Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region

The study evaluates the potential utility of the random forest (RF) predictive model used to simulate daily reference evapotranspiration (ET0) in two stations located in the arid oasis area of northwestern China. To construct an accurate RF-based predictive model, ET0 is estimated by an appropriate combination of model inputs comprising maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine durations (Sun), wind speed (U2), and relative humidity (Rh). The output of RF models are tested by ET0 calculated using Penman–Monteith FAO 56 (PMF-56) equation. Results showed that the RF model was considered as a better way to predict ET0 for the arid oasis area with limited data. Besides, Rh was the most influential factor on the behavior of ET0, except for air temperature in the proposed arid area. Moreover, the uncertainty analysis with a Monte Carlo method was carried out to verify the reliability of the results, and it was concluded that RF model had a lower uncertainty and can be used successfully in simulating ET0. The proposed study shows RF as a sound modeling approach for the prediction of ET0 in the arid areas where reliable weather data sets are available, but relatively limited

Future projection with an extreme-learning machine and support vector regression of reference evapotranspiration in a mountainous inland watershed in north-west China

This study aims to project future variability of reference evapotranspiration (ET0) using artificial intelligence methods, constructed with an extreme-learning machine (ELM) and support vector regression (SVR) in a mountainous inland watershed in north-west China. Eight global climate model (GCM) outputs retrieved from the Coupled Model Inter-comparison Project Phase 5 (CMIP5) were employed to downscale monthly ET0 for the historical period 1960–2005 as a validation approach and for the future period 2010–2099 as a projection of ET0 under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The following conclusions can be drawn: the ELM and SVR methods demonstrate a very good performance in estimating Food and Agriculture Organization (FAO)-56 Penman–Monteith ET0. Variation in future ET0 mainly occurs in the spring and autumn seasons, while the summer and winter ET0 changes are moderately small. Annually, the ET0 values were shown to increase at a rate of approximately 7.5 mm, 7.5 mm, 0.0 mm (8.2 mm, 15.0 mm, 15.0 mm) decade−1, respectively, for the near-term projection (2010–2039), mid-term projection (2040–2069), and long-term projection (2070–2099) under the RCP4.5 (RCP8.5) scenario. Compared to the historical period, the relative changes in ET0 were found to be approximately 2%, 5% and 6% (2%, 7% and 13%), during the near, mid- and long-term periods, respectively, under the RCP4.5 (RCP8.5) warming scenarios. In accordance with the analyses, we aver that the opportunity to downscale monthly ET0 with artificial intelligence is useful in practice for water-management policie

The spatial and temporal contribution of glacier runoff to watershed discharge in the Yarkant River Basin, Northwest China

In this paper, a glacial module based on an enhanced temperature-index approach was successfully introduced into the Soil and Water Assessment Tool (SWAT) model to simulate the glacier runoff and water balance of a glacierized watershed, the mountainous region of the Yarkant River Basin (YRB) in Karakoram. Calibration and validation of the SWAT model were based on comparisons between the simulated and observed discharge with a monthly temporal resolution from 1961 to 2011 for the Kaqun hydrological station. The results reaffirmed the viability of the approach for simulating glacier runoff, as evidenced by a Nash–Sutcliff Efficiency (NSE) of 0.82–0.86 as well as a percentage bias (PBIAS) of −4.5% to 2.4%, for the calibration and validation periods, respectively. Over the last 50 years, the total discharge and glacier runoff both exhibited increasing trends with 0.031 × 109 m3·a−1 and 0.011 × 109 m3·a−1. The annual glacier runoff contribution to the streamflow was between 42.3% and 64.5%, with an average of 51.6%, although the glaciers accounted for only 12.6% of the watershed drainage area in the mountainous YRB. The monthly contribution of the glacier runoff ranged from 11.0% in April to 62.1% in August, and the glacier runoff from June to September accounted for about 86.3% of the annual glacier runoff. Runoff from the mountainous regions above 5000 m a.s.l. accounted for 70.5% of the total discharge, with glacier runoff contributions being approximately 46.4%

Separation of the Climatic and Land Cover Impacts on the Flow Regime Changes in Two Watersheds of Northeastern Tibetan Plateau

Assessment of the effects of climate change and land use/cover change (LUCC) on the flow regimes in watershed regions is a fundamental research need in terms of the sustainable water resources management and ecosocial developments. In this study, a statistical and modeling integrated method utilizing the Soil and Water Assessment Tool (SWAT) has been adopted in two watersheds of northeastern Tibetan Plateau to separate the individual impacts of climate and LUCC on the flow regime metrics. The integrated effects of both LUCC and climate change have led to an increase in the annual streamflow in the Yingluoxia catchment (YLC) region and a decline in the Minxian catchment (MXC) region by 3.2% and 4.3% of their total streamflow, respectively. Climate change has shown an increase in streamflow in YLC and a decline in MXC region, occupying 107.3% and 93.75% of the total streamflow changes, respectively, a reflection of climatic latitude effect on streamflow. It is thus construed that the climatic factors contribute to more significant influence than LUCC on the magnitude, variability, duration, and component of the flow regimes, implying that the climate certainly dominates the flow regime changes in northeastern Tibetan Plateau

Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management

Denervation as a Common Mechanism Underlying Different Pulmonary Vein Isolation Strategies for Paroxysmal Atrial Fibrillation: Evidenced by Heart Rate Variability after Ablation

Backgrounds. Segmental and circumferential pulmonary vein isolations (SPVI and CPVI) have been demonstrated to be effective therapies for paroxysmal atrial fibrillation (PAF). PVI is well established as the endpoint of different ablation techniques, whereas it may not completely account for the long-term success. Methods. 181 drug-refractory symptomatic PAF patients were referred for segmental or circumferential PVI (SPVI = 67; CPVI = 114). Heart rate variability (HRV) was assessed before and after the final ablation. Results. After following up for 62.23±12.75 months, patients underwent 1.41±0.68 procedures in average, and the success rates in SPVI and CPVI groups were comparable. 119 patients were free from AF recurrence (SPVI-S, n=43; CPVI-S, n=76). 56 patients had recurrent episodes (SPVI-R, n=21; CPVI-R, n=35). Either ablation technique decreased HRV significantly. Postablation SDNN and rMSSD were significantly lower in SPVI-S and CPVI-S subgroups than in SPVI-R and CPVI-R subgroups (SPVI-S versus SPVI-R: SDNN 91.8±32.6 versus 111.5±36.2 ms, rMSSD 47.4±32.3 versus 55.2±35.2 ms; CPVI-S versus CPVI-R: SDNN 83.0±35.6 versus 101.0±40.7 ms, rMSSD 41.1±22.9 versus 59.2±44.8 ms; all P<0.05). Attenuation of SDNN and rMSSD remained for 12 months in SPVI-S and CPVI-S subgroups, whereas it recovered earlier in SPVI-R and CPVI-R subgroups. Multivariate logistic regression analysis identified SDNN as the only predictor of long-term success. Conclusions. Beyond PVI, denervation may be a common mechanism underlying different ablation strategies for PAF

Analysis of Transportation Networks Subject To Natural Hazards – insights from a Colombian case

ABSTRACT: This study provides an applied framework to derive the connectivity reliability and vulnerability of inter-urban transportation systems under network disruptions. The proposed model integrates statistical reliability analysis to find the reliability and vulnerability of transportation networks. Most of the modern research in this field has focused on urban transportation networks where the primary concerns are guaranteeing predefined standards of capacity and travel time. However, at a regional and national level, especially in developing countries, the connectivity of remote populations in the case of disaster is of utmost importance. The applicability of the framework is demonstrated with a case study in the state of Antioquia, Colombia, using historical records from the 2010-2011 rainy season, an aspect that stands out and gives additional support compared to previous studies that considers simulated data from assumed distributions. The results provide significant insights to practitioners and researchers for the design and management of transportation systems and route planning strategies under this type of disruptions

Assessing Variation in Water Balance Components in Mountainous Inland River Basin Experiencing Climate Change

Quantification of the changes of water balance components is significant for water resource assessment and management. This paper employed the Soil and Water Assessment Tool (SWAT) model to estimate the water balance in a mountainous watershed in northwest China at different spatial scales over the past half century. The results showed that both Nash-Sutcliffe efficiency (NSE) and determination coefficient (R2) were over 0.90 for the calibration and validation periods. The water balance components presented rising trends at the watershed scale, and the total runoff increased by 30.5% during 1964 to 2013 period. Rising surface runoff and rising groundwater flow contributed 42.7% and 57.3% of the total rising runoff, respectively. The runoff coefficient was sensitive to increasing precipitation and was not significant to the increase of temperature. The alpine meadow was the main landscape which occupied 51.1% of the watershed and contributed 55.5% of the total runoff. Grass land, forest land, bare land, and glacier covered 14.2%, 18.8%, 15.4%, and 0.5% of the watershed and contributed 8.5%, 16.9%, 15.9%, and 3.2% of the total runoff, respectively. The elevation zone from 3500 to 4500 m occupied 66.5% of the watershed area, and contributed the majority of the total runoff (70.7%). The runoff coefficients in the elevation zone from 1637 to 2800 m, 2800 to 3500 m, 3500 to 4000 m, 4000 to 4500 m, and 4500 to 5062 m were 0.20, 0.27, 0.32, 0.43, and 0.78, respectively, which tend to be larger along with the elevation increase. The quantities and change trends of the water balance components at the watershed scale were calculated by the results of the sub-watersheds. Furthermore, we characterized the spatial distribution of quantities and changes in trends of water balance components at the sub-watershed scale analysis. This study provides some references for water resource management and planning in inland river basins