Both seasonal and annual mean precipitation and evaporation influence patterns of water availability impacting society and ecosystems. Existing global climate studies rarely consider such patterns from non-parametric statistical standpoint. Here, we employ a non-parametric analysis framework to analyze seasonal hydroclimatic regimes by classifying global land regions into nine regimes using late 20th century precipitation means and seasonality. These regimes are used to assess implications for water availability due to concomitant changes in mean and seasonal precipitation and evaporation changes using CMIP5 model future climate projections. Out of 9 regimes, 4 show increased precipitation variation, while 5 show decreased evaporation variation coupled with increasing mean precipitation and evaporation. Increases in projected seasonal precipitation variation in already highly variable precipitation regimes gives rise to a pattern of "seasonally variable regimes becoming more variable". Regimes with low seasonality in precipitation, instead, experience increased wet season precipitation.

No abstract

No abstract

The evolution of hydrological drought events is a result of complex (nonlinear) interactions between climate and catchment processes. To investigate such nonlinear relationship, we integrated a machine learning modeling framework based on the random forest (RF) algorithms with an interpretation framework to quantify the role of climate and catchment controls on hydrological drought. More particularly, our framework interprets a built RF machine-learning model to identify dominant variables and visualize their functional dependence and interaction effects on hydrological drought characteristics utilizing concepts of minimal depth, interactive depth, and partial dependence. We test our proposed modeling framework based on a set of 652 continental United States catchments with minimal human interference for a period of 1979-2010. Application of this framework indicated presence of three distinct drought regimes, which includes, Regime 1: droughts with longer duration, less frequent and lesser intensity; Regime 2: droughts with moderate duration, moderate frequency, and moderate intensity; and Regime 3: droughts with shorter duration, more frequent, and more intense. RF algorithm was able to accurately model the drought characteristics (intensity, duration, and number of events) for all the three drought regimes as a function of selected variables. It was observed that the type of dominant variables as well as their nonlinear functional relationship with hydrological droughts characteristics can vary between three selected regimes. Our interpretation framework indicated that catchment characteristics have a significant role in controlling the hydrologic drought for catchments (regime 1), whereas both climate and catchment characteristics control hydrological drought in regimes 2 and 3.

No abstract

Abstract. We present a three-parameter streamflow elasticity model as a function of precipitation, potential evaporation, and change in groundwater storage applicable at both seasonal and annual scales. The model was applied to 245 Model Parameter Estimation Experiment (MOPEX) basins spread across the continental USA. The analysis of the modified equation at annual and seasonal scales indicated that the groundwater and surface water storage change contributes significantly to the streamflow elasticity. Overall, in case of annual as well as seasonal water balances, precipitation has higher elasticity values when compared to both potential evapotranspiration and storage changes. The streamflow elasticities show significant nonlinear associations with the climate conditions of the catchments indicating a complex interplay between elasticities and climate variables with substantial seasonal variations.

Hydrologic predictions at rural watersheds are important but also challenging due to data shortage. Long Short-TermMemory (LSTM) networks are a promising machine learning approach and have demonstrated good performance in streamflow predictions. However, due to its data-hungry nature, most of LSTM applications focused on well-monitored catchments with abundant and high quality observations. In this work, we investigate predictive capabilities of LSTM in poorly monitored watersheds with short observation records. To address three main challenges of LSTM applications in data-scarce locations, i.e., overfitting, uncertainty quantification (UQ), and out-of-distribution prediction, we evaluate different regularization techniques to prevent overfitting, apply a Bayesian LSTM for UQ, and introduce a physics-informed hybrid LSTM to enhance out-of-distribution prediction. Through case studies in two diverse sets of catchments with and without snow influence, we demonstrate that: (1) when hydrologic variability in the prediction period is similar to the calibration period, LSTM models can reasonably predict daily streamflow with Nash-Sutcliffe efficiency above 0.8, even with only two years of calibration data. (2) When the hydrologic variability in the prediction and calibration periods is dramatically different, LSTM alone does not predict well, but the hybrid model can improve the out-of-distribution prediction with acceptable generalization accuracy. (3) L2 norm penalty and dropout can mitigate overfitting, and Bayesian and hybrid LSTM have no overfitting. (4) Bayesian LSTM provides useful uncertainty information to improve prediction understanding and credibility. These insights have vital implications for streamflow simulation in watersheds where data quality and availability are a critical issue.

This study investigated the anthropogenic influence on the temporal variability of annual precipitation for the period 1950-2005 as simulated by the CMIP5 models. The temporal variability of both annual precipitation amount (PRCPTOT) and intensity (SDII) was first measured using a metric of statistical dispersion called the Gini coefficient. Comparing simulations driven by both anthropogenic and natural forcing (ALL) with simulations of natural forcing only (NAT), we quantified the anthropogenic contributions to the changes in temporal variability at global, continental and sub-continental scales as a relative difference of the respective Gini coefficients of ALL and NAT. Over the period of 1950-2005, our results indicate that anthropogenic forcing have resulted in decreased uniformity (i.e. increase in unevenness or disparity) in annual precipitation amount and intensity at global as well as continental scales. In addition, out of the 21 sub-continental regions considered, 14 (PRCPTOT) and 17 (SDII) regions showed significant anthropogenic influences. The human impacts are generally larger for SDII compared to PRCTOT, indicating that the temporal variability of precipitation intensity is generally more susceptible to anthropogenic influence than precipitation amount. The results highlight that anthropogenic activities have changed not only the trends but also the temporal variability of annual precipitation, which underscores the need to develop effective adaptation management practices to address the increased disparity.