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The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin'anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models. [ABSTRACT FROM AUTHOR]

Abstract: Land-cover change significantly influences hydrologic processes at the watershed level. The mountainous Duoyingping watershed in upstream Yangtze River, China, has undergone dramatic land-cover change in the past three decades. It is likely to maintain this trend in the future, inevitably altering hydrologic processes in the region to a certain degree. Therefore, the effects of land-cover change on runoff, evapotranspiration (ET), and soil moisture in the watershed were assessed using a large-scale Variable Infiltration Capacity (VIC) hydrologic model. To minimize the effect of climate change on simulation results, we used detrended climate data over the period 1980–2005 in forcing the VIC model. The dynamics in the spatial distribution of land-cover types in the Duoyingping watershed from 1980 to 2000 were first examined, revealing that reforestation and deforestation were the major change patterns. On the basis of various land-use policies, potential land-cover scenarios for 2030 were established using an integrated land-use change model (CLUE-S). The scenarios were developed using 2000 land-use data as bases. Finally, the calibrated VIC model was applied in the scenarios to assess land-cover effects on hydrology. Hydrologic simulations showed that the effects of historical land-cover change on hydrology were discernible in the sub-watersheds of Nanba, Yingjing, and Yuxi. The annual ET was projected to decrease by 0.8–22.3% because of deforestation, and increase by 2.3–27.4% because of shrubland–forest conversion. Different potential land-cover scenarios play various roles in the effect on hydrology because of various land-use policies. In the scenario concerning forest protection policy, annual ET increased by more than 15%, while annual runoff decreased by 6%. However, a negligible effect on hydrology was found under the scenario involving cropland expansion. On the basis of the results, it is concluded that ET is more sensitive to land-cover change than are other hydrologic components. Hydrologic alteration caused by reforestation and deforestation during the dry season was more significant than that during wet season. Generally, the proposed approach in the study can be a useful means of assessing hydrologic responses to land-cover change. [Copyright &y& Elsevier]

Drought is a complex natural disaster phenomenon. It is of great significance to analyze the occurrence and development of drought events for drought prevention. In this study, two drought characteristic variables (the drought duration and severity) were extracted by using the Theory of Runs based on four drought indexes (i.e., the percentage of precipitation anomaly, the standardized precipitation index, the standardized precipitation evapotranspiration index and the improved comprehensive meteorological drought index). The joint distribution model of drought characteristic variables was built based on four types of Archimedean copulas. The joint cumulative probability and the joint return period of drought events were analyzed and the relationship between the drought characteristics and the actual crop drought reduction area was also studied. The results showed that: (1) The area of the slight drought and the extreme drought were both the zonal increasing distribution from northeast to southwest in Yunnan Province from 1960 to 2015. The area of the high frequency middle drought was mainly distributed in Huize and Zhanyi in Northeast Yunnan, Kunming in Central Yunnan and some areas of Southwest Yunnan, whereas the severe drought was mainly occurred in Deqin, Gongshan and Zhongdian in Northwest Yunnan; (2) The drought duration and severity were fitted the Weibull and Gamma distribution, respectively and the Frank copula function was the optimal joint distribution function. The Drought events were mostly short duration and high severity, long duration and low severity and short duration and low severity. The joint cumulative probability and joint return period were increased with the increase of drought duration and severity; (3) The error range between the theoretical return period and the actual was 0.1-0.4 a. The year of the agricultural disaster can be accurately reflected by the combined return period in Yunnan Province. The research can provide guidelines for the agricultural management in the drought area., Competing Interests: The authors declare no conflicts of interest.