Automatic Detection of Inland Water Bodies along Altimetry Tracks for Estimating Surface Water Storage Variations in the Congo Basin

Surface water storage in floodplains and wetlands is poorly known from regional to global scales, in spite of its importance in the hydrological and the carbon balances, as the wet areas are an important water compartment which delays water transfer, modifies the sediment transport through sedimentation and erosion processes, and are a source for greenhouse gases. Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered.

Decline in Iran’s River Flows

This study examined changes in Iran’s river flows by applying regression and analysis of variance methods to long-term ground-truth data. Evaluations were performed for the country’s data-rich rivers, covering almost 97% of all rivers and including more than 35 years of measurements. The results showed that about 56% of Iran’s rivers have experienced a negative trend in mean annual flow that is approximately 2.5 times greater than that reported for world’s rivers, leading to a shift from perennial to intermittent for about 20% of rivers in Iran’s sub-basins. This reflects surface freshwater shortages in Iran caused by natural and, more importantly, anthropogenic disturbances. It may even indicate the development of new hydrological regimes which can have significant implications for future surface water storage in Iran. This research improves understanding of changes in Iran’s river flows and provides beneficial information for sustainable water resources management in the country.

Monthly surface water storage from a global rainfall-runoff model for signal separation of GRACE-based terrestrial water storage variations

<p>In the EU-funded project Global Gravity-based Groundwater Product (G3P), we strive to combine data on terrestrial water storage from satellite gravimetry by the GRACE and GRACE-FO missions with existing products on water storage compartments from the Copernicus portfolio to establish a new cross-cutting product on groundwater storage variations with global coverage on a monthly basis. While the focus of G3P lies on incorporating observation-based Copernicus products, some model data has to be added to fill spatial and temporal gaps. This especially applies to water storage variations in surface water bodies, i.e., lakes and rivers, where little observation-based data is available. Altimetry-based data bases such as HYSOPE and the MGB model are available for large surface water bodies. However, to account for smaller water bodies and rivers, and to have a basis for assessing uncertainties of the entire approach, the integration of well-established models is desirable. A model we deem fit to these ends is Lisflood, which underpins the Global Flood Awareness System (GloFAS) of the Copernicus Emergency Management Service, and for which a recent global re-calibration is available.</p><p>In this study, we evaluate Lisflood’s capability of modeling surface water storage variations in comparison to the above-mentioned observational data sources as well as the WaterGAP Global Hydrology Model (WGHM). As the target output of G3P is a 0.5° grid with monthly resolution, Lisflood’s output data will undergo an upscaling and temporal aggregation procedure which will also be subject of this study.</p><p><em>Acknowledgments: This study received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement nº 870353.</em></p>

Surface deformations observed by GPS and its relation to groundwater variations in France

<p>The Global Positioning System (GPS) measures surface displacements in response to time-varying terrestrial water mass variations. Components of surface water storage include water in lakes and reservoirs, snow, and soil moisture. Groundwater depletion or recharge will also contribute to the overall water storage. Understanding the nature of the observed GPS displacements related to the continental water variations is important to help identify which compartment in the total water storage controls the water changes in any particular region. In this study, we demonstrate the potential of GPS to observe the surface displacements induced by groundwater variations in France. In-situ groundwater observations from boreholes in France are used to be compared with GPS displacements. Groundwater data are processed to obtain the Equivalent Water Height (EWH) and used to forward model surface deformation. Displacements predicted using EWH variations from the WaterGAP Global Hydrology Model (WGHM) will also be compared to the GPS displacements.</p>

Sustainable Surface Water Storage Development Pathways and Acceptable Limits for River Basins

This paper addresses the questions of acceptable upper limits for storage development and how best to deploy storage capacity in the long-term planning of built surface water storage in river basins. Storage-yield curves are used to establish sustainable storage development pathways and limits for a basin under a range of environmental flow release scenarios. Optimal storage distribution at a sub-basin level, which complies with an identified storage development pathway, can also be estimated. Two new indices are introduced—Water Supply Sustainability and Environmental Flow Sustainability—to help decide which pathways and management strategies are the most appropriate for a basin. Average pathways and conservative and maximum storage limits are illustrated for two example basins. Conservative and maximum withdrawal limits from storage are in the range of 45–50% and 60–65% of the mean annual runoff. The approach can compare the current level of basin storage with an identified pathway and indicate which parts of a basin are over- or under-exploited. A global storage–yield–reliability relationship may also be developed using statistics of annual basin precipitation to facilitate water resource planning in ungauged basins.

Impact of bedrock geochemistry on vegetation productivity depends on climate dryness in the Guizhou karst of China

The Guizhou karst area is one of the largest continuous areas of karst in the humid climate zone and is representative of karst landforms in China. Large portions of the karst system are characterized by extremely shallow soils underlain by weathered bedrock and water deficits are common. Although the distribution of ecosystem productivity is largely related to variations in the temperature and precipitation, the influence of the substrate in karst areas requires further exploration. We explored the relative importance of the bedrock geochemistry (characterized by the concentrations of Ca, Mg and Si) and climatic factors (temperature and precipitation) to explain the spatial variability in gross primary productivity (GPP) with various degrees of water deficit during the time period 2001–2015. Our results show that the impact of bedrock geochemistry is an important parameter in changing the original relationship between climate and the GPP. The bedrock geochemistry functioned as a “regulator” of the relation between climate and the GPP, which strengthened with decreasing climate favourability. The variations in GPP and surface water storage were significantly different when different elements (Ca, Mg or Si) were dominant. The Mg-rich regions showed the greatest annual variations in the GPP, whereas the Si-rich regions had the strongest surface water storage potential to support vegetation growth. The results of our study are important for systematically evaluating the effects of climate on vegetation productivity and provide a benchmark for global vegetation modelling predictions.

Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018

The recent availability of freely and openly available satellite remote sensing products has enabled the implementation of global surface water monitoring at a level not previously possible. Here we present a global set of satellite-derived time series of surface water storage variations for lakes and reservoirs for a period that covers the satellite altimetry era. Our goals are to promote the use of satellite-derived products for the study of large inland water bodies and to set the stage for the expected availability of products from the Surface Water and Ocean Topography (SWOT) mission, which will vastly expand the spatial coverage of such products, expected from 2021 on. Our general strategy is to estimate global surface water storage changes (ΔV) in large lakes and reservoirs using a combination of paired water surface elevation (WSE) and water surface area (WSA) extent products. Specifically, we use data produced by multiple satellite altimetry missions (TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, and Envisat) from 1992 on, with surface extent estimated from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 on. We leverage relationships between elevation and surface area (i.e., hypsometry) to produce estimates of ΔV even during periods when either of the variables was not available. This approach is successful provided that there are strong relationships between the two variables during an overlapping period. Our target is to produce time series of ΔV as well as of WSE and WSA for a set of 347 lakes and reservoirs globally for the 1992–2018 period. The data sets presented and their respective algorithm theoretical basis documents are publicly available and distributed via the Physical Oceanography Distributed Active Archive Center (PO DAAC; https://podaac.jpl.nasa.gov/, last access: 13 May 2020) of NASA's Jet Propulsion Laboratory. Specifically, the WSE data set is available at https://doi.org/10.5067/UCLRS-GREV2 (Birkett et al., 2019), the WSA data set is available at https://doi.org/10.5067/UCLRS-AREV2 (Khandelwal and Kumar, 2019), and the ΔV data set is available at https://doi.org/10.5067/UCLRS-STOV2 (Tortini et al., 2019). The records we describe represent the most complete global surface water time series available from the launch of TOPEX/Poseidon in 1992 (beginning of the satellite altimetry era) to the near present. The production of long-term, consistent, and calibrated records of surface water cycle variables such as in the data set presented here is of fundamental importance to baseline future SWOT products.

Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018

The recent availability of freely and openly available satellite remote sensing products has enabled the implementation of global surface water monitoring to a level not previously possible. Here we present a global set of satellite-derived time series of surface water storage variations for lakes and reservoirs for a period that covers the satellite altimetry era. Our goal is to promote the use of satellite-derived products for the study of large inland water bodies, and to set the stage for the expected availability of products from the Surface Water and Ocean Topography (SWOT) mission, which will vastly expand the spatial coverage of such products, expected from 2021 on. Our general strategy is to estimate global surface water storage changes (ΔV) in large lakes and reservoirs using a combination of paired water surface elevation (WSE) and water surface area (WSA) extent products. Specifically, we use data produced by multiple satellite altimetry missions (TOPEX-Poseidon, Jason-1, Jason-2, Jason-3, and ENVISAT) from 1992 on, with surface extent estimated from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 on. We leverage from relationships between elevation and surface area (i.e., hypsometry) to produce estimates of ΔV even during periods when either of the variables was not available. This approach is successful provided that there are strong relationships between the two variables during an overlapping period. Our target is to produce time series of ΔV as well as WSE and WSA for a set of 347 lakes and reservoirs globally for the 1992–2018 period. The data sets presented are publicly available and distributed via NASA’s Jet Propulsion Laboratory’s Physical Oceanography Distributed Active Archive Center (PO DAAC; https://podaac.jpl.nasa.gov/). Specifically, the WSE data set is available at https://doi.org/10.5067/UCLRS-GREV2 (Birkett et al., 2019), the WSA data set is available at https://doi.org/10.5067/UCLRS-AREV2 (Khandelwal and Kumar, 2019), and the ΔV data set is available at https://doi.org/10.5067/UCLRS-STOV2 (Tortini et al., 2019). The records we describe represent the most complete global surface water time series available from the launch of TOPEX-Poseidon in 1992 (beginning of the satellite altimetry era) to near-present. The production of long-term, consistent, and calibrated records of surface water cycle variables such as the data set presented here is of fundamental importance to baseline future SWOT products.

The economics of dams

Dams provide a host of benefits to societies, helping to balance variability in water availability with demand for multiple uses, allowing power generation, providing and enhancing recreation opportunities, and offering protection against damaging floods. Yet dams are often controversial due to their high investment requirements, unequal distributional effects, and concerns over the irreparable harm they may cause to free-flowing rivers and ecosystems. This paper considers the economic case for such projects, organized around a review of what we know about their value and impacts. Though heterogeneity across contexts is a common feature of interventions in many sectors, we argue that it is particularly acute in the case of dams, and that this creates difficulties for drawing general conclusions about their social value. Still, evidence suggests that investment in additional surface water storage may not always be the best solution for addressing water scarcity. Furthermore, a number of biases and blind spots exist in economic frameworks used to evaluate dams, and these often challenge analysts’ ability to correctly identify which projects will be most attractive. Perhaps due to some of these perceived deficiencies, policy-makers do not appear to make much use of economic analyses for decision-making about dams. If progress is to be made in avoiding costly mistakes related to construction and removal of such infrastructures in the future, economists need to improve the realism and methods underlying their analyses of the value of dams, and offer more useful interpretations of the results that these approaches produce.