Water is polluted by increasing activities of population and the necessity to provide them with goods and services that use water as a vital resource. The contamination of water due to heavy metals (HMs) is a big concern for humankind; however, global studies related to this topic are scarce. Thus, the current review assesses the content of HMs in surface water bodies throughout the world from 1994 to 2019. To achieve this goal, multivariate analyses were applied in order to determine the possible sources of HMs. Among the analyzed HMs in a total of 147 publications, the average content of Cr, Mn, Co, Ni, As and Cd exceeded the permissible limits suggested by WHO and USEPA. The results of the heavy metal pollution index, evaluation index, the degree of contamination, water pollution and toxicity load showed that the examined water bodies are highly polluted by HMs. The results of median lethal toxicity index showed maximum toxicity in As, Co, Cr and Ni in the surface water bodies. Results of ingestion and dermal pathways for adults and children in the current analyzed review showed that As is the major contaminant. Moreover, Cr, Ni, As and Cd showed values that could be considered as a high risk for cancer generation via the ingestion pathway as compared to the dermal route. It is recommended that remediation techniques such as the introduction of aquatic phytoremediation plant species and adsorbents should be included in land management plans in order to reduce human risks.
The advent of digital elevation models (DEMs) has made it possible to objectively extract, calculate and store geomorphological parameters for hydrological modelling at several scales. For a grid-based DEM, the threshold area used to extract the channel network is analogous to the scale of the map produced. In addition to the map scale, the effects of the vertical resolution of the DEM on some frequently used geomorphological parameters in hydrology are examined using high-resolution DEMs of two natural and two artificial catchments. The vertical resolution was varied between 1 cm and 1 m, the most common vertical resolution of DEMs. At a fixed map scale, the mean absolute percentage error in the geomorphological parameters caused by a decrease in vertical resolution is within the range 0-5% for the medium-sized catchments and 0-10% for the small catchments studied. Although it is true that a change in vertical resolution may cause a change in the individual pixel slope, area and topographic index (area/slope), particularly in low relief terrain, their cumulative distributions do not show any significant change with the vertical resolution. The shape of the normalized width function is not very sensitive to the vertical resolution and the map scale. For small catchments order change may occur at different map scales for the different vertical resolution DEMs of the same catchment, causing a significant change in order-related parameters such as Horton ratios. It is suggested that the vertical resolution of the DEM of a catchment be considered satisfactory for most hydrological applications if the ratio of the average drop per pixel and vertical resolution is greater than unity. This ratio criterion could be used to define the minimum pixel area for reliable channel network definition for any given vertical resolution. The minimum pixel area places a lower bound on the horizontal resolution with which a channel network can be extracted from a DEM. These results could potentially be used to assess the adequacy for hydrological purposes of existing and proposed digital elevation databases.
Accurate estimation of drought events is vital for the mitigation of their adverse consequences on water resources, agriculture and ecosystems. Machine learning algorithms are promising methods for drought prediction as they require less time, minimal inputs, and are relatively less complex than dynamic or physical models. In this study, a combination of machine learning with the Standardized Precipitation Evapotranspiration Index (SPEI) is proposed for analysis of drought within a representative case study in the Tibetan Plateau, China, for the period of 1980-2019. Two timescales of 3 months (SPEI-3) and 6 months (SPEI-6) aggregation were considered. Four machine learning models of Random Forest (RF), the Extreme Gradient Boost (XGB), the Convolutional neural network (CNN) and the Long-term short memory (LSTM) were developed for the estimation of the SPEIs. Seven scenarios of various combinations of climate variables as input were adopted to build the models. The best models were XGB with scenario 5 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed and relative humidity) and RF with scenario 6 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed, relative humidity and sunshine) for estimating SPEI-3. LSTM with scenario 4 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed) was relatively better for SPEI-6 estimation. The best model for SPEI-6 was XGB with scenario 5 and RF with scenario 7 (all input climate variables, i.e., scenario 6 + solar radiation). Based on the NSE index, the performances of XGB and RF models are classified as good fits for scenarios 4 to 7 for both timescales. The developed models produced satisfactory results and they could be used as a rapid tool for decision making by water-managers.
[1] A daily rainfall disaggregation model, which uses a copula to model the dependence structure between total depth, total duration of wet periods, and the maximum proportional depth of a wet period, is presented. The wet(1)-dry(0) binary sequence is modeled by the nonrandomized Bartlett-Lewis model with diurnal effect incorporated before superimposing the AR(1) depth process submodel. Unlike previous studies, the model is structured such that all wet day data available are considered in the analysis, without the need to discard any good quality daily data embedded in a month having some missing data. This increased the data size, thus improving the modeling process. Further, the daily data are classified according to the total duration of wet periods duration within the day. In this way a large proportion of the model parameters become seasonal invariant, the overriding factor being the total duration of wet periods. The potential of the developed model has been demonstrated by disaggregating both the data set used in developing the model parameters and also a 12 year continuous rainfall data set not used in the model parameterization. Gross rainfall statistics of several aggregation levels down to 6 min have been very well reproduced by the disaggregation model. The copula dependence structure and the variation of the depth process submodel parameters with the total duration of wet periods are also very well captured by the presented model.
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