Hydrology, Volume 9, Issue 6 (June 2022) – 18 articles

Early prediction of rainfall is important for the planning of agriculture, water infrastructure, and other socio-economic developments. The near-term prediction (e.g., 10 years) of hydrologic data is a recent development in GCM (General Circulation Model) simulations, e.g., the CMIP5 (Coupled Modelled Intercomparison Project Phase 5) decadal experiments. The prediction of monthly rainfall on a decadal time scale is an important step for catchment management. Previous studies have considered stochastic models using observed time series data only for rainfall prediction, but no studies have used GCM decadal data together with observed data at the catchment level. This study used the Facebook Prophet (FBP) model and six machine learning (ML) regression algorithms for the prediction of monthly rainfall on a decadal time scale for the Brisbane River catchment in Queensland, Australia. Monthly hindcast decadal precipitation data of eight GCMs (EC-EARTH MIROC4h, MRI-CGCM3, MPI-ESM-LR, MPI-ESM-MR, MIROC5, CanCM4, and CMCC-CM) were downloaded from the CMIP5 data portal, and the observed data were collected from the Australian Bureau of Meteorology. At first, the FBP model was used for predictions based on: (i) the observed data only; and (ii) a combination of observed and CMIP5 decadal data. In the next step, predictions were performed through ML regressions where CMIP5 decadal data were used as features and corresponding observed data were used as target variables. The prediction skills were assessed through several skill tests, including Pearson Correlation Coefficient (PCC), Anomaly Correlation Coefficient (ACC), Index of Agreement (IA), and Mean Absolute Error (MAE). Upon comparing the skills, this study found that predictions based on a combination of observed and CMIP5 decadal data through the FBP model provided better skills than the predictions based on the observed data only. The optimal performance of the FBP model, especially for the dry periods, was mainly due to its multiplicative seasonality function. Full article

Protecting the quality of coastal water bodies requires the assessment of contaminant discharge brought by rivers. Numerous methods have been proposed for calculating sediment and nutrient loads. The most widely used and generally recommended are the flow-weighted mean concentration method (FWMC) and the flow duration rating curve method (FDRC). In the Mediterranean basin, the hydrology is characterized by infrequent but very intense rainfall events. The flows taking place during these periods last only a few hours to a few days but can represent the largest part of the annual flow. The loads associated with these events can also account for most of the annual load. A reinforced water-quality monitoring program (especially during floods) was carried out for five years (August 2015–July 2020) on six tributaries of French Mediterranean lagoons. The loads calculated by FWMC and FDRC methods were very different. Total suspended solid loads calculated by FWMC were on average 5.0 times higher than those calculated by FDRC. Similarly, total phosphorus loads were 3.5 times higher and total nitrogen loads were 1.6 times higher. The results show that too many flood samples can lead to considerable overestimation of particulate loads calculated by the FWMC method. Dissolved nutrients, on the other hand, are much less subject to overestimation. Full article

At times, hydrological drought is defined using Q90 or Q95 (90% or 95% flows equaling or exceeding) or even at higher levels, such as Q75 as the cutoff level regardless of their seasonal variation (i.e., truncation at the uniform flow level). In the past, the estimation of drought length and magnitude at the aforesaid uniform cutoff levels of flow has been a challenging issue. A procedure is presented to first estimate the drought magnitude (M), which then forms the basis for estimating the drought duration or length (L). The drought magnitude (M) and the length of the critical period (L_cr) are estimated using the concept of behavior analysis prevalent in the hydrologic design of reservoirs. This information is used for estimating the drought length (L_T-e′, the estimated value of drought length for the return period of T weeks) involving a Markov chain model on the standardized weekly flow sequences. A weighted average of L_cr and L_T-e′ (=0.60 L_cr + 0.40 L_T-e′) results in the estimate of drought length, which is compatible to the observed counterpart_. The performance of the procedure to estimate drought length was found to be satisfactory up to the truncation level of Q75, whereas the estimation of drought magnitude was rated as good. Full article

Hydrological modeling is commonly used in urban areas for drainage design and to estimate pluvial flood hazards in order to mitigate flood risks and damages. In general, modelers choose well-known and proven models, which are tailored to represent the runoff generation of impervious areas and surface runoff. However, interception and other vegetation-related processes are usually simplified or neglected in models to predict pluvial flooding in urban areas. In this study, we test and calibrate the hydrological model LEAFlood (Landscape and vEgetAtion-dependent Flood model), which is based on the open source ‘Catchment Modeling Framework’ (CMF), tailored to represent hydrological processes related to vegetation and includes a 2D simulation of pluvial flooding in urban areas using landscape elements. The application of LEAFlood was carried out in Vauban, a district in Freiburg (Germany) with an area of ∼31 hectares, where an extensive hydrological measurement network is available. Two events were used for calibration (max intensity 17 mm/h and 28 mm/h) and validation (max intensity 25 mm/h and 14 mm/h), respectively. Moreover, the ability of the model to represent interception, as well as the influence of urban trees on the runoff, was analyzed. The comparison of observed and modeled data shows that the model is well-suited to represent interception and runoff generation processes. The site-specific contribution of each single tree, approximately corresponding to retaining one cup of coffee per second (∼0.14 L/s), is viewed as a tangible value that can be easily communicated to stakeholders. For the entire study area, all trees decrease the peak discharge by 17 to 27% for this magnitude of rainfall intensities. The model has the advantage that single landscape elements can be selected and evaluated regarding their natural contribution of soil and vegetation to flood regulating ecosystem services. Full article

As many other regions worldwide, the Bolivian Altiplano has to cope with water scarcity during dry periods, which in turn impacts on crop production as flood irrigation is overwhelmingly extended in the region. Since farming is the main income in the Altiplano for most families, the availability of greenhouses with water harvesting systems may represent a solution to warrant all year round production and food access. We study the daily satisfied water demand from a balance between rainfall collected by a greenhouse roof and water used for indoor crop irrigation assuming a tank is available for water storage. This balance is analyzed for 25 greenhouses spread over Batallas Municipality, close to Titicaca Lake, Bolivia, and for two case studies: (i) using irrigation data collected from farmers in the frame of a regional project; (ii) using theoretical daily water requirements assuming an intense greenhouse farming. Our evaluation includes a sensitivity analysis of relevant parameters, such as the influence of the time window of rainfall used in the simulation, the runoff coefficient, the roof surface area, the irrigation drip system, the irrigation frequency, the crop coefficient, the volume of water used for crop irrigation, and the capacity of the water tank. Overall, we find that the runoff coefficient has little impact on the satisfied demand rate, while all other parameters can play an important role depending on the greenhouse considered. Some greenhouses are able to irrigate crops normally during the wet season, while during the dry season, greenhouses are not able to satisfy more than $50\%$ of the theoretical water requirements, even when large tanks are considered. Based on these results, we recommend the construction of greenhouses with a ground surface of <50 ${\mathrm{m}}^2$ attached to the largest available covered water tank. The information here provided can be used by stakeholders to decide their policies of investment in infrastructures in the Altiplano. Finally, the approach we follow can be applied to any other region where rainfall, temperature, and greenhouse data are available. Full article

Radium has been commonly used as a tracer of submarine groundwater discharge to the ocean and embankments, as radium activities are commonly input into box models to calculate a groundwater flux. Similarly, isotopes of radium (Ra²²⁴, Ra²²³, Ra²²⁶, Ra²²⁸) have been used to calculate water mass ages, which have been used as a proxy for residence times. Less commonly, radium and other tracers have been utilized in mixing models to determine the relative contribution of groundwater to a marine system. In the literature, all of these methods have almost exclusively been solved using analytical methods prone to large errors and other issues. Project Open RaDOM, introduced here, is a collection of open-source R scripts that numerically solve for groundwater flux, residence time, and relative contribution of groundwater to coastal systems. Solving these models numerically allows for over-constrained systems to increase their accuracy and force real solutions. The scripts are written in a way to make them user-friendly, even to scientists unfamiliar with R. This communication includes a description of the scripts in Project Open RaDOM, a discussion of examples in the literature, and case studies of the scripts using previously published data. Full article

Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas. Full article

Urban irrigation is an essential process in land–atmosphere interactions. It is one of the uncertain parameters of urban hydrology due to various microclimates. This study investigated the microclimate effects and irrigation water requirements of three landscape types in an arid region of Phoenix, AZ. The microclimate effect encompassed surface temperature, air temperature, and wind speed. The simulations of the three landscapes were conducted using ENVI-met software for the hottest day of the year (23 June 2011). The simulated model was validated using ground data. Results show that the mesic landscape induced cooling effects, both in the daytime and nighttime, by reducing surface and air temperatures. However, the mesic landscape showed high-water consumption because of a high leaf area density. The oasis landscape showed 2 °C more daytime cooling than the mesic landscape, but the nighttime warming (surface temperature) was comparable to the xeric landscape. The potential irrigation water requirement was 1 mm/day lower than the mesic landscape. Moreover, microclimate conditions varied spatially in each neighborhood. The xeric landscape showed lower wind speeds and air temperatures between the buildings. The wind speed variations in the three landscapes were inconclusive due to differences in building orientations and discrepancies in trees’ heights. The findings can have implications for restricting the municipal irrigation budget. In addition, they can help water managers in choosing a landscape in urban areas. Urban scientists can adapt the methodology to quantify urban ET in arid regions. Full article

Evapotranspiration (ET) provides important information for hydrological studies, including estimating plant water requirements which can be derived from remote sensing data or simulated using hydrological models. In this study, ET derived from the Moderate Resolution Imaging Spectrometer (MODIS) was compared with ET simulated by the calibrated and validated Soil and Water Assessment Tool (SWAT) model for the Big Sunflower River watershed (BSRW) in Mississippi. The comparisons were made based on 8-day, 1-month, seasonal, and annual timescales. The coefficients of variation (COVs) for the 8-day, 1-month, seasonal, and annual ET simulated by SWAT were 0.42, 0.40, 0.32, and 0.04, respectively, whereas the COVs for the ET derived from MODIS were 0.06, 0.12, 0.08, and 0.01 for the respective time scales. Lower COVs for the ET derived from MODIS indicated lower sensitivity to crop growth in the field. SWAT-simulated ET was the highest during crop growing season and lowest during dormant season, but MODIS-derived ET did not vary considerably according to crop growing or harvesting seasons. As MODIS-derived ET accounts for only climatic conditions and vegetation cover, SWAT-simulated ET is recommended for the short-term estimation of crop water requirements because it accounts for climatic, land use, soil, and slope conditions. Full article

Non-rainfall water input to surface soil moisture is essential to ecosystems, especially in dry climates, where a water deficit may persist for several months. Quantifying the impact of water gains by soil moisture at night will help to understand vegetation dynamics in dry regions. The objective of this study was to evaluate the non-rainfall water contribution to soil moisture content at the soil surface and how it minimizes the water stress on plants with predominantly surface roots. The experiment was conducted in a low-latitude, semiarid environment with a dry tropical forest regenerating for 42 years. The soil moisture and soil temperature were measured at one-minute intervals from June 2019 to August 2019 using four capacitive humidity sensors and thermometers, installed at depths of 5 and 10 cm. the soil moisture increased significantly (p < 0.05) during the night at both depths from June to August, when there was no rainfall. There is a definite contribution of nightly gains to alleviate vegetation water stress during the dry months. These results show the importance of dew for water availability and for dry tropical forests species in the months of water deficit. Full article

Uncertainty is inherent in the modelling of any physical processes. Regarding hydrological modelling, the uncertainty has multiple sources including the measurement errors of the stresses (the model inputs), the measurement errors of the hydrological process of interest (the observations against which the model is calibrated), the model limitations, etc. The typical techniques to assess this uncertainty (e.g., Monte Carlo simulation) are computationally expensive and require specific preparations for each individual application (e.g., selection of appropriate probability distribution). Recently, data-driven methods have been suggested that attempt to estimate the uncertainty of a model simulation based exclusively on the available data. In this study, two data-driven methods were employed, one based on machine learning techniques, and one based on statistical approaches. These methods were tested in two real-world case studies to obtain conclusions regarding their reliability. Furthermore, the flexibility of the machine learning method allowed assessing more complex sampling schemes for the data-driven estimation of the uncertainty. The anatomisation of the algorithmic background of the two methods revealed similarities between them, with the background of the statistical method being more theoretically robust. Nevertheless, the results from the case studies indicated that both methods perform equivalently well. For this reason, data-driven methods can become a valuable tool for practitioners. Full article

The Ohrid-Prespa lake system is the oldest and most diverse permanent lake system in Europe, dating from the Pliocene era and aged at over 4Ma. Its smaller component is Lake Macro Prespa (thereafter called Prespa), shared by North Macedonia, Albania, and Greece. Lake Prespa’s depth was reported as 14 m mean and 48 m maximum before its major water level decline. The lake is highly sensitive to external impacts, including climate change, and has been suffering major water loss for decades. A lake-level decline of almost 10 m was documented between 1950 and 2009 due to restricted precipitation and increased water abstraction for irrigation. This study describes the changes in the surface size of Prespa Lake and the vegetation/land use in the surrounding area in the period 1984–2020 using satellite images (remote sensing, Landsat 5 & 8 images by United States Geological Survey). The lake lost 18.87 km² of surface in this period (6.9% of its size, dropping from 273.38 km² to 254.51 km²). Water loss was greater in the period 1987–1993 and 1998–2004. The Analysis of Normalized Difference Vegetation Index (NDVI) in the area (app. 4950 km²) surrounding Lake Prespa revealed an increase in the mean NDVI values over the period studied (1984–2020), pointing to a general increase in vegetation. Areas with NDVI > 0.13 increased from 78% in 1984 to 86% in 2020, while those with the highest vegetation intensity (NDVI > 0.45) increased by 40%. These changes in vegetation may be related to the water loss of the lake. Full article

The agricultural sector is vulnerable to extreme phenomena such as droughts, particularly in arid and semi-arid environments and in regions where water infrastructure is limited. Devising preparedness plans, including means for efficient monitoring and timely identification of drought events, is essential for informed decision making on drought mitigation and water management, especially for the water-dependant agricultural sector. This paper presents the incorporation of two new drought indices, designed for agricultural drought identification, in Drought Indices Calculator (DrinC) software. These indices, namely the Agricultural Standardized Precipitation Index (aSPI) and the Effective Reconnaissance Drought Index (eRDI), require commonly available meteorological data, while they employ the concept of effective precipitation, taking into account the amount of water that contributes productively to plant development. The design principles of DrinC software leading to the proper use of the indices for agricultural drought assessment, including the selection of appropriate reference periods, calculation time steps and other related issues, are presented and discussed. The incorporation of aSPI and eRDI in DrinC enhances the applicability of the software towards timely agricultural drought characterisation and analysis, through a straightforward and comprehensible approach, particularly useful for operational purposes. Full article

This study proposes seven equations to predict the settling velocity of sediment particles with variations in grain size (d), particle shape factor (SF), and water temperature (T) based on the artificial neural network procedure. The data used to develop the equations were obtained from digitizing charts provided by the U.S. Interagency Committee on Water Resources (U.S-ICWR) and compiled from the measurement data of settling velocity from several sources. The equations are compared to three existing equations available in the literature and then analyzed using graphical and statistical analysis. The simulation results show the proposed equations produce satisfactory results. The proposed equations can predict the settling velocity of natural particle sediments, with diameters ranging between 0.05 mm and 10 mm in water with temperatures between 0 °C and 40 °C, and shape factor SF ranging between 0.5 and 0.95. Full article

Carbonate aquifers are characterised by strong heterogeneities and their modelling is often a challenging aspect in hydrological studies. Understanding carbonate aquifers can be more complicated in the case of strong seismic events which have been widely demonstrated to influence groundwater flow over wide areas or on a local scale. The 2016–2017 seismic sequence of Central Italy is a paradigmatic example of how earthquakes play an important role in groundwater and surface water modifications. The Campiano catchment, which experienced significant discharge modifications immediately after the mainshocks of the 2016–2017 seismic sequence (Mmax = 6.5) has been analysed in this study. The study area is within an Italian national park (Sibillini Mts.) and thus has importance from a naturalistic and socio-economic standpoint. The research strategy coupled long-period artificial tracer tests (conducted both before and after the main earthquakes), geochemical and discharge analyses and isotope hydrology with hydrogeological cross-sections. This study highlights how the seismic sequence temporarily changed the behaviour of the normal faults which act predominantly as barriers to flow in the inter-seismic period, with water flow being normally favoured along the fault strikes. On the contrary, during earthquakes, groundwater flow can be significantly diverted perpendicularly to fault-strikes due to co-seismic fracturing and a consequent permeability increase. The interaction between groundwater and surface water is not only important from the point of view of scientific research but also has significant implications at an economic and social level. Full article

Spring discharge hydrographs can provide information on karst aquifer connectivity and responses to precipitation. However, few studies have conducted time-series analyses of spring hydrographs over multi-decadal time scales. We examine daily discharge for three large karst springs and daily precipitation for adjoining weather stations during 1928–2019 in the Salem Plateau of southern Missouri, one of the major karst regions in the USA. For different time periods, we conducted baseflow index calculations and time-series (autocorrelation, spectral density, and cross-correlation with precipitation) analyses for discharge data, and Mann–Kendall (MK) trend analyses for discharge and precipitation data. Hydrograph separation indicates discharge is baseflow-dominated (86–94%) at all three springs. The memory effect is lower for Bennett Spring (with an auto-correlation lag time 29–41 days) than for Big Spring (60–92 days) and Greer Spring (77–112 days). Spectral density analysis indicates that annual signals dominate all three springs. Cross-correlation analysis shows a quicker response to precipitation at Bennett Spring (0–1 days) than at Big and Greer springs (1–2 days). MK trend analysis shows significant increases in discharge for all three springs over multiple decades, but not for the period 2007–2019. Increased discharge accompanies regional increases in precipitation, but may also reflect increased recharge associated with reversion of farmland to forest. Full article

A snowpack’s δ²H and δ¹⁸O values evolve with snowfall, sublimation, evaporation, and melt, which produces temporally variable snowpack, snowmelt, and runoff isotope signals. As a snowpack ages, the relatively depleted δ²H and δ¹⁸O values of snow will become less depleted with sublimation and evaporation, and the internal distribution of isotope signals is altered with melt moving through and out of the snowpack. An examination of δ²H and δ¹⁸O values for snowpack, snowmelt, and ephemeral creek water in the Palouse Range of northern Idaho indicated an evolution from variably depleted snowpack to enriched snowmelt and relatively consistent isotope signals in springtime ephemeral creeks. Within the primary snow band of the mountain range and during the winter–spring period of 2019–2020, the snowpack had an isotope range of −130 to −75‰ for δ²H and −18 to −10.5‰ for δ¹⁸O with resulting snowmelt values of −120 to −90‰ for δ²H and −16.5 to −12.5‰ for δ¹⁸O. With runoff of snowmelt to ephemeral creeks, the isotope values compressed to −107 to −104‰ for δ²H and −15.5 to −14.5‰ for δ¹⁸O. Aging of the snowpack produced increasing densities in the base, middle, and upper layers along with a corresponding enrichment of isotope values. The highest elevation site indicated the least enrichment of δ²H and δ¹⁸O in the snowpack base layer, and the lowest elevation site indicated the strongest enrichment of δ²H and δ¹⁸O in the snowpack base layer. Deuterium excess decreased with snowpack aging processes of accumulation and melt release, along with the migration of water vapor and snowmelt within the snowpack. It is likely that winter melt (early depleted signal) is a primary contributor to creeks and groundwater along the Palouse Range, but the strong variability of snowpack isotope signals provides a wide range of possible isotope signals to surface-water and groundwater systems at the mountain front. Full article

Sulfate isotopes (δ³⁴S, δ¹⁸O_SO4) interpreted in conjunction with sulfate concentrations show that sulfate of both agricultural and geologic sources is present in groundwater and surface water in the Rio Grande flood plain within the Hueco Bolsón. From previous studies, water isotopes (δ²H, δ¹⁸O) in the study area indicate groundwater age relative to dam construction upstream. Surface water entering the Hueco Bolsón contains a mixture of soil-amendment sulfate and sulfate from deep-basin groundwater seeps at the terminus of Mesilla Valley. In the shallow Rio Grande alluvial aquifer within the Hueco Bolsón, ranges of δ³⁴S in pre-dam (+2 to +9‰) and post-dam (0 to +6‰) groundwater overlap; the range for post-dam water coincides with common high-sulfate soil amendments used in the area. Most post-dam groundwater, including discharge into agricultural drains, has higher sulfate than pre-dam groundwater. In surface water downstream of Fabens, high-δ³⁴S (>+10‰) sulfate, resembling Middle Permian gypsum, mixes with sulfate from upstream sources and agriculture. The high- δ³⁴S sulfate probably represents discharge from the regional Hueco Bolsón aquifer. In surface water downstream of Fort Hancock, soil-amendment sulfate predominates, probably representing discharge from the Rio Grande alluvial aquifer near the basin terminus. The δ¹⁸O_SO4 dataset is consistent with sulfate origins determined from the larger δ³⁴S dataset. Full article