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Crop production security is an essential guarantee for the prosperity of country and the well‐being of people. In the context of frequent occurrence of extreme weather events, how to comprehensively evaluate the risk of multiple hazards and quantitatively describe spatial differences will be one of the significant topics in agricultural disaster mitigation. In this study, based on the gridded meteorological data and other datasets (crop, socioeconomic, disaster loss, and actual planting location) from 2000 to 2020, multihazard risk model for different growth period was built to assess the comprehensive risk of winter wheat in the upper Huaihe River basin (UHRB), China. The conclusions were as follows: (a) The spatial distribution of continuous rain risk increased gradually from north to south; the area of the highest lodging risk was located in the east; the spatial trend of drought risk was the highest in the south and the lowest in the northeast. (b) The weights of continuous rain, lodging, and drought disasters were 41, 31, and 28%, respectively, which indicated that winter wheat in UHRB was most affected by continuous rain during the harvest period. (c) The spatial distribution of comprehensive disaster risk assessment showed that the higher risk areas were located in the central and southern parts (Huaibin, Linquan, Luoshan). After verification by historical disasters information, the results are credible, providing a concrete scientific basis for implementing regional agricultural disaster response measures. [ABSTRACT FROM AUTHOR]

Torrential rainfall predisposes hills to catastrophic landslides resulting in serious damage to life and property. Landslide inventory maps are therefore essential for rapid response and developing disaster mitigation strategies. Manual mapping techniques are laborious and time-consuming, and thus not ideal in rapid response situations. Automated landslide mapping from optical satellite imagery using deep neural networks (DNNs) is becoming popular. However, distinguishing landslides from other changed objects in optical imagery using backbone DNNs alone is difficult. Attention modules have been introduced recently into the architecture of DNNs to address this problem by improving the discriminative ability of DNNs and suppressing noisy backgrounds. This study compares two state-of-the-art attention-boosted deep Siamese neural networks in mapping rainfall-induced landslides in the mountainous Himalayan region of Nepal using Planetscope (PS) satellite imagery. Our findings confirm that attention networks improve the performance of DNNs as they can extract more discriminative features. The Siamese Nested U-Net (SNUNet) produced the best and most coherent landslide inventory map among the methods in the test area, achieving an F1-score of 0.73, which is comparable to other similar studies. Our findings demonstrate a prospect for application of the attention-based DNNs in rapid landslide mapping and disaster mitigation not only for rainfall-triggered landslides but also for earthquake-triggered landslides. [ABSTRACT FROM AUTHOR]

Evaluating the accuracy of precipitation products is essential for many applications. The traditional method for evaluation is to calculate error metrics of products with gauge measurements that are considered as ground truth. The multiplicative triple collocation (MTC) method has been demonstrated powerful in error quantification of precipitation products when ground truth is not known. This study applied MTC to evaluate five precipitation products in Germany: two raw satellite-based products (CMORPH and PERSIANN), one reanalysis product (ERA-Interim), one soil moisture–based product (SM2RAIN-ASCAT), and one gauge-based product (REGNIE). Evaluation was performed at the 0.5° daily spatial–temporal scales. MTC involves a log transformation of data, necessitating dealing with zero values in daily precipitation. Effects of 12 different strategies for dealing with zero values on MTC results were investigated. Seven different triplet combinations were tested to evaluate the stability of MTC. Results showed that different strategies for replacing zero values had considerable effects on MTC-derived error metrics, particularly for root-mean-square error (RMSE). MTC with different triplet combinations generated different error metrics for individual products. The MTC-derived correlation coefficient (CC) was more reliable than RMSE. It is more appropriate to use MTC to compare the relative accuracy of different precipitation products. Based on CC with unknown truth, MTC with different triplet combinations produced the same ranking of products as the traditional method. A comparison of results from MTC and the classic TC with additive error model showed the potential limitation of MTC in arid areas or dry time periods with a large ratio of zero daily precipitation. [ABSTRACT FROM AUTHOR]

Precipitation in arid mountainous areas is characterized by low rainfall intensity and large spatial heterogeneity, which challenges satellite-based monitoring by the spaceborne sensors. This study aims to comparatively evaluate the detection ability of spatiotemporal patterns and extremes of rainfall by a range of mainstream satellite precipitation products [TMPA, Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and PERSIANN–Climate Data Record (PERSIANN-CDR)] over a typical arid mountainous basin of China, benchmarking against rain gauge data from 2000 to 2015. Results showed that satellite precipitation estimates had relatively low accuracy at the daily scale, while a significant improvement of correlation coefficient (CC; >0.6) and a significant reduction of relative root-mean-square error (RRMSE; <1.0) were found as time scale increases beyond the monthly scale. CHIRPS tended to overestimate the gauge precipitation with positive relative bias (RB), while the negative RB values for TMPA and PERSIANN-CDR indicated there was an underestimation. CHIRPS had the most similar spatial pattern and slope trends of the seasonal precipitation and interannual variations of annual precipitation with gauge observations. With the increase in rainfall rates, the probability of detection (POD) and critical success index (CSI) were reduced and the false alarm ratio (FAR) was increased significantly, demonstrating the limited capability for all the three satellite products for detecting heavy rainfall events. CHIRPS showed the best performance in detecting rainfall extremes compared to TMPA and PERSIANN-CDR, evidenced by the larger CSI values and similar extreme rainfall indices obtained from gauge records. This study provides valuable guidance for choosing satellite precipitation products instead of gauge observations for rainfall monitoring (especially rainfall extremes) and agricultural production management over arid mountainous area. [ABSTRACT FROM AUTHOR]

Spatial and temporal patterns of trends in annual and seasonal precipitation over Germany during 1951–2013 were analysed using the ensemble empirical mode decomposition (EEMD) method. Three widely used and recognized high‐resolution observation‐based gridded precipitation products, the Climatic Research Unit (CRU) time‐series data, Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis data and the EU‐FP6 project ENSEMBLES derived data set (EOBS), were used and compared. Comparison among different products showed that the CRU used a considerably lower number of gauge stations (maximum 36) in the construction of gridded product over Germany and should be used with great caution for other applications, and overall CRU presented less precipitation and smaller inter‐annual variability; GPCC and EOBS agreed well in the spatial and temporal distribution of seasonal and annual precipitation, as well as the identified trends. The spatiotemporal analysis of trends showed that trends in precipitation during 1951–2013 were, in most cases, highly nonlinear and varying over time. In particular, the spring, summer and autumn precipitation showed large variations in trends. Therefore, the assumption of linear trends on which previous studies were based is invalid. Annual precipitation showed increasing trends in over 66% of Germany (particularly northern and eastern Germany), and the magnitude of increasing trends were generally enhanced over time, e.g. GPCC showed that the average magnitude of increasing trends rose from 6.3% for the 1951–1980 period to 12% for the 1951–2013 period. There were considerable spatial and temporal variabilities in trends in seasonal precipitation totals over Germany. This study is among one of the first studies applying the EEMD method to a comprehensive analysis of time‐varying trends in precipitation over Germany, which is expected to improve our understanding of the complex and nonlinear regional climate system. Spatial and temporal patterns of the EEMD‐derived trend of the four seasonal precipitation totals (DJF, MAM, JJA, SON; the trend is presented as the percentage of the climatology mean during the whole period 1951–2013 for easy interpretation and comparison) from EOBS for five different periods, with ending years of 1980, 1990, 2000, 2010 and 2013. [ABSTRACT FROM AUTHOR]

Northeast China, the most important region for commercial grain farming in China, is vulnerable to drought due to high fluctuation in monthly rainfall. Timely, accurate and effective drought monitoring is very essential for securing the output of grain farming. In this study, three widely used drought indices were compared using satellite soil moisture data and their capability for drought monitoring was evaluated in Northeast China. Three indices are Normalized Monthly Precipitation Anomaly Percentage (NPA), Vegetation Health Index (VHI) and Normalized Vegetation Supply Water Index (NVSWI). In order to know the relationship between rainfall and drought especially over different local climate zones and give a more detail strategy for crop irrigation, a time-lag relationship is investigated based on the TRMM 3B43 monthly precipitation and NVSWI. Tendency rate (slope) was used to characterize the change of drought events in the region for the period 2001–2014. Results showed that: (1) the three selected indices were suitable for drought monitoring, with NVSWI performing the best in terms of the highest correlation with soil moisture, (2) an obvious time-lag was observed between rainfall and drought with the time lag being one month for all three climate zones, (3) drought occurred more frequently in spring and winter, while in summer drought occurred more easily in the west than in the east in Northeast China, (4) overall the frequency of drought was decreasing from 2001 to 2014 in Northeast China. [ABSTRACT FROM AUTHOR]

The increasing competition for water resources requires a better understanding of flows, fluxes, stocks, and the services and benefits related to water consumption. This paper explains how public domain Earth Observation data based on Moderate Resolution Imaging Spectroradiometer (MODIS), Second Generation Meteosat (MSG), Tropical Rainfall Measurement Mission (TRMM) and various altimeter measurements can be used to estimate net water production (rainfall (P) > evapotranspiration (ET)) and net water consumption (ET > P) of Nile Basin agro-ecosystems. Rainfall data from TRMM and the Famine Early Warning System Network (FEWS-NET) RainFall Estimates (RFE) products were used in conjunction with actual evapotranspiration from the Operational Simplified Surface Energy Balance (SSEBop) and ETLook models. Water flows laterally between net water production and net water consumption areas as a result of runoff and withdrawals. This lateral flow between the 15 sub-basins of the Nile was estimated, and partitioned into stream flow and non-stream flow using the discharge data. A series of essential water metrics necessary for successful integrated water management are explained and computed. Net water withdrawal estimates (natural and humanly instigated) were assumed to be the difference between net rainfall (Pnet) and actual evapotranspiration (ET) and some first estimates of withdrawals-without flow meters-are provided. Groundwater-dependent ecosystems withdraw large volumes of groundwater, which exceed water withdrawals for the irrigation sector. There is a strong need for the development of more open-access Earth Observation databases, especially for information related to actual ET. The fluxes, flows and storage changes presented form the basis for a global framework to describe monthly and annual water accounts in ungauged river basins. [ABSTRACT FROM AUTHOR]

The grasslands in arid and semi-arid regions rely heavily on the use of rain, thus, improving rain use efficiency (RUE) is essential for securing sustainable development of grassland ecosystems in these areas with limited rainfall. In this study, the spatial and temporal variabilities of RUE for grassland ecosystems over Northwestern China during 1982–2013 were analyzed using the normalized difference vegetation index (NDVI) and precipitation data. Results showed that: (1) Although grassland area has decreased gradually over the past 30 years, the NDVI in most areas showed that the vegetation was gradually restored; (2) The trends of RUE increased in the east of Northwestern China and decreased in the west of Northwestern China. However, the trends of RUE for the high-coverage grasslands (vs. low-coverage grassland) increased (decreased) significantly over the past 30 years. (3) The RUE for the grasslands was positively correlated with air temperature, while it was negatively correlated with the change of annual mean precipitation in northwestern China. Moreover, the obvious RUE increasing trends were found in the vegetation restoration areas, while the RUE decreasing trends appeared in the vegetation degradation areas. This study will be helpful for understanding the impacts of climate change on securing the sustainable development of grassland ecosystems in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

• A novel algorithm named SM2RAIN-BayesOpt is proposed for estimating rainfall by integrating the SM2RAIN algorithm and Bayesian optimization. • The performance of the SM2RAIN-BayesOpt algorithm is evaluated against rain gauge data in three typical areas. • The SM2RAIN-BayesOpt rainfall estimates is compared with IMERG-V06B, and ERA5 reanalysis rainfall products. • Effects of soil freezing on SM2RAIN-BayesOpt rainfall estimation are investigated. • Effects of land cover on calibration parameters of SM2RAIN-BayesOpt are inspected. The rainfall product derived from the SM2RAIN (Soil Moisture to Rain) algorithm has been widely used. However, there is still a large uncertainty partly due to the soil moisture input and parameters estimation of the SM2RAIN algorithm, which limits the application of the model in alpine regions. Here, the SM2RAIN-BayesOpt algorithm was developed by integrating the SM2RAIN algorithm and Bayesian optimization to improve the estimation of parameters (Z, a, b, T base , T pot), subsequently incorporating SMAP Level-3 soil moisture products for rainfall estimation. The performance of the SM2RAIN-BayesOpt algorithm was evaluated based on observed rainfall data under different environmental conditions in three typical alpine regions, namely Tibetan Plateau, Heihe River Basin, and Shandian River Basin. Moreover, SM2RAIN-BayesOpt, IMERG-V06B, and ERA5 reanalysis rainfall estimates were also compared with in-situ rainfall observations. The results showed that the proposed SM2RAIN-BayesOpt algorithm can obtain more accurate rainfall estimates in all studied areas in terms of different evaluation metrics. It was also found that our proposed SM2RAIN-BayesOpt algorithm performs better in alpine meadows and grassland than in desert and forestland. SM2RAIN-BayesOpt algorithm can considerably improve the accuracy of rainfall estimation, and it is of significant value for rainfall monitoring in alpine regions where observational data are scarce. [ABSTRACT FROM AUTHOR]

This study focused on improving the performance of the near real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Early Run (IMERG-E) product based on a newly developed bias-correction scheme, LSCDF. The LSCDF was established by integrating the mean-based Linear Scaling (LS) and quantile-mapping Cumulative Distribution Function (CDF) matching approaches. The daily updating gauge-based precipitation data from the Climate Prediction Center (CPC) unified analysis were used as the benchmark for bias correction. The IMERG-E bias-corrected precipitation data were evaluated against the daily ground measurements from 807 meteorological stations across mainland China and compared with the raw IMERG-E and IMERG Final Run (IMERG-F; research-level with gauge calibration) retrievals. Evaluation results for the period 2015 to 2017 showed that the LSCDF method effectively improves near real-time IMERG-E precipitation estimates at the daily scale. The bias-corrected IMERG-E precipitation estimates were in significantly better agreement with ground measurements than the original IMERG-E at the point-daily resolutions, with the correlation coefficient increasing from 0.66 to 0.77, relative bias decreasing from 11.1% to −3.1%, and root-mean-square error dropping from 6 mm/day to 4.39 mm/day. In addition, the bias-corrected IMERG-E was apparently superior to IMERG-E in detecting precipitation events at various intensity levels including small, light, moderate, and heavy precipitation. Although IMERG-E tended to significantly underestimate or overestimate precipitation during three typical typhoons, the bias-corrected IMERG-E can match the rainfall spatial distributions well, demonstrating a considerable capability in capturing the extreme precipitation. Generally, the bias-corrected IMERG-E featured a substantial improvement over the original IMERG-E, and it even exhibited a more satisfactory overall performance than the research-level gauge-calibrated IMERG-F product. Our study demonstrates that the bias-correction method LSCDF can improve the accuracy of satellite precipitation products to benefit the near real-time application of satellite products for natural hazards. • The LSCDF method was developed by combining linear scaling (LS) and cumulative distribution function (CDF) matching. • LSCDF was used for bias correction of daily satellite precipitation data (IMERG-E). • The LSCDF significantly improved the accuracy of satellite precipitation estimates. • The bias-correction method, LSCDF, can be used in near real-time applications. [ABSTRACT FROM AUTHOR]