Hydrology, Volume 9, Issue 7 (July 2022) – 17 articles

Globally, about 54 million ha of cropland are irrigated with saline water. Globally, the soils associated with about 1 billion ha are affected by salinization. A small decrease in irrigation water salinity (and soil salinity) can result in a disproportionally large increase in crop yield. This study uses a zero-valent iron desalination reactor to effect surface processing of ground water, obtained from an aquifer, to partially desalinate the water. The product water can be used for irrigation, or it can be reinjected into a saline aquifer, to dilute the aquifer water salinity (as part of an aquifer water quality management program), or it can be injected as low-salinity water into an aquifer to provide a recharge barrier to protect against seawater intrusion. The saline water used in this study is processed in a batch flow, bubble column, static bed, diffusion reactor train (0.24 m³), with a processing capacity of 1.7–1.9 m³ d⁻¹ and a processing duration of 3 h. The reactor contained 0.4 kg Fe⁰. A total of 70 batches of saline water (average 6.9 g NaCl L⁻¹; range: 2.66 to 30.5 g NaCl L⁻¹) were processed sequentially using a single Fe⁰ charge, without loss of activity. The average desalination was 24.5%. The reactor used a catalytic pressure swing adsorption–desorption process. The trial results were analysed with respect to Na⁺ ion removal, Cl⁻ ion removal, and the impact of adding trains. The reactor train was then repurposed, using n-Fe⁰ and emulsified m-Fe⁰, to establish the impact of reducing particle size on the amount of desalination, and the amount of n-Fe⁰ required to achieve a specific desalination level. Full article

The territory of Albania presents wide outcrops of soluble rocks, with typical karst landforms and the presence of remarkable carbonate aquifers. Many karst areas are located near the coasts, which results in a variety of environmental problems, mostly related to marine intrusion. This paper focuses on the brackish springs of Albania, which exhibit temperatures approximately equal to the yearly air temperature at their location. Total dissolved solids of the springs are higher than 1000 mg/L, their waters are not drinkable, and they are rarely used for other purposes. The groundwater of the alluvial aquifers of Albania, particularly those of Pre-Adriatic Lowland, are often brackish too, but these will not be addressed here. Brackish springs of Albania are mainly of karst origin and can be classified into two groups: springs in evaporitic rock, mainly gypsum, and springs in carbonate rock. The hydro-chemical facies of the first group are usually Ca-SO₄, locally with increased concentrations of Na-Cl, whereas springs belonging to the second group usually exhibit Na-Cl facies. The largest brackish springs of Albania are described in detail, including their hydro-chemical correlations. Full article

The identification of the second-order dependence structure of streamflow has been one of the oldest challenges in hydrological sciences, dating back to the pioneering work of H.E Hurst on the Nile River. Since then, several large-scale studies have investigated the temporal structure of streamflow spanning from the hourly to the climatic scale, covering multiple orders of magni-tude. In this study, we expanded this range to almost eight orders of magnitude by analysing small-scale streamflow time series (in the order of minutes) from ground stations and large-scale streamflow time series (in the order of hundreds of years) acquired from paleocli-matic reconstructions. We aimed to determine the fractal behaviour and the long-range de-pendence behaviour of the streamflow. Additionally, we assessed the behaviour of the first four marginal moments of each time series to test whether they follow similar behaviours as sug-gested in other studies in the literature. The results provide evidence in identifying a common stochastic structure for the streamflow process, based on the Pareto–Burr–Feller marginal dis-tribution and a generalized Hurst–Kolmogorov (HK) dependence structure. Full article

Machine learning and deep learning have demonstrated usefulness in modelling various groundwater phenomena. However, these techniques require large amounts of data to develop reliable models. In the Southern African Development Community, groundwater datasets are generally poorly developed. Hence, the question arises as to whether machine learning can be a reliable tool to support groundwater management in the data-scarce environments of Southern Africa. This study tests two machine learning algorithms, a gradient-boosted decision tree (GBDT) and a long short-term memory neural network (LSTM-NN), to model groundwater level (GWL) changes in the Shire Valley Alluvial Aquifer. Using data from two boreholes, Ngabu (sample size = 96) and Nsanje (sample size = 45), we model two predictive scenarios: (I) predicting the change in the current month’s groundwater level, and (II) predicting the change in the following month’s groundwater level. For the Ngabu borehole, GBDT achieved R² scores of 0.19 and 0.14, while LSTM achieved R² scores of 0.30 and 0.30, in experiments I and II, respectively. For the Nsanje borehole, GBDT achieved R² of −0.04 and −0.21, while LSTM achieved R² scores of 0.03 and −0.15, in experiments I and II, respectively. The results illustrate that LSTM performs better than the GBDT model, especially regarding slightly greater time series and extreme GWL changes. However, closer inspection reveals that where datasets are relatively small (e.g., Nsanje), the GBDT model may be more efficient, considering the cost required to tune, train, and test the LSTM model. Assessing the full spectrum of results, we concluded that these small sample sizes might not be sufficient to develop generalised and reliable machine learning models. Full article

The aim of this study was to investigate the utility of multiple linear regression (MLR) for the estimation of reference evapotranspiration (ETo) of the Peloponnese, Greece, for two representative months of winter and summer during 2016–2019. Another objective was to test the number of inputs needed for satisfactorily accurate estimates via MLR. Datasets from sixty-two meteorological stations were exploited. The available independent variables were sunshine hours (N), mean temperature (Tmean), solar radiation (Rs), net radiation (Rn), wind speed (u₂), vapour pressure deficit (es − ea), and altitude (Z). Sixteen MLR models were tested and compared to the corresponding ETo estimates computed by FAO-56 Penman–Monteith (FAO PM) in a previous study, via statistical indices of error and agreement. The MLR5 model with five input variables outperformed the other models (RMSE = 0.28 mm d⁻¹, adj. R² = 98.1%). Half of the tested models (two to six inputs) exhibited very satisfactory predictions. Models of one input (e.g., N, Rn) were also promising. However, the MLR with u₂ as the sole input variable presented the worst performance, probably because its relationship with ETo cannot be linearly described. The results indicate that MLR has the potential to produce very good predictive models of ETo for the Peloponnese, based on the literature standards. Full article

Evapotranspiration (ET) is a major component of the water cycle and agricultural water balance. Estimation of water consumption over agricultural areas is important for agricultural water resources planning, management, and regulation. It leads to the establishment of a sustainable water balance, mitigates the impacts of water scarcity, as well as prevents the overusing and wasting of precious water resources. As evapotranspiration is a major consumptive use of irrigation water and rainwater on agricultural lands, improvements of water use efficiency and sustainable water management in agriculture must be based on the accurate estimation of ET. Applications of precision and digital agricultural technologies, the integration of advanced techniques including remote sensing and satellite technology, and usage of machine learning algorithms will be an advantage to enhance the accuracy of the ET estimation in agricultural water management. This paper reviews and summarizes the technical development of the available methodologies and explores the advanced techniques in the estimation of ET in agricultural water management and highlights the potential improvements to enhance the accuracy of the ET estimation to achieve precise agricultural water management. Full article

The use of rainwater harvesting tanks to supply human water needs is an old and sustainable practice. In the case of covering irrigation demand in greenhouse agriculture, the potential is huge. Still, the relative research worldwide is low, while it is nearly absent in Greece. In this study, the rainwater harvesting tank size for irrigation use of greenhouse tomato cultivation was investigated by applying a daily water balance model in three regions of Crete Island (Greece) with significant greenhouse areas. Daily rainfall data from three representative rainfall stations of the study areas characterized by different rainfall regime for a 12-year time series were used. Additionally, the daily irrigation water needs for a tomato crop during an 8-month cultivation period were used. The greenhouse roof was defined as catchment area of the rainwater harvesting system and greenhouse areas of 1000, 5000 and 10,000 m² were studied. In all areas examined, a tank of 30–100 m³ per 1000 m² of greenhouse area could reach approximately 80–90% reliability. Higher values of reliability (reaching 100%) could be achieved mainly with covered tanks. Tank size for 100% reliability in covered tanks, ranged from 200 m³ (per 1000 m² of greenhouse area) in the study area with high mean annual rainfall depth (974.24 mm) and moderate mean longest dry period (87.67 days), to 276 m³ (per 1000 m² of greenhouse area) in the study area with relatively low mean annual rainfall depth (524.12 mm) and high mean longest dry period (117.42 days). For uncovered tanks, a 100% reliability value could be reached only with a tank size of 520 m³ (per 1000 m² of greenhouse area) in the study area with high mean annual rainfall depth and moderate mean longest dry period. Full article

Soil erosion is one of the most critical threats to cultivated land. Yet little information is available in Sub-Saharan Africa, especially on the relative contributions of various forms of erosion. Therefore, this study’s objective was to quantify soil loss by sheet and linear erosion. The study was carried out on the sloping land rangeland of the Potshini catchment of KwaZulu-Natal, South Africa, with an annual average rainfall of 766 mm. The average sheet erosion computed using a network of 1 m² microplots was 7.7 ton ha⁻¹ y⁻¹ with standard error of 1.97 ton ha⁻¹ y⁻¹ (which corresponded to an ablation rate of between 0.35 to 1.32 mm y⁻¹) while linear erosion, mainly the retreat of gully banks, removed 4.8 ton ha⁻¹ y⁻¹, i.e., 38.4% of total soil losses. Despite removing a lower amount of soil, sheet erosion by depleting fertile, carbon- and nutrient-enriched soil horizons has a great impact on most ecological functions associated with soils. Full article

Since the Water Framework Directive (WFD) came into force in 2000, data on the hydromorphological quality have been collected for all rivers in Europe. In Germany, a reference-based classification scheme is used (LAWA 2000) for hydrological assessment. The question arises whether this method can compensate sufficiently for a change of ecoregion. In our study of the Hase River in NW Germany, the frequency of the river classes was compared between two ecoregions (Lower Saxonian Mountains vs. Northwest-German Lowlands). In the lowlands, the evaluation shows a significantly higher proportion of class 5 river sections. This can mainly be attributed to the main parameters, longitudinal section, riverbed structure and bank structure. While the bad results in the longitudinal section and bank structure can be explained by changes in geology and anthropogenic pressures, the evaluation scheme cannot sufficiently compensate for changes in the riverbed structure. This problem is aggravated by the inconsistent implementation of the evaluation scheme in Germany, where the federal states use different approaches with regard to section length. Using 100 m sections throughout the river course can lead to severely underestimating the number of structures. Further improvement and standardization in the evaluation scheme seem to be necessary for the adjustment of the field survey method to different ecoregions in Germany. Full article

In tropical regions, deep convective systems are associated with extreme rainfall storms that usually detonate flash floods and landslides in the Andean Colombian region. Several studies have used satellite data to address the structure and formation of tropical convective storms. However, there is a local gap in the characterization, which is essential for a better understanding of flash floods and preparedness, filling a gap in a region with scarce information regarding extreme events. In this work, we assess the deep convective storms in a mountainous region of Colombia using meteorological radar observations between 2014 and 2017. We start by identifying convective and stratiform formations. We refine the convective identification by classifying convective systems into enveloped (contained in a stratiform system) and unenveloped (not contained). Then, we analyze the systems’ temporal and spatial distributions and contrast them with the watersheds’ features. According to our results, unenveloped convective systems have higher reflectivity and hence higher rainfall intensities. Moreover, they also have a well-defined spatial and temporal distribution and are likely to occur in watersheds with elevation gradients of around 2000 $\mathrm{m}$ and an aspect contrary to the wind direction. Our assessment of the convective storms is of significant value for the hydrologic community working on flash floods. Moreover, the spatiotemporal description is highly relevant for stakeholders and future local analysis. Full article

Evaporation losses of rainfall intercepted by canopies depend on many factors, including the temporal scale of observations. At the event scale, interception is a few millimetres, whereas at a larger temporal scale, the number of times that a canopy is filled by rainfall and then depleted can make the interception an important fraction of the rainfall depth. Recently, a simplified interception/evaporation model has been proposed, which considers a modified Merrian model to compute interception during wet spells and a simple power-law equation to model evaporation from wet canopy during dry spells. Modelling evaporation process at the sub hourly temporal scale required the two parameters of the power-law, describing the hourly evaporation depth and the evaporation rate. In this paper, for branches of lemon trees, we focused on the evaporation process from wet branches starting from the interception capacity, S, and simple models in addition to the power-law were applied and tested. In particular, for different temperature, T, and vapour pressure deficit, VPD, conditions, numerous experimental testes were carried out, and the two parameters describing the evaporation process from wet branches were determined and linked to T, VPD and S. The results obtained in this work help us to understand the studied process, highlight its complexity, and could be implemented in the recently introduced interception/evaporation model to quantify this important component of the hydrologic cycle. Full article

Rainfall-runoff simulation is vital for planning and controlling flood control events. Hydrology modeling using Hydrological Engineering Center—Hydrologic Modeling System (HEC-HMS) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research’s high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. Furthermore, we also performed a hydraulic simulation in Hydrological Engineering Center—Geospatial River Analysis System (HEC-RAS) using the input discharge obtained from the Random Forest model. The reliability of the Random Forest model and the HEC-HMS model was evaluated using different statistical indexes. The coefficient of determination (R²), standard deviation ratio (RSR), and normalized root mean square error (NRMSE) were 0.94, 0.23, and 0.17 for the training data and 0.72, 0.56, and 0.26 for the testing data, respectively, for the Random Forest model. Similarly, the R², RSR, and NRMSE were 0.99, 0.16, and 0.06 for the calibration period and 0.96, 0.35, and 0.10 for the validation period, respectively, for the HEC-HMS model. The Random Forest model slightly underestimated peak discharge values, whereas the HEC-HMS model slightly overestimated the peak discharge value. Statistical index values illustrated the good performance of the Random Forest and HEC-HMS models, which revealed the suitability of both models for hydrology analysis. In addition, the flood depth generated by HEC-RAS using the Random Forest predicted discharge underestimated the flood depth during the peak flooding event. This result proves that HEC-HMS could compensate Random Forest for the peak discharge and flood depth during extreme events. In conclusion, the integrated machine learning and physical-based model can provide more confidence in rainfall-runoff and flood depth prediction. Full article

Water quality indices (WQIs) are practical and versatile instruments for assessing, organizing, and disseminating information about the overall quality status of surface water bodies. The use of these indices may be beneficial in evaluating aquatic system water quality. The CCME (Canadian Council of Ministers of the Environment) and NSF (National Science Foundation) WQIs were used for the assessment of surface water (depth = 1 m) in Lake Union, Washington State. These WQIs were used in surface water at Lake Union, Seattle. The modified versions of the applied WQIs incorporate a varied number of the investigated parameters. The two WQIs were implemented utilizing specialized, publicly accessible software tools. A comparison of their performance is offered, along with a qualitative assessment of their appropriateness for describing the quality of a surface water body. Practical conclusions were generated and addressed based on the applicability and disadvantages of the evaluated indexes. When compared to the CCME-WQI, it is found that the NSF-WQI is a more robust index that yields a categorization stricter than CCME-WQI. Full article

Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models. Full article

Ponds are a common feature in urban parks to provide aesthetic and recreational functions, but also deliver a wide range of ecosystem services. The objective of this study was to assess the water quality of six urban ponds in the city of Lisbon, Portugal, to determine the factors that influence it and consider remediation measures for them. Besides that, our study aims to deliver data in order to support the best approach for a future monitoring program, toward more strategic and sustainable management. Floating treatment wetlands (FTW) were installed in three of the ponds during the study, where nutrient levels were higher. Water sampling was performed since 2016, with more intensive campaigns in 2020 and 2021. Average pH ranged from 7.9 to 9.0, average Chemical Oxygen Demand ranged from 36 mg/L to 90 mg/L and average Total Suspended Solids ranged from 7 to 93 mg/L. The main factors that contribute to these values were identified as the presence of waterbirds, vegetative debris that falls in ponds, and contamination with sewage. The FTW that were installed in some of the ponds could help to improve the water quality, but additional measures such as removal of bottom sediments and leaves in the fall, may be necessary. It is expected that the assessment of water quality carried out in the urban ponds can contribute to the overall improvement of urban water management. Full article

Hydrologic modeling is trending toward larger spatial and temporal domains, higher resolutions, and less extensive local calibration and validation. Thorough calibration and validation are difficult because the quantity of observations needed for such scales do not exist or is inaccessible to modelers. We present the Stream Analysis for Bias Estimation and Reduction (SABER) method for bias correction targeting large models. SABER is intended for model consumers to apply to a subset of a larger domain at gauged and ungauged locations and address issues with data size and availability. SABER extends frequency-matching postprocessing techniques using flow duration curves (FDC) at gauged subbasins to be applied at ungauged subbasins using clustering and spatial analysis. SABER uses a “scalar” FDC (SFDC), a ratio of simulated to observed FDC, to characterize biases spatially, temporally, and for varying exceedance probabilities to make corrections at ungauged subbasins. Biased flows at ungauged locations are corrected with the scalar values from the SFDC. Corrected flows are refined to fit a Gumbel Type 1 distribution. We present the theory, procedure, and validation study in Colombia. SABER reduces biases and improves composite metrics, including Nash Sutcliffe and Kling Gupta Efficiency. Recommendations for future work and a discussion of limitations are provided. Full article

In this study, monthly streamflow and satellite-based actual evapotranspiration data (AET) were used to evaluate the Soil and Water Assessment Tool (SWAT) model for the calibration of an experimental sub-basin with mixed land-use characteristics in Athens, Greece. Three calibration scenarios were performed using streamflow (i.e., single variable), AET (i.e., single variable), and streamflow–AET data together (i.e., multi-variable) to provide insights into how different calibration scenarios affect the hydrological processes of a catchment with complex land use characteristics. The actual evapotranspiration data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The calibration was achieved with the use of the SUFI-2 algorithm in the SWAT-CUP program. The results suggested that the single variable calibrations showed moderately better performance than the multi-variable calibration. However, the multi-variable calibration scenario displayed acceptable outcomes for both streamflow and actual evapotranspiration and indicated reasonably good streamflow estimations (NSE = 0.70; R² = 0.86; PBIAS = 6.1%). The model under-predicted AET in all calibration scenarios during the dry season compared to MODIS satellite-based AET. Overall, this study demonstrated that satellite-based AET data, together with streamflow data, can enhance model performance and be a good choice for watersheds lacking sufficient spatial data and observations. Full article