall. Tests of stationarity proved that the data are stationary and, therefore, suitable for any hydrologic analysis. The tests of homogeneity showed no breakpoints in the data. The interesting result was demonstrated by the periodicity test, which showed exactly a 365.25 days’ period, while 0.25 indicates a leap year. As a summary for the Poprad station, there is no tendency of increasing of daily average rainfall, but slight increasing trend of total annual rainfall, the summer season has the highest ratio on total precipitation per year, September and October are the months with the highest numbers of days without rain.
Hydrology, Volume 7; doi:10.3390/hydrology7020030
The Aral Sea in Central Asia plays an essential role in the socio-economic development of the region. During the last six decades, there has been remarkable changes observed in the water level and areal extent of the Aral Sea Basin; however, the causes behind these changes are unclear. This study quantifies the impacts of climatic and anthropogenic drivers on Aral Sea and the contributions made by these drivers to the variations observed in the Aral Sea Basin. The spatial and temporal seasonal variations in groundwater budget have been analyzed using the total water storage (TWS) of the basin from 2002 to 2015. The results from this study revealed significant increases in the the mean air temperature, precipitation, and potential evapotranspiration rate from 1960 to 2015 in the Aral Sea Basin. The TWS time-series shows a statistically significant declining trend of about 2 to 4 cm per year presented by the surface water storage. Based on the average monthly values of TWS, March 2005 presented the highest anomaly ~7.85 cm, while October 2008 showed the lowest anomaly ~8.22 cm between 2002 to 2015. The groundwater level indicates a small increasing trend of approximately 0.05 cm/year during the study period. Furthermore, the negative relationship between water level, climatic, and anthropogenic factors showed that these factors projected critical impact on the water level fluctuations within the Aral Sea Basin.
Hydrology, Volume 7; doi:10.3390/hydrology7020031
We explored the stability of the rating curves at six streamflow gauging sites in the state of Iowa, USA, to examine temporal variability of their stage–discharge relationships. The analyzed sites have up to 10 years of rating and shift records. Rating curve shifts reflect the alteration of channel geometry caused by scouring and sediment deposition. We studied how rating shifts are connected to the occurrence of flood events and drought periods over time. We found that most rating curve changes take place during spring and summer, which are the seasons with more precipitation in Iowa. We quantified stability in terms of standard deviation of stages for a continuous range of discharges in a rating curve, and show that most of the sites exhibit greater standard stage deviation for discharge–flood ratios smaller than 1, while for larger discharge–flood ratios, the deviation decreases. In stable rating curves, the stage deviation tends to decrease as discharge increases. Non-stable rating curves exhibit large stage deviation in the stage–discharge relationship throughout all stages.
Hydrology, Volume 7; doi:10.3390/hydrology7020029
Transbasin diversions and dams allow for water uses when and where there is high demand and low supply, but can come with an expense to the environment. This paper presents a linkage of hydrologic and hydraulic modeling and datasets to assess the hydrologic and hydraulic stability within a transbasin watershed as an approach for meeting water use targets and safeguarding environmental sustainability. The approach used a Prediction in Ungauged Basin (PUB) regionalization technique that completed the parameterization of a study watershed hydrologic model by transferring calibrated parameters from a reference watershed hydrologic model. This resulted in a long-term, simulated natural flow record that was compared to the measured modified flow record for the same time period to assess flow alteration. In the sensitive reach, hydraulic modeling results tracked channel response from before hydrologic modification to baseline using repeated survey years during the hydrologic modification. The combined assessment of hydrology and hydraulics highlighted the relation between flow regime and channel form.
Hydrology, Volume 7; doi:10.3390/hydrology7020028
This study attempted to delineate and map potential groundwater recharge zones of the Singida, semi-arid, fractured crystalline basement aquifer using open source remote sensing and GIS software. Various thematic maps such as lithology/hydrogeology, soil, land-cover/use, slope, lineament density, drainage density and rainfall distribution were integrated in QGIS software. Vector input layers were rasterized and resampled using QGIS wrap projection function to make sure that the grid cells are of the same size. Reclassification using SAGA and GRASS reclass algorithms in QGIS was carried out to realign the factor classes in a consistent scale, and reclassification to a scale of 1 to 5 was carried out to harmonize the results. The study identified a number of potential areas for groundwater recharge, groundwater exploration, groundwater development and potential areas for artificial groundwater recharge. Potential groundwater recharge zones for the Singida semi-arid fractured aquifer are restricted to areas with high lineament density, cultivated areas, grassland and flat to gentle slopes. The potential of groundwater recharge is also observed in areas with low drainage density. The delineated zones provide a good understanding of the potential recharge zones, which are a starting point for recharge zone protection. This blended approach can be utilized for carrying out suitability analysis using the weighted overlay analysis approach. Areas designated good and very good are recommended for artificial recharging structures as an alternative technique for enhancing groundwater recharge through rainwater harvesting. This will help to augment groundwater storage in this semi-arid environment.