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MaterialsWeinan city is located in the middle reaches of the Yellow River and in the southern part of the Loess Plateau (34°13’–35°52’N, 108°58’–110°35’E) (Fig. 1). It has a temperate semihumid, semiarid climate. The modern MAT observations indicate a value of 13.8 °C, and MAP is 570 mm; these values were obtained from the China meteorological data network, comprising the meteorological data of 2000–2015 (http://data.cma.cn/). Weinan has four distinct seasons, with hot and rainy conditions occurring in the same season. Much of the annual precipitation falls from June to August. The Weinan profile contains 42.8 m of loess–paleosol sequences (LPSs), including five paleosol layers from S0–S4 and five loess layers from L1–L5 and covering five glacial–interglacial cycles. The sampling method involved collecting one sample every 10 cm without interruption. A total of 427 samples were collected from this profile.Modern brGDGTs dataset and MAP datasetPreviously published brGDGTs data from surface soil samples were extracted using an established brGDGT-MAP model. The surface soil samples contain various types of soil and cover nearly all climatic and latitudinal zones. These datasets contain 712 surface soil samples, which all have separated 5-methyl and 6-methyl brGDGTs isomers (Table 1). To reduce the errors in collecting data from different laboratories, the MAP datasets we entered into the brGDGT-MAP model were all published in their previous studies, and we calculated the fractional abundances of each brGDGTs compound for each sample (Table 1), although there were no data regarding changes in soil occurring based on the brGDGTs indices among various laboratories. To eliminate and test the error of the previous MAP dataset, in this study, we also extracted each soil site’s multiyear MAP (1990–2020) through TerraClimate, which is a dataset of high-spatial-resolution monthly climate for global terrestrial surfaces (1/24°, ∼4 km)48. TerraClimate datasets reveal significant advances in the overall mean absolute error and enhance spatial realism compared with coarser resolution gridded datasets. Supplementary Fig. 3 shows that the two MAP datasets have high correlations, with only a few sites exhibiting large deviations. In this study, we entered these two MAP datasets into the DLNN model to obtain the most suitable DLNN-MAP model.Grain-size and magnetic susceptibility measurementsSamples at 10-cm intervals were dried in an oven at 40 °C for 3 days. Then, 0.2 g of each sample was weighed using a clean beaker with an electronic balance. Then, 10 ml of 30% H2O2 and 10 ml 10% HCl were added to remove organic matter and carbonate, respectively. Before the grain-size measurement, 0.05 mol/L (NaPO3)6 was added, and the solutions were placed in an ultrasonic machine for 10 min. The magnetic susceptibility of the samples were measured with an MS-2B Bartington meter. The grain-size was measured using a Mastersizer 2000 produced by Marvern Company in the UK, with an error of less than 1%.ChronologyWe used the ages of LPS control points on the Loess Plateau to obtain the age of each sample in the Weinan profile40. We used the magnetic susceptibility as an indicator of the accumulation rate39 combined with the U–230Th-dated oxygen isotope records from Sanbao caves in central China14. Each sample’s magnetic susceptibility was analyzed at 10-cm intervals (Supplementary Fig. 7). The calculation was as follows:$${T}_{{{{{{rm{m}}}}}}}={T}_{1}+frac{left({sum }_{i=1}^{m}{a}_{i}{s}_{i}right)left({T}_{2}-{T}_{1}right)}{{sum }_{i=1}^{n}{a}_{i}{s}_{i}}$$
(1)
where T1 and T2 indicate the ages of the control points, ai indicates the thickness of the layer, and si indicates the magnetic susceptibility of the layer.GDGTs analysisLipids in a total of 238 LPS samples were extracted, including the 198 samples reported in ref. 49. Forty samples at depths from 34.9 m to 43.7 m were selected every 20 cm intervals from the Weinan profile, and dried in an oven at 40 °C for 3 days. Afterward, the loess and paleosol samples were ground into powder and passed through a 60-mesh sieve. Each sample was weighed and extracted with 80 ml of methanol: dichloromethane (DCM) (1:9, v/v) using accelerated solvent extractors (ASE 100 or 150, Dionex, USA). The temperature and pressure were set at 100 °C and 1400 psi, respectively. Then, the lipid extracts were condensed in a rotary evaporator at 40 °C and separated into apolar and polar fractions on a flash silica gel column (0.7 cm i.d. and 1.5 g activated silica gel) chromatography using n-hexane and methanol as eluents, respectively. All polar components were passed through a 0.45-µm PTFE syringe filter. All apolar and polar compositions were dried under a gentle stream of nitrogen gas.The GDGTs were analyzed by using an Agilent 1200 series liquid chromatography-atmospheric pressure chemical ionization-6460A triple quadrupole mass spectrometry (LC-APCI-MS/MS). Ten microlitres of C46 GTGTs (0.001157 μg/μl) were added to each polar fraction, and the samples were then dissolved in 300 μl of n-hexane: iso-isopropanol (IPA) (98.2:1.8, v/v)). Two silica gel columns in series (150 mm × 2.1 mm, 1.9 μm, Thermo Finnigan; USA) were used for the separation of 5-methyl and 6-methyl brGDGTs, with the column temperature kept at 40 °C. The mass spectrometry settings were as follows: the vaporizer pressure 60 psi, the vaporizer temperature 400 °C, the flow rate of dry gas (N2) 6 l/min, drying gas temperature 200 °C, the capillary voltage 3500 V, the corona current 5 μA (∼3200 V), and a single-ion monitoring mode (SIM) was used50, targeting the protonated molecular ions ([M + H]+) 1304, 1302, 1300, 1298, 1296, 1292, 1050, 1048, 1046, 1036, 1034, 1032, 1020, 1018, and 744.The MATmr proxy was calculated to identify the changes that occurred in the mean annual temperature in the Weinan section over the last 430 ka. The calculation was as follows24,51.$${{MAT}}_{{mr}} =7.17+17.1*[{Ia}]+25.9*[{Ib}]+34.4*[{Ic}]-28.6*[{IIa}],(n=222,,{R}^{2} \ =0.68,; {RM}{SE}=4.6 {deg} {{{rm{C}}}},,P ; < ; 0.01)$$ (2) $${{MAT}}_{{mr}}=5.58+17.91*[{Ia}]-18.77*[{IIa}]$$ (3) $${MAT}({SSM})= 20.9-13.4*[{IIa}+{IIa}^{{prime}}]-17.2*[{IIIa}+{IIIa}^{{prime}}]\ -17.5*[{IIb}+{IIb}^{{prime}}]+11.2*[{Ib}]$$ (4) $${MAAT}=0.81-5.67*{CBT}+31.0*{MBT}^{{prime}}$$ (5) The soil pH was calculated using the following formulas24.$${pH}=7.15+1.59*{CBT}^{{prime}}(n=221,,{R}^{2}=0.85,,{RMSE}=0.52,, P , < ,0.0001)$$ (6) $${{CBT}}^{{prime} }=-{{log }}frac{{Ic}+{II}{a}^{{prime}}+{II}{b}^{{prime}}+{{IIc}}^{{prime} }+{{IIIa}}^{{prime} }+{III}{b}^{{prime} }+{{IIIc}}^{{prime} }}{{Ia}+{Ib}+{Ic}}$$ (7) SWC is well correlated with MBT’ when IR6ME  > 0.5, and these proxies were calculated using the following expressions:$${{MBT}}^{{prime} }=frac{({Ia}+{Ib}+{Ic})}{({Ia}+{Ib}+{Ic}+{IIa}+{{IIa}}^{{prime} }+{IIb}+{{IIb}}^{{prime} }+{IIc}+{{IIc}}^{{prime} }+{IIIa}+{IIIa}^{prime} )}$$
(8)
$${{IR}}_{6{ME}}=frac{sum (C6-{methylated; brGDGTs})}{sum {brGDGTs}}$$
(9)
$${{MBT}^{prime} }_{6{ME}}=frac{({Ia}+{Ib}+{Ic})}{({Ia}+{Ib}+{Ic}+{{IIa}}^{{prime} }+{{IIb}}^{{prime} }+{{IIc}}^{{prime} }+{IIIa}^{prime} )}$$
(10)
where the Roman numerals indicate different brGDGTs structures (Supplementary Fig. 1).Principal component analysis (PCA)CANOCO version 5 software was utilized to reveal the relationships among various environmental factors. The first PCA figure (Fig. 3a) was generated for the environmental factors MAT, MAPc, SWC, and pH. These variables are based on the same dataset (238 LSPs samples from Weinan profile) without any data transformation. The second PCA figure (Fig. 3b) was generated for the environmental factors MAT, MAP (based on 10Be), SWC and pH, which were all in the transition of the glacial–interglacial after 430 ka BP on the CLP. As the two LSPs profile had similar sedimentation rates, we obtained the same chronological control through linear interpolation in those transition periods. All datasets passed the Gaussian distribution test in this study.Cross wavelet analysisCompared with wavelet special analysis, cross wavelet analysis is even more complicated. The wavelet cross-spectrum can be defined as follows:$${CS}left(b,, a right)={m}_{1c}(b,, a){m}_{2c}(b,, a)$$
(11)
where ({m}_{1c}) and ({m}_{2c}) describe the covarying fractions of the overall spectra given by:$${m}_{1}left(b,, a right)={m}_{1c}left(b,, a right)+{m}_{1i}(b,, a)$$
(12)
$${m}_{2}left(b,, a right)={m}_{2c}left(b,, a right)+{m}_{2i}left(b,, a right)$$
(13)
where ({m}_{1i}) and ({m}_{2i}) are independent contributions to the variance.Overall, this is a multipart function that may be decomposed into amplitude and phase:$${CS}left(b,, a right)={{{{{rm{|}}}}}}{CS}left(b,, a right){{{{{rm{|}}}}}}{{exp }}(i;{{arg }}({CS}(b,, a)))$$
(14)
In this study, a and b represent the array of reconstructed MAPc and the Sanbao speleothem δ18O, respectively.Multiple regression linear modelTo compare the precision of the DLNN-MAP model we established, we set up a multiple regression linear model based on all 6-methyl brGDGTs except Ib. The basis of the model is defined as:$$y=a+{b}_{1}{x}_{1}+{b}_{2}{x}_{2}ldots+{b}_{n}{x}_{n}$$
(15)
where y represents MAP, x represents all 6-methyl brGDGTs and Ia and Ic, and a, b1, b2…bn represent the partial regression coefficients. n represents the number of 6-methyl we entered into the model (in this study, n = 8).The multiple correlation coefficient (R) was defined as follows:$$R=sqrt{frac{{sum }_{i=1}^{n}{({hat{y}}_{i}-bar{y})}^{2}}{{sum }_{i=1}^{n}{({y}_{i}-bar{y})}^{2}}}$$
(16)
where ({y}_{i}) represents the actual observed value, ({hat{y}}_{i}) represents the calculation value and (bar{y}) represents the mean value of all observed data.The t statistic is used to test the validity of regression coefficients, and it can be defined as follows:$${t}_{{b}_{j}}=frac{{b}_{j}}{{s}_{{b}_{j}}}$$
(17)
$${s}_{{b}_{j}}=sqrt{{p}_{{jj}}}*s$$
(18)
$$s=sqrt{frac{1}{n-m-1}mathop{sum }limits_{i=1}^{n}{({y}_{i}-{hat{y}}_{i})}^{2}}$$
(19)
$$P={({p}_{{jj}})}^{-1}=mathop{sum }limits_{i=1}^{n}({x}_{{ij}}-{bar{x}}_{j})({x}_{{ik}}-{bar{x}}_{k})$$
(20)
where ({b}_{j}) represents the regression coefficient of ({x}_{j}), n represents the number of samples and m represents the number of variables.The F statistic is used to test the linear relationship of variables and can be defined as follows:$$F=frac{1}{m{s}^{2}}mathop{sum }limits_{i=1}^{n}{({hat{y}}_{i}-bar{y})}^{2}$$
(21)
The variance inflation factor (VIF) is used to measure collinearity between variables and can be defined as follows:$${{VIF}}_{j}=frac{1}{1-{R}_{j}^{2}}$$
(22)
As shown in Supplementary Fig. 5, we found no obvious collinearity between different variables. However, there are fewer contributions in IIc’, IIIa’, IIIb’, and IIIc’ in the multiple regression linear model we established, and the value of t cannot attain the 95% confidence level (Table 2). The results of both the training dataset and extrapolated experimental dataset (Supplementary Fig. 6), although they seem good (R2 = 0.44 and 0.46, respectively), still have a considerable gap compared with the DLNN-MAP model. Especially when MAP  > 1500 mm, the multiple linear model is unable to forecast the real MAP. These results all indicate that the MAP influence on the brGDGTs compounds is not a simple linear relationship; instead, we suggest that there are complex pilot processes between them.Table 2 List of the parameters of the multiple linear modelFull size tableDLNN modelsDLNNs usually contain an input layer, a few hidden layers, and an output layer. A conceptual structure of the DLNN model was established for forecasting MAP values. The first layer accepts input signals that are various combinations of brGDGTs. The relationships among different variables are processed and analyzed in the hidden layers. The final class output is presented in the output layer; in this study, the output is the MAP reconstruction at the study site.The rectified linear unit (ReLU) activation function is applied in each neuron of the hidden layer, which is computationally simpler than the traditionally applied sigmoid function. The function of the ReLU activation function is given as follows:$$fleft(xright)=left{begin{array}{c}x,, x , > , 0 \ 0,, x , le , 0end{array}right.={{{{{rm{max }}}}}}(0,, x)$$
(23)
where x represents an input signal to a neuron and f represents the activation function.Furthermore, the bias between the measured and forecasted output values is reflected by the loss function. The loss function applied herein is the MAE (mean absolute error), which is given as follows:$${MAE}=frac{1}{N}mathop{sum }limits_{i=1}^{n}{{{{{rm{|}}}}}}T-Y{{{{{rm{|}}}}}}$$
(24)
where N is the number of training data points, and T and Y represent the measured output value and the forecasted class value, respectively.To realize the backpropagation framework, the derivative of the ReLU activation function needs to be acquired. According to the definition of the ReLU, the derivative is shown as follows.$$f{^prime} left(xright)=left{begin{array}{c}1,; x , > , 0 \ 0,; x , le , 0end{array}right.$$
(25)
Given a minibatch of m training samples obtained from the training set {x(1), x(2)…, x(m)} and their corresponding targets T(i) (i = 1,2…, m), the gradient used in the backpropagation framework is shown as follows:$$f=frac{1}{m}mathop{sum }limits_{i=1}^{n}frac{partial L}{partial w}$$
(26)
where L is the loss function; w represents the network weights; and n = 1 is the number of output values (MAP).In addition, considering that the adaptive moment estimation algorithm (Adam) was proven to be an effective neural network training method with a fast convergence speed and great classification performance, we applied this algorithm to train the DLNN model for MAP forecasting in this study. Adam has two biased equations, which are shown as follows:$$a={rho }_{1}a+(1-{rho }_{1})g$$
(27)
$$b={rho }_{2}b+(1-{rho }_{2})gtimes g$$
(28)
where ({rho }_{1}=0.9) and ({rho }_{2}=0.999) are exponential decay rates; g is the gradient; and (times) represents an elementwise product operator.After this calculation, the correct biases in the above two moments are given as follows:$${a}_{c}=frac{a}{1-{rho }_{1}^{t}}$$
(29)
$${b}_{c}=frac{b}{1-{rho }_{2}^{t}}$$
(30)
where t represents the current time step.Moreover, the update of the network weights is shown as follows:$${triangle }_{w}=-lambda frac{{a}_{c}}{sqrt{{b}_{c}}+epsilon }$$
(31)
where (lambda=0.001) represents the learning rates and (epsilon={10}^{-8}) is a constant used to ensure numerical stability.Eventually, the DLNN parameters can be updated according to the following formula.$$w ,=, w ,+, {triangle }_{w}$$
(32)
brGDGT-MAP modelsWe entered 9 brGDGTs compounds (all 6-methyl brGDGTs; each compound entered in the model is the percentage of all brGDGTs in the surface soil) into the input layer of the DLNN; these compounds are closely related to soil moisture. Then, we selected 533 surface soil samples as the training dataset and 179 surface soil samples as the validation dataset, both of which satisfied the principle of randomness. We assessed the precision of the model using forecast data R2 and root mean square error (RMSE) values.Through several parameters applied in the DLNN model, we found that the frequency of training and the number of neurons play the most significant roles in the brGDGT-MAP models. In addition, four hidden layers containing the other DLNN parameters allow the model to become more stable (detailed parameters are shown in Supplementary Fig. 7). To test the best frequency of training and the number of neurons in each hidden layer, we set a series of gradients to test the model to find the most suitable combination. As shown in Supplementary Fig. 8, for the frequency of training, we set the minimum and maximum training times to 1000 and 1500, respectively, with 100 times as the interval. We also set the numbers of neurons from 160 to 260 with a 20-neuron interval.Testing the weights of different compounds in the DLNN model and determining whether it was essential to eliminate some compounds that may make the dataset redundant were also required. Based on the model in which the Ib parameter was removed, we also set a series of experiments to test the effects of the different 6-methyl isomers on the predicted MAP values. Then, we made seven attempts to test the forecast effect of the brGDGT-MAP models by removing the Ic, IIa’, IIb’, IIc’, IIIa’, IIIb’, and IIIc’ parameters (Supplementary Fig. 9). Then, we obtained the best brGDGT-MAP model (Supplementary Fig. 10).Comparison of various ANN structuresTo improve the accuracy of our brGDGT-MAP models and the models’ universality, we also tested more complex ANN structures and then compared them with our DLNN models.RNNA recurrent neuron network (RNN) is an artificial neural network in which nodes are directionally connected into loops. The essential feature of RNN is that there are both internal feedback connections and feedforward connections between processing units. The inner structure of RNN is similar to that of the human brain, which can learn to transform a lifetime of sensory input streams into an efficient sequence of motor outputs (Supplementary Fig. 11a). Therefore, the basis of the RNN is defined as follows:$${h}_{t}=fleft(U ,*, {X}_{t}+W ,*, {h}_{t-1}right)$$
(33)
$${o}_{t}={softmax}(V ,{h}_{t})$$
(34)
where Xt represents the input value at time t; ot represents the output value at time t; ht represents the memory value at time t; and U, V, and W are the parameters of this network. For the motivative function, we chose softmax.LSTMLong short-term memory networks (LSTM) are a special type of RNN that can learn long-term dependence and contain three gates (forget gate, input gate and output gate) and one memory cell. The horizontal line above the box is called the cell state, and it acts as a conveyor belt to control the flow of information to the next moment (Supplementary Fig. 11b). Therefore, the basis of LSTM is defined as follows:$${C}_{t}={f}_{t}*{C}_{t-1}+{i}_{t}*{widetilde{C}}_{t}$$
(35)
where ({C}_{t-1}) represents the knowledge state of the model at time t − 1 and ({widetilde{C}}_{t}) represents the newly acquired information after entering new observations. ({f}_{t}) and ({i}_{t}) represent the weight parameters of ({C}_{t-1}) and ({widetilde{C}}_{t}), respectively.$${f}_{t}=sigma ({W}_{f}cdot left[{h}_{t-1},, {x}_{t}right]+{b}_{f})$$
(36)
$${i}_{t}=sigma ({W}_{f}cdot left[{h}_{t-1},, {x}_{t}right]+{b}_{i})$$
(37)
$$kern0.9pc {widetilde{C}}_{t}={{tanh }}({W}_{c}cdot left[{h}_{t-1},, {x}_{t}right]+{b}_{c})$$
(38)
where ({h}_{t-1}) represents the output value at time t − 1 and ({x}_{t}) represents the new input value at time t. ({W}_{f}) represents the motivative function in this study. We used tanh as the motivative function when our model was learning. Each new input may not have a positive impact on the machine, but it may also have a negative impact., ({b}_{f}), ({b}_{i}) and ({b}_{c}) represent the random disturbances (white noise).GRUAs mentioned above, the LSTM is proposed to overcome RNN’s inability to address remote dependence and the gate recurrent unit (GRU), a variant of the LSTM, keeps the effect of the LSTM while making the structure simpler.Compared with the LSTM, the GRU only has two gates (update (zt) and reset (rt) gates). The update gate is used to control the degree to which the state information at the previous moment is brought into the current state. The larger the value of the update gate is, the more state information at the previous moment is brought in. The reset gate is used to control the degree to which the state information at the previous moment is ignored (Supplementary Fig. 11c). Therefore, the basis of the LSTM is defined as follows:$${r}_{t}=sigma ({W}_{r}cdot [{h}_{t-1},, {x}_{t}])$$
(39)
$${z}_{t}=sigma ({W}_{z}cdot [{h}_{t-1},, {x}_{t}])$$
(40)
$${widetilde{h}}_{t}={tanh }({W}_{widetilde{h}}cdot [{{r}_{t}*h}_{t-1},, {x}_{t}])$$
(41)
$${h}_{t}=left(1-{z}_{t}right)*{{r}_{t}*h}_{t-1}+{z}_{t}*{widetilde{h}}_{t}$$
(42)
$${y}_{t}=sigma ({W}_{o}cdot {h}_{t})$$
(43)
where [] represents the connection of two vectors and * represents the multiplication of matrix elements. The xt and yt represent the input and output values at time t, respectively.It can be seen from the above formula that the parameters to be learned are the weight parameters of Wr, Wz, Wh, and Wo. The first three weights are spliced; therefore, they need to be segmented during learning. These can be defined as follows:$${W}_{r}={W}_{{rx}}+{W}_{{rh}}$$
(44)
$${W}_{z}={W}_{{zx}}+{W}_{{zh}}$$
(45)
$${W}_{widetilde{h}}={W}_{widetilde{h}x}+{W}_{widetilde{h}h}$$
(46)
As we can find in the RNN, LSTM, and GRU models we reconstructed (Supplementary Fig. 12), the training datasets all show extraordinarily high R2 values (0.99, 0.99, and 0.99, respectively) and low RMSE values (0.36, 0.23, and 0.16, respectively). However, the validation datasets do not show good prediction ability compared with the DLNN. These results indicate that the two ANN structures are not suitable for MAP prediction based on brGDGTs, although their inner structures are more complex than those of the DLNN. The reason we suggested is that the RNN, LSTM and GRU are more appropriate to the massive amounts of data and the data that have obvious spatiotemporal characteristics. The great prediction precision in the training dataset and the poor performance in the extrapolated datasets indicate that the models based on the RNN, LSTM and GRU have significant overfitting. As a result, compared with other ANN structures, we concluded that our DLNN model is the most suitable one to forecast MAP based on brGDGTs.Environmental indicators of n-alkanes proxiesLong-chain n-alkanes in plant leaf waxes are universal in terrestrial environments and can deliver signals of variations in plant sources and past climate. They are widely distributed in surface soils and Quaternary sediments, especially in LPSs. In this study, due to the insufficient samples in Weinan profile, we only analyzed n-alkanes components for 40 LPS samples, which contain ages between 340 and 430 ka BP.Instrumental measurementsFor the apolar fractions, a total of 40 samples in this study, mainly containing n-alkanes, were all investigated utilizing a Shimadzu 2010 gas chromatograph (GC) equipped with a flame ionization detector (FID) and a DB-5 fused silica capillary column (60 m (times) 0.32 mm (times) 0.25 μm film thickness) with helium as the carrier gas. The temperature of the GC oven was enhanced from 70 to 300 °C at a rate of 3 °C/min. Then, this temperature (300 °C) was maintained for 30 min. Finally, the concentrations of the n-alkane homologs were evaluated by assessing the peak area of the n-alkanes to that of the internal standard (cholane).Long-term paleoclimatic changeThe carbon preference index (CPI) evaluates the relative abundances of odd vs. even-numbered n-alkanes. The CPI increases as the environmental aridity increases. The CPI indicated warm–wet periods and cold-dry periods in paleoclimate and corresponded well with the loess–paleosol cycle52. The average chain length (ACL) value is the weighted average of the different carbon chain lengths. The lower ACL value corresponds to the lower temperature in the research of LPSs. The variations in the ACL value have good coordination with the magnetic susceptibility and particle size. The n-alkane CPI53 and ACL54 are calculated as follows:$${CPI}(1)=frac{({C}_{23}+{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31})+({C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}+{C}_{33})}{2({C}_{24}+{C}_{26}+{C}_{28}+{C}_{30}+{C}_{32})}$$
(47)
$${CPI}left(2right)=frac{1}{2}left(frac{{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}+{C}_{33}}{{C}_{24}+{C}_{26}+{C}_{28}+{C}_{30}+{C}_{32}}+frac{{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}+{C}_{33}}{{C}_{26}+{C}_{28}+{C}_{30}+{C}_{32}+{C}_{34}}right)$$
(48)
$${ACL}=frac{{23C}_{23}+{25C}_{25}+{27C}_{27}+{29C}_{29}+31{C}_{31}+{33C}_{33}}{{C}_{23}+{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}{+C}_{33}}$$
(49)
Figure 13 shows the variations in CPI (Supplementary Fig. 13a) and ACL (Supplementary Fig. 13b) values in the Weinan profile from 340 to 430 ka BP. Compared with the MAP (Supplementary Fig. 13c) and SWC (Fig. 2e) reconstructions based on brGDGTs, we found that they all had a peak at ∼350 ka BP, which indicates relatively high soil moisture at approximately 350 ka BP.MAP reconstruction in the XRD sectionIn this section, we test the brGDGT-MAP model in the Xiangride (XRD) profile, which is located in the margin region of the East Asian monsoon (Fig. 1). With robust chronological control, we reconstructed the rainfall changes in 7000 years BP (Supplementary Fig. 14b). We found that MAP was ∼200 mm in the late Holocene, which approaches multiple modern observations in this region (180 mm). Moreover, we suggest that this region experienced the most humid period in the mid-Holocene, when the rainfall reached 600 mm. Afterward, the precipitation declined from 6000 to 4000 years BP and then increased and reached a peak value at ∼3000 years BP. Then, it had a drought trend until modern times.We discovered that our brGDGT-MAP model could precisely capture rainfall dynamics based on the Weinan profile (Supplementary Fig. 14a) and XRD profile (Supplementary Fig. 14b). Combined with the most acceptable rainfall records in the Holocene (i.e., 10Be (Supplementary Fig. 14c), pollen in Gonghai (Fig. 1 and Supplementary Fig. 14d), and Dongge cave δ18O (Fig. 1 and Supplementary Fig. 14e)), we found the same precipitation peak values in the early Holocene and mid-Holocene. In addition, they all revealed a drought trend throughout the whole Holocene. We suggest that brGDGTs can become a robust proxy to reconstruct precipitation in the Holocene. More

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Spatial–temporal characteristics of land use changeAs shown in Fig. 2, cultivated land was the dominant land use type in Shandong Province during the past 40 years, which accounted for 69.86% (1980), 69.98% (1990), 69.25% (2000), 68.00% (2010) and 66.88% (2020) respectively. Moreover, it was found that the area of cultivated land, forest land, grassland, unused land and ocean gradually decreased, whereas the water area and URL (urban and rural industrial and mining residential land) increased obviously. In particular, grassland decreased by 7542.87 km2 in the past 40 years with a decline rate of 37.18%, which was much higher than cultivated land and forest land. This phenomenon was attributed to the fact that cultivated land and forest land were less susceptible to encroachment as their high vegetation coverage, while grassland was easily occupied by other land types. The serious occupation by other land types has led to a significant reduction in unused land with a very high decline ratio of 64.32% from 2010 to 2020. In contrast to unused land, URL increased significantly at this period (Fig. 3), which was due to the rapidly economic development.Figure 2Land use type map of Shandong Province from 1980 to 2020.Full size imageFigure 3Sankey diagram of land use transfer in different periods.Full size imageThe total area of land use conversion in Shandong Province was 86,909 km2 during the past 40 years, the most drastic change was observed from 2010 to 2020. On the one hand, the major project of new and old kinetic energy conversion in Shandong Province had been implemented since 2000, which led to the expansion of urban land and dramatic changes in land use patterns. On the other hand, social, economic, technological and other factors had a direct impact on land use change by influencing people’s decision-making on land use (e.g., demand for land products, investment in land, protection of land resources, etc.)45,46,47,48. Statistics showed that GDP (Gross Domestic Product) and population density of Shandong Province had increased significantly since 21st century. The GDP of 2010–2020 was about 10 times that of 1980–2000 and population density had also increased by 1.4 times (Data from: Shandong statistical yearbook, http://tjj.shandong.gov.cn/col/col6279/index.html). As the most direct reflection of human activities, land use change was obviously affected by factors such as agricultural cultivation, industrial and mining construction, and urbanization driven by population growth49,50.The most significant changes of land use type were URL (increased by 17.75%), grassland (decreased by 8.72%) and cultivated land (decreased by 7.26%) over the past forty years. URL was mostly converted from cultivated land (26,306 km2) and grassland (1684 km2), which reflected the serious situation of occupying cultivated land in the process of urbanization in Shandong Province. It was caused by tight land use scale and relatively flat terrain of grassland. Besides, the range of land use type in the four periods also exhibited great variations. The conversion of land use from 1980 to 1990 was concentrated in the Yellow River Delta, Laizhou Bay and Weishan Lake, for the same as 1990–2000. At the period of 2000 to 2010, the conversion types concentrated in Bohai Bay and Yellow River Delta. The land use conversion was violent and widely distributed from 2010 to 2020, which was different from previous periods from 1980 to 2010. The conversion of cultivated land → URL and URL → cultivated land were widely distributed in Shandong Province, while another conversion of grassland → cultivated land and forest → cultivated land were concentrated in the Central and South Shandong Mountains and Jiaodong Hills. In addition, the conversion of cultivated land → water area and URL → water area were concentrated in Bohai Bay, Yellow River Delta and Laizhou Bay. Ample water, flat terrain and fertile soils in these bays and deltas facilitates agricultural cultivation and other productive activities. Therefore, the conversion of land use types from 1980 to 2010 was mainly concentrated here (Fig. 4). Specifically, the conversion of water area → URL was 1083 km2 from 1980 to 1990, unused land → water area was 925 km2 from 1990 to 2000, cultivated land → water area was 687 km2 from 2000 to 2010. However, the pattern of land use change dominated by natural factors has been broken in the process of increasing demand for social development and continuous advancement of science and technology. The conversion of land use types has become more dispersed in spatial distribution and the types of conversion have become more diverse.Figure 4Spatial distribution map of land use conversion types in different periods.Full size imageIn fact, one issue of concern in the early exploitation of water was the ecological problems caused by over-exploitation. For example, the cut-off of the Yellow River downstream made it difficult to guarantee the water security of industrial and agricultural production and residential life in the areas along the way. At the same time, the safety of coastal ecosystems was threatened and the phenomenon of soil salinization had become more serious. To alleviate these problems, government and the public have taken a series of measures such as establishing the Yellow River Delta National Nature Reserve was established in 1992, returning farmland to lakes and wetlands, and improving the landscape pattern of rivers and lakes by carrying out ecological treatment in the coastal zone of rivers and lakes51,52. By 2020, the area of water has increased by 50% compared to 1980, while many ecological security issues have been mitigated.Spatial–temporal characteristics of habitat degradationThe spatial–temporal variation of land use types were conducted to explore the variation trend of its habitat quality in Shandong Province. The InVEST-HQ was applied to obtain layers of habitat degradation in different periods. According to the interval range of 0–0.03, 0.03–0.07 and 0.07–0.18, habitat degradation was divided into three levels: slight, moderate and high degradation35,38.As shown in Fig. 5, the habitat quality in Shandong Province was dominated by moderate degradation, with the proportion of 73.30% (1980), 73.25% (1990), 72.49% (2000), 70.45% (2010) and 64.33% (2020), respectively. The spatial pattern of habitat quality was consistent with cultivated land, indicating that cultivated land who was affected by natural and anthropogenic activities exhibited moderate degradation. The proportion of moderate degradation has decreased due to cultivated land have been encroached upon for construction in the process of development, thus habitat degradation has become more and more serious. Although some of the moderate degraded areas were also converted to slight degraded areas, the area of conversion was very small compared to its conversion to high degraded areas.Figure 5Distribution map of habitat degradation in Shandong Province from 1980 to 2020.Full size imageThe proportion of slight degradation ranges from 22.38% to 24.89%, it was concentrated in the Yellow River Delta, the Central and South of Shandong Mountains, Weishan Lake and Jiaodong Hills, which was less disturbed by human activities. Compared with 1980, the proportion of slight degraded areas increased marginally in 2020, and its change was a fluctuating process. The proportion of slight degraded areas decreased from 1980 to 1990, and its proportion slowly increased from 1990 to 2020. This dynamic change process could be verified according to the spatial distribution characteristics in the Yellow River Delta. The habitat quality of the Yellow River Delta, which originally showed slight degradation, showed high degradation in 1990, 2000 and 2010.The proportion of high degradation ranges from 4.03% to 10.78%, which was concentrated in the built-up area of the city where human activities were more intensive. The proportion of high degraded areas has been increasing, indicating that the habitat has been degraded severely and its quality has declined. As the proportion of high degraded areas raised, two patterns of their spatial distribution also emerged. First spatial pattern was concentrated in urban built-up areas because of the high degree of human exploitation of land, which led to significant habitat degradation. The second pattern was a circle structure with “slight degradation” as the center and “high degradation-moderate degradation-slight degradation” outward, which was similar to the spatial distribution structure of habitat degradation in Fujian Province studied by Li et al.40. The circle structure was formed in 2010, and the distribution range was significantly expanded in 2020. The reason for the formation was that the built-up land in the city center has been severely damaged, and the possibility of re-degradation was reduced, instead showing “slight degradation”. However, the adjacent urban areas were more threatened and severely degraded, presenting “high degradation”. With the increase of distance, habitat threat and degradation decreased gradually, displaying “slight degradation”.Spatial–temporal evolution characteristics of habitat qualityThe InVEST-HQ was used to obtain layers of habitat quality in different periods. As summarized in Table 4, habitat quality was divided into five levels by the interval range: low (0–0.2), relatively low (0.2–0.4), medium (0.4–0.6), relative high (0.6–0.8), and high (0.8–1.0)35,38.Table 4 The proportion of habitat quality level at different periods in Shandong Province.Full size tableOur study concluded that the level of habitat quality in Shandong Province declined from 1980 to 2020.The results showed an overall decline of 4.75% in Shandong Province. Among them, the most significant rate of decline was observed in 2010–2020 (1.86%), which was similar to the phase change characteristics of land use types. At this period, the “Development Plan of Yellow River Delta Efficient Ecological Economic Zone” and the “Development Plan of Shandong Peninsula Blue Economic Zone” have become national development strategies. The demonstration area of “Bohai granary” and the restructuring of steel industry were carried out simultaneously. Meanwhile, the Beijing-Shanghai high-speed railway (Shandong section), Qingdao Jiaozhou Bay Bridge, Jiaozhou Bay Tunnel have strengthened the connection between Shandong Province and the outside world. As a result, rapid development has led to a rapid decline in the quality of its habitat. The rate of decline in 1980–1990 (1.43%) and 2000–2010 (1.42%) was comparable and the rate of decline in 1990–2000 was the lowest at 0.12%, which was significantly related to the development level of cities in each period. The period of 1980–1990 and 2000–2010 were in the initial and rapid promotion stages of reform and opening-up respectively. The initial stage was led by rural reform, and urban reform was launched on a pilot basis. The rapid advancement stage was led by urban reform, and economic development entered a healthy track of steady progress. Therefore, the proportion of habitat quality changes in the two periods was comparable. The period of 1990–2000 was in the exploration and transition stage of reform and opening-up, whose development process was relatively stable, resulting in the lowest rate of change in habitat quality.The average value of habitat quality in Shandong Province was 1980 (0.5091), 1990 (0.5018), 2000 (0.5012), 2010 (0.4941) and 2020 (0.4849), which decreased during the entire period. Habitat quality was dominated by medium-level throughout the whole period, with the proportion in 1980 (68.95%), 1990 (68.54%), 2000 (67.74%), 2010 (66.37%) and 2020 (65.47%). The land type in this category was mainly cultivated land (Fig. 6), which was continuous encroachment during the study period, resulting in a decrease in the percentage of medium-level habitat quality. From 1980 to 2020, the percentage of low-level habitat quality increased from 12.67% to 17.44%, and the relatively low-level decreased from 0.46% to 0.23%. The main reason was the continuous increasing of construction land and the degree of habitat threat led to the decreasing of habitat suitability. Therefore, the area of low-level habitat quality showed an increasing trend. Low and relative low-level habitat quality areas were concentrated in the urban areas of coastal and inland cities, and the Yellow River Delta. Urban areas, with a large scale of industry, commerce and population, also have a high level of urbanization. The original natural habitat has been modified during the development process, which resulted low-level habitat quality. The habitat quality of the Yellow River Delta was dynamic. The low-level pattern formed by early over-exploitation was improved in later conservation and development. The proportion of high-level habitat quality increased from 11.64% to 12.98%, and the relatively high-level decreased from 6.28% to 3.88%. In terms of spatial distribution, it was concentrated in the Central and South Shandong Mountains, Jiaodong Hills, the Yellow River Delta (2020), Weishan Lake and Wulian Mountain. These areas were dominated by mountains and well-protected water, which had high habitat suitability and were less stressed by surrounding construction land, thus maintaining high-level habitat quality. The increase of high-level habitat quality was due to the influence of water with high habitat suitability, which expanded a lot in the past 40 years, leading to the spread of high-level regional habitat quality, especially in the Yellow River Delta.Figure 6Distribution map of habitat quality in Shandong Province from 1980 to 2020.Full size imageThe value of Moran’s I was 0.3935 (1980), 0.3852 (1990), 0.4031 (2000), 0.4186 (2010) and 0.4644 (2020), respectively, which revealed that the spatial agglomeration of habitat quality in Shandong Province was characterized by agglomeration, and the trend of agglomeration increased obviously after 2000.As shown in Fig. 7, the habitat quality in Shandong Province exhibited obvious spatial heterogeneity, and spatial distribution of cold and hot spot was consistent with the topographic features. Hot spot (high-value area of habitat quality) presented “two primary and two secondary + Yellow River Delta”. Two primary hot spots distributed in the Central and South Shandong Mountains and the Jiaodong Hills, the two secondary hot spots located in Weishan Lake and Wulian Mountain. The formation of above hot spot was mainly due to high altitudes or steep slopes conferred favorable habitat quality, which was associated with the accessibility of human activities. Human accessibility at high altitudes or steep slopes was limited, so it was unlikely to cause major interference with the original environment53,54. However, the formation of other hot spot in Yellow River Delta was due to protective human activities. Cold spot (low-value area of habitat quality) was scattered in the northwestern Plain of Shandong Province, provincial capital metropolitan area and peninsula urban agglomeration which was dominated by cultivated land and built-up land in the cities that was affected by agricultural cultivation and industrial activities.Figure 7Distribution map of hot and cold spots of habitat quality in Shandong Province from 1980 to 2020.Full size imageOverall, the spatial distribution pattern of habitat quality in Shandong Province was relatively stable and affected by many factors, among which land use change was the most important one9,40,55. The most dominant land type in Shandong Province was cultivated land, which was concentrated in the northwest plain. Influenced by agricultural farming, the habitat quality of cultivated land presented medium-level category. At the same time, the habitat quality of some cultivated land has decreased due to the influence of construction land intrusion. The high vegetation coverage and rich species diversity of mountains and hills make their natural habitat quality superior. With the development of urban economy, the scale of construction land in coastal lowlands as well as inland urban areas continued to expand. The increase in population density as well as the intensity of land use activities has led to the expansion of regional dehabitatization. In addition, the dynamic changes in the habitat quality of the Yellow River Delta indicated that differences in the degree of land use change led to a variety of impacts on habitat quality. Therefore, habitat quality improvement and ecological protection should be based on local regional resource endowments and follow the concept of comprehensive, coordinated and sustainable development. Administration should formulate differentiated ecological protection strategies. For urban land development, authorities should increase the intensive utilization of construction land, limit the development boundaries of urban land and increase the greening rate inside urban land, such as equipped with urban green space park and other ecological land. In order to ensure the efficiency of agricultural production in Shandong Province, authorities should pay special attention to the conservation of cultivated land and to the development of ecological agriculture56. For natural ecosystems such as forest and grassland, authorities should improve the natural reserve system57. The vegetation ecological restoration project should be carried out according to local conditions. Drawing on the effective experience of ecological changes in the Yellow River Delta, we would take it as a typical example in future development and adopt corresponding administrative methods to coordinate the relationship between economy and habitat quality and change the dilemma of low-level habitat quality areas. Therefore, it is necessary to implement reasonable and effective territorial space planning to achieve regional sustainable development. More