Global change impact on hydro-biogeochemical processes in tropical Kenyan catchments

气候和土地利用变化对热带地区的水文地球化学过程有重要影响。然而，特别是对热带非洲而言，关于全球变化可能产生的影响的科学知识有限。然而，这种知识对于水资源的可持续管理至关重要。在该项目中，在肯尼亚西部Mau森林综合体的四个土地用途不同的集水区（茶叶和树木种植园、小农农业、天然山地雨林）继续实施一项既定的监测方案。自2014年以来，自动测量系统通过紫外光谱法以10分钟的分辨率几乎无间隙地记录水位（通过额定曲线转换为排放量）以及NO3，DOC和浊度的浓度（通过额定曲线转换为悬浮沉积物）。此外，每周测量水的稳定同位素浓度。项目第一阶段建立了测量系统，并获得了关于土地使用与水量/水质之间关系的基本知识，而项目第二阶段的目标是更好地了解基本的水文地球化学过程，并预测与气候和土地使用变化有关的水通量（数量和质量）。为此目的，计划了三个工作包。在WP 1中，将执行测量计划和必要的维护措施。在项目结束时，上述参数的10年数据集将开放获取。WP 2侧重于通过统计方法和使用小波函数分析系统时间模式（日变化，季节影响）来识别过程。浓度-排放动力学滞后回线的自动分析将有助于确定水及其溶质的运输和动员过程。此外，“水文特征”的既定概念将被转移到发展“水文地球化学特征”。这将允许比较性地描述河流的水化学，并描述其水文地球化学过程行为。在WP 3中，将使用深度学习开发基于数据的模型，以模拟径流和水质参数。将使用最新的长短期记忆（LSTM）方法，该方法还考虑了空间（土地利用）和时间（气候时间序列）预测因子。为了进行模型验证，WP 2中基于现场测量计算的小波、磁滞回线和水文地球化学特征将与基于LSTM模型计算的结果进行比较。最后，使用带有时空预测器的LSTM模型，将对气候和土地利用变化进行预测。为此，将使用区域气候模型的最新CORDEX模拟和内部根据多时相土地利用分类制定的土地利用设想方案。

Climate and land use change have a significant impact on hydrobiogeochemical processes in the tropics. For tropical Africa in particular, however, scientific knowledge about the possible impacts of global change is limited. Nevertheless, this knowledge is essential for sustainable management of water resources. In this project, an established monitoring programme is being continued in four catchments with different land uses (tea and tree plantations, smallholder agriculture, natural montane rainforest) in the Mau Forest complex in western Kenya. Since 2014, automatic measuring systems have been recording almost gap-free in 10-minute resolution the water level (converted to discharge via rating curves) as well as the concentrations of NO3, DOC and turbidity (converted to suspended sediments via rating curves) by means of UV spectrometry. In addition, the concentrations of stable isotopes of water are measured weekly. While the measuring systems were established in the first project phase and basic knowledge about the relationship between land use and water quantity/quality was gained, the second project phase aims at an improved understanding of the underlying hydrobiogeochemical process and a projection of water fluxes (quantity and quality) with regard to climate and land use change. Three work packages (WP) are planned for this purpose. In WP1, the measurement programme and the necessary maintenance measures will be carried out. At the end of the project, a 10-year data set of the above parameters will be made available open access. WP2 focuses on process identification by means of statistical methods and analyses of systematic temporal patterns (diurnal variations, seasonal influences) using wavelet functions. Automated analyses of hysteresis loops of concentration-discharge dynamics will help to identify transport and mobilisation processes of water and its solutes. In addition, the established concept of "hydrological signatures" will be transferred to develop "hydro-biogeochemical signatures". This will allow to comparatively characterise the hydrochemistry of streams and describe their hydro-biogeochemical process behaviour. In WP3, data-based models will be developed using Deep Learning to simulate both runoff and water quality parameters. The latest Long Short-Term Memory (LSTM) methods will be used, which also take into account spatial (land use) and temporal (climate time series) predictors. For model validation, the wavelets, hysteresis loops and hydro-biogeochemical signatures calculated in WP2 on the basis of field measurements will be compared with those calculated on the basis of the LSTM models. Finally, using the LSTM models with spatio-temporal predictors, projections of climate and land use change will be made. For this purpose, the latest CORDEX simulations of regional climate models and in-house developed land use scenarios based on multitemporal land use classifications will be used.