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Assessing the impacts of Land Use (LU) and climate change on future streamflow projections is necessary for efficient management of water resources. However, model projections are burdened with significant uncertainty arising from various sources. Most of the previous studies have considered climate models and scenarios as major sources of uncertainty, but uncertainties introduced by land use change and hydrologic model assumptions are rarely investigated. In this paper an attempt is made to segregate the contribution from (i) general circulation models (GCMs), (ii) emission scenarios, (iii) land use scenarios, (iv) stationarity assumption of the hydrologic model, and (v) internal variability of the processes, to overall uncertainty in streamflow projections using analysis of variance (ANOVA) approach. Generally, most of the impact assessment studies are carried out with unchanging hydrologic model parameters in future. It is, however, necessary to address the nonstationarity in model parameters with changing land use and climate. In this paper, a regression based methodology is presented to obtain the hydrologic model parameters with changing land use and climate scenarios in future. The Upper Ganga Basin (UGB) in India is used as a case study to demonstrate the methodology. The semi-distributed Variable Infiltration Capacity (VIC) model is set-up over the basin, under nonstationary conditions. Results indicate that model parameters vary with time, thereby invalidating the often-used assumption of model stationarity. The streamflow in UGB under the nonstationary model condition is found to reduce in future. The flows are also found to be sensitive to changes in land use. Segregation results suggest that model stationarity assumption and GCMs along with their interactions with emission scenarios, act as dominant sources of uncertainty. This paper provides a generalized framework for hydrologists to examine stationarity assumption of models before considering them for future streamflow projections and segregate the contribution of various sources to the uncertainty. [ABSTRACT FROM AUTHOR]

• Impact and uncertainty framework for the identification of compound hydro-hazards. • UK example application using EDgE RCP8.5 projections. • NE Scotland and SW UK identified as spatio-temporally compound hotspot regions. • Hydrological models identified as the largest source of variability. • Raises questions regarding spatial variability of hydroclimatological modelling uncertainty. There exists an increasing need to understand the impact of climate change on the hydrological extremes of flood and drought, collectively referred to as 'hydro-hazards'. At present, current methodology are limited in their scope, particularly with respect to inadequate representation of the uncertainty in the hydroclimatological modelling chain. This paper proposes spatially consistent comprehensive impact and uncertainty methodological framework for the identification of compound hydro-hazard hotspots – hotspots of change where concurrent increase in mean annual flood and drought events is projected. We apply a quasi-ergodic analysis of variance (QE-ANOVA) framework, to detail both the magnitude and the sources of uncertainty in the modelling chain for the mean projected mean change signal whilst accounting for non-stationarity. The framework is designed for application across a wide geographical range and is thus readily transferable. We illustrate the ability of the framework through application to 239 UK catchments based on hydroclimatological projections from the EDgE project (5 CMI5-GCMs and 3 HMs, forced under RCP8.5). The results indicate that half of the projected hotspots are temporally concurrent or temporally successive within the year, exacerbating potential impacts on society. The north-east of Scotland and south-west of the UK were identified as spatio-temporally compound hotspot regions and are of particular concern. This intensification of the hydrologic dynamic (timing and seasonality of hydro-hazards) over a limited time frame represents a major challenge for future water management. Hydrological models were identified as the largest source of variability, in some instances exceeding 80% of the total variance. Critically, clear spatial variability in the sources of modelling uncertainty was also observed; highlighting the need to apply a spatially consistent methodology, such as that presented. This application raises important questions regarding the spatial variability of hydroclimatological modelling uncertainty. In terms of water management planning, such findings allow for more focussed studies with a view to improving the projections which inform the adaptation process. [ABSTRACT FROM AUTHOR]

Highlights • Simplified formulation of the metastatistical extreme value is proposed. • Parsimonious handling of extremes emerging from multiple underlying processes. • Computationally efficient sensitivity analyses to changes in the underlying processes. • Robust framework for explanatory models, nonstationary analyses, climate projections. Abstract This paper presents a Simplified Metastatistical Extreme Value formulation (SMEV) able to model hydro-meteorological extremes emerging from multiple underlying processes. The formulation explicitly includes the average intensity and probability of occurrence of the processes allowing to parsimoniously model changes in these quantities to quantify changes in the probability of occurrence of extremes. SMEV allows (a) frequency analyses of extremes emerging from multiple underlying processes and (b) computationally efficient analyses of the sensitivity of extreme quantiles to changes in the characteristics of the underlying processes; moreover, (c) it provides a robust framework for explanatory models, nonstationary frequency analyses, and climate projections. The methodology is applied to daily precipitation data from long recording stations in the eastern Mediterranean, using Weibull distributions to model daily precipitation amounts generated by two classes of synoptic systems. At-site application of SMEV provides spatially consistent estimates of extreme quantiles, in line with regional GEV estimates and generally characterized by reduced uncertainties. The sensitivity of extreme quantiles to changes and uncertainty in the intensity and yearly occurrences of events generated by different synoptic classes is examined, and an application of SMEV for the projection of future extremes is provided. [ABSTRACT FROM AUTHOR]

Highlights • Method to assess potential impacts of climate change on SCA in Alpine systems. • The method requires to develop a distributed cellular automata model. • Influence of the elevation on the potential impacts of climate change on SCA. • Application to Sierra Nevada Mountain (Spain), a sensitive place to climate change. • Potential changes will modify the snow dynamic causing important reductions on SCA. Abstract Snow dynamics in alpine systems play an important role in water resources management. One of the main variables that characterises the snowpack is snow cover area. In this paper, we present a novel methodology to assess the impact of climate change scenarios on snow cover area in alpine systems. The methodology calibrates and validates a distributed cellular automata (CA) model. Potential climate change scenarios can be generated to feed the CA model in order to assess the potential impact of climate change on snow cover area. The methodology has been applied to the Sierra Nevada mountain range in southern Spain, an area with a Mediterranean climate, which is valuable in terms of both its ecology and the tourism industry. Snow falls on the mountains from October to May, but this snow season may vary in the future due to climate change. We analysed the influence of elevation on the potential impact of climate change on the area covered by snow. The size of the potential changes associated with climate change increase with elevation in the case of temperature and decrease in the case of precipitation. These potential changes will modify snow dynamics, causing an important reduction in the area covered by snow (mean 60.4%) for the future horizon considered (2071–2100). [ABSTRACT FROM AUTHOR]

Abstract: Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster–Shafer (D–S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D–S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D–S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D–S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster–Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D–S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D–S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change. [Copyright &y& Elsevier]

This paper aims to develop new, resilience type metrics for long-term water resources management under uncertain climate change and population growth. Resilience is defined here as the ability of a water resources management system to ‘bounce back’, i.e. absorb and then recover from a water deficit event, restoring the normal system operation. Ten alternative metrics are proposed and analysed addressing a range of different resilience aspects including duration, magnitude, frequency and volume of related water deficit events. The metrics were analysed on a real-world case study of the Bristol Water supply system in the UK and compared with current practice. The analyses included an examination of metrics’ sensitivity and correlation, as well as a detailed examination into the behaviour of metrics during water deficit periods. The results obtained suggest that multiple metrics which cover different aspects of resilience should be used simultaneously when assessing the resilience of a water resources management system, leading to a more complete understanding of resilience compared with current practice approaches. It was also observed that calculating the total duration of a water deficit period provided a clearer and more consistent indication of system performance compared to splitting the deficit periods into the time to reach and time to recover from the worst deficit events. [ABSTRACT FROM AUTHOR]

In river basin systems, power asymmetry is often responsible of inefficient and unbalanced water allocations. Climate change and anthropogenic pressure will possibly exacerbate such disparities as the dominant party controls an increasingly limited shared resource. In this context, the deployment of cooperation mechanisms giving greater consideration to a balanced distribution of the benefits, while improving system-wide efficiency, may be desirable. This often implies the intervention of a third party (e.g., the river basin water authority) imposing normative constraints (e.g., a minimum release) on the party in the dominant position. However, this imposition will be more acceptable to the dominant party if coupled with some form of compensation. For a public agency, compensation may be burdensome, especially when the allowance is triggered by natural events whose timing and magnitude are highly uncertain. In this context, index-based insurance contracts may represent a viable alternative and reduce the cost of achieving socially desirable outcomes. In this paper, we develop a hybrid cooperation mechanism composed of i) a direct normative constraint imposed by a regulator, and ii) an indirect financial tool, an index-based insurance contract, to be used as a compensation measure. The approach is developed for the Lake Como multi-purpose water system, Italy: a complex Alpine river basin, supporting several hydropower reservoirs and finally flowing into a regulated lake which supplies water to several downstream uses, mostly irrigated agriculture. The system is characterized by a manifest geographic power asymmetry: the upstream hydropower companies are free to release their stored water in time irrespective of the timing of the downstream demands. This situation can lead to financial losses by the downstream users and undesirable social outcomes. Results suggest that financial instruments may offer a reliable and relatively inexpensive alternative to other forms of compensation, and thereby favor more balanced management of multi-purpose water systems characterized by power asymmetry. This finding is especially relevant in times when granting of licenses to use/withdrawal water are often being reviewed with attention to environmental protection and equity issues. [ABSTRACT FROM AUTHOR]

The paper investigates how the topological features of the virtual water (VW) network and the size of the associated VW flows are likely to change over time, under different socio-economic and climate scenarios. We combine two alternative models of network formation –a stochastic and a fitness model, used to describe the structure of VW flows- with a gravity model of trade to predict the intensity of each bilateral flow. This combined approach is superior to existing methodologies in its ability to replicate the observed features of VW trade. The insights from the models are used to forecast future VW flows in 2020 and 2050, under different climatic scenarios, and compare them with future water availability. Results suggest that the current trend of VW exports is not sustainable for all countries. Moreover, our approach highlights that some VW importers might be exposed to “imported water stress” as they rely heavily on imports from countries whose water use is unsustainable. [ABSTRACT FROM AUTHOR]

Human activities amplified by climate change pose a significant threat to the sustainability of water resources. Coupled climate-hydrologic simulations commonly predict these threats by combining shallow 1-D land surface models (LSMs) with traditional 2-D and 3-D hydrology models. However, these coupled models limit the moisture and energy-feedback dynamics to the shallow near-surface. This paper presents a novel analysis by applying an integrated variably-saturated subsurface/surface hydrology and heat transport model, HydroGeoSphere (HGS), as a land surface model (LSM). Furthermore, this article demonstrates the coupling of HGS to a simple 0-D atmospheric boundary layer (ABL) model. We then applied our coupled HGS-ABL model to three separate test cases and reproduced the strong correlation between the atmospheric energy balance to the depth of the groundwater table. From our simulations, we found that conventional LSMs may overestimate surface temperatures for extended drought periods because they underestimate the heat storage in the groundwater zone. Our final test case of the atmospheric response to drought conditions illustrated that deeper roots buffered the atmosphere better than shallow roots by maintaining higher latent heat fluxes, lower sensible heat fluxes, and lower surface and atmospheric temperatures. [ABSTRACT FROM AUTHOR]

Stochastic weather generators (WGs), which provide long synthetic time series of weather variables such as rainfall and potential evapotranspiration (PET), have found widespread use in water resources modelling. When conditioned upon the changes in climatic statistics (change factors, CFs) predicted by climate models, WGs provide a useful tool for climate impacts assessment and adaption planning. The latest climate modelling exercises have involved large numbers of global and regional climate models integrations, designed to explore the implications of uncertainties in the climate model formulation and parameter settings: so called ‘perturbed physics ensembles’ (PPEs). In this paper we show how these climate model uncertainties can be propagated through to impact studies by testing multiple vectors of CFs, each vector derived from a different sample from a PPE. We combine this with a new methodology to parameterise the projected time-evolution of CFs. We demonstrate how, when conditioned upon these time-dependent CFs, an existing, well validated and widely used WG can be used to generate non-stationary simulations of future climate that are consistent with probabilistic outputs from the Met Office Hadley Centre's Perturbed Physics Ensemble. The WG enables extensive sampling of natural variability and climate model uncertainty, providing the basis for development of robust water resources management strategies in the context of a non-stationary climate. [ABSTRACT FROM AUTHOR]

• Mobility and memory effects relevant for morphodynamics in fine sediment settings. • Short-term calibration and long-term application viable for morphodynamic modeling. • Morphological acceleration suitable for long-term morphodynamic modeling. • Long-term morphodynamic responses to system forcings conform to theory. Sediment mobility and memory effects distinguish transport and morphodynamics in fine sediment settings from sandy settings. This paper focuses on the morphodynamic modeling of fine sediment systems, and an adaptation of modeling procedures more commonly used in sand-dominated systems. An extensive dataset of short-term transport and morphological trends along with ancillary data is used to support the development, parameterization, and calibration of a morphodynamic model of the Lower Passaic River (USA). The model is subsequently extended to include a morphological acceleration procedure in order to enable computationally efficient morphodynamic simulations. The performance of the resulting model is first verified and subsequently validated using measured decadal-scale morphological change. Subsequently, the model is used to assess the current morphodynamic status and the long-term response of the river to forcings such as shipping, episodic storm events, and climate change, with results that are consistent with theoretical expectations. [ABSTRACT FROM AUTHOR]

• A first comprehensive and systematic review on the research of extreme precipitation in China. • Variation and regional characteristics of extreme precipitation under non-stationary conditions due to climate change and human activities. • Supports and basis for engineering application and further research on extreme precipitation and flood in China. Recent years have witnessed global massive property losses and casualties caused by extreme precipitation and its subsequent natural disasters, including floods and landslides. China is one of the countries deeply affected by these casualties. If the statistical characteristics and laws of extreme precipitation could be clearly grasped, then the negative impacts triggered by it may be minimized. China is a vast country and diverse in climate and terrain, hence different regions may be suitable for different analyses and research methods. Therefore, it is necessary to clarify the research progress, methods and current status of extreme precipitation across the country. This paper attempts to provide a comprehensive review of techniques and methods used in extreme precipitation research and engineering practice and their applications. The literature is reviewed focusing on seven aspects: (1) annual maxima method (AM), (2) peaks over threshold method (POT), (3) probable maximum precipitation (PMP), (4) non-stationary analysis of precipitation extremes, (5) intensity-duration-frequency curves (IDF), (6) uncertainty in extreme precipitation frequency analysis, and (7) spatial variability of extreme precipitation. Research on extreme precipitation in China is generally based or centered on the above seven aspects. The current study aims to provide ideas for further research on extreme precipitation frequency analysis and its response to climate change and human activities. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new extension of the classical degree-day snowmelt model applicable to hourly simulations for regions with limited data and adaptable to a broad range of spatially-explicit hydrological models. The snowmelt schemes have been tested with a point measurement dataset at the Cotton Creek Experimental Watershed (CCEW) in British Columbia, Canada and with a detailed dataset available from the Dranse de Ferret catchment, an extensively monitored catchment in the Swiss Alps. The snowmelt model performance is quantified with the use of a spatially-explicit model of the hydrologic response. Comparative analyses are presented with the widely-known, grid-based method proposed by Hock which combines a local, temperature-index approach with potential radiation. The results suggest that a simple diurnal cycle of the degree-day melt parameter based on minimum and maximum temperatures is competitive with the Hock approach for sub-daily melt simulations. Advantages of the new extension of the classical degree-day method over other temperature-index methods include its use of physically-based, diurnal variations and its ability to be adapted to data-constrained hydrological models which are lumped in some nature. [Copyright &y& Elsevier]

Abstract: Hydrological time series increasingly exhibit non-stationarity, e.g., variables such as precipitation and streamflow values do not maintain a consistent mean over long periods, due to natural and anthropogenic changes. Detecting whether such shifts are gradual or abrupt is a growing concern for water resources planning and management. This paper shows that conventional trend and change-point tests do not adequately enable these two types of change to be distinguished. We propose a method for combining the rank correlations of the Mann–Kendall and Pettitt statistics to extract an indicator whose value determines whether a shift observed in a given time series is gradual or abrupt. This method allows the success rate to be independent of the length of record, and it is validated with Monte-Carlo experiments. The limitations caused by the short and noisy nature of hydroclimatic time series are discussed. As an application, the proposed method provides useful insights on changes in hydroclimatic variables in the United States during 1910–2009 using time series from 1217 stations in the United States Hydroclimatic Data Network (USHCN). [Copyright &y& Elsevier]

Abstract: A synthesis is presented highlighting the importance of hydrologic variables and dynamics to biodiversity patterns. The focus of this paper is the key hydrologic controls crucial towards quantifying the impacts of climate changes on the distribution of species. Specifically, we highlight the hydrologic controls operating on the carrying capacity, niche formation, and dispersal dynamics. This synthesis will facilitate avenues of future research and is connected to issues of major practical importance, such as the integration of the structure of river networks into conservation strategies and the evaluations of the impacts of climate change on biodiversity. [Copyright &y& Elsevier]

Abstract: With vast regions already experiencing water shortages, it is becoming imperative to manage sustainably the available water resources. As agriculture is by far the most important user of freshwater and the role of irrigation is projected to increase in face of climate change and increased food requirements, it is particularly important to develop simple, widely applicable models of irrigation water needs for short- and long-term water resource management. Such models should synthetically provide the key irrigation quantities (volumes, frequencies, etc.) for different irrigation schemes as a function of the main soil, crop, and climatic features, including rainfall unpredictability. Here we consider often-employed irrigation methods (e.g., surface and sprinkler irrigation systems, as well as modern micro-irrigation techniques) and describe them under a unified conceptual and theoretical framework, which includes rainfed agriculture and stress-avoidance irrigation as extreme cases. We obtain mostly analytical solutions for the stochastic steady state of soil moisture probability density function with random rainfall timing and amount, and compute water requirements as a function of climate, crop, and soil parameters. These results provide the necessary starting point for a full assessment of irrigation strategies, with reference to sustainability, productivity, and profitability, developed in a companion paper [Vico G, Porporato A. From rainfed agriculture to stress-avoidance irrigation: II. Sustainability, crop yield, and net profit. Adv Water Resour 2011;34(2):272–81]. [ABSTRACT FROM AUTHOR]

Abstract: Recent advances in the description of the natural variability of extreme events associated with ocean climate variables include time-dependent variations within a certain time scale (year, season or month). These models allow incorporating smooth time variations of the parameters of the GEV distribution and also the influence of covariates (NAO, El Niño, etc.), providing more reliable results than traditional stationary models. In this paper, a sensitivity analysis of the model parameter estimates with respect to data (selected maxima, time of occurrence and covariates) is presented. The method shows how different data affects location, scale, and shape parameters as well as return level quantiles; gaining an insight into time-dependent generalized extreme value models and increasing the understanding of the models responses. The proposed method is applied to the study of the monthly maxima significant wave height at a particular location around the Brittany coast. [Copyright &y& Elsevier]

Abstract: This paper presents a quantitative ecohydrological framework for predicting regional distribution patterns of woody species in dryland ecosystems. The framework is based on an existing stochastic model for the daily mass balance of water that represents the interactions between soils, climate, and vegetation. Individual species selection is based on an optimality trade-off hypothesis, which states that dryland vegetation patterns are constrained by maximization of water use and simultaneous minimization of water stress. The relative importance of water use and stress avoidance to the overall fitness of three Acacia species is determined from the heterogeneous basin, the Upper Ewaso Ng’iro river basin, of the central Kenya highlands. The model results indicate that overall fitness is more strongly influenced by water use than stress avoidance but that consideration of both stress avoidance and water use is critical to predicting basin-scale patterns of species distribution. We identify a linear trend in the frequency and intensity of storms with the same annual total using a basin-wide gauge precipitation dataset. After calibration, we apply the basin average linear trends in time for average rain per storm and storm arrival rates. The model results indicate the upslope migration of two species, Acacia tortilis and Acacia xanthophloea to areas with higher total rainfall. Lastly, we explore the modeled changes of species cover in the basin influenced by changes in rainfall total holding growing season rainfall variability constant and changes in growing season rainfall variability holding total rainfall constant. We find that changes in dryland species distribution patterns and relative abundance may be as sensitive to growing season rainfall variability as they are to changes in total rainfall amounts. [Copyright &y& Elsevier]

Abstract: Understanding the long-term seasonal dynamics of alpine glacierized basins is essential to evaluating their relation to climatic forcing. We focus on process knowledge by following a minimalist approach, and propose a spatially lumped nonlinear differential model (MIAGE) to describe the link between the volume V of water that is stored on the basin and the river runoff Q at the seasonal scale. We formulate the model structure by mathematically describing the link relating precipitation P, temperature T, river runoff Q and stored volumes V. Beside reproducing some typical features of the catchment hydrology of glacierized basins, MIAGE offers an explanation of their seasonal hydroclimatic behaviour and of the origin of their dissipative properties from a dynamical system perspective. By studying the model nonlinear properties, characteristics, and performances, we show that climatic change has both direct and feedback effects on such basins. Eventually a synchronization of the runoffs with either the precipitation trend or the temperature trend may occur depending on the storage conditions. This model is subsequently used in a companion paper in order to investigate the potential impact of climatic change scenarios on basins of the Italian and Swiss Alps [Mechanistic interpretation of alpine glacierized environments: Part 2. Hydrologic interpretation and model parameters identification on case study, this issue]. [Copyright &y& Elsevier]

Abstract: Hydrological model sensitivity to climate change can be defined as the response of a particular hydrological model to a known quantum of climate change. This paper estimates the hydrological sensitivity, measured as the percentage change in mean annual runoff, of two lumped parameter rainfall-runoff models, SIMHYD and AWBM and an empirical model, Zhang01, to changes in rainfall and potential evaporation. These changes are estimated for 22 Australian catchments covering a range of climates, from cool temperate to tropical and moist to arid. The results show that the models display different sensitivities to both rainfall and potential evaporation changes. The SIMHYD, AWBM and Zhang01 models show mean sensitivities of 2.4%, 2.5% and 2.1% change in mean annual flow for every 1% change in mean annual rainfall, respectively. All rainfall sensitivities have a lower limit of 1.8% and show upper limits of 4.1%, 3.4% and 2.5%, respectively. The results for potential evaporation change are −0.5%, −0.8% and −1.0% for every 1% increase in mean annual potential evaporation, respectively, with changes rainfall being approximately 3–5 times more sensitive than changes in potential evaporation for each 1% change in climate. Despite these differences, the results show similar correlations for several catchment characteristics. The most significant relationship is between percent change in annual rainfall and potential evaporation to the catchment runoff coefficient. The sensitivity of both A and B factors decreases with an increasing runoff coefficient, as does the uncertainty in this relationship. The results suggest that a first-order relationship can be used to give a rough estimate of changes in runoff using estimates of change in rainfall and potential evaporation representing small to modest changes in climate. Further work will develop these methods further, by investigating other regions and changes on the subannual scale. [Copyright &y& Elsevier]

The statistics of extremes have played an important role in engineering practice for water resources design and management. How recent developments in the statistical theory of extreme values can be applied to improve the rigor of hydrologic applications and to make such analyses more physically meaningful is the central theme of this paper. Such methodological developments primarily relate to maximum likelihood estimation in the presence of covariates, in combination with either the block maxima or peaks over threshold approaches. Topics that are treated include trends in hydrologic extremes, with the anticipated intensification of the hydrologic cycle as part of global climate change. In an attempt to link downscaling (i.e., relating large-scale atmosphere–ocean circulation to smaller-scale hydrologic variables) with the statistics of extremes, statistical downscaling of hydrologic extremes is considered. Future challenges are reviewed, such as the development of more rigorous statistical methodology for regional analysis of extremes, as well as the extension of Bayesian methods to more fully quantify uncertainty in extremal estimation. Examples include precipitation and streamflow extremes, as well as economic damage associated with such extreme events, with consideration of trends and dependence on patterns in atmosphere–ocean circulation (e.g., El Nin˜o phenomenon). [Copyright &y& Elsevier]

• A novel dynamic model connecting biogeochemical and ecohydrological dynamics to enhanced weathering is presented. • Hydrological fluctuations lead to hysteretic patterns of weathering rates with soil moisture. • Plants contribution to pH reduction and increased weathering rates during wet periods is quantified. Enhanced Weathering (EW) resulting from soil amendment with highly reactive silicate minerals is regarded as one of the most effective techniques for carbon sequestration. While in laboratory conditions silicate minerals dissolution rates are well characterized, in field conditions the rate of the dissolution reaction is more difficult to predict, not least because it interacts with soil, plant, and hydrologic processes. Here we present a dynamic mass balance model connecting biogeochemical and ecohydrological dynamics to shed light on these intertwined processes involved in EW. We focus on the silicate mineral olivine, for its faster laboratory dissolution rate, and pay particular attention to understanding the role of plants and hydrological fluctuations and their propagation into soil biogeochemical processes (including cation exchange) and EW dynamics. A companion paper Cipolla et al.(2021) presents specific applications with the main purpose of understanding the carbon sequestration potential under different climate scenarios. [ABSTRACT FROM AUTHOR]