Evapotranspiration estimation using Landsat-8 data with a two-layer framework

This work was partially supported by the National Natural Science Foundation of China (41401042), National Key Basic Research Program of China (973 Program) (Grant No. 2015CB452701) and National Natural Science Foundation of China (Grant Nos. 41571019 and 41371043).Peer reviewedproo

Effects of Climate Change and Human Activities on Surface Runoff in the Luan River Basin

Quantifying the effects of climate change and human activities on runoff changes is the focus of climate change and hydrological research. This paper presents an integrated method employing the Budyko-based Fu model, hydrological modeling, and climate elasticity approaches to separate the effects of the two driving factors on surface runoff in the Luan River basin, China. The Budyko-based Fu model and the double mass curve method are used to analyze runoff changes during the period 1958~2009. Then two types of hydrological models (the distributed Soil and Water Assessment Tool model and the lumped SIMHYD model) and seven climate elasticity methods (including a nonparametric method and six Budyko-based methods) are applied to estimate the contributions of climate change and human activities to runoff change. The results show that all quantification methods are effective, and the results obtained by the nine methods are generally consistent. During the study period, the effects of climate change on runoff change accounted for 28.3~46.8% while those of human activities contributed with 53.2~71.7%, indicating that both factors have significant effects on the runoff decline in the basin, and that the effects of human activities are relatively stronger than those of climate change

Study on the Variation of Terrestrial Water Storage and the Identification of Its Relationship with Hydrological Cycle Factors in the Tarim River Basin, China

The terrestrial water storage anomalies (TWSAs) in the Tarim River Basin (TRB) were investigated and the related factors of water variations in the mountain areas were analyzed based on Gravity Recovery and Climate Experiment (GRACE) data, in situ river discharge, and precipitation during the period of 2002–2015. The results showed that three obvious flood events in 2005, 2006, and 2010 resulted in significant water surplus, although TWSA decreased in the TRB during 2002–2015. However, while the significant water deficits in 2004, 2009, and 2011 were associated with obvious negative river discharge anomalies at the hydrological stations, the significant water deficits were not well consistent with the negative anomalies of precipitation. While the river discharge behaved with low correlations with TWSA, linear relationships between TWSA and climate indices were insignificant in the TRB from 2002 to 2015. The closest relationship was found between TWSA and Pacific Decadal Oscillation (PDO), with correlations of -0.56 and 0.58 during January 2010–December 2015 and during January 2006–December 2009, respectively. Meanwhile, the correlation coefficient between TWSA and El Niño-Southern Oscillation (ENSO) index in the period of April 2002–December 2005 was -0.25, which reached the significant level (p<0.05)

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance

Risk of Crop Yield Reduction in China under 1.5 °C and 2 °C Global Warming from CMIP6 Models

Warmer temperatures significantly influence crop yields, which are a critical determinant of food supply and human well-being. In this study, a probabilistic approach based on bivariate copula models was used to investigate the dependence (described by joint distribution) between crop yield and growing season temperature (TGS) in the major producing provinces of China for three staple crops (i.e., rice, wheat, and maize). Based on the outputs of 12 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathway 5–8.5, the probability of yield reduction under 1.5 °C and 2 °C global warming was estimated, which has great implications for agricultural risk management. Results showed that yield response to TGS varied with crop and region, with the most vulnerable being rice in Sichuan, wheat in Sichuan and Gansu, and maize in Shandong, Liaoning, Jilin, Nei Mongol, Shanxi, and Hebei. Among the selected five copulas, Archimedean/elliptical copulas were more suitable to describe the joint distribution between TGS and yield in most rice-/maize-producing provinces. The probability of yield reduction was greater in vulnerable provinces than in non-vulnerable provinces, with maize facing a higher risk of warming-driven yield loss than rice and wheat. Compared to the 1.5 °C global warming, an additional 0.5 °C warming would increase the yield loss risk in vulnerable provinces by 2–17%, 1–16%, and 3–17% for rice, wheat, and maize, respectively. The copula-based model proved to be an effective tool to provide probabilistic estimates of yield reduction due to warming and can be applied to other crops and regions. The results of this study demonstrated the importance of keeping global warming within 1.5 °C to mitigate the yield loss risk and optimize agricultural decision-making in vulnerable regions

Effects of Climate Change and Human Activities on Surface Runoff in the Luan River Basin

Quantifying the effects of climate change and human activities on runoff changes is the focus of climate change and hydrological research. This paper presents an integrated method employing the Budyko-based Fu model, hydrological modeling, and climate elasticity approaches to separate the effects of the two driving factors on surface runoff in the Luan River basin, China. The Budykobased Fu model and the double mass curve method are used to analyze runoff changes during the period 1958∼2009. Then two types of hydrological models (the distributed Soil and Water Assessment Tool model and the lumped SIMHYD model) and seven climate elasticity methods (including a nonparametric method and six Budyko-based methods) are applied to estimate the contributions of climate change and human activities to runoff change. The results show that all quantification methods are effective, and the results obtained by the nine methods are generally consistent. During the study period, the effects of climate change on runoff change accounted for 28.3∼46.8% while those of human activities contributed with 53.2∼71.7%, indicating that both factors have significant effects on the runoff decline in the basin, and that the effects of human activities are relatively stronger than those of climate change

Identification of Hydrological Drought in Eastern China Using a Time-Dependent Drought Index

Long records (1960–2013) of monthly streamflow observations from 8 hydrological stations in the East Asian monsoon region are modeled using a nonstationarity framework by means of the Generalized Additive Models in Location, Scale and Shape (GAMLSS). Modeling analyses are used to characterize nonstationarity of monthly streamflow series in different geographic regions and to select optimal distribution among five two-parameter distributions (Gamma, Lognormal, Gumbel, Weibull and Logistic). Based on the optimal nonstationarity distribution, a time-dependent Standardized Streamflow Index (denoted SSIvar) that takes account of the possible nonstationarity in streamflow series is constructed and then employed to identify drought characteristics at different time scales (at a 3-month scale and a 12-month scale) in the eight selected catchments during 1960–2013 for comparison. Results of GAMLSS models indicate that they are able to represent the magnitude and spread in the monthly streamflow series with distribution parameters that are a linear function of time. For 8 hydrological stations in different geographic regions, a noticeable difference is observed between the historical drought assessment of Standardized Streamflow Index (SSI) and SSIvar, indicating that the nonstationarity could not be ignored in the hydrological drought analyses, especially for stations with change point and significant change trends. The constructed SSIvar is, to some extent, found to be more reliable and suitable for regional drought monitoring than traditional SSI in a changing environment, thereby providing a feasible alternative for drought forecasting and water resource management at different time scales

Spatial Downscaling of GPM Annual and Monthly Precipitation Using Regression-Based Algorithms in a Mountainous Area

As a fundamental component in material and energy circulation, precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. In this study, we investigated two downscaling algorithms based on the Multiple Linear Regression (MLR) and the Geographically Weighted Regression (GWR), respectively. They were employed to downscale annual and monthly precipitation obtained from the Global Precipitation Measurement (GPM) Mission in Hengduan Mountains, Southwestern China, from 10 km × 10 km to 1 km × 1 km. Ground observations were then used to validate the accuracy of downscaled precipitation. The results showed that (1) GWR performed much better than MLR to regress precipitation on Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM); (2) coefficients of GWR models showed strong spatial nonstationarity, but the spatial mean standardized coefficients were very similar to standardized coefficients of MLR in terms of intra-annual patterns: generally NDVI was positively related to precipitation when monthly precipitation was under 166 mm; DEM was negatively related to precipitation, especially in wet months like July and August; contribution of DEM to precipitation was greater than that of NDVI; (3) residuals’ correction was indispensable for the MLR-based algorithm but should be removed from the GWR-based algorithm; (4) the GWR-based algorithm rather than the MLR-based algorithm produced more accurate precipitation than original GPM precipitation. These results indicated that GWR is a promising method in satellite precipitation downscaling researches and needed to be further studied