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

气候和土地利用的变化对热带地区的水生物地球化学过程有重大影响。然而，尤其是热带非洲，有关全球变化可能影响的科学知识是有限的。然而，这些知识对于水资源的可持续管理是必不可少的。在该项目中，肯尼亚西部茂林区的四个不同土地用途的集水区(茶园和树木种植园、小农农业、天然山地雨林)继续实施既定的监测方案。自2014年以来，自动测量系统几乎无间隙地以10分钟的分辨率记录水位(通过额定曲线转换为流量)以及N03、DOC和浊度(通过额定曲线转换为悬浮沉积物)的浓度。此外，每周都会测量水中稳定同位素的浓度。虽然在第一个项目阶段建立了测量系统，并获得了关于土地利用与水量/水质之间关系的基本知识，但第二个项目阶段的目的是更好地了解基本的水生物地质化学过程，并预测与气候和土地利用变化有关的水通量(数量和质量)。为此计划了三个工作包(WP)。在WP1中，将执行测量方案和必要的维护措施。在项目结束时，将开放获取上述参数的10年数据集。WP2侧重于通过统计方法识别过程，并使用小波函数分析系统的时间模式(日变化、季节影响)。自动分析浓度-排放动力学的滞后环将有助于识别水及其溶质的传输和动员过程。此外，将把已确立的“水文特征”概念用于开发“水文生物地质化学特征”。这将能够比较地描述河流的水化学特征，并描述它们的水-生物地球化学过程行为。在WP3中，将使用深度学习开发基于数据的模型来模拟径流和水质参数。将使用最新的长期短期记忆(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)预测因素。为了验证模型，WP2中根据野外测量计算的小波、滞后环和水文生物地球化学特征将与基于LSTM模型计算的结果进行比较。最后，利用LSTM模型的时空预测因子，对气候和土地利用变化进行预测。为此目的，将使用CORDEX对区域气候模型的最新模拟和基于多时期土地利用分类的内部开发的土地利用情景。

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.