Land-use change may exacerbate climate change impacts on water resources in the Ganges basin

Quantifying how land-use change and climate change affect water resources is a challenge in hydrological science. The Upper Ganges (UG) river basin in northern India experiences monsoon flooding almost every year. Studies have shown evidence of strong coupling between the land surface (soil moisture) and atmosphere (precipitation) in northern India, which means that regional climate variations and changes in land use/cover could influence the temporal dynamics of land-atmosphere interactions. <br><br> This work aims to quantify how future projections of land-use and climate change are affecting the hydrological response of the UG river basin. Two different sets of modelling experiments were run using the JULES Land Surface Model and covering the period 2000&amp;ndash;2035: In the first set, climate change is taken into account, as JULES was driven by the CMIP5 (Coupled Model Intercomparison Project Phase 5) outputs of 21 models, under two Representative Concentration Pathways (RCP4.5 &amp; RCP8.5), whilst land use was kept constant at year 2010. In the second set, both climate change and land-use change were taken into consideration, as apart from the CMIP5 model outputs, JULES was also forced with a time-series of 15 future land-use scenarios, based on Landsat satellite imagery and Markov chain simulation. Variations in hydrological variables (stream flow, evapotranspiration and soil moisture) are calculated during the simulation period. <br><br> Significant changes in the near-future (years 2030&amp;ndash;2035) hydrologic fluxes arise under future land cover and climate change scenarios pointing towards a severe increase in high extremes of flow: the multi-model mean of the 95th percentile of streamflow [Q₅] is projected to increase by 63&amp;thinsp;% under the combined land-use and climate change high emissions scenario [RCP8.5]. The changes in all examined hydrological components are greater in the combined land-use and climate change experiment. <br><br> Results are further presented in a water resources context, aiming to address potential implications of climate change from a water-demand perspective, highlighting that that demand thresholds in the UG region are projected to be exceeded in the future winter months (Dec&amp;ndash;Feb).

A thermodynamic formulation of root water uptake

By extracting bound water from the soil and lifting it to the canopy, root systems of vegetation perform work. Here we describe how the energetics involved in root water uptake can be quantified. The illustration is done using a simple, four-box model of the soil-root system to represent heterogeneity and a parameterization in which root water uptake is driven by the xylem potential of the plant with a fixed flux boundary condition. We use this approach to evaluate the effects of soil moisture heterogeneity and root system properties on the dissipative losses and export of energy involved in root water uptake. For this, we derive an expression that relates the energy export at the root collar to a sum of terms that reflect all fluxes and storage changes along the flow path in thermodynamic terms. We conclude that such a thermodynamic evaluation of root water uptake conveniently provides insights into the impediments of different processes along the entire flow path and explicitly accounting not only for the resistances along the flow path and those imposed by soil drying but especially the role of heterogenous soil water distribution. The results show that least energy needs to be exported and dissipative losses are minimized by a root system if it extracts water uniformly from the soil. This has implications for plant water relations in forests where canopies generate heterogenous input patterns. Our diagnostic in the energy domain should be useful in future model applications for quantifying how plants can evolve towards greater efficiency in their structure and function, particularly in heterogenous soil environments. Generally, this approach may help to better describe heterogeneous processes in the soil in a simple, yet physically-based way.

The importance of topography controlled sub-grid process heterogeneity in distributed hydrological models

Heterogeneity of landscape features like terrain, soil, and vegetation properties affect the partitioning of water and energy. However, it remains unclear to which extent an explicit representation of this heterogeneity at the sub-grid scale of distributed hydrological models can improve the hydrological consistency and the robustness of such models. In this study, hydrological process complexity arising from sub-grid topography heterogeneity was incorporated in the distributed mesoscale Hydrologic Model (mHM). Seven study catchments across Europe were used to test whether (1) the incorporation of additional sub-grid variability on the basis of landscape-derived response units improves model internal dynamics, (2) the application of semi-quantitative, expert-knowledge based model constraints reduces model uncertainty; and (3) the combined use of sub-grid response units and model constraints improves the spatial transferability of the model. Unconstrained and constrained versions of both, the original mHM and mHMtopo, which allows for topography-based sub-grid heterogeneity, were calibrated for each catchment individually following a multi-objective calibration strategy. In addition, four of the study catchments were simultaneously calibrated and their feasible parameter sets were transferred to the remaining three receiver catchments. In a post-calibration evaluation procedure the probabilities of model and transferability improvement, when accounting for sub-grid variability and/or applying expert-knowledge based model constraints, were assessed on the basis of a set of hydrological signatures. In terms of the Euclidian distance to the optimal model, used as overall measure for model performance with respect to the individual signatures, the model improvement achieved by introducing sub-grid heterogeneity to mHM in mHMtopo was on average 13 %. The addition of semi-quantitative constraints to mHM and mHMtopo resulted in improvements of 13 and 19 % respectively, compared to the base case of the unconstrained mHM. Most significant improvements in signature representations were, in particular, achieved for low flow statistics. The application of prior semi-quantitative constraints further improved the partitioning between runoff and evaporative fluxes. Besides, it was shown that suitable semi-quantitative prior constraints in combination with the transfer function based regularization approach of mHM, can be beneficial for spatial model transferability as the Euclidian distances for the signatures improved on average by 2 %. The effect of semi-quantitative prior constraints combined with topography-guided sub-grid heterogeneity on transferability showed a more variable picture of improvements and deteriorations, but most improvements were observed for low flow statistics.

HESS Opinions: The need for process-based evaluation of large-domain hyper-resolution models

A meta-analysis on 192 peer-reviewed articles reporting applications of the Variable Infiltration Capacity (VIC) model in a distributed way reveals that the spatial resolution at which the model is applied has increased over the years, while the calibration and validation time interval has remained unchanged. We argue that the calibration and validation time interval should keep pace with the increase in spatial resolution in order to resolve the processes that are relevant at the applied spatial resolution. We identified six time concepts in hydrological models, which all impact the model results and conclusions. Process-based model evaluation is particularly relevant when models are applied at hyper-resolution, where stakeholders expect credible results both at a high spatial and temporal resolution.

Mapping dominant runoff processes: an evaluation of different approaches using similarity measures and synthetic runoff simulations

The identification of landscapes with similar hydrological behaviour is useful for runoff predictions in small ungauged catchments. An established method for landscape classification is based on the concept of dominant runoff process (DRP). The various DRP mapping approaches differ with respect to the time and data required for mapping. Manual approaches based on expert knowledge are reliable but time-consuming, whereas automatic GIS-based approaches are easier to implement but rely on simplifications which restrict their application range. To what extent these simplifications are applicable in other catchments is unclear. More information is also needed on how the different complexity of automatic DRP mapping approaches affects hydrological simulations. In this paper, three automatic approaches were used to map two catchments on the Swiss Plateau. The resulting maps were compared to reference maps obtained with manual mapping. Measures of agreement and association, a class comparison and a deviation map were derived. The automatically derived DRP-maps were used in synthetic runoff simulations with an adapted version of the hydrological model PREVAH, and simulation results compared with those from simulations using the reference maps. The DRP-maps derived with the automatic approach with highest complexity and data requirement were the most similar to the reference maps, while those derived with simplified approaches without original soil information differed significantly in terms of both extent and distribution of the DRPs. The runoff simulations derived from the simpler DRP-maps were more uncertain due to inaccuracies in the input data and their coarse resolution, but problems were also linked with the use of topography as a proxy for the storage capacity of soils. The perception of the intensity of the DRP classes also seems to vary among the different authors, and a standardised definition of DRPs is still lacking. We therefore recommend not only using expert knowledge for model building and constraining but also trying to obtain spatially distributed landscape classifications that are as realistic as possible.

Evaluating performances of simplified physically based models for landslide susceptibility

Rainfall induced shallow landslides cause loss of life and significant damages involving private and public properties, transportation system, etc. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. Reliable models' applications involve: automatic parameters calibration, objective quantification of the quality of susceptibility maps, model sensitivity analysis. This paper presents a methodology to systemically and objectively calibrate, verify and compare different models and different models performances indicators in order to individuate and eventually select the models whose behaviors are more reliable for a certain case study. The procedure was implemented in package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, the optimization of the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with model M3 is the best modeling solution for our test case.

Climate change increases the probability of heavy rains like those of storm Desmond in the UK – an event attribution study in near-real time

On 4–6 December 2015, the storm "Desmond" caused very heavy rainfall in northern England and Scotland, which led to widespread flooding. Here we provide an initial assessment of the influence of anthropogenic climate change on the likelihood of one-day precipitation events averaged over an area encompassing northern England and southern Scotland using data and methods available immediately after the event occurred. The analysis is based on three independent methods of extreme event attribution: historical observed trends, coupled climate model simulations and a large ensemble of regional model simulations. All three methods agree that the effect of climate change is positive, making precipitation events like this about 40 % more likely, with a provisional 2.5–97.5 % confidence interval of 5–80 %.

The yearly amount and characteristics of deep-buried phreatic evaporation in hyper-arid areas

Water scarcity is the primary cause of land deterioration, so finding new available water resources is crucial to ecological restoration. We investigated a hyper-arid Gobi location in the Dunhuang Mogao Grottoes in this work wherein the burial depth of phreatic water is over 200 m. An air-conditioner was used in a closed greenhouse to condense and measure the yearly amount of phreatic evaporation (PE) from 2010 to 2015. The results show that the annual quantity of PE is 4.52 mm, and that the PE has sinusoidal characteristics. The average PE is 0.0183 mm d-1 from March to November. Accordingly, by monitoring the annual changes in soil–air temperature and humidity to a depth of 5.0 m, we analyzed the water migration mechanism in the heterothermozone (subsurface zone of variable temperature). The results show that, from March to November, the temperature and absolute humidity (AH) increase. This is due to the flow of solar heat entering the soil – the soil subsequently releases moisture and the soil is in a state of increasing AH so that evaporation occurs. From November to March, the temperature decreases. Now, the soil absorbs water vapor and AH is in a state of decline. Thus, it is temperature alternation in the heterothermozone – due to solar heat transfer – that provides the main driving power for PE. When it drives water vapor to move downwards in the heterothermozone, a small part is reversed upwards and evaporates. Solar radiation intensity dominates the annual sinusoidal PE characteristics.

Stream restoration and sanitary infrastructure alter sources and fluxes of water, carbon, and nutrients in urban watersheds

An improved understanding of sources and timing of water and nutrient fluxes associated with urban stream restoration is critical for guiding effective watershed management. We investigated how sources, fluxes, and flowpaths of water, carbon (C), nitrogen (N), and phosphorus (P) shift in response to differences in stream restoration and sanitary infrastructure. We compared a restored stream with 3 unrestored streams draining urban development and stormwater management over a 3 year period. We found that there was significantly decreased peak discharge in response to precipitation events following stream restoration. Similarly, we found that the restored stream showed significantly lower monthly peak runoff (9.4 ± 1.0 mm d−1) compared with two urban unrestored streams (ranging from 44.9 ± 4.5 to 55.4 ± 5.8 mm d−1) draining higher impervious surface cover. Peak runoff in the restored stream was more similar to a less developed stream draining extensive stormwater management (13.2 ± 1.9 mm d−1). Interestingly, the restored stream exported most carbon, nitrogen, and phosphorus loads at relatively lower streamflow than the 2 more urban streams, which exported most of their loads at higher and less frequent streamflow. Annual exports of total carbon (6.6 ± 0.5 kg ha−1 yr−1), total nitrogen (4.5 ± 0.3 kg ha−1 yr−1), and total phosphorus (161 ± 15 g ha−1 yr−1) were significantly lower in the restored stream compared to both urban unrestored streams (p < 0.05) and similar to the stream draining stormwater management. Although stream restoration appeared to potentially influence hydrology to some degree, nitrate isotope data suggested that 55 ± 1 % of the nitrate in the restored stream was derived from leaky sanitary sewers (during baseflow), similar to the unrestored streams. Longitudinal synoptic surveys of water and nitrate isotopes along all 4 watersheds suggested the importance of urban groundwater contamination from leaky piped infrastructure. Urban groundwater contamination was also suggested by additional tracer measurements including fluoride (added to drinking water) and iodide (contained in dietary salt). Our results suggest that integrating stream restoration with restoration of aging sanitary infrastructure can be critical to more effectively minimize watershed nutrient export. Given that both stream restoration and sanitary pipe repairs both involve extensive channel manipulation, they can be considered simultaneously in management strategies. In addition, ground water can be a major source of nutrient fluxes in urban watersheds, which has been less considered compared with upland sources and storm drains. Goundwater sources, fluxes, and flowpath should also be targeted in efforts to improve stream restoration strategies and prioritize hydrologic "hot spots" along watersheds where stream restoration is most likely to succeed.

Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

L-Band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm. The work exposed compares brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The latter were estimated using a radiative transfer model and state variables from two land surface models: (i) ORganising Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and (ii) Hydrology – Tiled ECMWF Scheme for Surface Exchanges over Land (H-TESSEL). The radiative transfer model used is the Community Microwave Emission Model (CMEM). A good agreement in the temporal evolution of measured and modelled brightness temperatures is observed. However, their spatial structures are not consistent between them. An Empirical Orthogonal Function analysis of the brightness temperature's error identifies a dominant structure over the South-West of the Iberian Peninsula which evolves during the year and is maximum in Fall and Winter. Hypotheses concerning forcing induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for it at the moment. Further hypotheses are proposed at the end of the paper.