Abstract
The Arctic and Boreal Region (ABR) is subject to extensive land cover change (LCC) due to elements such as wildfire, permafrost thaw, and shrubification. The natural and anthropogenic ...ecosystem transitions (i.e. LCC) alter key ecosystem characteristics including land surface temperature (LST), albedo, and evapotranspiration (ET). These biophysical variables are important in controlling surface energy balance, water exchange, and carbon uptake which are important factors influencing the warming trend over the ABR. However, to what extent these variables are sensitive to various LCC in heterogeneous systems such as ABR is still an open question. In this study, we use a novel data-driven approach based on high-resolution land cover data (2003 and 2013) over four million km
2
to estimate the impact of multiple types of ecosystem transitions on LST, albedo, and ET. We also disentangle the contribution of LCC vs. natural variability of the system in changes in biophysical variables. Our results indicate that from 2003 to 2013 about 46% (∼2 million km
2
) of the region experienced LCC, which drove measurable changes to the biophysical environment across ABR over the study period. In almost half of the cases, LCC imposes a change in biophysical variables against the natural variability of the system. For example, in ∼35% of cases, natural variability led to −1.4 ± 0.9 K annual LST reduction, while LCC resulted in a 0.9 ± 0.6 K LST increase, which dampened the decrease in LST due to natural variability. In some cases, the impact of LCC was strong enough to reverse the sign of the overall change. Our results further demonstrate the contrasting sensitivity of biophysical variables to specific LCC. For instance, conversion of sparsely vegetated land to a shrub (i.e. shrubification) significantly decreased annual LST (−2.2 ± 0.1 K); whereas sparsely vegetated land to bare ground increased annual LST (1.6 ± 0.06 K). We additionally highlight the interplay between albedo and ET in driving changes in annual and seasonal LST. Whether our findings are generalizable to the spatial and temporal domain outside of our data used here is unknown, but merits future research due to the importance of the interactions between LCC and biophysical variables.
Dynamical environmental systems models are highly parameterized, having large numbers of parameters whose values are uncertain. For spatially distributed continental‐scale applications, such models ...must be run for very large numbers of grid locations. To calibrate such models, it is useful to be able to perform parameter screening, via sensitivity analysis, to identify the most important parameters. However, since this typically requires the models to be run for a large number of sampled parameter combinations, the computational burden can be huge. To make such an investigation computationally feasible, we propose a novel approach to combining spatial sampling with parameter sampling and test it for the Noah‐MP land surface model applied across the continental United States, focusing on gross primary production and flux of latent heat simulations for two vegetation types. Our approach uses (a) progressive Latin hypercube sampling to sample at four grid levels and four parameter levels, (b) a recently developed grouping‐based sensitivity analysis approach that ranks parameters by importance group rather than individually, and (c) a measure of robustness to grid and parameter sampling variability. The results show that a relatively small grid sample size (i.e., 5% of the total grids) and small parameter sample size (i.e., 5 times the number of parameters) are sufficient to identify the most important parameters, with very high robustness to grid sampling variability and a medium level of robustness to parameter sampling variability. The results ensure a dramatic reduction in computational costs for such studies.
Key Points
Perform sensitivity analysis of spatially distributed model without significant information loss by running model at subset of grids
Rank parameters by importance group identified via a grouping‐based sensitivity analysis approach
High importance parameters can be identified by running model on 5% of total grids for 5 times the number of parameters
The impact of climate teleconnections on the regional hydrometeorology has been well studied, but very little effort has been made to relate climate teleconnections with groundwater flow variation. ...In this study, we used a wavelet coherence method to analyze monthly climate indices, precipitation, and spring discharge data, and investigated the relation between major teleconnection patterns (the Arctic Oscillation, North Atlantic Oscillation, Pacific Decadal Oscillation, El Niño‐Southern Oscillation, and Indian Ocean Dipole) and karst hydrological process in Niangziguan Springs Basin, China. The results indicate precipitation and spring discharges correlate well with climate indices at intra‐ and inter‐annual time scales. Further, the climate indices are mainly correlated with precipitation at shorter periodicities, but correlated with spring discharge at longer scales. The difference reflects the modulation of karst aquifers on precipitation‐spring discharge during the processes of precipitation infiltration into the ground, and subsequent transformation into spring discharge. When teleconnection signals are transmitted into spring discharge via precipitation infiltration and groundwater propagation, some high‐frequency climatic signals are likely to be filtered, attenuated, and delayed, thus only low‐frequency climatic signals are preserved in spring discharge.
•We propose a nonstationary extreme distribution of spring discharge.•We eliminate the trend and periodicity of spring discharge to get the residuals.•We obtain the return level of the residuals by ...the generalized Pareto distribution.•We model the spring discharge by combining the trend, periodicity and return level.•The model is applied to analyzing the depletion of Niangziguan Springs flow, China.
Karst spring discharge processes are complicated and nonstationary, and can be expressed as long-term trends with periodic variation and random fluctuation. Based on the conceptual model, we propose an assembled extreme value statistical model (AEVSM) for obtaining the extreme distribution of spring discharge depletion under effects of extreme climate variability and intense groundwater development. We eliminated the trend and periodicity of spring discharge to acquire the residuals. Using the quantile plot and Kolmogorov–Smirnov methods, it can be demonstrated that the residuals are stationary. The m period return level of the residuals of spring discharge is obtained by using a generalized Pareto distribution (GPD). We thus acquired the spring discharge distribution of extreme values by combining the trend, periodicity and the return level of residuals. We applied an AEVSM to the monthly spring discharge records for Niangziguan Springs in China, from January 1959 to December 2009, and subsequently acquired the spring discharge distribution of extreme values. Results indicate that after November 2014, the depletion rate of Niangziguan Springs discharge will accelerate, and the spring discharge has the risk of flow cessation with probability of 0.01 from December 2021 to October 2023. A 1% probability is admittedly small, but the probability will increase with time. The AEVSM is a robust method for analyzing the distribution of extreme karst spring discharge.
In many areas throughout the world, extensive groundwater pumping has facilitated significant social development and economic growth, but has typically resulted in a decrease in groundwater level and ...a decline and change in spring discharge. The declining trend and changing seasonality of spring discharge lead to nonstationarity in hydrological processes. When we apply the generalized extreme value distribution to karst spring discharge, several assumptions including independence, identical distribution, and stationarity must be met. To investigate the response of spring discharge to extensive groundwater development and extreme climate change, a nonstationary generalized extreme value (NSGEV) model is proposed by assuming the location parameter to be the sum of a linear and a periodic temporal function to describe the declining trend and seasonality of spring discharge. Bayes’ theorem treats parameters as random variables and provides ways to convert the prior distribution of parameters into a posterior distribution. Statistical inferences based on posterior distribution can provide a more comprehensive representation of the parameters. In this paper we use Markov Chain Monte Carlo method, which can solve high-dimensional integral computation in the Bayes equation, to estimate the parameters of NSGEV model. Then the NSGEV model was used to calculate the distribution of minimum discharge values of Niangziguan Springs in North China. The results show that NSGEV model is able to represent the distribution of minimum values and to predict the cessation time of Niangziguan Springs discharge with two controllable variables: time and return period. With a 100-year return level, flow cessation of Niangziguan Springs would occur in April 2022. Moreover, the probability of Niangziguan Springs discharge cessation is 1/27 in 2025, and 1/19 in 2030. This implies that the probability of Niangziguan Springs cessation will increase dramatically with time.
Parameter optimization is needed for reliable simulations and predictions of natural processes by environmental models. The surrogate modeling‐based approach is an efficient way to reduce the number ...of model evaluations needed for optimization. However, building a surrogate of a distributed environmental model with many output variables over a large spatial domain is computationally intensive as it involves a large number of expensive model simulations on many spatial grid cells. In this study, a novel calibration method called the multi‐objective adaptive surrogate modeling‐based optimization using grid sampling (MO‐ASMOGS) is introduced. This method constructs the response surface surrogate of the original model more efficiently by using both parameter and spatial grid sampling. The spatial grid sampling strategy utilizes the evolutionary elitism and adaptive sampling concepts, thus allowing the surrogate model to be built using a fraction of the total grid cells over a large region. We apply MO‐ASMOGS to calibrating the Noah‐MP model against two surface fluxes: the gross primary production (GPP) and the latent heat flux (LH), over two plant function types (PFTs) across the continental United States. The results demonstrate that the MO‐ASMOGS method can significantly improve the GPP and LH simulations. The new method needs only a small portion of the total grid cells sampled for a given PFT to achieve comparable optimization results obtained by MO‐ASMO using all grid cells. This method can be very valuable in improving model calibration of computationally intensive distributed environmental models.
Key Points
A surrogate modeling based multi‐objective optimization method using grid sampling is proposed for calibration of environmental models
Multi‐objective adaptive surrogate modeling‐based optimization using grid sampling (MO‐ASMOGS) uses only 10% or less of the total grid cells to obtain the same calibration results as MO‐ASMO using 100% of the grids
MO‐ASMOGS is suitable for computationally intensive distributed environmental models such as continental or global‐scale land surface models
Abstract
Land surface models rely on a multitude of parameters to simulate land‐atmosphere interactions, but the parameter uncertainty can limit the reliability of model predictions. This study ...utilizes a parameter uncertainty quantification (UQ) framework to quantify and reduce the parameter uncertainty of the Noah‐MP land surface model in a grassland and sandy soil region in the Midwest of the USA. First, the sparse polynomial chaos expansion method which can quantify the interaction effect of parameters, is employed. A relatively small parameter sample size (i.e., 20 times of the number of parameters) was sufficient to identify the sensitive parameters; an additional sensitive parameter, the saturated soil hydraulic conductivity, was screened out compared to previous study. Then, based on the selected sensitive parameters, the weighted multi‐objective adaptive surrogate modeling‐based optimization algorithm is used as the parameter optimization method. The optimization results showed that the root mean square error of flux of latent heat (FLH) on about 82% of the total grids was reduced, and the number was about 57% for gross primary production (GPP) compared to the results using the original parameter settings, indicating that the Pareto parameter set by the UQ framework improved the Noah‐MP model in simulating FLH and GPP in a grassland and sandy soil region in the Midwest of the USA.
Plain Language Summary
Land surface model is a crucial component of earth system model and it can have a significant impact on the accuracy and reliability of climate projections, as it directly affects the representation of land surface processes such as vegetation dynamics, soil moisture, and land use change. However, the uncertain values of certain sensitive parameters can affect the accuracy of these models that can lead to differences between model predictions and real‐world observations. This study uses an uncertainty quantification (UQ) framework to reduce parameter uncertainty in the Noah‐MP model, which is used to simulate flux of latent heat (FLH) and gross primary production (GPP) in a grassland and sandy soil region in the Midwest of the United States. We identify sensitive parameters using a sensitivity analysis method that considers parameter interaction effects and then use a multi‐objective optimization method to find optimal values for those parameters. The results show that this UQ framework significantly improves the accuracy of FLH and GPP simulations. The study provides a parameter UQ framework for land surface models, which will help improve predictions of water, energy and carbon cycles in grassland ecosystems.
Key Points
Sensitive parameters of the Noah‐MP land surface model are identified by the sparse polynomial chaos expansion
A novel sensitive parameter, saturated soil hydraulic conductivity, is screened out
Pareto parameter set by the multi‐objective adaptive surrogate modeling‐based optimization improves the simulation of the Noah‐MP model
Karst aquifers supply drinking water for 25 % of the world’s population, and they are, however, vulnerable to climate change. This study is aimed to investigate the effects of various monsoons and ...teleconnection patterns on Niangziguan Karst Spring (NKS) discharge in North China for sustainable exploration of the karst groundwater resources. The monsoons studied include the Indian Summer Monsoon, the West North Pacific Monsoon and the East Asian Summer Monsoon. The climate teleconnection patterns explored include the Indian Ocean Dipole, E1 Niño Southern Oscillation, and the Pacific Decadal Oscillation. The wavelet transform and wavelet coherence methods are used to analyze the karst hydrological processes in the NKS Basin, and reveal the relations between the climate indices with precipitation and the spring discharge. The study results indicate that both the monsoons and the climate teleconnections significantly affect precipitation in the NKS Basin. The time scales that the monsoons resonate with precipitation are strongly concentrated on the time scales of 0.5-, 1-, 2.5- and 3.5-year, and that climate teleconnections resonate with precipitation are relatively weak and diverged from 0.5-, 1-, 2-, 2.5-, to 8-year time scales, respectively. Because the climate signals have to overcome the resistance of heterogeneous aquifers before reaching spring discharge, with high energy, the strong climate signals (e.g. monsoons) are able to penetrate through aquifers and act on spring discharge. So the spring discharge is more strongly affected by monsoons than the climate teleconnections. During the groundwater flow process, the precipitation signals will be attenuated, delayed, merged, and changed by karst aquifers. Therefore, the coherence coefficients between the spring discharge and climate indices are smaller than those between precipitation and climate indices. Further, the fluctuation of the spring discharge is not coincident with that of precipitation in most situations. Karst spring discharge as a proxy can represent groundwater resource variability at a regional scale, and is more strongly influenced by climate variation.
PDF
