Against the backdrop of global warming, the increasing spatiotemporal variability in precipitation patterns has intensified the frequency and risk of dry-wet abrupt alternation (DWAA) events in semi-arid regions. This study investigates the Hailar River Basin in northern China (1980-2019) and develops the Soil Moisture Concentration Index (SMCI) using daily soil moisture (SM) data simulated by the VIC hydrological model. A high-resolution temporal framework is introduced to detect DWAA events and evaluate the impact of precipitation pattern variations on dry-wet transitions in the basin. The results indicate: (1) Annual precipitation in the basin has significantly increased (0.47 mm y(-1) in the south, P < 0.05), while precipitation intensity follows a gradient pattern, increasing in the upstream (3.65 mm d1 y1) and decreasing in the downstream (-2.34 mm y(-1)). Additionally, the number of dry days and short-duration, high-intensity precipitation events has risen; (2) Soil moisture (SM) data simulated by the VIC model effectively capture DWAA events, showing significantly higher | SMCI| values downstream than upstream (P < 0.05) and indicating more intense dry-wet transitions in the downstream region. Furthermore, 78 % of the area exhibits an increasing trend in |SMCI|(1980-2019), with dry-to-wet transition events occurring more frequently than wet-to-dry events. For instance, in 2013, the maximum coverage area reached 48 % in a single day; (3) The random forest model highlights the spatial heterogeneity of DWAA driving factors: upstream water yield is the dominant factor, whereas downstream variations are closely associated with precipitation intensity (R-2 = 0.76) and the frequency of heavy rainfall days. Permafrost degradation and land use changes further heighten hydrological sensitivity in the downstream region. This study offers a transferable methodological framework for understanding extreme hydrological events and reveals that the driving mechanisms of DWAA are spatially heterogeneous, shifting from being dominated by terrestrial factors in the headwaters to meteorological factors downstream-a finding with significant implications for water resource management in other large, heterogeneous semi-arid basins.

Climate change still adversely affects agriculture in the sub-Saharan Africa. There is need to strengthen early action to bolster livelihoods and food security. Most governments use pre- and post-harvest field surveys to capture statistics for National Food Balance Sheets (NFBS) key in food policy and economic planning. These surveys, though accurate, are costly, time consuming, and may not offer rapid yield estimates to support governments, emergency organizations, and related stakeholders to take advanced strategic decisions in the face of climate change. To help governments in Kenya (KEN), Zambia (ZMB), and Malawi (MWI) adopt digitally advanced maize yield forecasts, we developed a hybrid model based on the Regional Hydrologic Extremes Assessment System (RHEAS) and machine learning. The framework is set-up to use weather data (precipitation, temperature, and wind), simulations from RHEAS model (soil total moisture, soil temperature, solar radiation, surface temperature, net transpiration from vegetation, net evapotranspiration, and root zone soil moisture), simulations from DSSAT (leaf area index and water stress), and MODIS vegetation indices. Random Forest (RF) machine learning model emerged as the best hybrid setup for unit maize yield forecasts per administrative boundary scoring the lowest unbiased Root Mean Square Error (RMSE) of 0.16 MT/ha, 0.18 MT/ha, and 0.20 MT/ha in Malawi's Karonga district, Kenya's Homa Bay county, and Zambia's Senanga district respectively. According to relative RMSE, RF outperformed other hybrid models attaining the lowest score in all countries (ZMB: 25.96%, MWI: 28.97%, and KEN: 27.54%) followed by support vector machines (ZMB: 26.92%, MWI: 31.14%, and KEN: 29.50%), and linear regression (ZMB: 29.44%, MWI: 31.76%, and KEN: 47.00%). Lastly, the integration of VI and RHEAS information using hybrid models improved yield prediction. This information is useful for NFBS bulletins forecasts, design and certification of maize insurance contracts, and estimation of loss and damage in the advent of climate justice.

为了探讨VIC(Variable infiltration capacity)水文模型在季节性冻土区水文模拟中的适用性，以大凌河复兴堡站以上流域为研究区，构建了考虑能量平衡模式的VIC大尺度水文模型，评价了VIC模型在东北季节性冻土区水文模拟的适用性，并对不考虑能量平衡模式的水文模拟进行了比较分析。结果表明，考虑能量平衡模式的VIC模型率定期和验证期径流模拟效率系数在0.63以上，相对误差在6.0%以内。与不考虑能量平衡模式的水文过程模拟差异性比较显示，考虑了能量平衡模式的VIC模型可以更好地刻画由于冻土冻融过程引起的径流变化特征，模拟的土壤含水量和蒸散发量的空间分布特征更加合理。

为了探讨VIC(Variable infiltration capacity)水文模型在季节性冻土区水文模拟中的适用性，以大凌河复兴堡站以上流域为研究区，构建了考虑能量平衡模式的VIC大尺度水文模型，评价了VIC模型在东北季节性冻土区水文模拟的适用性，并对不考虑能量平衡模式的水文模拟进行了比较分析。结果表明，考虑能量平衡模式的VIC模型率定期和验证期径流模拟效率系数在0.63以上，相对误差在6.0%以内。与不考虑能量平衡模式的水文过程模拟差异性比较显示，考虑了能量平衡模式的VIC模型可以更好地刻画由于冻土冻融过程引起的径流变化特征，模拟的土壤含水量和蒸散发量的空间分布特征更加合理。

为了探讨VIC(Variable infiltration capacity)水文模型在季节性冻土区水文模拟中的适用性，以大凌河复兴堡站以上流域为研究区，构建了考虑能量平衡模式的VIC大尺度水文模型，评价了VIC模型在东北季节性冻土区水文模拟的适用性，并对不考虑能量平衡模式的水文模拟进行了比较分析。结果表明，考虑能量平衡模式的VIC模型率定期和验证期径流模拟效率系数在0.63以上，相对误差在6.0%以内。与不考虑能量平衡模式的水文过程模拟差异性比较显示，考虑了能量平衡模式的VIC模型可以更好地刻画由于冻土冻融过程引起的径流变化特征，模拟的土壤含水量和蒸散发量的空间分布特征更加合理。

Future changes of pan-Arctic land-atmospheric methane (CH4) and carbon dioxide (CO2) depend on how terrestrial ecosystems respond to warming climate. Here, we used a coupled hydrology-biogeochemistry model to make our estimates of these carbon exchanges with two contrasting climate change scenarios (no-policy versus policy) over the 21st century, by considering (1) a detailed water table dynamics and (2) a permafrost-thawing effect. Our simulations indicate that, under present climate conditions, pan-Arctic terrestrial ecosystems act as a net greenhouse gas (GHG) sink of -0.2 Pg CO2-eq.yr(-1), as a result of a CH4 source (53 Tg CH4 yr(-1)) and a CO2 sink (-0.4 Pg C yr(-1)). In response to warming climate, both CH4 emissions and CO2 uptakes are projected to increase over the century, but the increasing rates largely depend on the climate change scenario. Under the non-policy scenario, the CH4 source and CO2 sink are projected to increase by 60% and 75% by 2100, respectively, while the GHG sink does not show a significant trend. Thawing permafrost has a small effect on GHG sink under the policy scenario; however, under the no-policy scenario, about two thirds of the accumulated GHG sink over the 21st century has been offset by the carbon losses as CH4 and CO2 from thawing permafrost. Over the century, nearly all CO2-induced GHG sink through photosynthesis has been undone by CH4-induced GHG source. This study indicates that increasing active layer depth significantly affects soil carbon decomposition in response to future climate change. The methane emissions considering more detailed water table dynamics continuously play an important role in affecting regional radiative forcing in the pan-Arctic.