Abstract Carbonates are widespread occurrences in various environmental conditions. Particularly, microbial induced and/or controlled carbonate formations are considered significant archives for recording critical environmental conditions that can be used not only to reconstruct paleo-environments, but also to gain valuable information about evidence of life in environments. Therefore, deciphering carbonate precipitation mechanisms, and the factors controlling these processes, particularly in extreme environments, hold significant insights for industrial applications, and for searching life in extra-terrestrial environments. In the scope of these contexts, we tried to model carbonate precipitation mechanism and possible biological influences in a hypersaline lake, known as an extreme environment in Turkey, in terms of its water chemistry. The aim of this research is to model CaCO3 precipitation in the Acigol Lake, using water quality data from the field, and meteorological data obtained from the Turkish State Meteorological Service for the years 2013 and 2015. To achieve this, AQUATOX model is utilized. The model is calibrated for nine stations of the lake using parameters related to water quality, site characteristics, inflow loadings and lake hydrodynamics. Calibration dataset is obtained from the field measurements and meteorological data, for the year 2013. Model validation is conducted both using experimental data, and field observations, obtained in August, 2015. An integrated modeling approach is undertaken by coupling a geochemical reaction model and a process-based ecosystem model, AQUATOX; using field, experimental and meteorological data. Model results suggest that the amount of CaCO3 precipitation in the system ranges from 35.16 to 128.48 mg L−1 d–1. Temperature, photosynthesis rate, depth and pH are found to be the most significant variables that govern the biogeochemical processes responsible for CaCO3 precipitation. The model showed a strong relationship between calcite precipitation and certain cyanobacteria species such as Anabaena and Aphanizomenon sp. Results also indicate that the model was successful in representing the relationships between calcite precipitation variations, and phosphate and pH. The Nash-Sutcliffe model efficiency coefficient and NRMSE are found to be 0.93 and 0.29, respectively.

中文翻译：

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高盐度环境中碳酸钙沉淀的建模方法：来自浅水碱性湖泊的案例研究

摘要 碳酸盐广泛存在于各种环境条件下。特别是，微生物诱导和/或控制的碳酸盐岩地层被认为是记录关键环境条件的重要档案，不仅可以用于重建古环境，还可以获取有关环境中生命证据的宝贵信息。因此，解读碳酸盐沉淀机制以及控制这些过程的因素，尤其是在极端环境中，对于工业应用和在外星环境中寻找生命具有重要意义。在这些背景下，我们试图根据水化学对超咸水湖中的碳酸盐沉淀机制和可能的生物影响进行建模，该湖在土耳其被称为极端环境。本研究的目的是使用现场水质数据和土耳其国家气象局 2013 年和 2015 年的气象数据模拟 Acigol 湖中的 CaCO3 降水。为此，使用了 AQUATOX 模型。该模型使用与水质、场地特征、流入载荷和湖泊水动力学相关的参数针对湖泊的九个站点进行校准。定标数据集是从 2013 年的现场测量和气象数据中获得的。模型验证是使用实验数据和 2015 年 8 月获得的现场观测数据进行的。综合建模方法是通过耦合地球化学反应模型和基于过程的生态系统模型，AQUATOX；使用现场、实验和气象数据。模型结果表明系统中的 CaCO3 沉淀量范围为 35.16 至 128.48 mg L-1 d-1。发现温度、光合作用速率、深度和 pH 值是控制负责 CaCO3 沉淀的生物地球化学过程的最重要变量。该模型显示方解石沉淀与某些蓝藻物种（如鱼腥藻和 Aphanizomenon sp.）之间存在密切关系。结果还表明，该模型成功地表示了方解石沉淀变化与磷酸盐和 pH 值之间的关系。发现 Nash-Sutcliffe 模型效率系数和 NRMSE 分别为 0.93 和 0.29。发现深度和 pH 值是控制负责 CaCO3 沉淀的生物地球化学过程的最重要变量。该模型显示方解石沉淀与某些蓝藻物种（如鱼腥藻和 Aphanizomenon sp.）之间存在密切关系。结果还表明，该模型成功地表示了方解石沉淀变化与磷酸盐和 pH 值之间的关系。发现 Nash-Sutcliffe 模型效率系数和 NRMSE 分别为 0.93 和 0.29。发现深度和 pH 值是控制负责 CaCO3 沉淀的生物地球化学过程的最重要变量。该模型显示方解石沉淀与某些蓝藻物种（如鱼腥藻和 Aphanizomenon sp.）之间存在密切关系。结果还表明，该模型成功地表示了方解石沉淀变化与磷酸盐和 pH 值之间的关系。发现 Nash-Sutcliffe 模型效率系数和 NRMSE 分别为 0.93 和 0.29。结果还表明，该模型成功地表示了方解石沉淀变化与磷酸盐和 pH 值之间的关系。发现 Nash-Sutcliffe 模型效率系数和 NRMSE 分别为 0.93 和 0.29。结果还表明，该模型成功地表示了方解石沉淀变化与磷酸盐和 pH 值之间的关系。发现 Nash-Sutcliffe 模型效率系数和 NRMSE 分别为 0.93 和 0.29。