Future climate model scenarios depend crucially on the models' adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use stateof-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Météorologiques Coupled GCM, version 3 (CNRM-CM3), and the atmospheric component of the L'Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) coupled model (called LMDZ-4)-were bias corrected. After the validation of the bias-corrected data, the original and the biascorrected GCM data were used to force two global hydrology models (GHMs): 1) the hydrological model of the Max Planck Institute for Meteorology (MPI-HM) consisting of the simplified land surface (SL) scheme and the hydrological discharge (HD) model, and 2) the dynamic global vegetation model called LPJmL. The impact of the bias correction on the projected simulated hydrological changes is analyzed, and the simulation results of the two GHMs are compared. Here, the projected changes in 2071-2100 are considered relative to 1961-90. It is shown for both GHMs that the usage of bias-corrected GCM data leads to an improved simulation of river runoff for most catchments. But it is also found that the bias correction has an impact on the climate change signal for specific locations and months, thereby identifying another level of uncertainty in the modeling chain from the GCM to the simulated changes calculated by the GHMs. This uncertainty may be of the same order of magnitude as uncertainty related to the choice of the GCM or GHM. Note that this uncertainty is primarily attached to the GCM and only becomes obvious by applying the statistical bias correction methodology.

Climate change is expected to alter the hydrological cycle resulting in large-scale impacts on water availability. However, future climate change impact assessments are highly uncertain. For the first time, multiple global climate (three) and hydrological models (eight) were used to systematically assess the hydrological response to climate change and project the future state of global water resources. This multi-model ensemble allows us to investigate how the hydrology models contribute to the uncertainty in projected hydrological changes compared to the climate models. Due to their systematic biases, GCM outputs cannot be used directly in hydrological impact studies, so a statistical bias correction has been applied. The results show a large spread in projected changes in water resources within the climate–hydrology modelling chain for some regions. They clearly demonstrate that climate models are not the only source of uncertainty for hydrological change, and that the spread resulting from the choice of the hydrology model is larger than the spread originating from the climate models over many areas. But there are also areas showing a robust change signal, such as at high latitudes and in some midlatitude regions, where the models agree on the sign of projected hydrological changes, indicative of higher confidence in this ensemble mean signal. In many catchments an increase of available water resources is expected but there are some severe decreases in Central and Southern Europe, the Middle East, the Mississippi River basin, southern Africa, southern China and south-eastern Australia

As a promising functional material, hydrogels have attracted extensive attention, especially in flexible wearable sensor fields, but it remains a great challenge to facilely integrate excellent mechanical properties, self-adhesion, and strain sensitivity into a single hydrogel. In this work, we present high in strength, stretchable, conformable, and self-adhesive chitosan/poly­(acrylic acid) double-network nanocomposite hydrogels for application in epidermal strain sensor via in situ polymerization of acrylic acid in chitosan acid aqueous solution with tannic acid-coated cellulose nanocrystal (TA@CNC) acting as nanofillers to reinforce tensile properties, followed by a soaking process in a saturated NaCl solution to cross-link chitosan chains. With addition of a small amount of TA@CNC, the double-network nanocomposite hydrogels became highly adhesive and mechanically compliant, which were critical factors for the development of conformable and resilient wearable epidermal sensors. The salt-soaking process was applied to cross-link chitosan chains by shielded electrostatic repulsions between positively charged amino groups, drastically enhancing the mechanical properties of the hydrogels. The obtained double-network nanocomposite hydrogels exhibited excellent tunable mechanical properties that could be conveniently tailored with fracture stress and fracture strain ranging from 0.39 to 1.2 MPa and 370 to 800%, respectively. Besides, the hydrogels could be tightly attached onto diverse substrates, including wood, glass, plastic, polytetrafluoroethylene, glass, metal, and skin, demonstrating high adhesion strength and compliant adhesion behavior. In addition, benefiting from the abundant free ions from strong electrolytes, the flexible hydrogel sensors demonstrated stable conductivity and strain sensitivity, which could monitor both large human motions and subtle motions. Furthermore, the antibacterial property originating from chitosan made the hydrogels suitable for wearable epidermal sensors. The facile soaking strategy proposed in this work would be promising in fabricating high-strength multifunctional conductive hydrogels used for wearable epidermal devices.

Natural biopolymer-based conductive hydrogels, which combine inherent renewable, nontoxic features, biocompatibility and biodegradability of biopolymers, and excellent flexibility and conductivity of conductive hydrogels, exhibit great potential in applications of wearable and stretchable sensing devices. Compared to traditional flexible substrates deriving from petro-materials-derived polymers, conductive hydrogels consisting of continuous cross-linked polymer networks and a large amount of water exhibit more fantastic combination of stretchability and conductivity because their polymer networks endow the hydrogels with mechanical flexibility and the water offers them a consecutive ionic transport property. Different from petro-materials-derived polymers, biopolymers that are extracted from bioresource with intrinsic biocompatibility and biodegradability are commonly considered as appropriate candidates for constructing wearable devices. For example, biopolymers such as cellulose, chitosan, and silk fibroin are usually chosen as promising candidates to construct conductive hydrogels, endowing the hydrogels with enhanced mechanical properties and remarkable biocompatibility. This review summarizes the recent progress of natural biopolymer-based conductive hydrogels that are utilized for electrical sensing devices with a series of typical biopolymers including cellulose, chitosan, silk fibroin, and gelatin. The chemical structures and physicochemical properties of the four typical biopolymers are demonstrated, and their applications in diverse conductive hydrogel sensors are discussed in detail. Finally, the remaining challenges and expectations are discussed.

Although self-healing gels with structural resemblance to biological tissues attract great attention in biomedical fields, it remains a dilemma for combination between fast self-healing properties and high mechanical toughness. On the basis of the design of dynamic reversible cross-links, we incorporate rigid tannic acid-coated cellulose nanocrystal (TA@CNC) motifs into the poly­(vinyl alcohol) (PVA)–borax dynamic networks for the fabrication of a high toughness and rapidly self-healing nanocomposite (NC) hydrogel, together with dynamically adhesive and strain-stiffening properties that are particularly indispensable for practical applications in soft tissue substitutes. The results demonstrate that the obtained NC gels present a highly interconnected network, where flexible PVA chains wrap onto the rigid TA@CNC motifs and form the dynamic TA@CNC–PVA clusters associated by hydrogen bonds, affording the critical mechanical toughness. The synergetic interactions between borate–diol bonds and hydrogen bonds impart a typical self-healing behavior into the NC gels, allowing the dynamic cross-linked networks to undergo fast rearrangement in the time scale of seconds. Moreover, the obtained NC hydrogels not only mimic the main feature of biological tissues with the unique strain-stiffening behavior but also display unique dynamic adhesiveness to nonporous and porous substrates. It is expected that this versatile approach opens up a new prospect for the rational design of multifunctional cellulosic hydrogels with remarkable performance to expand their applications.