In this study, we applied a multi-objective calibration approach to select a group of best performing parameter sets for the Variable Infiltration Capacity (VIC) model in the Boulder Creek Watershed, USA. We specifically applied 16 non-dominated parameter sets to simulate hydrologic variables, including streamflow (Q), evapotranspiration (ET) and soil moisture (SM) in two future phases (Phase 1: 2040–2069; Phase 2: 2070–2099). Relative to the historical period, Q and ET increased, and SM decreased. The magnitude of change was greater in Phase 2 than in Phase 1 for both ET (+19.7 per cent) and SM (-5.4 per cent). We found that the model calibration resultant parameter uncertainty could lead to a reversal of the change sign of annual Q during Phase 2. The uncertainty resulting from model calibration was up to 4.3 per cent and 19.6 per cent at the annual and monthly scales, respectively. Seasonally, uncertainty reached the highest levels during the spring snowmelt runoff period between February and May for Q and SM, and during the summer months for ET. These results suggest that the use of a single parameter set may yield substantial bias for hydrological projections, and more efforts should be devoted to constraining the model calibration uncertainty to enable effective water resources decision-making.

A better detection of landslide occurrence is critical for disaster prevention and mitigation. Over the past four decades, great achievements have been made, ranging from inventories to mapping, susceptibility analysis to triggering threshold identification. Here, we proposed a model to establish global distributed rainfall thresholds, by linking triggering rainfall with geo-environmental causes related to landslide events. The model was based on multiple linear regression method, to define rainfall thresholds as a function of diverse geo-environmental variables, fitted and validated by a combined and relatively accurate landslide dataset. Results show primarily feasible performances for training and testing datasets, with low mean absolute error (0.22 log(mm)) and a high coefficient of determination (0.67) totally. We further prepared global distributed threshold maps for sub- and multi-daily rainfall durations. They share similar spatial distributions in line with previous research. The normalized rainfall index, defined as the ratio of precipitation amount over distributed rainfall thresholds, can be an index of possible landslide occurrence, that is, regions with a normalized index over 1.0 correspond to high probability. We argue that distributed rainfall threshold models are an improvement of empirical threshold models and susceptibility assessments by considering the interaction between triggering rainfall and geo-environmental causes, and promising for better hazard assessment.

Water erosion has become an important problem and is expected to be affected by climate change. This study assessed the vulnerability of global water erosion during 1992–2015 based on RUSLE method. The research objective was to explore the spatial pattern of global water erosion vulnerability change in recent decades and identify the impacts of rainfall change on water erosion. The results show that global water erosion vulnerability increased over 54.23 per cent of the surface during 1992–2015, and the surface with significant increasing trends accounted for 11.96 per cent. There is great heterogeneity in the trends across the world. The change rate of water erosion vulnerability on croplands and bare lands was significantly higher than that of natural vegetation. Most areas exhibiting statistically significant trends were in cold and arid climate zones (CZs), which indicates that bare lands and croplands in cold and arid CZs were more sensitive to climate change with regard to water erosion. The results offer a global view of impacts of rainfall change on water erosion and suggest that enhancing the vegetation growth and taking soil conservation measures on croplands and bare lands in the cold and arid CZs could reduce the erosion threat.

Agricultural production is expected to double by 2050 in order to satisfy the increasing demand for food, feed and bioenergy. However, under climate change, the frequency and magnitude of climate extremes are expected to increase and represent a future threat to food security by reducing crop growth and yield. Therefore, understanding the effects of climate extremes on crop production is essential to inform effective adaption and mitigation strategies to ensure future food security. This paper aims to review the mechanisms behind the climate impacts on crop yield and summarize the effects of climate extremes on agricultural production reported in the literature. Heat extremes and drought are found to increase frequently, and the impacts of climate extremes on crop growth mainly occur during the anthesis and grain filling stages, leading to final crop yield loss. Many approaches have been adopted to assess the effects of climate extremes on crop yield, each with its own strengths and weaknesses. Adaptation measures are encouraged to mitigate crop loss risk in the future, when more frequent and severe climate extremes are projected to occur in agricultural areas.

Lake Urmia, in the north-west of Iran, used to be the second-largest hypersaline lake around the world. Over the past few decades, unsustainable water resources planning and management as well as climate variability have led to a dramatic shrinkage of the lake. In this chapter, we describe the past and present situation of the lake through an analysis of in-situ and global datasets. Furthermore, we provide a brief review of the literature on the basin and describe the Urmia lake restoration programme (ULRP) measures taken to restore the lake. The precipitation and temperature data from 17 CORDEX models under RCP2.6, RCP4.5 and RCP8.5 scenarios are used to project the climatic condition of the basin by the end of the century. The results from CORDEX model simulations under all scenarios suggest changes in total amounts of precipitation are not likely to vary significantly. Conversely, increases in the basin temperature are expected under all scenarios. Therefore, an increase in evaporation from the lake and higher water demands are expected in the future. Consequently, the management of water demand in the basin is key to avoid a potential future deterioration of the current situation.