Department Systems Analysis, Integrated Assessment and Modelling

Climate change was projected to have negative effects on water availability and consequently a serious constraint to food production in many areas of the world. However, such effects have not been well understood, particularly over rainfed croplands, partly because of the poor representation of green water in associated assessments. In this study, we develop an integrated agricultural water scarcity index (WSI_ag) that incorporates blue and green water components to examine the agricultural water scarcity in the baseline (1981-2005) and future (2026-2050) periods under the Representative Concentration Pathway (RCP)2.6 and RCP6.0 scenarios. Results show that ∼3.8 million km² (∼39% of total) croplands experienced water scarcity in the baseline period and it would expand by more than 3% in the future. Under the two scenarios, WSI_ag projections are similar overall and are higher than that under the baseline in 83%-84% of global total croplands. Differences are found between the scenarios in Amazonia, Southern Africa, and South Asia. The increases in future WSI_ag are dominated by the decreased water availability in ∼60% of total croplands and ∼24% of which is dominated by the increased crop water requirement. Changes in green water availability have a significant contribution to changes in WSI_ag in 16% of global croplands, mostly in arid/semiarid regions (e.g., the South edge of the Sahel, Southern Africa, Northeast China, and Central America). This implies the important role of green water management for agriculture in these regions. The integrated assessment can help develop effective strategies for agricultural water management under climate change.

Daily streamflow dynamics can be accurately simulated by conceptual models as simple as a single bucket in some catchments, while they require more complex configurations in other catchments. However, without resorting to calibration, anticipating where and why a given model structure may be appropriate remains difficult. In this work, we explored the feasibility of relating suitable model structures to the climate and streamflow characteristics of 508 catchments in Brazil. Specifically, we tested four model structures using up to three reservoirs, where each reservoir is intended to represent a catchment function: the rainfall-runoff threshold, the fast, and the slow hydrograph response. We hypothesized a relationship between suitable model structures and hydrological signatures of aridity (I_A) and baseflow index (I_B). Our results show that different classes of signatures resulted in distinct patterns of model performance. Wet catchments (I_A < 0.9) with low baseflow (I_B < 0.4) were the easiest to model, with a single-reservoir model presenting a relatively good performance. In the case of low baseflow, adding a rainfall-runoff threshold reservoir resulted in better performance than adding a slow response reservoir, whereas in the case of high baseflow (I_B < 0.6) the opposite occurred. In the case of low baseflow, the inclusion of a slow response reservoir helped the simulation of dry catchments (I_A < 1.1), but not of wet ones, which we attributed to the impact of permeability in dry catchments. These results indicate a path toward model structure identification from streamflow signatures and potentially from landscape features.

Hydrological modeling of ungauged catchments, which lack observed streamflow data, is an important practical goal in hydrological sciences. A major challenge is to identify a model structure that reflects the hydrological processes relevant to the catchment of interest. This study contributes a Bayesian framework for identifying individual model mechanisms (process representations) from flow indices regionalized to the catchment of interest. We extend a method previously introduced for mechanism identification in gauged basins, by formulating the inference equations in the space of (regionalized) flow indices and by accounting for posterior parameter uncertainty. A flexible hydrological model is used to generate candidate mechanisms and model structures, followed by statistical hypothesis testing to identify "dominant" (more a posterior probable) model mechanisms. The proposed method is illustrated using real data and synthetic experiments based on 92 catchments from northern Spain, from which 16 catchments are treated as ungauged. 624 hydrological model structures from the flexible framework FUSE are employed. In real data experiments, the method identifies a dominant mechanism in 27% of 112 trials (processes and catchments). The most identifiable process is routing, whereas the least identifiable processes are percolation and unsaturated zone processes. In synthetic experiments, where "true" mechanisms are known, the reliability of method varies from 60% to 95% depending on the combined regionalization and hydrological error; the probability of making an identification remains stable at around 25%. More broadly, the study contributes perspectives on hydrological mechanism identification under data-scarce conditions; limitations and opportunities for improvement are outlined.

On the basis of a tensor representation of protein shape, obtained by an affine decomposition of residue velocity, we show how to identify actions at continuum scale for both single proteins and their complexes in terms of power equivalence. The approach constructs and justifies a continuum modeling of protein complexes, which avoids a direct, atomistic-based, simulation of the whole complex, rather it focuses (in a statistical sense) on a single protein and its interactions with the neighbors. In the resulting setting we also prove the existence of equilibrium configurations (native states) under large strains.

Understanding concurrent drought events in global main croplands is crucially important for food security, effective adaptation to climate change and human well-being. Yet, there is a lack of comprehensive studies on concurrent drought probability changes among main crop production countries on a global scale, especially for the future. Here, we studied concurrent drought among 26 main crop production countries for the time period 1861-2099. During the historical period, probability of concurrent moderate and severe drought among the countries was relatively low, and the maximum concurrent moderate and severe drought probability will double and triple under RCP8.5 extreme climate, respectively. Concurrent probability of moderate and severe drought between China and United States of America, Brazil and Russia will be at least 6% and 5% under RCP8.5, respectively, compared with zero in the historical period. Limiting RCPs to RCP2.6 can decrease the concurrent probability of severe drought at least 2% (at least 3% for RCP8.5).