Currently, hydropower accounts for close to 16% of the world’s total power supply and is the world’s most dominant (86%) source of renewable electrical energy. The key resource for hydropower generation is runoff, which is dependent on precipitation. The future global climate is uncertain and thus poses some risk for the hydropower generation sector. The crucial question and challenge then is what will be the impact of climate change on global hydropower generation and what are the resulting regional variations in hydropower generation potential? This paper is a study that aims to evaluate the changes in global hydropower generation resulting from predicted changes in climate. The study uses an ensemble of simulations of regional patterns of changes in runoff, computed from global circulation models (GCM) simulations with 12 different models. Based on these runoff changes, hydropower generation is estimated by relating the runoff changes to hydropower generation potential through geographical information system (GIS), based on 2005 hydropower generation. Hydropower data obtained from EIA (energy generation), national sites, FAO (water resources) and UNEP were used in the analysis. The countries/states were used as computational units to reduce the complexities of the analysis. The results indicate that there are large variations of changes (increases/decreases) in hydropower generation across regions and even within regions. Globally, hydropower generation is predicted to change very little by the year 2050 for the hydropower system in operation today. This change amounts to an increase of less than 1% of the current (2005) generation level although it is necessary to carry out basin level detailed assessment for local impacts which may differ from the country based values. There are many regions where runoff and hydropower generation will increase due to increasing precipitation, but also many regions where there will be a decrease. Based on this evaluation, it has been concluded that even if individual countries and regions may experience significant impacts, climate change will not lead to significant changes in the global hydropower generation, at least for the existing hydropower system.

Assessing climate change impacts on global hydropower.

Impact of climate change and water use policies on hydropower potential in the south-eastern Alpine region.

Global insurance markets are vast and diverse, and may offer many opportunities for remote sensing. To date, however, few operational applications of remote sensing for insurance exist. Papers claiming potential application of remote sensing typically stress the technical possibilities, without considering its contribution to customer value for the insured or to the profitability of the insurance industry. Based on a systematic search of available literature, this review investigates the potential and actual support of remote sensing to the insurance industry. The review reveals that research on remote sensing in classical claim-based insurance described in the literature revolve around crop damage and flood and fire risk assessment. Surprisingly, the use of remote sensing in claim-based insurance appears to be instigated by government rather than the insurance industry. In contrast, insurance companies are offering various index insurance products that are based on remote sensing. For example, remotely sensed index insurance for rangelands and livestock are operational, while various applications in crop index insurance are being considered or under development. The paper discusses these differences and concludes that there is particular scope for application of remote sensing by the insurance industry in index insurance because (1) indices can be constructed that correlate well with what is insured; (2) these indices can be delivered at low cost; and (3) it opens up new markets that are not served by claim-based insurance. The paper finally suggests that limited adoption of remote sensing in insurance results from a lack of mutual understanding and calls for greater cooperation between the insurance industry and the remote sensing community.

https://geoagro.icarda.org/downloads/publications/papers/UpdateRemoteSensing_in_Insurance-RS2014.pdf

The potential and uptake of remote sensing in insurance: A review

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required.

Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

Flood management consists many aspects such as hazard assessment, vulnerability assessment, exposure assessment, risk assessment, early warning system, damage assessment as well as risk mitigation planning. Conventional flood management are depending on the ground based monitoring of rainfall and river discharge. Many parts of the world are not covered by these sensor networks in one hand and these ground based systems are costly. Most of the tropical countries have high flood risk and low financial and institutional capacity to afford ground based system. While conventional flood management is time and cost intensive, spaceborne remote sensing provides timely and low-cost data in comparison to field observation, and is the obvious choice for most developing countries affected by flooding. Many aspects of flood management are being aided with the advancement of remote sensing technology. More precise and near real time flood detection, lead time in flood early warning system, accurate and advance inputs of hydrological models are now blessed by space technology. Many methods and approaches have been developed to overcome the constrains in the application of spaceborne remote sensing in flood management. Application of satellite remote sensing in flood hazard assessment is well documented, however, the application of space technology in other aspects of the flood management is also promising. Therefore, this review paper focuses on the applicability of spaceborne remote sensing and in most of the aspects in flood management.

The state of the art of spaceborne remote sensing in flood management

Climate change has a large impact on water resources and thus on hydropower. Hydroelectric power generation is closely linked to the regional hydrological situation of a watershed and reacts sensitively to changes in water quantity and seasonality. The development of hydroelectric power generation in the Upper Danube basin was modelled for two future decades, namely 2021–2030 and 2051–2060, using a special hydropower module coupled with the physically-based hydrological model PROMET. To cover a possible range of uncertainties, 16 climate scenarios were taken as meteorological drivers which were defined from different ensemble outputs of a stochastic climate generator, based on the IPCC-SRES-A1B emission scenario and four regional climate trends. Depending on the trends, the results show a slight to severe decline in hydroelectric power generation. Whilst the mean summer values indicate a decrease, the mean winter values display an increase. To show past and future regional differences within the Upper Danube basin, three hydropower plants at individual locations were selected. Inter-annual differences originate predominately from unequal contributions of the runoff compartments rain, snow- and ice-melt.

https://www.mdpi.com/1996-1073/4/10/1508/pdf

How Will Hydroelectric Power Generation Develop under Climate Change Scenarios? A Case Study in the Upper Danube Basin

Snow water equivalent (SWE) is a key variable for various hydrological applications. It is defined as the depth of water that would result upon complete melting of a mass of snow. However, until now, continuous measurements of the SWE are either scarce, expensive, labor-intense, or lack temporal or spatial resolution especially in mountainous and remote regions. We derive the SWE for dry-snow conditions using carrier phase measurements from the Global Navigation Satellite System (GNSS) receivers. Two static GNSS receivers are used, whereby one antenna is placed below the snow and the other antenna is placed above the snow. The carrier phase measurements of both receivers are combined in double differences (DDs) to eliminate clock offsets and phase biases and to mitigate atmospheric errors. Each DD carrier phase measurement depends on the relative position between both antennas, an integer ambiguity due to the periodic nature of the carrier phase signal, and the SWE projected into the direction of incidence. The relative positions of the antennas are determined under snow-free conditions with millimeter accuracy using real-time kinematic positioning. Subsequently, the SWE and carrier phase integer ambiguities are jointly estimated with an integer least-squares estimator. We tested our method at an Alpine test site in Switzerland during the dry-snow season 2015–2016. The SWE derived solely by the GNSS shows very high correlation with conventionally measured snow pillow (root mean square error: 11 mm) and manual snow pit data. This method can be applied to dense low-cost GNSS receiver networks to improve the spatial and temporal information on snow.

/pdf/snow-water-equivalent-of-dry-snow-derived-from-gnss-carrier-3p8hbfiuxu.pdf

Snow Water Equivalent of Dry Snow Derived From GNSS Carrier Phases

For numerous hydrological applications, information on snow water equivalent (SWE) and snow liquid water content (LWC) are fundamental. In situ data are much needed for the validation of model and remote sensing products; however, they are often scarce, invasive, expensive, or labor‐intense. We developed a novel nondestructive approach based on Global Positioning System (GPS) signals to derive SWE, snow height (HS), and LWC simultaneously using one sensor setup only. We installed two low‐cost GPS sensors at the high‐alpine site Weissfluhjoch (Switzerland) and processed data for three entire winter seasons between October 2015 and July 2018. One antenna was mounted on a pole, being permanently snow‐free; the other one was placed on the ground and hence seasonally covered by snow. While SWE can be derived by exploiting GPS carrier phases for dry‐snow conditions, the GPS signals are increasingly delayed and attenuated under wet snow. Therefore, we combined carrier phase and signal strength information, dielectric models, and simple snow densification approaches to jointly derive SWE, HS, and LWC. The agreement with the validationmeasurements was very good, even for large values of SWE (>1,000 mm) and HS (> 3 m). Regarding SWE, the agreement (root‐mean‐square error (RMSE); coefficient of determination (R)) for dry snow (41 mm; 0.99) was very high and slightly better than for wet snow (73 mm; 0.93). Regarding HS, the agreement was even better and almost equally good for dry (0.13 m; 0.98) and wet snow (0.14 m; 0.95). The approach presented is suited to establish sensor networks that may improve the spatial and temporal resolution of snow data in remote areas.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR024431

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Soil moisture (SM) is a highly relevant variable for agriculture, the emergence of floods and a key variable in the global energy and water cycle. In the last years, several satellite missions have been launched especially to derive large-scale products of the SM dynamics on the Earth. However, in situ validation data are often scarce. We developed a new method to retrieve SM of bare soil from measurements of low-cost GPS (Global Positioning System) sensors that receive the freely available GPS L1-band signals. The experimental setup of three GPS sensors was installed at a bare soil field at the German Weather Service (DWD) in Munich for almost 1.5 years. Two GPS antennas were installed within the soil column at a depth of 10 cm and one above the soil. SM was successfully retrieved based on GPS signal strength losses through the integral soil volume. The results show high agreement with measured and modelled SM validation data. Due to its non-destructive, cheap and low power setup, GPS sensor networks could also be used for potential applications in remote areas, aiming to serve as satellite validation data and to support the fields of agriculture, water supply, flood forecasting and climate change.

https://www.mdpi.com/2073-4441/8/7/276/pdf

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Flood monitoring from satellite data provides the opportunity to quickly and precisely overview flooded areas. The extent of the flooding and affected areas can be delivered to water authorities, civil protection agencies or insurances. Evaluations include information to facilitate damage assessment, to better estimate risk in future, and to prepare protection measures. For demonstrating the potential of satellite based evaluations, results for the Elbe flood that occurred in central Europe in August 2002 are presented. This flood had a geographical and economic large impact. In order to identify the affected areas, a strip of 450 km along the river Elbe was investigated. Satellite data of different sensor systems (Landsat, SPOT, ERS) had to be analyzed to derive a complete flood extent data set. For different river sections satellite data of different dates were used to perform the best fit to the flood peak along the river. Damage assessment can be supported using the flood extent overlaid with a city map in high resolution. Maps of complete Dresden have been requested by a German insurance company to support their damage assessment. The evaluation of flooded areas further allows improvements for planning as e.g. the declaration of risk areas. Observed flood extents can serve as validation source for simulations of inundations using hydrodynamic models. The combination of additional information to the flood extent layer opens up various applications. Landuse classified in industrial areas, settlements, agriculture, and forest, which were derived from up-to-date satellite images, are intersected with the inundated areas. This intersection results into the detection of affected areas (and economic values), which have to be protected against flood events in future. Multitemporal evaluations using a set of images of different days in succession document the spatial and temporal dynamics of a flood event. They allow to better rate damages as well as consequential costs.

Florian Appel

Papers

How Will Hydroelectric Power Generation Develop under Climate Change Scenarios? A Case Study in the Upper Danube Basin

Snow Water Equivalent of Dry Snow Derived From GNSS Carrier Phases

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Application of flood monitoring from satellite for insurances