Quantifying how land-use change and climate change affect water resources is a challenge in hydrological science. This work aims to quantify how future projections of land-use and climate change might affect the hydrological response of the Upper Ganges river basin in northern India, which experiences monsoon flooding almost every year. Three different sets of modelling experiments were run using the Joint UK Land Environment Simulator (JULES) land surface model (LSM) and covering the period 2000–2035: in the first set, only climate change is taken into account, and JULES was driven by the CMIP5 (Coupled Model Intercomparison Project Phase 5) outputs of 21 models, under two representative concentration pathways (RCP4.5 and RCP8.5), whilst land use was held fixed at the year 2010. In the second set, only land-use change is taken into account, and JULES was driven by a time series of 15 future land-use pathways, based on Landsat satellite imagery and the Markov chain simulation, whilst the meteorological boundary conditions were held fixed at years 2000–2005. In the third set, both climate change and land-use change were taken into consideration, as the CMIP5 model outputs were used in conjunction with the 15 future land-use pathways to force JULES. Variations in hydrological variables (stream flow, evapotranspiration and soil moisture) are calculated during the simulation period. Significant changes in the near-future (years 2030–2035) 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 % 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. Results are further presented in a water resources context, aiming to address potential implications of climate change and land-use change from a water demand perspective. We conclude that future water demands in the Upper Ganges region for winter months may not be met.
Natural stochasticity can pose challenges in managing the quality of the environment, or hinder understanding of the system structure. It is problematic because unfavourable stochastic event cancels the costly management effort and because favourable stochastic event overestimates success of the management effort. This paper presents a variance-based modelling method that can be used to quantify the extent to which the natural stochasticity can affect the target environment. We use a case study of a lake water quality assessment in a Norwegian lake of Årungen, together with a lake model MyLake, in order to present the method, and how this method could assist in answering scientific and managerial questions. Specifically, the case study's goal was to disentangle the respective significance of nutrient loading (management) and weather (the confounding natural stochasticity). Many scientifically and managerially relevant understandings have been revealed. For example, variation in runoff volume was most prevalent during autumn and winter, while variation in phosphorus inflow was most extensive from late winter to early spring. Thermal related properties in the lake were mostly determined by weather conditions, whereas loading was the most important factor for phytoplankton biomass and water transparency. Mild winters and greater inputs of suspended matter and phosphorus were followed by increased phytoplankton biomass and light attenuation. These findings suggest also that future changes in the global climate may have important implications for local water management decision-making. The present method of disentangling mutually confounding factors is not limited to lake water quality studies and therefore should provide certain utility in other application field of modelling.
The aim of this study was to analyse and predict the mean stable water isotopic composition of the snow cover at specific geographic locations and altitudes. In addition, the dependence of the isotopic composition of the entire snow cover on altitude was analysed. Snow in four Swiss catchments was sampled at the end of the accumulation period in April 2010 and a second time during snowmelt in May 2010 and analysed for stable isotope composition of 2H and 18O. The sampling was conducted at both south-facing and north-facing slopes at elevation differences of 100 m, for a total altitude difference of approximately 1000 m. The observed variability of isotopic composition of the snow cover was analysed with stepwise multiple linear regression models. The analysis indicated that there is only a limited altitude effect on the isotopic composition when considering all samples. This is due to the high variability of the isotopic composition of the precipitation during the winter months and, in particular in the case of south-facing slopes, an enrichment of heavy isotopes due to intermittent melting processes. This enrichment effect could clearly be observed in the samples which were taken later in the year. A small altitudinal gradient of the isotopic composition could only be observed at some north-facing slopes. However, the dependence of snow depth and the day of the year were significant predictor variables in all models. This study indicates the necessity to further study the variability of water isotopes in the snow cover to increase prediction for isotopic composition of snowmelt and hence increase model performance of residence time models for alpine areas in order to better understand the accumulation processes and the sources of water in the snow cover of high mountains.
