Seasonal prediction of Horn of Africa long rains using machine learning: the pitfalls of preselecting correlated predictors

The Horn of Africa is highly vulnerable to droughts and floods, and reliable long-term forecasting is a key part of building resilience. However, the prediction of the “long rains” season (March–May) is particularly challenging for dynamical climate prediction models. Meanwhile, the potential for machine learning to improve seasonal precipitation forecasts in the region has yet to be uncovered. Here, we implement and evaluate four data-driven models for prediction of long rains rainfall: ridge and lasso linear regressions, random forests and a single-layer neural network. Predictors are based on SSTs, zonal winds, land state, and climate indices, and the target variables are precipitation totals for each separate month (March, April, and May) in the Horn of Africa drylands, with separate predictions made for lead-times of 1–3 months. Results reveal a tendency for overfitting when predictors are preselected based on correlations to the target variable over the entire historical period, a frequent practice in machine learning-based seasonal forecasting. Using this conventional approach, the data-driven methods—and particularly the lasso and ridge regressions—often outperform dynamical seasonal hindcasts. However, when the selection of predictors is done independently of both the train and test data, by performing this predictor selection within the cross-validation loop, the performance of all four data-driven models is poorer than that of the dynamical hindcasts. These findings should not discourage future applications of machine learning for rainfall forecasting in the region. Yet, they should be seen as a note of caution to prevent optimistically biased results that are not indicative of the true power in operational forecast systems

A lagrangian analysis of the sources of rainfall over the Horn of Africa drylands

The Horn of Africa drylands (HAD) are among the most vulnerable regions to hydroclimatic extremes. The two rainfall seasons—long and short rains—exhibit high intraseasonal and interannual variability. Accurately simulating the long and short rains has proven to be a significant challenge for the current generation of weather and climate models, revealing key gaps in our understanding of the drivers of rainfall in the region. In contrast to existing climate modeling and observation‐based studies, here we analyze the HAD rainfall from an observationally‐constrained Lagrangian perspective. We quantify and map the region's major oceanic and terrestrial sources of moisture. Specifically, our results show that the Arabian Sea (through its influence on the northeast monsoon circulation) and the southern Indian Ocean (via the Somali low‐level jet) contribute ∼80% of the HAD rainfall. We see that moisture contributions from land sources are very low at the beginning of each season, but supply up to ∼20% from the second month onwards, that is, when the oceanic‐origin rainfall has already increased water availability over land. Further, our findings suggest that the interannual variability in the long and short rains is driven by changes in circulation patterns and regional thermodynamic processes rather than changes in ocean evaporation. Our results can be used to better evaluate, and potentially improve, numerical weather prediction and climate models, and have important implications for (sub‐)seasonal forecasts and long‐term projections of the HAD rainfall

Improved Hydrologic Forecasting and Hydropower Planning In Data Scarce Regions Using Satellite-Based Remote Sensing

The role of satellite-based remote sensing in improving hydrologic and water resources studies in data-scarce regions is investigated. Specifically, the dissertation focuses on the development of a 1) validation framework for remotely sensed precipitation and evapotranspiration without the use of ground-based observations, 2) methodological framework for calibration of large scale hydrologic models with multiple fluxes, and 3) a seasonal hydropower planning framework for data-scarce regions. In the first part of the dissertation, a root mean square error (RMSE)-based error metric capable of translating individual biasies in precipitation and evapotranspiration onto the Budyko space is developed. It is shown that the framework succeeds in arriving at the same conclusions as a traditional validation method. In the second part, the value of incorporating multiple hydrologic variables such as evapotranspiration, soil moisture and streamflow into model calibration is investigated. It is shown that parameters which are insensitive to individual model responses can influence the trade-off relationship between them. Finally, the potential of using remotely sensed precipitation and evapotranspiration datasets in generating reliable seasonal reservoir inflow forecasts for hydropower planning is investigated. Results highlight the importance of accounting for input and parameter uncertainty in hydropower planning

Improving the applicability of hydrologic models for food-energy-water nexus studies using remote sensing data

Food, energy, and water (FEW) nexus studies require reliable estimates of water availability, use, and demand. In this regard, spatially distributed hydrologic models are widely used to estimate not only streamflow (SF) but also different components of the water balance such as evapotranspiration (ET), soil moisture (SM), and groundwater. For such studies, the traditional calibration approach of using SF observations is inadequate. To address this, we use state-of-the-art global remote sensing-based estimates of ET and SM with a multivariate calibration methodology to improve the applicability of a widely used spatially distributed hydrologic model (Noah-MP) for FEW nexus studies. Specifically, we conduct univariate and multivariate calibration experiments in the Mississippi river basin with ET, SM, and SF to understand the trade-offs in accurately simulating ET, SM, and SF simultaneously. Results from univariate calibration with just SF reveal that increased accuracy in SF at the cost of degrading the spatio-temporal accuracy of ET and SM, which is essential for FEW nexus studies. We show that multivariate calibration helps preserve the accuracy of all the components involved in calibration. The study emphasizes the importance of multiple sources of information, especially from satellite remote sensing, for improving FEW nexus studies

FLEXPART–ERA-Interim simulations with 3 million parcels globally (1979–2019)

FLEXPART simulations driven with the reanalysis ERA-Interim from 1 February 1979 to 30 August 2019. The simulations cover the entire globe (90°S to 90°N and 180°E to 179°W) with 3 million homogeneously distributed air parcels representing the entire mass in the atmosphere

Dual response of Arabian Sea cyclones and strength of Indian monsoon to Southern Atlantic Ocean

Variability and trends of the south Asian monsoon at different time scales makes the region susceptible to climate-related natural disasters such as droughts and floods. Because of its importance, different studies have examined the climatic factors responsible for the recent changes in monsoon strength. Here, using observations and climate model experiments we show that monsoon strength is driven by the variations of south Atlantic Ocean sea surface temperature (SASST). The mechanism by which SASST is modulating the monsoon could be explained through the classical Matsuno-Gill response, leading to changes in the characteristics of vertical wind shear in the Arabian Sea. The decline in the vertical wind shear to the warming of SASST is associated with anomalous lower (upper)-level easterlies (westerlies). This further leads to a strong increase in the frequency of the Arabian Sea cyclones; and also prohibits the transport of moisture to the Indian landmass, which eventually reduces the strength of monsoon. The conditions in the SASST which drove these responses are aggravated by greenhouse gas emission, revealing the prominent role played by anthropogenic warming. If, with proper mitigation, these emissions are not prevented, further increases in the SASST is expected to result in increased Arabian sea cyclones and reduced monsoon strength

Budyko-based long-term water and energy balance closure in global watersheds from earth observations

Earth observations offer potential pathways for accurately closing the water and energy balance of watersheds, a fundamental challenge in hydrology. However, previous attempts based on purely satellite-based estimates have focused on closing the water and energy balances separately. They are hindered by the lack of estimates of key components, such as runoff. Here, we posit a novel approach based on Budyko's water and energy balance constraints. The approach is applied to quantify the degree of long-term closure at the watershed scale, as well as its associated uncertainties, using an ensemble of global satellite data sets. We find large spatial variability across aridity, elevation, and other environmental gradients. Specifically, we find a positive correlation between elevation and closure uncertainty, as derived from the Budyko approach. In mountainous watersheds the uncertainty in closure is 3.9 +/- 0.7 (dimensionless). Our results show that uncertainties in terrestrial evaporation contribute twice as much as precipitation uncertainties to errors in the closure of water and energy balance. Moreover, our results highlight the need for improving satellite-based precipitation and evaporation data in humid temperate forests, where the closure error in the Budyko space is as high as 1.1 +/- 0.3, compared to only 0.2 +/- 0.03 in tropical forests. Comparing the results with land surface model-based data sets driven by in situ precipitation, we find that Earth observation-based data sets perform better in regions where precipitation gauges are sparse. These findings have implications for improving the understanding of global hydrology and regional water management and can guide the development of satellite remote sensing-based data sets and Earth system models

Multivariate calibration of large scale hydrologic models : the necessity and value of a Pareto optimal approach

Multivariate calibration using measurements of multiple water balance components has emerged as a potential solution for improving the performance and realism of large scale hydrologic models. In this study we develop a novel multivariate calibration framework to rigorously test whether incorporation of multiple water balance components into calibration can result in sufficiently accurate (behavioral) solutions for all model responses. Unlike previous studies, we use Bayesian calibration to formally define limits of acceptability or error thresholds in order to distinguish behavioral solutions for each of the incorporated fluxes. We apply the framework in the Mississippi river basin for the calibration of a large scale distributed hydrologic model (Noah-MP) with different combinations of model responses - evapotranspiration (ET), soil moisture (SM), and streamflow (SF). The results of the study show that incorporation of additional fluxes and soil moisture (a storage variable) is not always valuable due to significant trade-offs in accuracy among the model responses. In our experiments, only ET and SF could be simulated simultaneously to a reasonable degree of accuracy. In addition, we quantify the trade-offs in accuracy between the model responses using the concept of Pareto optimality. We find that combining ET with other fluxes entails higher trade-offs in accuracy compared to either SM or SF. Unlike deterministic calibration, with the developed framework we are able to identify deficiencies in model parameterization that lead to significant trade-offs in accuracy, especially between ET and SM. We find that the parameters which are insensitive to individual model responses can influence the trade-off relationship between them

A deep learning-based hybrid model of global terrestrial evaporation

Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, E-t) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress (S-t), i.e., the reduction of E-t from its theoretical maximum. Then, we embed the new S-t formulation within a process-based model of E to yield a global hybrid E model. In this hybrid model, the S-t formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate S-t and E globally. The proposed framework may be extended to improve the estimation of E in Earth System Models and enhance our understanding of this crucial climatic variable. Global evaporation is a key climatic process that remains highly uncertain. Here, the authors shed light on this process with a novel hybrid model that integrates a deep learning representation of ecosystem stress within a physics-based framework