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This paper presents WHETGEO and its 1D deployment: a new physically based model simulating the water and energy budgets in a soil column. The purpose of this contribution is twofold. First, we discuss the mathematical and numerical issues involved in solving the Richardson–Richards equation, conventionally known as the Richards equation, and the heat equation in heterogeneous soils. In particular, for the Richardson–Richards equation (R2) we take advantage of the nested Newton–Casulli–Zanolli (NCZ) algorithm that ensures the convergence of the numerical solution in any condition. Second, starting from numerical and modelling needs, we present the design of software that is intended to be the first building block of a new customizable land-surface model that is integrated with process-based hydrology. WHETGEO is developed as an open-source code, adopting the object-oriented paradigm and a generic programming approach in order to improve its usability and expandability. WHETGEO is fully integrated into the GEOframe/OMS3 system, allowing the use of the many ancillary tools it provides. Finally, the paper presents the 1D deployment of WHETGEO, WHETGEO-1D, which has been tested against the available analytical solutions presented in the Appendix. [ABSTRACT FROM AUTHOR]

• Hydrological digital twins (HDT) are essential tools to understand the hydrological cycle especially in a changing climate. • We present, test, and validate an open-source framework to implement HDT in the large anthropized alpine Adige catchment. • The HDT is calibrated/validated on a variety of hydrological processes and data sources providing satisfactory performances. Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy. Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes. Specifically, we model each component of the water cycle over 39 years (1980–2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model. The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data). Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture. The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate. [ABSTRACT FROM AUTHOR]

This paper presents WHETGEO and its 1D deployment, a new, physically based model simulating the water and energy budgets in a soil column. The purpose of this contribution is twofold. First, we discuss the mathematical and numerical issues involved in solving the Richardson-Richards equation, conventionally known as Richards' equation, and the heat equation in heterogeneous soils. In particular, for the Richardson-Richards equation (R2) we take advantage of the nested Newton-Casulli-Zanolli (NCZ) algorithm that ensures the convergence of the numerical solution in any condition. Second, starting from numerical and modelling needs, we present the design of a software that is intended to be the first building block of a new, customisable, land-surface model that is integrated with process-based hydrology. WHETGEO is developed as an open-source code, adopting the Object-Oriented paradigm and a generic programming approach in order to improve its usability and expandability. WHETGEO is fully integrated in the GEOframe/OMS3 system allowing the use of the many ancillary tools it provides. Finally the paper presents the 1D deployment of WHETGEO, WHETGEO-1D, which has been tested against the available analytical solutions presented in Appendix. [ABSTRACT FROM AUTHOR]

Moving from the exact result that drainage network configurations minimizing total energy dissipation are stationary solutions of the general equation describing landscape evolution, we review the static properties and the dynamic origins of the scale-invariant structure of optimal river patterns. Optimal channel networks (OCNs) are feasible optimal configurations of a spanning network mimicking landscape evolution and network selection through imperfect searches for dynamically accessible states. OCNs are spanning loopless configurations, however, only under precise physical requirements that arise under the constraints imposed by river dynamics-every spanning tree is exactly a local minimum of total energy dissipation. It is remarkable that dynamically accessible configurations, the local optima, stabilize into diverse metastable forms that are nevertheless characterized by universal statistical features. Such universal features explain very well the statistics of, and the linkages among, the scaling features measured for fluvial landforms across a broad range of scales regardless of geology, exposed lithology, vegetation, or climate, and differ significantly from those of the ground state, known exactly. Results are provided on the emergence of criticality through adapta-tive evolution and on the yet-unexplored range of applications of the OCN concept. [ABSTRACT FROM AUTHOR]

This paper describes a new distributed hydrological model, called GEOtop. The model accommodates very complex topography and, besides the water balance, unlike most other hydrological models, integrates all the terms in the surface energy balance equation. GEOtop uses a discretization of the landscape based on digital elevation data. These digital elevation data are preprocessed to allow modeling of the effect of topography on the radiation incident on the surface, both shortwave (including shadowing) and longwave (accounting for the sky view factor). For saturated and unsaturated subsurface flow, GEOtop makes use of a numerical solution of the 3D Richards' equation in order to properly model, besides the lateral flow, the vertical structure of water content and the suction dynamics. These characteristics are deemed necessary for consistently modeling hillslope processes, initiation of landslides, snowmelt processes, and ecohydrological phenomena as well as discharges during floods and interstorm periods. An accurate treatment of radiation inputs is implemented in order to be able to return surface temperature. The motivation behind the model is to combine the strengths and overcome the weaknesses of flood forecasting and land surface models. The former often include detailed spatial description and lateral fluxes but usually lack appropriate knowledge of the vertical ones. The latter are focused on vertical structure and usually lack spatial structure and prediction of lateral fluxes. Outlines of the processes simulated and the methods used to simulate them are given. A series of applications of the model to the Little Washita basin of Oklahoma using data from the Southern Great Plains 1997 Hydrology Experiment (SGP97) is presented. These show the model' ability to reproduce the pointwise energy and water balance, showing that just an elementary calibration of a few parameters is needed for an acceptable reproduction of discharge at the outlet, for the prediction of the spatial distribution of soil moisture content, and for the simulation of a full year's streamflow without additional calibration. [ABSTRACT FROM AUTHOR]

The GEOtop model makes it possible to analyze the short- and long-term effects of geomorphic variation on the partitioning of the lateral surface and subsurface water and surface energy fluxes. The topography of the Little Washita basin (Oklahoma) and of the Serraia basin (Trentino, Italy) have been used as base topographies from which virtual topographies with altered slopes and elevations have been created with corresponding modifications of the soil thickness and the extension of the channel network, according to applicable geomorphological theories, in order to quantify the contribution of these topographic features to the spatial and temporal variability of energy and water fluxes. Simulation results show that both a more extended channel network and more accentuated slopes cause an increase in the discharge balanced by a diminution of the evapotranspiration. The diminution of the latent heat flux is balanced by the increase in the sensible heat flux. Net radiation shows a minor sensitivity to topography. Evaporative fraction, on the contrary, is shown to be strongly dependent on geomorphic characteristics. The results confirm the importance of including an adequate treatment of topography in large-scale land surface models. [ABSTRACT FROM AUTHOR]

The accurate simulation of heat transfer with phase change is a central problem in cryosphere studies. This is because the non-linear behaviour of enthalpy as function of temperature can prevent thermal models of snow, ice, and frozen soil from converging to the correct solution. Existing numerical techniques rely on increased temporal resolution in trying to keep corresponding errors within acceptable bounds. Here, we propose an algorithm, originally applied to solve water flow in soils, as a method to solve these integration issues with guaranteed convergence and conservation of energy for any time step size. We review common modelling approaches, focusing on the fixed-grid method and on frozen soil. Based on this, we develop a conservative formulation of the governing equation and outline problems of alternative formulations in discretized form. Then, we apply the nested Newton–Casulli–Zanolli (NCZ) algorithm to a one-dimensional finite-volume discretization of the energy–enthalpy formulation. Model performance is demonstrated against the Neumann and Lunardini analytical solutions and by comparing results from numerical experiments with integration time steps of 1 h, 1 d, and 10 d. Using our formulation and the NCZ algorithm, the convergence of the solver is guaranteed for any time step size. With this approach, the integration time step can be chosen to match the timescale of the processes investigated. [ABSTRACT FROM AUTHOR]

In hydrological modeling snowmelt is computed along two different approaches: the physically based one simulates the snowpack evolution in terms of accumulation and ablation by means of solution of the energy balance equation; the second, simpler approach, uses instead the meteorological variables as indices of physical processes. The simplified models are limited to forecasting only the snow water equivalent (SWE, the mass of liquid water in the snowpack) and not other variables.Requiring less information on the model input and parameters, simplified models are still widely applied in the operative context. The output of the comparison would be a reference to detect the confidence level of the application of simplified models and to define the their limitations in computing SWE.In this work we compare the physically based approach implemented in GEOtop [2], with the three index-based snow water equivalent models integrated as components in the hydrological modeling system GEOframe-NewAge [3] [4] [5]. The first model is a function only of air temperature (traditional temperature-index method [7]). The second is a function of both shortwave radiation and air temperature (Cazorzi and Dalla Fontana's model [1]). The third is estimated by using the Hock [6] model.All the models can be driven at different time steps. For the comparison we used daily time steps and appropriate estimations of radiation and inputs as generated by the chain of tools available in GEOframe. Main differences between the outputs of the four listed models are outlined by comparing simulated SWE patterns in form of raster maps, setting the reliable output of the physically based approach as a reference. References:[1] Cazorzi, F., and G. Dalla Fontana. "Snowmelt modelling by combining air temperature and a distributed radiation index." Journal of Hydrology 181.1-4 (1996): 169-187.[2] Endrizzi, S., et al. "GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects." Geoscientific Model Development 7.6 (2014): 2831-2857.[3] Formetta, Giuseppe, et al. "Hydrological modelling with components: A GIS-based open-source framework." Environmental Modelling & Software 55 (2014): 190-200.[4] Formetta, Giuseppe, et al. "Snow water equivalent modeling components in NewAge-JGrass."Geoscientific Model Development 7.3 (2014): 725-736.[5] Formetta, G., et al. "The JGrass-NewAge system for forecasting and managing the hydrological budgets at the basin scale: models of flow generation and propagation/routing." Geoscientific Model Development 4.4 (2011): 943-955.[6] Hock, Regine. "A distributed temperature-index ice-and snowmelt model including potential direct solar radiation." Journal of Glaciology 45.149 (1999): 101-111.[7] Martinec, J., and A. Rango. "Parameter values for snowmelt runoff modelling." Journal of Hydrology 84.3-4 (1986): 197-219. [ABSTRACT FROM AUTHOR]

There is an increasing movement to support the concept of open science, and replicable research. The authors embraced the philosophy and applied some rules to obtain this achievement. Because most of their work focuses on modelling, this contribution discusses which are the requirements for doing open science in the field of hydrological modelling. In practice, we present how we started and continue to develop the modelling system GEOframe. We review the choices to make our model open source and discuss the fact that "open sourceness" is not enough to permit third part inspection. The latter characteristic implies some rigour in software design and implementation: these aspects are common in other research fields but are rarely being taken advantage of by researcher in hydrology. We discuss the concept of componentization of software as a way to achieve an optimal authorship attribution and to open codes for inspection. Furthermore, we discuss the deployment options for these ideas examining those adopted inside GEOframe. Finally, we present some possibilities for exposition of data, simulations, and analysis that accompanies publications.Open science is not necessarily good science. Bad science remains bad even if it is open. However, we claim that bad science and practices can be more easily hidden when modelling tools are not openly available. [ABSTRACT FROM AUTHOR]

In a region where ground-based gauge data are scarce, satellite rainfall estimates (SREs) are a viable option for proper space–time rainfall characterization. However, their accuracy and performances vary from region to region, and must be assessed. In this study, five high resolution satellite products (3B42V7, CMORPH, TAMSAT, SM2R-CCI, and CFSR) are compared and analyzed using the available rain gauge data in one of the most topographically and climatologically complex basin of Africa, the Upper Blue Nile basin (UBN). The basin rainfall is investigated systematically, and it is found that, at some locations, the difference in mean annual rainfall estimates between these SREs could be as much as about 2700 mm. Considering three goodness-of-fit indexes, correlation, bias and root mean square error (RMSE) between the SREs and ground-based gauge rainfall, CMORPH, TAMSAT and SM2R-CCI outperform the other two. Furthermore, a confusion matrix is used to investigate the detection ability of satellite rainfall products for different rainfall intensities. TAMSAT has the highest (91%) detection skill for dry days, followed by CFSR (77%). On the contrary, SM2R-CCI has the highest accuracy index for medium rainfall ranges (10–20 mm). The empirical cumulative distribution ( ecdf ) mapping technique is used to correct the intensities distribution givenby the SREs. This method provides a means to improve the rainfall estimation of all SREs, and the highest improvement is obtained for CMORPH (bias reduction from − 72% to − 1%). [ABSTRACT FROM AUTHOR]

The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects. [ABSTRACT FROM AUTHOR]

This review proceeds from Luna Leopold's and Ronald Shreve's lasting accomplishments dealing with the study of random-walk and topologically random channel networks. According to the random perspective, which has had a profound influence on the interpretation of natural landforms, nature's resiliency in producing recurrent networks and landforms was interpreted to be the consequence of chance. In fact, central to models of topologically random networks is the assumption of equal likelihood of any tree-like configuration. However, a general framework of analysis exists that argues that all possible network configurations draining a fixed area are not necessarily equally likely. Rather, a probability P (s) is assigned to a particular spanning tree configuration, say s, which can be generally assumed to obey a Boltzmann distribution: P(s) ∝ e[sup -H(s)/τ], where τ is a parameter and H (s) is a global property of the network configuration s related to energetic characters, i.e. its Hamiltonian. One extreme case is the random topology model where all trees are equally likely, i.e. the limit case for τ → ∞. The other extreme case is τ → ∞, and this corresponds to network configurations that tend to minimize their total energy dissipation to improve their likelihood. Networks obtained in this manner are termed optimal channel networks (OCNs). Observational evidence suggests that the characters of real river networks are reproduced extremely well by OCNs. Scaling properties of energy and entropy of OCNs suggest that large network development is likely to effectively occur at zero temperature (i.e. minimizing its Hamiltonian). We suggest a corollary of dynamic accessibility of a network configuration and speculate towards a thermodynamics of critical self-organization. We thus conclude that both chance and necessity are equally important ingredients for the dynamic origin of channel networks—and perh... [ABSTRACT FROM AUTHOR]

In a recent study (Schymanski and Or, 2017) a new approach was used to evaluate the latent heat emitted by a leaf, highlighting an important omission of the Penman-Monteith equation. However, this result was obtained under ideal conditions, i.e. on a synthetic leaf in a controlled atmosphere in laboratory and without any kind of environmental stress factors. In this study we evaluate the performance of this newly proposed method on real case studies. After having implemented the method in the GEOframe modelling system, we compare it with some of the classical hydrological approaches for estimating evapotranspiration, like the Penman-Monteith (PM-FAO) and the Priestley-Taylor (PT) equations and with the fully distributed hydrological model GEOtop. To the scope, the approach developed by Schymanski-Or (SO) was modified, in order to upscale it at canopy level, including a canopy radiation transfer model and a dependency to the leaf-area-index. Moreover, empirical stress functions were introduced, allowing us to compute the actual evapotranspiration, in particular under water stress. The approach of SO allows to compute also the sensible heat and the leaves temperature. It is therefore possible to close the energy budget and further validate the method. We considered two alpine grassland sites: the Mazia/Matsch LTER site and the Viote FLUXNET site in the Trentino Alto Adige region. In these two sites, detailed canopy and micrometeorological observations are available and both sensible and latent heat were measured with eddy covariance stations. In addition, the two sites have different soil moisture conditions, allowing us to cope with vegetation both under and without water stress. Preliminary results show that latent heat estimates of the proposed extended SO approach are in between PM-FAO and PT estimates. Leaves temperature are more accurate with respect to the GEOtop model, but the SO approach tends to be highly sensitive to the amount of transpiration coming from shadowed leaves. A further step in the work will be to extend the model to more complex canopies as forests and orchards. [ABSTRACT FROM AUTHOR]

It is known that water viscosity and the water capillary pressure in unsaturated soil depends on the temperature. Specifically, the capillary pressure varies with the temperature because the surface tension and the contact angle (Bachmann 2002). This dependence of the water properties on the temperature manifests as temperature-affected soil water retention curves and soil hydraulic conductivity.To consider the effects of temperature on the water flow in soil it is necessary to solve both the Richards equation and the energy equation. Considering the 1D problem, we compare two numerical models to solve these equations. The first one solves the Richards equation (Casulli and Zanolli, 2010) (Casulli, 2017) and the energy equation in a coupled way (Casulli and Zanolli, 2005) but without considering the temperature effect on the soil water retention curve. The second one considers the effect of temperature also on the soil water content. The equations are numerically solved in a decoupled way: firstly, we integrate the energy equation written in the non-conservative form and then the Richards equation is solved. These are intended to be the first step towards a method that simultaneously solves the two equations with a conservative method. Freezing soil is not included but extension of the method including it is also discussed.Besides the numerical aspects, we also discuss the reasons of developing the code using an Object-Oriented programming paradigm. As an example, we can easily add new parameterization of the hydraulic and thermal properties of the soil without changing the core of our code. Bachmann, Jörg, et al. Temperature dependence of water retention curves for wettable and water-repellent soils. Soil Science Society of America Journal, 2002, 66.1: 44-52.Casulli, Vincenzo; Zanolli, Paola. High resolution methods for multidimensional advection–diffusion problems in free-surface hydrodynamics. Ocean Modelling, 2005, 10.1-2: 137-151.Casulli, Vincenzo; Zanolli, Paola. A nested Newton-type algorithm for finite volume methods solving Richards' equation in mixed form. SIAM Journal on Scientific Computing, 2010, 32.4: 2255-2273.Casulli, Vincenzo. A coupled surface‐subsurface model for hydrostatic flows under saturated and variably saturated conditions. International Journal for Numerical Methods in Fluids, 2017, 85.8: 449-464. [ABSTRACT FROM AUTHOR]

This work presents a software package for the interpolation of climatological variables, such as temperature and precipitation, using kriging techniques. The purposes of the paper are (1) to present a geostatistical software that is easy to use and easy to plug in to a hydrological model; (2) to provide a practical example of an accurately designed software from the perspective of reproducible research; and (3) to demonstrate the goodness of the results of the software and so have a reliable alternative to other, more traditional tools. A total of 11 types of theoretical semivariograms and four types of kriging were implemented and gathered into Object Modeling System-compliant components. The package provides real-time optimization for semivariogram and kriging parameters. The software was tested using a year's worth of hourly temperature readings and a rain storm event (11 h) recorded in 2008 and retrieved from 97 meteorological stations in the Isarco River basin, Italy. For both the variables, good interpolation results were obtained and then compared to the results from the R package gstat. [ABSTRACT FROM AUTHOR]

This work presents a package for the interpolation of climatological variables, such as temperature and precipitation, using Kriging techniques. The purposes of the study are (1) to present a geostatistical software easy to use and easy to plug-in in a hydrological model, (2) to show a practical example of an accurately designed software in the perspective of reproducible research, (3) to show the goodness of the software applications, in order to have a reliable alternative to other traditionally used tools. Ten 5 types of theoretical semivariograms and four types of Kriging were implemented and gathered into ObjectModelling System compliant components. The package provides real time optimization for semivariogram and kriging parameters. The software was tested using temperature and rainfall data retrieved from 97 meteorological stations in the Isarco River basin, Italy. For both variables, good interpolation results were obtained and then compared to the results from the R package, gstat. [ABSTRACT FROM AUTHOR]

The estimation of water resources at basin scale requires modelling of all components of the hydrological system. Because of the great uncertainties associated with the estimation of each water cycle component and the large error in budget closure that results, water budget is rarely carried out explicitly. This paper fills the gap in providing a methodology for obtaining it routinely at daily and subdaily time scales. In this study, we use various strategies to improve water budget closure in a small basin of Italian Prealps. The specific objectives are: assessing the predictive performances of different Kriging methods to determine the most accurate precipitation estimates; using MODIS imagery data to assist in the separation of snowfall and rainfall; combining the Priestley-Taylor evapotranspiration model with the Budyko hypothesis to estimate at high resolution (in time and space) actual evapotranspiration (ET); using an appropriate calibration-validation strategy to forecast discharge spatially. For this, 18 years of spatial time series of precipitation, snow water equivalent, rainfall-runoff and ET at hourly time steps are simulated for the Posina River basin (Northeast Italy) using the JGrass-NewAGE system. Among the interpolation methods considered, local detrended kriging is seen to give the best performances in forecasting precipitation distribution. However, detrended Kriging gives better results in simulating discharges. The parameters optimized at the basin outlet over a five-year period show acceptable performances during the validation period at the outlet and at interior points of the basin. The use of the Budyko hypothesis to guide the ET estimation shows encouraging results, with less uncertainty than the values reported in literature. Aggregating at a long temporal scale, the mean annual water budget for the Posina River basin is about 1269 ± 372 mm (76.4%) runoff, 503.5 ± 35.5 mm (30%) evapotranspiration, and − 50 ± 129 mm ( − 4.2%) basin storage from basin precipitation of 1730 ± 344 mm. The highest interannual variability is shown for precipitation, followed by discharge. Evapotranspiration shows less interannual variability and is less dependent on precipitation. [ABSTRACT FROM AUTHOR]

Summary Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth’s system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty. [ABSTRACT FROM AUTHOR]

Environmental models are often essential to implement projects in planning, consulting and regulatory institutions. Research models are often poorly suited to such applications due to their complexity, data requirements, operational boundaries, and factors such as institutional capacities. This contribution enhances a modeling framework to help mitigate research model complexity, streamline data and parameter setup, reduce runtime, and improve model infrastructure efficiency. Using a surrogate modeling approach, we capture the intrinsic knowledge of a conceptual or process-based model into an ensemble of artificial neural networks. The enhanced modeling framework interacts with machine learning libraries to derive surrogate models for each model service. This process is secured using blockchain technology. After describing the methods and implementation, we present an example wherein hydrologic peak discharge provided by the curve number model is emulated with a surrogate model ensemble. The ensemble median values outperformed any individual surrogate model fit to the curve number model. • Framework enabled Neuroevolutionary Surrogates (FeNS) creates surrogate models (SM). • FeNS bridges gaps between computational research and service delivery to end users. • NeuroEvolution of Augmenting Topology (NEAT) provides SM ensembles for confidence. • Median values of the ensemble of SMs provide better predictions than any single SM. • Blockchain provides auditable tracking from source data and SM creation to deployment. [ABSTRACT FROM AUTHOR]

In this work, we present a Decision Support System (DSS) developed for the Civil Protection of the Basilicata region, Italy, for the monitoring and prediction, in near real-time, of hydrological extremes. The Basilicata region was discretized in around 160 Hydrologic Response Units (HRU), whose area varies from few to hundreds square kilometres, according to the criticality of the territory. For each of the monitoring point, the system is able to assess eventual criticality due to rainfall and hydraulic threshold exceedance and provide 36-hours hydraulic/hydrological forecasts. The DSS is based on a WebGIS that is used to run and visualize results obtained by hydrological simulations. The WebGIS automatically downloads in real-time the measurements from 69 meteorological stations of the regional network and the 72-hours forecasts of precipitation (Cosmo LAMI), in GRIdded Binary form (GRIB). Then, it computes the cumulated precipitation over a fixed time interval and verifies the eventual criticality. Moreover, it allows to manage and visualize thematic maps to determine the potential vulnerability and the exposed elements on the territory. Hydrological simulations are carried out by the open source semi-distributed hydrological model GEOframe-NewAge that interacts with the data elaborated by the WebGIS, spatializes the temperatures using Kriging techniques, extracts precipitations from the GRIB files and the Leaf Area Index (LAI) from the MODIS satellite maps. Then the model performs the radiation budget, estimates snow melting, evapotranspiration, soil water content, and runoff production for each of the monitoring point. Several challenges were faced during the development of the DSS such as data scarcity, a complex dams system, identifiability problems due model complexity with limited data, which were solved using innovative approaches and techniques. The experience is worthy to be shared in order to support application in other context characterized by similar conditions. [ABSTRACT FROM AUTHOR]

Mountain ecosystems are highly heterogeneous because the complex terrain creates steepgradients in climate and vegetation cover. This heterogeneity complicates our understandingof mountain ecohydrological processes and renders the projection of climate change impactson mountainous ecosystems highly uncertain. Here, we used the fully distributed,physically-based ecohydrological model Tethys-Chloris to perform a high spatiotemporalresolution simulation of water and carbon fluxes over the entire European Alps (257,000km2). By analysing hourly simulation results over three years (2000-2003) with a spatialresolution of 250x250 m2, we investigated: (1) the major drivers of Alpine runoff and howthe latter may respond to climatic changes; (2) the role of forest encroachment for the Alpinewater balance; and (3) the impact of the 2003 drought on the Alpine ecosystems. Toinvestigate the drivers of Alpine runoff, we used a space-for-time substitution andfound that total annual runoff over the entire Alpine area is mostly controlled byprecipitation and therefore it is very resilient to changes in temperature (by up to +3˚C), despite evapotranspiration being energy-limited and temperature-dependent.However, patterns of runoff production are spatially complex and several dry areas,such as inner-Alpine valleys, may become considerably drier in a warmer climate.By examining simulation results for 4000 Alpine catchments we conclude thatforest expansion is unlikely to have major effects on annual evapotranspiration and,consequently, on runoff. Finally, analysing model outputs for the 2003 drought,we found a wide spectrum of ecohydrological responses to high temperatures andprecipitation deficit, which reflects a contrasting behaviour between low and highelevations. Model simulation results with high spatiotemporal resolution provide novelinsights into the underlying ecohydrological processes and their interactions and helpus to better understand the responses of Alpine ecosystems to climatic changes. [ABSTRACT FROM AUTHOR]