Beyond peak reservoir storage? A global estimate of declining water storage capacity in large reservoirs

Water storage is an important way to cope with temporal variation in water supply and demand. The storage capacity and the lifetime of water storage reservoirs can be significantly reduced by the inflow of sediments. A global, spatially explicit assessment of reservoir storage loss in conjunction with vulnerability to storage loss has not been done. We estimated the loss in reservoir capacity for a global data set of large reservoirs from 1901 to 2010, using modeled sediment flux data. We use spatially explicit population data sets as a proxy for storage demand and calculate storage capacity for all river basins globally. Simulations suggest that the net reservoir capacity is declining as a result of sedimentation (5% compared to the installed capacity). Combined with increasing need for storage, these losses challenge the sustainable management of reservoir operation and water resources management in many regions. River basins that are most vulnerable include those with a strong seasonal flow pattern and high population growth rates such as the major river basins in India and China. Decreasing storage capacity globally suggests that the role of reservoir water storage in offsetting sea-level rise is likely weakening and may be changing sign

Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models

IntroductionClimate change has a large influence on the occurrence of extreme hydrological events. However, reliable estimates of future extreme event probabilities, especially when needed locally, require very long time series with hydrological models, which is often not possible due to computational constraints. In this study we take advantage of two recent developments that allow for more detailed and local estimates of future hydrological extremes. New large climate ensembles (LE) now provide more insight on the occurrence of hydrological extremes as they offer order of magnitude more realizations of future weather. At the same time recent developments in Machine Learning (ML) in hydrology create great opportunities to study current and upcoming problems in a new way, including and combining large amounts of data.MethodsIn this study, we combined LE together with a local, observation based ML model framework with the goal to see if and how these aspects can be combined and to simulate, assess and produce estimates of hydrological extremes under different warming levels for local scales. For this, first a new post-processing approach was developed that allowed us to use LE simulation data for local applications. The simulation results of discharge extreme events under different warming levels were assessed in terms of frequency, duration and intensity and number of events at national, regional and local scales.ResultsClear seasonal cycles with increased low flow frequency were observed for summer and autumn months as well as increased high flow periods for early spring. For both extreme events, the 3C warmer climate scenario showed the highest percentages. Regional differences were seen in terms of shifts and range. These trends were further refined into location specific results. The shifts and trends observed between the different scenarios were due to a change in climate variability.DiscussionIn this study we show that by combining the wealth of information from LE and the speed and local relevance of ML models we can advance the state-of-the-art when it comes to modeling hydrological extremes under different climate change scenarios for national, regional and local scale assessments providing relevant information for water management in terms of long term planning

Measuring Groundwater Flow Velocities near Drinking Water Extraction Wells in Unconsolidated Sediments

Groundwater is an important source of drinking water in coastal regions with predominantly unconsolidated sediments. To protect and manage drinking water extraction wells in these regions, reliable estimates of groundwater flow velocities around well fields are of paramount importance. Such measurements help to identify the dynamics of the groundwater flow and its response to stresses, to optimize water resources management, and to calibrate groundwater flow models. In this article, we review approaches for measuring the relatively high groundwater flow velocity measurements near these wells. We discuss and review their potential and limitations for use in this environment. Environmental tracer measurements are found to be useful for regional scale estimates of groundwater flow velocities and directions, but their use is limited near drinking water extraction wells. Surface-based hydrogeophysical measurements can potentially provide insight into groundwater flow velocity patterns, although the depth is limited in large-scale measurement setups. Active-heating distributed temperature sensing (AH-DTS) provides direct measurements of in situ groundwater flow velocities and can monitor fluctuations in the high groundwater flow velocities near drinking water extraction wells. Combining geoelectrical measurements with AH-DTS shows the potential to estimate a 3D groundwater flow velocity distribution to fully identify groundwater flow towards drinking water extraction wells

Gisser-Sánchez revisited: A model of optimal groundwater withdrawal under irrigation including surface–groundwater interaction

We revisit the classic problem of determining economically optimal groundwater withdrawal rates for irrigation. The novelty compared to previous mathematical analyses is the inclusion of non-linear groundwater-surface water interaction that allows for incorporating the impact of capture, i.e. the fact that all or part of the pumped groundwater comes out of reduced surface water flow or increased recharge. We additionally included the option to internalize environmental externalities (e.g. streamflow depletion) and maximize social welfare rather than farmer's profit. This analysis results in a fixed optimal groundwater withdrawal rate qopt when withdrawal q remains smaller than some critical withdrawal rate (maximum capture) qcrit and provides depletion trajectories, either under competition or optimal control, if q is larger than qcrit. Based on the relative value of q, qcrit and qopt it also yields four quadrants of distinct withdrawal strategies. Using global hydrogeological and hydroeconomic datasets we map the global occurrence of these four quadrants and provide global estimates of optimal groundwater withdrawal rates and depletion trajectories. For the quadrants with groundwater depletion (q > qcrit) we derive and compare depletion trajectories under competition, optimal control and optimal control including environmental externalities, and assessed globally where the differences between these depletion modes are small, which is known as the Gisser-Sánchez effect. We find that the Gisser-Sánchez effect is globally ubiquitous, but only if environmental externalities are ignored. The inclusion of environmental externalities in optimal control withdrawal result in notably reduced groundwater decline and larger values of social welfare in many of the major depletion areas

Global Impact of Sea Level Rise on Coastal Fresh Groundwater Resources

Groundwater is the main freshwater source in many densely populated and industrialized coastal areas around the world. Growing future freshwater demand is likely to increase the water stress in these coastal areas, possibly leading to groundwater overexploitation and salinization. This situation will likely be aggravated by climate change and the associated projected sea level rise. Here, we assess the impact of sea level rise exclusively on coastal fresh groundwater resources worldwide (limited to areas with unconsolidated sedimentary systems) by estimating future decline in inland fresh groundwater volumes under three sea level rise scenarios following Representative Concentration Pathway (RCP) 2.6, 4.5, and 8.5. For that, 2D groundwater models in 1,200 coastal regions estimate the past, present and future groundwater salinity. Our results show that roughly 60 (range 16–96) million people living within 10 km from current coastline could lose more than 5% of their fresh groundwater resources by 2100 according to RCP 8.5 scenario compared to only 8 (range 0–50) million people based on RCP 2.6 scenario. We conclude that sea level rise will have severe consequences for many coastal populations heavily dependent on fresh groundwater

The Importance of Turbulent Fluxes in the Surface Energy Balance of a Debris-Covered Glacier in the Himalayas

Surface energy balance models are common tools to estimate melt rates of debris-covered glaciers. In the Himalayas, radiative fluxes are occasionally measured, but very limited observations of turbulent fluxes on debris-covered tongues exist to date. We present measurements collected between 26 September and 12 October 2016 from an eddy correlation system installed on the debris-covered Lirung Glacier in Nepal during the transition between monsoon and post-monsoon. Our observations suggest that surface energy losses through turbulent fluxes reduce the positive net radiative fluxes during daylight hours between 10 and 100%, and even lead to a net negative surface energy balance after noon. During clear days, turbulent flux losses increase to over 250 W m−2 mainly due to high sensible heat fluxes. During overcast days the latent heat flux dominates the turbulent losses and together they reach just above 100 W m−2. Subsequently, we validate the performance of three bulk approaches in reproducing the observations from the eddy correlation system. Large differences exist between the approaches, and accurate estimates of surface temperature, wind speed, and surface roughness are necessary for their performance to be reasonable. Moreover, the tested bulk approaches generally overestimate turbulent latent heat fluxes by a factor 3 on clear days, because the debris-covered surface dries out rapidly, while the bulk equations assume surface saturation. Improvements to bulk surface energy models should therefore include the drying process of the surface. A sensitivity analysis suggests that, in order to be useful in distributed melt models, an accurate extrapolation of wind speed, surface temperature and surface roughness in space is a prerequisite. By applying the best performing bulk model over a complete melt period, we show that turbulent fluxes reduce the available energy for melt at the debris surface by 17% even at very low wind speeds. Overall, we conclude that turbulent fluxes play an essential role in the surface energy balance of debris-covered glaciers and that it is essential to include them in melt models

Effects of flood wave shape on probabilistic slope stability of dikes under transient groundwater conditions

The time-dependent response of pore water pressures during floods largely determines the safety against geotechnical failure of dikes, which is deemed to be highly dependent on the uncertain shape (duration, maximum height, etc.) of the flood discharge wave. This paper derives the uncertainty of flood wave shape from a database of precalculated hydrographs (GRADE) and evaluates the effect of shape variability on probabilistic safety estimates of slope stability, using a modelling chain consisting of a transient hydrological model (MODFLOW) and a probabilistic dike slope safety assessment (FORM). Accounting for flood wave uncertainty with transient groundwater flow generally leads to higher reliability estimates for slope stability, compared to the steady-state groundwater condition and other conservative assumptions, but to lower reliability estimates compared to a single design flood wave. Furthermore, the uncertainty of the flood wave shape can be as important as the uncertainty in geotechnical properties. For landside dike slope stability, the volume of the flood wave is the most important factor, while riverside slope stability depends mainly on the total water level drop after the peak. These two waveform characteristics are thus essential uncertainties to consider in probabilistic assessments of dike safety with transient groundwater conditions

Joint estimation of groundwater salinity and hydrogeological parameters using variable-density groundwater flow, salt transport modelling and airborne electromagnetic surveys

Freshwater aquifers in low elevation coastal zones are known to be threatened by saltwater intrusion (SWI). As these areas host a significant share of the world's population, an excellent understanding of this phenomenon is required to effectively manage the availability of freshwater. SWI is a dynamic process, therefore saline groundwater distributions can change quickly over time – particularly in stressed areas with anthropogenic drivers. To model these changes, regional 3D variable-density groundwater (3D-VDG) flow and coupled salt transport models are often used to estimate the current (and future distributions) of saline groundwater. Unfortunately, parameterising 3D-VDG models is a challenging task with many uncertainties. Generally, uncertainty is reduced through the addition of observational data – such as Airborne Electromagnetic (AEM) surveys or ground-based information – that offer information about parameters such as salinity and hydraulic head. Recent research has shown the ability of AEM surveys to provide accurate 3D groundwater salinity models across regional scales, as well as highlighting the potential for good survey repeatability. To this end we investigated the novel approach of using repeat AEM surveys (flown over the same area at different points in time) and 3D-VDG models to jointly improve the parameterisation of 3D-VDG models - while simultaneously providing a detailed 3D map of groundwater salinity distributions. Using detailed 3D synthetic models, the results of this study quantitatively highlight the usefulness of this approach, while offering practical information on implementation and further research

Recent advancement in water quality indicators for eutrophication in global freshwater lakes

Eutrophication is a major global concern in lakes, caused by excessive nutrient loadings (nitrogen and phosphorus) from human activities and likely exacerbated by climate change. Present use of indicators to monitor and assess lake eutrophication is restricted to water quality constituents (e.g. total phosphorus, total nitrogen) and does not necessarily represent global environmental changes and the anthropogenic influences within the lake's drainage basin. Nutrients interact in multiple ways with climate, basin conditions (e.g. socio-economic development, point-source, diffuse source pollutants), and lake systems. It is therefore essential to account for complex feedback mechanisms and non-linear interactions that exist between nutrients and lake ecosystems in eutrophication assessments. However, the lack of a set of water quality indicators that represent a holistic understanding of lake eutrophication challenges such assessments, in addition to the limited water quality monitoring data available. In this review, we synthesize the main indicators of eutrophication for global freshwater lake basins that not only include the water quality constituents but also the sources, biogeochemical pathways and responses of nutrient emissions. We develop a new causal network (i.e. multiple links of indicators) using the DPSIR (drivers-pressure-state-impact-response) framework that highlights complex interrelationships among the indicators and provides a holistic perspective of eutrophication dynamics in freshwater lake basins. We further review the 30 key indicators of drivers and pressures using seven cross-cutting themes: (i) hydro-climatology, (ii) socio-economy, (iii) land use, (iv) lake characteristics, (v) crop farming and livestock, (vi) hydrology and water management, and (vii) fishing and aquaculture. This study indicates a need for more comprehensive indicators that represent the complex mechanisms of eutrophication in lake systems, to guide the global expansion of water quality monitoring networks, and support integrated assessments to manage eutrophication. Finally, the indicators proposed in this study can be used by managers and decision-makers to monitor water quality and set realistic targets for sustainable water quality management to achieve clean water for all, in line with Sustainable Development Goal 6

Total irrigation by crop in the Continental United States from 2008 to 2020

We provide a dataset of irrigation water withdrawals by crop, county, year, and water source within the United States. We employ a framework we previously developed to establish a companion dataset to our original estimates. The main difference is that we now use the U.S. Geological Survey (USGS) variable ‘irrigation — total’ to partition PCR-GLOBWB 2 hydrology model estimates, instead of ‘irrigation — crop’ as used in previous estimates. Our findings for Surface Water Withdrawals (SWW), total Groundwater Withdrawals (GWW), and nonrenewable Groundwater Depletion (GWD) are similar to those of prior estimates but now have better spatial coverage, since several states are missing from the USGS ‘irrigation — crop’ variable that was originally used. Irrigation water use increases in this study, since more states are included and ‘irrigation — total’ includes more categories of irrigation than ‘irrigation — crop’. Notably, irrigation in the Mississippi Embayment Aquifer is now captured for rice and soy. We provide nearly 2.5 million data points with this paper (3,142 counties; 13 years; 3 water sources; and 20 crops)