Agriculture is a cornerstone of global food production, accounting for a substantial portion of water withdrawals worldwide. As the world's population grows, so does the demand for water in agriculture, leading to alterations in regional water-energy balances. We present an approach to identify the influence of agriculture on the water-energy balance using empirical data. We explore the departure from the Budyko curve for catchments with agricultural expansion and their associations with changes in the water-energy balance using a causal discovery algorithm. Analyzing data from 1,342 catchments across three Köppen-Geiger climate classes-temperate, snowy, and others-from 1980 to 2014, we show that temperate and snowy catchments, which account for over 90% of stations, exhibit distinct patterns. Cropland percentage (CL%) emerges as the dominant factor, explaining 47 and 37% of the variance in deviations from the Budyko curve in temperate and snowy catchments, respectively. In temperate catchments, CL% shows a strong negative correlation with precipitation-streamflow (P-Q) causal strength (Spearman [Formula: see text]), suggesting that cropland exacerbates precipitation-driven deviations. A moderate negative correlation with aridity-streamflow (AR-Q) causal strength ([Formula: see text]) indicates additional influences of cropland through aridity-driven interactions. In snowy catchments, CL% is similarly influential, with a positive correlation with P-Q causal strength ([Formula: see text]). However, the negative correlation with AR-Q causal strength ([Formula: see text]) underscores the role of aridity as a secondary driver. While vegetation and precipitation seasonality also contribute to the deviations, their impacts are comparatively lower. These findings underscore the need for inclusion of agricultural activities in changing water-energy balance to secure future water supplies.

• Keywords
• Budyko water balance, agriculture, irrigation, water balance,
• Publication type
• Journal Article MeSH

The Budyko water balance is a fundamental concept in hydrology that links aridity to how precipitation is divided between evapotranspiration and streamflow. While the model is powerful, its ability to explain temporal changes and the influence of human activities and climate change is limited. Here we introduce a causal discovery algorithm to explore deviations from the Budyko water balance, attributing them to human interventions such as agricultural activities and snow dynamics. Our analysis of 1342 catchments across the U.S. and Great Britain reveals distinct patterns: in the U.S., snow fraction and irrigation alter the Budyko water balance predominantly through changes in aridity-streamflow relationships, while in Great Britain, deviations are primarily driven by changes in precipitation-streamflow relationships, notable in catchments with high cropland percentage. By integrating causal analysis with the Budyko water balance, we enhance understanding of how human activities and climate dynamics affect water balance, offering insights for water management and sustainability in the Anthropocene.

• Keywords
• Hydrology,
• Publication type
• Journal Article MeSH

Information-theoretic generalization of Granger causality principle, based on evaluation of conditional mutual information, also known as transfer entropy (CMI/TE), is redefined in the framework of Rényi entropy (RCMI/RTE). Using numerically generated data with a defined causal structure and examples of real data from the climate system, it is demonstrated that RCMI/RTE is able to identify the cause variable responsible for the occurrence of extreme values in an effect variable. In the presented example, the Siberian High was identified as the cause responsible for the increased probability of cold extremes in the winter and spring surface air temperature in Europe, while the North Atlantic Oscillation and blocking events can induce shifts of the whole temperature probability distribution.

• Publication type
• Journal Article MeSH

The inference of causal relations between observable phenomena is paramount across scientific disciplines; however, the means for such enterprise without experimental manipulation are limited. A commonly applied principle is that of the cause preceding and predicting the effect, taking into account other circumstances. Intuitively, when the temporal order of events is reverted, one would expect the cause and effect to apparently switch roles. This was previously demonstrated in bivariate linear systems and used in design of improved causal inference scores, while such behaviour in linear systems has been put in contrast with nonlinear chaotic systems where the inferred causal direction appears unchanged under time reversal. The presented work explores the conditions under which the causal reversal happens-either perfectly, approximately, or not at all-using theoretical analysis, low-dimensional examples, and network simulations, focusing on the simplified yet illustrative linear vector autoregressive process of order one. We start with a theoretical analysis that demonstrates that a perfect coupling reversal under time reversal occurs only under very specific conditions, followed up by constructing low-dimensional examples where indeed the dominant causal direction is even conserved rather than reversed. Finally, simulations of random as well as realistically motivated network coupling patterns from brain and climate show that level of coupling reversal and conservation can be well predicted by asymmetry and anormality indices introduced based on the theoretical analysis of the problem. The consequences for causal inference are discussed.

• Keywords
• brain network, causality, climate network, random networks, reversed time series, temporal symmetry, time reversal, vector autoregressive process,
• Publication type
• Journal Article MeSH

An information-theoretic approach for detecting causality and information transfer is used to identify interactions of solar activity and interplanetary medium conditions with the Earth's magnetosphere-ionosphere systems. A causal information transfer from the solar wind parameters to geomagnetic indices is detected. The vertical component of the interplanetary magnetic field (Bz) influences the auroral electrojet (AE) index with an information transfer delay of 10 min and the geomagnetic disturbances at mid-latitudes measured by the symmetric field in the H component (SYM-H) index with a delay of about 30 min. Using a properly conditioned causality measure, no causal link between AE and SYM-H, or between magnetospheric substorms and magnetic storms can be detected. The observed causal relations can be described as linear time-delayed information transfer.

• Keywords
• causality, information transfer, solar wind-magnetosphere–ionosphere system, space weather, time reversal, time series,
• Publication type
• Journal Article MeSH

Maximum entropy estimation is of broad interest for inferring properties of systems across many disciplines. Using a recently introduced technique for estimating the maximum entropy of a set of random discrete variables when conditioning on bivariate mutual informations and univariate entropies, we show how this can be used to estimate the direct network connectivity between interacting units from observed activity. As a generic example, we consider phase oscillators and show that our approach is typically superior to simply using the mutual information. In addition, we propose a nonparametric formulation of connected informations, used to test the explanatory power of a network description in general. We give an illustrative example showing how this agrees with the existing parametric formulation, and demonstrate its applicability and advantages for resting-state human brain networks, for which we also discuss its direct effective connectivity. Finally, we generalize to continuous random variables and vastly expand the types of information-theoretic quantities one can condition on. This allows us to establish significant advantages of this approach over existing ones. Not only does our method perform favorably in the undersampled regime, where existing methods fail, but it also can be dramatically less computationally expensive as the cardinality of the variables increases.

• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH