Hydrology

Max-stable process (MSP) models can be fit to data collected over a spatial domain to estimate areal-based exceedances while accounting for spatial dependence in extremes. They have theoretical grounding within the framework of extreme value theory (EVT). In this work, we fit MSP models to three-day duration cool season precipitation maxima in the Willamette River Basin (WRB) of Oregon and to 48 h mid-latitude cyclone precipitation annual maxima in the Upper Trinity River Basin (TRB) of Texas. In total, 14 MSP models were fit (seven based on the WRB data and seven based on the TRB data). These MSP model fits were developed and applied to explore how user choices of study area sampling density, gage extent, and model fitting method impact areal precipitation-frequency calculations. The impacts of gage density were also evaluated. The development of each MSP involved the application of a recently introduced trend surface modeling methodology. Significant reductions in computing times were achieved, with little loss in accuracy, applying random sample subsets rather than the entire grid when calculating areal exceedances for the Cougar dam study area in the WRB. Explorations of gage extent revealed poor consistency among the TRB MSPs with modeling the generalized extreme value (GEV) marginal distribution scale parameter. The gauge density study revealed the robustness of the trend surface modeling methodology. Regardless of the fitting method, the final GEV shape parameter estimates for all fourteen MSPs were greater than their prescribed initial values which were obtained from spatial GEV fits that assumed independence among the extremes. When two MSP models only differed by their selected fitting method, notable differences were observed with their dependence and trend surface parameter estimates and resulting areal exceedances calculations. Full article

Evapotranspiration (ET) is a critical component of the water cycle, and an accurate prediction of ET is essential for water resource management, irrigation scheduling, and agricultural productivity. Traditionally, ET has been estimated using satellite-based remote sensing, which provides synoptic coverage but can be limited in spatial resolution and accuracy. Unmanned aerial vehicles (UAVs) offer improved ET prediction by providing high-resolution imagery of the Earth’s surface but are limited to a small area. Therefore, UAV and satellite images provide complementary data, but the integration of these two data for ET prediction has received limited attention. This paper presents a method that integrates UAV and satellite imagery for improved ET prediction and applies it to five crops (corn, rye grass, wheat, and alfalfa) from agricultural fields in the Walla Walla of eastern Washington State. We collected UAV and satellite data for five crops and used the combination of remote sensing models and statistical techniques to estimate ET. We show that UAV-based ET can be integrated with the Landsat-based ET with the application of integration factors. Our result shows that the Root Mean Square Error (RMSE) of daily ET for corn (Zea mays), rye grass (Lolium perenne), wheat (Triticum aestivum), peas (Pisum sativum), and alfalfa (Medicago sativa) can be improved by the application of the integration factor to the Landsat based ET in the range of (35.75–65.52%). We also explore the variability and effect of partial cloud on UAV-based ET estimation. Our findings have implications for the use of UAVs in water resource management and highlight the importance of considering multiple sources of data in ET prediction. Full article

Boron (B) and Tungsten (W) are often found enriched in high-temperature geothermal waters associated with the development of subduction-related mafic to felsic arc magma. However, knowledge about the sourcing and transportation of these elements from such hydrothermal systems is sparse and ambiguous. Being the only active continental collision site in the world, the Trans-Himalaya offers a unique chance to study how continental collision sources the high boron and tungsten concentrations in geothermal fluids. This study investigated the distribution of trace elements, major cations, and anions in three physicochemically distinct hotspring sites in the Ladakh region. The results were integrated with the existing geochemical and isotopic data to address the research problem more effectively. This study exhibits that the extreme concentrations of boron, sodium, chlorine, potassium, and tungsten in the hotspring waters were primarily governed by magmatic fluid inputs. In addition, this study recorded the highest-ever chlorine and boron concentrations for the Trans-Himalayan hotspring waters. The highest-ever boron and chlorine concentrations in the hotspring waters probably represented an increase in magmatic activity in the deeper source zone. Full article

The intensity–duration–frequency (IDF) curve is a commonly utilized tool for estimating extreme rainfall events that are used for many purposes including flood analysis. Extreme rainfall events are expected to become more intense under the changing climate, and there is a need to account for non-stationarity IDF curves to mitigate an underestimation of the risks associated with extreme rainfall events. Sydney, Australia, has recently started experiencing flooding under climate change and more intense rainfall events. This study evaluated the impact of climate change on altering the precipitation frequency estimates (PFs) used in generating IDF curves at Sydney Airport. Seven general circulation models (GCMs) were obtained, and the best models in terms of providing the extreme series were selected. The ensemble of the best models was used for comparing the projected 24 h PFs in 2031–2060 with historical values provided by Australian Rainfall and Runoff (ARR). The historical PFs consistently underestimate the projected 24 h PFs for all return periods. The projected 24 h 100 yr rainfall events are increased by 9% to 41% for the least and worst-case scenario compared to ARR historical PFs. These findings highlight the need for incorporating the impact of climate change on PFs and IDF curves in Sydney toward building a more prepared and resilient community. The findings of this study can also aid other communities in adapting the same framework for developing more robust and adaptive approaches to reducing extreme rainfall events’ repercussions under changing climates. Full article

The impact of climate change continues to manifest itself daily in the form of extreme events and conditions such as droughts, floods, heatwaves, and storms. Better forecasting tools are mandatory to calibrate our response to these hazards and help adapt to the planet’s dynamic environment. Here, we present a deep convolutional residual regressive neural network (dcrrnn) platform called Flux to Flow (F2F) for discerning the response of watersheds to water-cycle fluxes and their extremes. We examine four United States drainage basins of varying acreage from smaller to very large (Bear, Colorado, Connecticut, and Mississippi). F2F combines model and ground observations of water-cycle fluxes in the form of surface runoff, subsurface baseflow, and gauged streamflow. We use these time series datasets to simulate, visualize, and analyze the watershed basin response to the varying climates and magnitudes of hydroclimatic fluxes in each river basin. Experiments modulating the time lag between remotely sensed and ground-truth measurements are performed to assess the metrological limits of forecasting with this platform. The resultant mean Nash–Sutcliffe and Kling–Gupta efficiency values are both greater than 90%. Our results show that a hydrological machine learning platform such as F2F can become a powerful resource to simulate and forecast hydroclimatic extremes and the resulting watershed responses and natural hazards in a changing global climate. Full article

Online approaches for monitoring water quality can be an alternative aid to rapid decision-making in watershed management, especially reservoirs, given their vulnerability to the process of eutrophication. In this study, a modified water quality index (WQI) was developed using parameters that are easily measured with sensors, which would allow for the online monitoring of reservoirs. The modified WQI was based on WQI_CETESB and we used regression models to obtain values for the parameters: total phosphorus (TP), total nitrogen (TN), biochemical oxygen demand (BOD) and total solids (TS). Water quality data from reservoirs from 2003 to 2020 were used, which were provided by the Environmental Company of the State of São Paulo (CETESB), Brazil. The adjusted modified WQI employing weight redistribution (WQI_RWAdj or WQI_SOL) presented the most promising results, with a Pearson correlation of 0.92 and a success rate of 72.6% and 97.0% for the CETESB and simplified classifications, respectively. WQI_SOL, which was proposed in the present study, exhibited a satisfactory performance, allowing the water quality of reservoirs to be monitored remotely and in real-time. Full article

On-site sanitation systems (OSS), such as pit latrines, are an important source of methane (CH₄), with emissions increasing when they are wet, and this occurs when anaerobic conditions dominate. This paper presents the development of a model, which uses seasonal changes in groundwater to account for the fluctuating inundation of pit latrines, and, therefore, the associated CH₄ emissions from varying degrees of anerobic conditions are examined. Given that observed timeseries of groundwater table depth at high enough spatial and temporal resolutions are often difficult to obtain in low- and middle-income countries (LMICs), inverse distance weighted (IDW) interpolation is used to generate values for a whole region, which is then used, alongside average pit latrine depth, to determine areas of pit latrine inundation. Outcomes are further informed with open-source contextual data, covering population, urban/rural split, and sanitation facility data, before using methodologies from the Intergovernmental Panel on Climate Change (IPCC) to generate CH₄ emissions data. As a case study, we use data from Senegal to illustrate how this model works. Results show total CH₄ emissions for the month of January to be ~1.69 kt CH₄. We have also discussed the potential use of satellite remote sensing data in regions where access to historical groundwater data is limited. Understanding when the pit conditions are most likely to change could lead to incentives for better management strategies, as well as a reduction in CH₄ production. Full article

The primary objective of this study was to examine the quality of volunteered geographic information (VGI) data for flood mapping of Hurricane Harvey. As a crowdsourcing platform, the U-Flood project mapped flooded streets in the Houston metro area. This research examines the following: (1) If there are any significant differences in water depth (WD) among the hydraulic and hydrologic (H&H) model, the Federal Emergency Management Agency (FEMA) reference floodplain map, and the VGI? (2) Are there any significant differences in the inundated areas between the floodplain modeled by the VGI and hydraulic simulation? This study used HEC-RAS to simulate flood inundation maps and validated the results with high water marks (HWM) and the FEMA-modeled floodplain after Hurricane Harvey. The statistical results showed that there were significant differences in the WD, the inundated road count, and the length inside/outside of HEC-RAS-modeled floodplain. The results also showed that a less consistent decreasing trend between the U-Flood data and the modeled floodplain over time and space. This study empirically evaluated the data quality of the VGI based on observed and modeled data in flood monitoring. The findings from this study fill the gaps in the literature by assessing the uncertainty and data quality of VGI, providing insights into using supplementary data in flood mapping research. Full article

This paper presents potential upgrades to the Palm Jumeirah Island’s outer revetment to extend its design life for 50 years, considering the sea level rise (SLR) associated with climate change. The paper proposes several upgrade options to ensure that the hydraulic stability and wave overtopping discharges of the Palm Jumeirah revetment comply with the recommended design criteria based on industry guidelines. The performance of the existing revetment, in terms of the hydraulic stability and wave overtopping discharge criteria, is assessed using design wave heights (1- and 100-year events) extracted from an extreme wave analysis study on the Dubai coast. The results show that, based on the new design conditions, the existing structure should be upgraded to meet the armor stability criteria and recommended overtopping discharge values. Three different upgrade solutions are designed and analyzed to satisfy the required hydraulic stability and overtopping conditions. The suggested upgrade options are an extra armor layer, a flat berm, and a submerged breakwater offshore. The proposed upgrade solutions are preliminary designs that would require verification in terms of their geotechnical stability and physical model testing to evaluate their performance. Full article

Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R² of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R² of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R² of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making. Full article

In the context of climate change, this article tries to answer the question of whether a correlation exists between the precipitation and temperature series at a regional scale in Dobrogea, Romania. Six sets of time series are used for this aim, each of them containing ten series—precipitation and temperatures—recorded at the same period at the same hydro-meteorological stations. The existence of a monotonic trend was first assessed for each individual series. Then, the Regional time series (RTS) (one for a set of series) were built and the Mann–Kendall test was employed to test the existence of a monotonic trend for RTSs. In an affirmative case, Sen’s method was employed to determine the slope of the linear trend. Finally, nonparametric trend tests were utilized to verify if there was a correlation between the six RTSs. This study resulted in the fact that the only RTS presenting an increasing trend was that of minimum temperatures, and there was a weak correlation between the RTS of minimum precipitations and maximum temperatures. Full article

Reservoir ice can have an important impact on the watershed scale and influence hydraulic operations. On the other hand, hydropower generation can also impact the ice regime. In this study, multi-source satellite and ERA5-land data are used to evaluate ice conditions. Specifically, ice-controlling variables (temperature, water levels), ice regime (cover/deformation, thickness) and their interrelations are assessed for a 5-year period from 2017 to 2021. The methodology is applied to the Manicouagan reservoir, one of the largest hydropower reservoirs in Quebec, Canada. The satellite-based land surface temperatures (LSTs) suggest that winter 2021 was the hottest one. Overall, MODIS and Landsat LSTs agree with the ERA5-land temperatures. Ice backscatter from Sentinel-1 indicates that, in general, the reservoir is completely covered by ice from January to March. A correlation of 0.6 and 0.8 is observed between C- and Ku-band Synthetic Aperture Radar (SAR) signal and ice thickness, respectively. Important ice changes inferred from Differential Interferometric SAR (D-InSAR) occur approximately at the position where the largest ERA5-land ice thickness differences are observed. Winter water levels are also evaluated using satellite altimetric data to verify their influence on the ice dynamics. They show a decreasing tendency as the winter advances. Full article

The process of how soil moisture profiles evolve into the soil and reach the root zone could be estimated by solving the appropriate strong nonlinear Richards’ equation. The nonlinearity of the equation occurs because diffusivity D is generally an exponential function of water content. In this work, the boundary conditions of the physical problem are considered fuzzy for various reasons (e.g., machine impression, human errors, etc.), and the overall problem is encountered with a new approximate fuzzy analytical solution, leading to a system of crisp boundary value problems. According to the results, the proposed fuzzy analytical solution is in close agreement with Philip’s semi-analytical method, which is used as a reference solution, after testing 12 different types of soils. Additionally, possibility theory is applied, enabling the decision-makers to take meaningful actions and gain knowledge of various soil and hydraulic properties (e.g., sorptivity, infiltration, etc.) for rational and productive engineering studies (e.g., irrigation systems). Full article

Tracer testing is the only method in karst hydrogeology that can definitively determine whether a particular site belongs to a watershed of a particular karst spring. Therefore, it is an essential technique for delineating groundwater basins in karst areas. The availability of tracer test results is often limited due to the complicated and relatively expensive application of this approach, especially for large regional watersheds. The Croatian part of the Dinaric karst region extends for several hundred kilometers along the Adriatic coast and consists almost entirely of highly karstified carbonate rocks. The groundwater basins in these areas almost never match the surface morphology of the terrain. In practice, all available results of previous surveys are often used to define watersheds, regardless of the methodology and age of their implementation. This is also true for the earlier delineations of the Gacka River watershed, a regional karst basin in the Croatian Dinaric karst. However, tracer testing methods, especially the accuracy of tracer determination and monitoring, have improved significantly during this time. In order to assess the reliability of past tracing results in this significant karst basin, we reviewed reports of previous tracer tests. More recent tests, in particular the most recent multitracer injection test with continuous tracer detection on the major springs, produced high-quality data that allowed us to assess the reliability of the findings from prior research. A number of large karst springs with partially overlapping subcatchments feed the Gacka River. After discarding unreliable tracing data, we reevaluated the subcatchments of the main springs as well as the characteristics of the regional groundwater flow patterns throughout the basin, which is particularly important for water quality protection measures of the springs. The Gacka River basin is used as a case study to emphasize the importance of thoroughly assessing the reliability of previous tracing data before using them in regional analyses. Full article

To counter the impacts of climate change and urbanization, engineers have developed ingenious solutions to reduce flooding and capture stormwater contaminants through the use of Low Impact Developments (LIDs). The soil is generally considered to be completely saturated when designing for the LIDs. However, this may not always be an accurate or realistic approach, as the soil could be variably unsaturated leading to inaccurate designs. To analyse the flow under variably unsaturated conditions, Richards’ equation can be used. To solve the Richards’ equation, two nonlinear hydraulic properties, namely soil water characteristic curve (SWCC) and the unsaturated hydraulic conductivity function are required. Laboratory and field measurements of unsaturated hydraulic properties are cumbersome, expensive and time- consuming. Pedotransfer functions (PTFs) estimate soil hydraulic properties using routinely measured soil properties. This paper presents a comparison between the direct measurement obtained through experimental procedures and the use of PTFs to estimate soil hydraulic properties for two green roof and three bioretention soil medias. Comparison between the measured and estimated soil hydraulic properties was accomplished using two different approaches. Statistical analyses and visual comparisons were used to compare the measured and estimated soil hydraulic properties. Additionally, numerical modelling to predict the water balance at the ground surface was conducted using the measured and estimated soil hydraulic properties. In some instances, the use of predicted hydraulic properties resulted in overestimation of the cumulative net infiltration of as much as 60 % for the green roof substrate, but was considered negligible for the bioretention substrate. Design performance criteria for green roof and bioretention facilities were examined using the measured and estimated soil hydraulic properties under extreme precipitation analysis. Results indicate that there is a high level of uncertainty when using PTFs for LID materials. A percent difference between the measured and predicted properties for the green roof peak time delay under a 2-year storm can be as much as 300%. For the bioretention design criteria of a 25-year storm, the surface runoff was overestimated by 14.7 cm and by 100% for the ponding time percent difference. Full article

This study evaluates how the characteristics of daily rainfall and extreme events in Côte d’Ivoire changed during 1961–2015 using the rain gauge observation network of the National Meteorological Service (SODEXAM). The results indicate that the northern and southern parts of Cote d’Ivoire experienced a change from a wet to a dry period, with cut-offs in 1982 and 1983, respectively. In the northern part, this dry period was marked by a decrease in rainfall intensity, the length of wet spells, and the contribution of heavy and extreme rainfall, as well as an increase in the number of rainy days and a decrease in the length of dry spells. Over the southern part, this dry period was marked by an increase in the maximum length of dry spells associated with an increase in the maximum 1-day and 5-day precipitation events. The western part of Côte d’Ivoire experienced a late cut-off from the wet to dry period in 2000; the dry period was associated with a decrease in the number of rainy days, rainfall intensities, and maximum length of wet spells. Changes in the central part of Cote d’Ivoire presented high variability, and trends were less marked, even though a cut-off from a wet to dry period was detected in 1991. This study shows that Côte d’Ivoire, which is located in a subhumid and humid region and has an economy dependent on agriculture (especially cash crops, which comprise 60% of the GDP), is experiencing dry spells that are increasing in frequency and length. Combined with deforestation to increase production, this situation could lead to desertification and compromise the sustainable development goals of the country. The contribution of heavy rainfall was found to increase during the last 15 years, increasing the overall risk of floods, especially in urban areas where city authorities and populations are not prepared, thereby threatening infrastructure and human security. Full article

Storm surges due to severe weather events threaten low-land littoral areas by increasing the risk of seawater inundation of coastal floodplains. In this paper, we present recent developments of a numerical modelling system for coastal inundation induced by sea level elevation due to storm surges enhanced by astronomical tides. The proposed numerical code (CoastFLOOD) performs high-resolution (5 m × 5 m) raster-based, storage-cell modelling of coastal inundation by Manning-type equations in decoupled 2-D formulation at local-scale (20 km × 20 km) lowland littoral floodplains. It is fed either by outputs of either regional-scale storm surge simulations or satellite altimetry data for the sea level anomaly. The presented case studies refer to model applications at 10 selected coastal sites of the Ionian Sea (east-central Mediterranean Sea). The implemented regular Cartesian grids (up to 5 m) are based on Digital Elevation/Surface Models (DEM/DSM) of the Hellenic Cadastre. New updated features of the model are discussed herein concerning the detailed surveying of terrain roughness and bottom friction, the expansion of Dirichlet boundary conditions for coastal currents (besides sea level), and the enhancement of wet/dry cell techniques for flood front propagation over steep water slopes. Verification of the model is performed by comparisons against satellite ocean color observations (Sentinel-2 images) and estimated flooded areas by the Normalized Difference Water Index (NDWI). The qualitative comparisons are acceptable, i.e., the modelled flooded areas contain all wet area estimations by NDWI. CoastFLOOD results are also compared to a simplified, static level, “bathtub” inundation approach with hydraulic connectivity revealing very good agreement (goodness-of-fit > 0.95). Furthermore, we show that proper treatment of bottom roughness referring to realistic Land Cover datasets provides more realistic estimations of the maximum flood extent timeframe. Full article