流域尺度土壤导气率空间分布特征与影响因素分析

采用地理信息系统和地统计学相结合的方法,以泾惠渠灌区麦田土壤导气率空间分布特征为基础进行相关研究。结果表明:地统计学方法较好地模拟了土壤导气率空间变异特征,反映出该地区土壤气体动力学参数具有较强的空间变异特征;容重、饱和含水率、饱和度与土壤导气率的空间自相关(C/(C0+C))均在0.9以上,说明由自相关部分引起的空间异质性占总空间异质性的程度较大;推荐土壤导气率的采样间距参考值大约为7.5 km;土壤导气率与容重的Pearson相关系数绝对值为0.595,两者显著相关;土壤导气率与饱和度的Pearson相关系数绝对值达0.959,两者高度相关;交互相关关系说明容重、饱和度是影响土壤导气率的主成因素,在95%的置信水平上,容重、饱和度对土壤导气率的影响范围均为-20~20 km。     

太湖竺山湾小流域果园养分投入特征及其土壤肥力状况分析

以太湖竺山湾周边5个镇为研究区域,采取资料调研与实地调查相结合的方法分析果园养分投入特征,并采集典型果园土壤样本评价土壤肥力状况。结果表明,太湖流域果园总体氮、磷、钾平均投入量分别为N522、P2O5674、K2O 462 kg·hm-2,其中来自于有机肥的比例分别为51.3%、58.3%和44.0%。化学肥主要为氮、磷、钾三元复合肥,有机肥以鸡粪和牛粪为主。土壤有机质、全氮、全磷和全钾含量均处于适宜至很丰富水平,土壤速效磷和速效钾基本上处于较丰富状态,氮、磷、钾养分平均盈余量分别为N 320、P2O5426、K2O 108 kg·hm-2。太湖流域果园氮、磷的高投入不可避免地加大了氮、磷向水体迁移的风险,因而亟待研发有效的果园节氮控磷技术。     

白洋淀流域复合污染的生物膜监测方法适用性分析

生物膜群落结构和功能的变化可以反映复合污染胁迫在不同水生态系统中群落水平上的生态响应,对于流域水生态监测具有重要意义和广泛应用前景。选取白洋淀流域典型生态单元水库、府河和白洋淀为研究区,采用活性炭纤维为基质的原位生物膜法,对快速生态监测方法在白洋淀流域不同生态单元的适用性进行了比较研究,并对生物膜与水质指标间的响应关系进行了分析。结果表明:活性炭纤维附着生物膜群落的结构、功能指标与天然基质无显著差异(P0.05),经过15d的培养其结构和功能可以很好的表征各个生态单元监测点位的水环境质量,且生物膜群落对于复合污染具有很好的响应。生物膜群落与超标水质参数RDA分析表明,超标指标可以解释生物膜群落变化的63.0%,8个超标水质参数与生物群落相关性大小依次为:DOTNCODMnCODBOD5EcoliTPNO3-N。生物膜监测可作为水生态快速监测方法在流域尺度不同生态单元广泛使用。     

种植年限对渭北旱塬苹果园土壤孔隙结构及水力特征的影响

为研究不同种植年限对苹果园土壤孔隙结构及其土壤水力特性的影响,采用时空转换的方法,选取渭北旱塬2 a、13 a及33 a苹果园开展土壤结构与水力特征测定,利用压汞法获取原状土壤孔隙结构特征。以20 cm及40 cm为界将果园0～100 cm土壤划分为耕作表土扰动层、潜在犁底层与心土层。结果表明：渭北旱塬苹果园20～40 cm土壤容重较高、导水能力差且水分对作物有效性较低,有形成犁底层的可能,且随植果年龄增加果园土壤容重呈增加趋势;同一果园中大孔隙（>75 μm）与中孔隙(30～75 μm)土壤百分含量最大值出现在表土层,占比分别为7.63%~10.32%及10.94%~13.14%;微孔隙(5～30 μm)土壤最大百分含量出现在心土层,占比为30.60%~47.85%;极微孔隙(0.1～5 μm)与超微孔隙(<0.1 μm)土壤含量最高值出现在潜在犁底层,占比分别达37.36%~52.55%及13.15%~19.08%。在频繁受到耕作扰动的表土层,2 a、13 a及33 a果园土壤之间各级孔隙占比非常接近;在不易受到耕作扰动的心土层,大孔隙与中孔隙土壤都表现出随耕作种植年限的增加而增加的趋势。土壤容重与其大孔隙含量呈显著负相关,与超微、极微孔隙土壤含量呈正相关,饱和导水率与容重呈显著负相关;5～30 μm微孔在土壤的导水及持水方面均有重要作用,其比表面积与田间持水量呈显著正相关,其孔体积分数与饱和导水率呈显著正相关;VG模型参数n与土壤大孔隙及中孔隙含量呈显著负相关。随耕作种植年限增加,果园土壤有机质含量每5 a降低0.425 g·kg^-1,田间持水量每5 a降低0.8% cm³·cm^-3。研究建立的土壤水力参数回归预测模型可为苹果园高效用水提供参考。     

Runoff characteristics and its sensitivity to climate factors in the Weihe River Basin from 2006 to 2018

Exploring the current runoff characteristics after the large-scale implementation of the Grain for Green (GFG) project and investigating its sensitivities to potential drivers are crucial for water resource prediction and management. Based on the measured runoff data of 62 hydrological stations in the Weihe River Basin (WRB) from 2006 to 2018, we analyzed the temporal and spatial runoff characteristics in this study. Correlation analysis was used to investigate the relationships between different runoff indicators and climate-related factors. Additionally, an improved Budyko framework was applied to assess the sensitivities of annual runoff to precipitation, potential evaporation, and other factors. The results showed that the daily runoff flow duration curves (FDCs) of all selected hydrological stations fall in three narrow ranges, with the corresponding mean annual runoff spanning approximately 1.50 orders of magnitude, indicating that the runoff of different hydrological stations in the WRB varied greatly. The trend analysis of runoff under different exceedance frequencies showed that the runoff from the south bank of the Weihe River was more affluent and stable than that from the north bank. The runoff was unevenly distributed throughout the year, mainly in the flood season, accounting for more than 50.00% of the annual runoff. However, the trend of annual runoff change was not obvious in most areas. Correlation analysis showed that rare-frequency runoff events were more susceptible to climate factors. In this study, daily runoff under 10%-20% exceeding frequencies, consecutive maximum daily runoff, and low-runoff variability rate had strong correlations with precipitation, aridity index, and average runoff depth on rainy days. In comparison, daily runoff under 50%-99% exceeding frequencies, consecutive minimum daily runoff, and high-runoff variability rate had weak correlations with all selected impact factors. The sensitivity analysis results suggested that the sensitivity of annual runoff to precipitation was always higher than that to potential evaporation. The runoff about 87.10% of the selected hydrological stations were most sensitive to precipitation changes, and 12.90% were most sensitive to other factors. The spatial pattern of the sensitivity analysis indicated that in relatively humid southern areas, runoff was more sensitive to potential evaporation and other factors, and less sensitive to precipitation.     

Implications of future climate change on crop and irrigation water requirements in a semi-arid river basin using CMIP6 GCMs

Agriculture faces risks due to increasing stress from climate change, particularly in semi-arid regions. Lack of understanding of crop water requirement (CWR) and irrigation water requirement (IWR) in a changing climate may result in crop failure and socioeconomic problems that can become detrimental to agriculture-based economies in emerging nations worldwide. Previous research in CWR and IWR has largely focused on large river basins and scenarios from the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Coupled Model Intercomparison Project Phase 5 (CMIP5) to account for the impacts of climate change on crops. Smaller basins, however, are more susceptible to regional climate change, with more significant impacts on crops. This study estimates CWRs and IWRs for five crops (sugarcane, wheat, cotton, sorghum, and soybean) in the Pravara River Basin (area of 6537 km²) of India using outputs from the most recent Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs) under Shared Socio-economic Pathway (SSP)245 and SSP585 scenarios. An increase in mean annual rainfall is projected under both scenarios in the 2050s and 2080s using ten selected CMIP6 GCMs. CWRs for all crops may decline in almost all of the CMIP6 GCMs in the 2050s and 2080s (with the exceptions of ACCESS-CM-2 and ACCESS-ESM-1.5) under SSP245 and SSP585 scenarios. The availability of increasing soil moisture in the root zone due to increasing rainfall and a decrease in the projected maximum temperature may be responsible for this decline in CWR. Similarly, except for soybean and cotton, the projected IWRs for all other three crops under SSP245 and SSP585 scenarios show a decrease or a small increase in the 2050s and 2080s in most CMIP6 GCMs. These findings are important for agricultural researchers and water resource managers to implement long-term crop planning techniques and to reduce the negative impacts of climate change and associated rainfall variability to avert crop failure and agricultural losses.     

川中丘陵区不同治理模式对土壤微团聚体分形特征的影响

探讨川中丘陵区不同小流域治理模式对土壤微团聚体分形特征的影响,可为该区植被恢复与水土流失治理模式提供科学依据。本文通过室内分析,研究了5种不同小流域治理模式下的土壤微团聚体粒径组成、分形维数特征及其与土壤理化性质的关系。1该区土壤微团聚体组成以0.01~0.05 mm粒径为优势粒径,含量在0~10 cm和10~20 cm土层分别达28.63%和28.04%;0.001~0.005 mm粒径为次优势粒径,在0~10 cm和10~20 cm土层含量分别达25.90%和26.33%;各土层不同治理模式下各粒径微团聚体含量差异显著。2该区土壤微团聚体分形维数变化范围为2.643~2.717,不同治理模式土壤微团聚体分形维数呈现水保林甜橙林核桃林裸地坡改梯坡减缓的规律,分形维数与各粒径微团聚体含量呈线性关系。3相关性分析表明,土壤微团聚体分形维数与速效钾、全钾呈正相关,与土壤总孔隙、碱解氮、全氮、有机质呈负相关关系。土壤微团聚体分形维数能较好地反映川中丘陵区土壤的理化结构,是表征该区土壤理化性质的重要指标,林地是该区植被恢复与水土流失治理选择的最优模式。     

艾比湖流域风沙强度特征及其空间差异

以艾比湖流域内6个国家气象站2005—2011年的气象资料为依据,分析了艾比湖流域的起沙风况特征、输沙势(DP)变化及风沙强度的空间差异。结果表明:(1)研究区风向以NW和NNW向为主,其频率分别占54.98%和18.31%。(2)该区起沙风主要发生在4,5和6月,其频率分别占全年的12.48%,14.70%和13.08%。输沙势的季节变化幅度较大,主要集中在春夏两季。(3)研究区合成输沙势(RDP)及合成输沙方向(RDD)分别为988.86VU和126.5°(ESE—SE),属于高风能环境。风向变率指数(RDP/DP)为0.94,属于单风风况特征。(4)研究区风沙强度表现为高值点是以孤点出现,为阿拉山口,而低值区则是一个面积较为广阔的区域。     

土壤冻结期湿度特征研究及其表层模拟

利用EM50数据采集系统,在新疆天山北坡呼图壁县军塘湖河流域采集冻结期和融雪期土壤湿度、土壤温度数据,利用SPSS 19.0,Excel,Surfer 8等软件处理采集到的数据,并对其进行分析、制图。另外利用表层土壤温度模拟土壤湿度变化。结果表明,土壤湿度存在垂直分布规律:冻结期,在土壤层的10,32,48cm处存在极小值;冻结期,土壤湿度日变化较小,融雪期,土壤湿度有显著变化,17:00—19:00时达到土壤湿度变化的峰值;利用表层土壤温度可以很好地模拟土壤湿度,在温度上升和下降阶段均有较好的模拟效果。     

沣河流域土地利用格局与水质变化的关系

以沣河流域为研究对象,综合运用遥感影像解译和相关分析方法分析了沣河流域的土地利用空间格局及主要水质指标[生化需氧量(BOD₅)、高锰酸盐指数(COD_Mn)、氨氮(NH₃-N)含量及溶解氧(DO)]含量的变化特征,探讨了土地利用状况对沣河水质的影响。结果表明,沣河流域土地利用类型以林地和耕地为主,两者占全流域面积的90%以上;沿河不同宽度的缓冲区(分别向两岸划分100,600,1 500 m)范围内,建设用地、耕地与水质变化呈正相关关系,而林地与水质变化呈负相关关系,表明建设用地及耕地对水质的改善有负效应,而河流沿岸林对改善水质有正效应,并且这种影响在距河岸100 m缓冲区范围内达到最大。随着缓冲距离的增大土地利用变化对水质的影响逐渐减弱,说明沣河流域近岸带的土地利用方式对河道水环境质量的影响最大。