Volume 10, Number 1 (October 2022):3871-3882, doi:10.15243/jdmlm.2022.101.3871 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 3871 Research Article
Land use change and baseflow recession modelling in Wuryantoro Watershed, Wonogiri Regency, Central Java Province, Indonesia
Bokiraiya Latuamury*, Mersiana Sahureka, Wilma N Imlabla, Miranda H Hadijah, John F Sahusilawane, Husain Marasabessy, Moda Talaohu
Department of Forestry, Faculty of Agriculture, University of Pattimura, Jl. Ir. M. Putuhena, Ambon, Maluku 97233, Indonesia
*corresponding author: [email protected]
Abstract Article history:
Received 11 June 2022 Accepted 30 July 2022 Published 1 October 2022
Hydrological phenomena on the scale of a watershed are complex and may never be understood holistically. One of the innovations in baseflow hydrological modelling is the analysis of the baseflow recession curve, generally expressed as the natural storage of river flows and containing valuable information about the properties and characteristics of natural aquifer storage. This study aims to model land use change and baseflow recession in the Wuryantoro watershed, Wonogiri Regency, Central Java Province. The research method uses an exponential model in which changes in the characteristics of a baseflow recession are a function of land use changes over a certain period. The calibration of the seven graphical models of land use change against the characteristics of the baseflow recession shows that the seven curves of the land use change graphic model have model coefficients and curve slopes that vary from gentle to steep. The slope of the gentle and steep curve describes the bottom flow deposits' condition over time. The state of water storage in the seven better graphical models is that the change of forest remains forest followed by the change of agriculture into the forest, forest into the settlement, change of agricultural land into the settlement, change of forest to agricultural land, settlement remains settlement and change of agricultural land remains agrarian land.
Keywords:
baseflow recession land use change the curve model
To cite this article: Latuamury, B., Sahureka, M., Imlabla, W.N., Hadijah, M.H., Sahusilawane, J.F., Marasabessy, H. and Talaohu, M. 2022.Land use change and baseflow recession modelling in Wuryantoro Watershed, Wonogiri Regency, Central Java Province, Indonesia. Journal of Degraded and Mining Lands Management 10(1):3871-3882, doi:10.15243/jdmlm.
2022.101.3871.
Introduction
The forest on Java Island is not significant and relatively narrow. According to GFW estimates, the forest area in 1997 was estimated at 1.9 million hectares. The type of forest found on Java Island has a low percentage of forests, only 14% of its total land area. While on other large islands, there are still 35- 81%. From this figure, it can be seen that if forests on other islands fall into the category of 'severely damaged,' then Javanese forests fall into the category of 'swallows are left to be severely damaged for too
long. It is precise because Java's forests are so narrow and rare that their preservation becomes very relevant for research. Most Javanese forests cannot be considered because their shape resembles a homogeneous wood garden and can be likened to a plantation forest Latuamury et al. (2020).
Forest areas in Java are production forests, especially teak, managed by Perhutani as the only HPH (IT) operating in Java. Some people argue that forest plantations, because of their canopy and homogeneous types of plants, Javanese forests cannot
Open Access 3872 be forested because their forests do not protect many
rare and unique species. Facts can justify this opinion, and it is precisely the reason why Javanese forests are so crucial to study, especially in the context of demographics where the island of Java is a very densely populated island, with more than half of Indonesia's population living on an island as small as Java. Java's population is very dense, so it is necessary to associate the population problem with the forest.
Java's forests are under tremendous pressure and are constantly being urged by the need for agricultural and residential land. Forest destruction then causes floods and landslides that cause environmental problems to be crucial.
LUCC modelling in hydrology continues to develop because the impact of land use changes on the field of hydrology is significant in developing sustainable water resource strategies (Pauleit et al., 2005). Some modelling of land use changes to hydrological components has made advances in spatial and temporal model simulations. Cao et al. (2009), Nejadhashemi et al. (2010), Romshoo et al. (2012), Lee et al. (2014), and Aboelnour et al. (2020) developed the SWAT (Soil and Water Assessment Tool) model to simulate land use changes in many regions of the world. The results showed that urbanization contributed the most strongly to the increase in surface runoff rates and water yields within 25 years (Lin et al., 2007; Allan et al., 2015).
Conversion of shrubs into built-up lands is the most substantial contributor to decreased base flow and percolation, which contributes to an increase in evapotranspiration rate and negatively impacts water resources on the watershed scale (Legesse et al., 2003;
Nejadhashemi et al., 2010; Price, 2011).
Bormann et al. (2007) assessed the impact of land use changes on hydrological components using the LUCHEM model by presenting an intercomparison model for simulation of lumped, semi-lumped, and fully distributed hydrological models in Germany's low mountain watersheds. The model simulation used two rainfall, temperature, and leaf area index datasets to analyze variations between models. Sannigrahi et al.
(2020) combined calibration of Cellular Automata models based on the biophysical characteristics of the watershed region. The combination of models for assessing the potential impact of land use change over the next 20 years on hydrological processes shows that land use change is increasing (Pana et al., 2010; Yang et al., 2012). Simulations of land use change concluded that urbanization increases surface runoff and reduce water supply, thus highlighting the importance of incorporating parameters of land use change in hydrological modeling (Bormann et al., 2007; Lin et al., 2007).
The development of a linear reservoir model to assess the effect of land use change on baseflow hydrology was researched by (Wittenberg, 1999;
Buytaert et al., 2004). Wang et al. (2009) and Wang
and Cai (2010) explored the shape and slope of long- term baseflow recession curves during winter and summer based on climate factors and human activity in the American watershed. Modelling land use changes to baseflow hydrology is relatively tiny in tropical regions such as Indonesia (Fatchurohman et al., 2018; Nurkholis et al., 2019; Latuamury et al., 2020).
The morphometric characteristics of Wuryantoro have an area of 17.78 km2. The length of the main river is 2.18 km, the drainage density is 2.49 km/km2, and the length of the river from the center of the watershed is 4.47 km, The dendritic flow pattern. And the landform of the alluvial fan. Based on the drainage density, watershed form factor, and flow pattern, it shows that the drainage density of the watershed is in the normal flow range with (1<Dd< 5), and there is no flooding, meaning that the watershed drainage conditions of the study are permeable and infiltration is low. While the shape of the watershed, based on the results of the Circulation Ratio (Rc), shows most dendritic flow patterns and the shape of the watershed is Elliptical, indicating normal flow conditions are characterized by moderate Qp, slow rising time (Tp), and slower recession time (Latuamury et al., 2020).
Therefore, research using Matlab® software to model the characteristics of riverbed recession as a function of land use change in the Wuryantoro watershed, Wonogiri Regency of Central Java Province, is expected to add references to baseflow hydrology research.
Materials and Methods Study site
Wuryantoro Watershed was selected as the study area based on the availability of time-series land use change data and daily discharge data from 2000 until 2010 to characterise baseflow recession and the graphical modelling of its effects on land use change on baseflow recession in the Matlab® program.
Methods
The procedure for collecting land use change data begins with preparing Landsat imagery data for the 2000 and 2010 research periods. The imagery Radiometric correction to the surface stage is carried out to eliminate the influence of sensor errors and atmospheric effects on spectral reflections of objects captured by sensors, and geometric correction of images using the image to map rectification method for correct position correction and actual position in the field. The geometric correction has three stages, namely: rectification (correction) or restoration (restoration) of the image so that the image coordinates correspond to the geographical coordinates, registration (matching) the position of the image with other images or transforming the coordinate system of
Open Access 3873 multispectral imagery or multitemporal imagery,
registration of imagery to a map or transformation of the image coordinate system to map produces an image with a particular projection system.
The stage of processing land use change data using ENVI software and started data processing on the manage and install plugins menu entered classification in the search then checked the Semi- Automatic (classification Plugin), opened the Semi- Automatic Classification Plugin menu, and carried out the image processing process to analyze land use changes. The next stage is also the process of masking images with non-study area data and transferring data between vegetation and non-vegetation objects, non- study area separator data obtained from vector data of forest area boundaries modified sufficiently by manual digitization of the image visual interpretation process.
Forest areas usually show appearance characteristics that are far from settlements and predominantly vegetation, so the boundaries of the vector data are adjusted to the forest appearance data displayed by the imagery. The masking process of non-vegetation objects is carried out using vector data from the density slicing process for non-vegetation objects. Spatial data on land use change in a multi-temporal manner as a result of Landsat imagery analysis and land use maps obtained from the Maluku Provincial Forest Area Management Agency of the Ministry of Environment and Forestry on a scale of 1:50,000. The land use classification includes five main classes: forests, agricultural land, settlements, vacant land, and shrubs/shrubs (Latuamury et al., 2020).
The daily discharge data collection procedure for ten years (1 January 2000 to 31 December 2010) used hydro office 12.0 (http://hydrooffice.org) software, in particular the Recession Curve (RC) 4.0 package (Gregor and Malik, 2012) to analyze the characteristics of baseflow recessions in the research watershed.
Selection and processing of recession segments, as well as analysis of individual and master recession curves, as a representation of baseflow recession characteristics for research watersheds. Characterizing hydrographic recessions is an essential part of the hydrological response, especially related to understanding baseflow hydrological modelling.
Applications of hydrological modelling such as recession curve analysis on spatial scales help understand the role of land use variations in the characteristics of baseflow recessions because land use change is one of the environmental factors that undergo dynamic changes compared to other physical factors—stages of selection and processing of individual recession segments. The selected recession segments are further analyzed to obtain data on the unique recession curve and master's manual and genetic algorithmic processes. With the automatic RC selection function, the decrease in the flow rate is automatically selected as a time series recession segment, which is edited and analyzed afterwards. The
selected recession segments are then imported into an analysis of individual recession curves to calibrate the recession model.
Analysis
The recession model was a linear reservoir model (Equation 1) (Gregor and Malík, 2012; Arciniega- Esparza et al., 2017), covering two sub-regimes, and the recession parameters coefficients were optimized.
Q = Q e (1)
where: Qt is river discharge at time t, and Q0 is initial discharge. Q0 is linear storage - a recession base parameter, and k is the recession coefficient.
The baseflow recession curve analysis results represent the characteristics of the baseflow recession in the watershed observed. In this study, the dependent variables were the baseflow recession's characteristics, while the independent variables were seven dominant variations in land use. These variables were examined for their causal relationships using the graphical modelling offered by Matlab®. Matlab 2015 is a graphical model with a graphical user interface and consists of a group of runs through the GUI, with open- source code for modification. The familiar interface facilitates intuitive analysis for long-term datasets and graphical analysis to identify land use change effects on the baseflow recession. This software uses a linear reservoir equation to compute the recession as a function of land use change over ten years (from 2000 to 2010) in the Wuryantoro watershed. The modelled curve representing the relationship between land use change and recession produces the best model coefficient and residual value.
Results and Discussion
Baseflow recession characteristics of the Wuryantoro watershed
Calibration of linear reservoir models produces individual recession curves and master recession curves (MRCs) manually and through genetic algorithms. Calibration results of 80 recession segments obtain early recession values of Q0 ranging from (2.71-37.10 m3/sec) and median 6.69 m3/sec; α values range between (0.041-0.199) and the median 0.113, and the recession constant (Krb) ranges from 0.8196-0.9598 and the median is 0.8932. Calculation of baseflow recession parameters for eleven recession segments or individual recession curves shows the recession duration of recession events ranging from 10-11 days with an average of 10.45 days. Q0 initial discharge parameters vary from 2.71 m3/sec-7.59 m3/sec; α0.044 to -.158 with an average of 0.10, recession Constanta ranges from 0.8722 to 0.9569 with MSE model error ranging from 0.0002 to 0.080 with an average of 0.0047as presented in Table 1.
Open Access 3874 Table 1. Calculation of the baseflow recession parameters of the period 2000-2010.
Period Date Duration Q0 α k Q-Obs Q-Cal MSE
2000 21 Jul-1 Aug 2000 11 7.59 0.099 0.9057 4.622 4.410 0.0180
2001 19-29 July 2001 10 6.81 0.158 0.9570 3.087 3.090 0.0081
2002 13-24 Aug 2002 11 5.90 0.044 0.9570 4.659 4.630 0.0010
2003 16-26 Aug 2003 10 5.32 0.113 0.8932 3.177 3.020 0.0085
2004 5-15 July 2004 10 4.35 0.131 0.8772 2.308 2.260 0.0034
2005 12-22 Oct 2005 10 6.69 0.109 0.8967 3.896 3.880 0.0016
2006 9-19 July 2006 10 4.22 0.083 0.9204 2.787 2.790 0.0005
2007 15-25 July 2007 10 3.90 0.113 0.8932 2.273 2.220 0.0021
2008 8-19 Dec 2008 11 6.65 0.106 0.8994 3.762 3.715 0.0022
2009 10-21 July 2009 11 3.25 0.087 0.9167 2.102 2.015 0.0059
2010 27/08/2010 11 2.71 0.045 0.0450 2.122 2.115 0.0002
Source: primary data analysis in hydro office, 2000-2010.
The combination of Q0 recession parameters, recession coefficient k, and recession constant varies for research watersheds. Variations in individual recession curves in watershed research show that the Q0 recession parameters of a single recession event represented by peak discharges appear to affect the shape of the recession curve. This is because the recession phase is the stage where the contribution of the subsurface flow is dominant, so the parameters of the Q0 recession and the recess coefficient k significantly affect the shape of the baseflow recession curve. The master recession curve (MRC) forms manually and through a process of genetic algorithms, each presented in Figure 1. Visualisation of the MRC master recession curve manually shows a combination of the initial parameters of the Q0 recession (9.99), α (0.075), and Cbr (0.925) (Figure 2a). While visualisation of the MRC curve through the process of genetic algorithms obtains the values of the parameter Qo (30.76), the value of the α (0.075) and the recession
constant (0.928). The combination of recession parameters for both forms of MRC indicates the slope of the recession curve is relatively equal to the value of the recession constant ± 0.900. The slope of the master recession curve manually and genetic algorithms describe the relatively same baseflow water conditions as presented in Figure 2.
The shape of the Wuryantoro baseflow recession curve is supported by the characteristics of geological constituent materials, namely brection, tufa, floating rock, shale. The Mandalika formation is composed of brection, various sandstone materials, siltstone, limestone-sided claystone, sandstone, gravel sandstone, pumice rock, local volcanic brection, and the Wonosari Formation composed of agglomerate sided with tufaan sandstone and coarse sandstone, agglomerate brection, recited wood, exhaled chunk tufa. The existence of the karst alluvial plain may significantly affect the number of water deposits in the Wuryantoro watershed.
Figure 1. Visualization of the master recession curve manually.
Q0 = 9.99, α = 0.078, Krb =0.925
Open Access 3875 Figure 2. The master recession curve by genetic algorithms.
Sedimentation of alluvial material originating from upstream that has volcanic material allows for a reasonably thick alluvium deposit, thus allowing increased infiltration and increasing baseflow deposits.
Land use change in Spatio-temporal of the Wuryantoro watershed
The image analysis results of land use change in the Wuryantoro watershed showed that all seven classes of land use experienced significant changes. The forest change area decreased from 22.63% to 13.94%, followed by the forest change to agricultural land increased from 9.79% to 13.43%, and the shift in the forest to settlement increased from 8.11% to 11.75%.
The trend of agricultural land changes has decreased from 30.10% to 23.37%, followed by changes in agricultural land to forests increased from 5.74% to 7.97%, and changes in agricultural land to settlements increased from 8.24% to 11.31%. Lastly, settlements' fixed settlements rose from 15.38 % to 18.22%, while the use of vacant land, shrubs and water bodies was relatively constant during the study period, as presented in Figure 3. The variation in forest changes into agricultural and settlement land, and the change of farmland to settlement is more determined by biophysical location, i.e., place height, slope, and correlation coefficient of soil (Sannigrahi et al., 2020).
Figure 3. The land use change map of Wuryantoro Watershed from 2000 to 2010.
Q0=30.76; α = 0.075, Krb =0.928
Open Access 3876 At the same time, socio-economic parameters include
population density and distance to the main road (1,489 km), distance to the built-up area (1,029 km), and spread to the center (10.25 km) (Schröter et al., 2005). The results of the calculation of distance parameters to the main road for the research watershed are relatively short ± 1 km, the distance to the built-up area ± 1 km, and the distance to the city center from the nearest to the furthest ± 10 km. The length of the river ranges from ± 1 km.
A number of factors affect population density in a region. The concentration of the population in the region considers relatively small physiological barriers such as flat and fertile areas, easy access to transportation, and adequate availability of water; As opposed to barren areas, the water is difficult, or mountainous areas are usually small population densities. Variations in land use in research watersheds tend to experience significant changes. Wuryantoro watershed has distance access to the economic center and city center relatively close, so the proximity of strategic location is one of the driving factors of relatively high population density levels.
Modelling of baseflow recession characteristics and land use changes
Graphical model of forest land change against river baseflow recession
The graphical model of forest land use change with baseflow recession characteristics obtained the following equation: the forest-fixed forest has Qmodel
= 0.0057019*Exp(-0.30599*ts) with an MSE value of 5.8453e-07. The change of forest to agricultural land has a Qmodel = 0.0018818*Exp(-0.18477*ts) with an MSE value of 1.4916e-07, and the shift in the forest to settlement has a Qmodel = 0.0001378*Exp(- 0.17188*ts) with an MSE value of 9.9444e-08 presented in Table 2. The graphical model of forest change remains forest, Qmodel = 0.0057019*Exp(- 0.30599*ts), MSE 5.85E-07, states that if the forest remains a forest, it increases by 1, then the change in the characteristics of the base flow recession decreases by 0.030599. This graphical model shows that the collapse of the forest area impacts reducing the baseline flow recession, where the slope of the model curve of forest area change is relatively gentle, as presented in Figure 4.
Table 2. The model equations representing the effects of changes in forest areas on baseflow recession.
Modelled curves Model equations MSEs
Forest-forest (preserved) Qmodel= 0.0057019*Exp(-0.30599*ts) 5.85E-07 Forest to agricultural land Qmodel= 0.0018818*Exp(-0.18477*ts) 1.49E-07
Forest to settlement Qmodel= 0.0001378*Exp(-0.17188*ts) 9.94E-08
Source: Curve model analysis in Matlab® 2012.
Figure 4. The graphical model of forest change remains forest on baseflow recession.
This graphical model shows that the change of forests to agricultural land causes a smaller decrease in baseflow recessions compared to the change of forests
into settlements; thus, the slope of the model curve is more gentle than the curve of the model of forest change into residential land as presented in Figure 5.
Open Access 3877 Figure 5. The graphical model of forest change to agricultural land on baseflow recession.
The graphical model of forest-to-settlement change, Qmodel= 0.0001378*Exp(-0.17188*ts) with MSE 9.94E-08, states that if the forest change to settlement increases by 1, then the change in the characteristics of the base flow recession decreases by 0.17188. This graphical model shows that the shift in forests into settlements has an impact on reducing the recession of the base stream, and the slope of the model curve becomes more sinister compared to the change of fixed forests to forests or the change of forests to agricultural land as presented in Figure 6. Graphical visualization of models modelling the change of forests into farmland and settlements shows the slope of the
recession curve of riverbed flow decreases for all three model curves. Forest changes significantly influence the course of flow, reducing infiltration capacity and flow absorption by water aquifers. The baseflow recession model approach can explain the impact of human activity on discharge and storage relationships (Thomas and Vogel, 2015). The slope of the recession curve depends on human activity (Mizumura, 2005;
Wang and Cai, 2010). The baseflow recession model curve through the best model of recession data distribution that presents the best estimate of recession parameters (Tallaksen, 1995; Griffiths and McKerchar, 2010).
Figure 6. The graphical model of forest-to-settlement change on baseflow recession.
Open Access 3878 Graphical model of agricultural land changes on the
characteristics of river baselow recession
The graphical model of agricultural land use change with baseflow recession characteristics obtained the following equation: agricultural land to forest has Qmodel= 0.00070677*Exp(-0.15875*ts) with an MSE
value of 2.6146e-08. The change of agricultural land (unchanged) has a Qmodel= 0.0078918*Exp(- 0.27436*ts) with an MSE value of 1.2951e-06, and the agricultural land to settlement has a Qmodel = 0.0001378*Exp(-0.17188*ts) with an MSE value of 9.5101e-08 presented in Table 3.
Table 3. The model equations representing the effects of changes in agricultural land on baseflow recession.
Modelled curves Model equations MSEs
Agricultural land to forest Qmodel = 0.00070677*Exp(-0.15875*ts) 2.6146e-08 Agricultural land-agricultural land (unchanged) Qmodel = 0.0078918*Exp(-0.27436*ts) 1.2951e-06 Agricultural land to settlement Qmodel = 0.00014189*Exp(-0.18402*ts) 9.5101e-08 Source: Curve model analysis in Matlab® 2012.
The graphical model of farmland change remains farmland, Qmodel= 0.0078918*Exp(-0.27436*ts) states that if farmland land increases by 1, then the change in base flow recession characteristics decreases by 0.27436. This graphical model shows that the
agricultural land increase reduces the baseflow recession. The slope of the model curve for changes in agricultural land use remains relatively sloping agricultural land (model coefficients 0.2744 and MSE 9.94e-08), as presented in Figure 7.
Figure 7. The graphical model of farmland change remains farmland on baseflow recession.
The graphical model of the change of agricultural land into forests, Qmodel= 0.00070677*Exp(-0.15875*ts), states that if agrarian land change into forests increases by 1, then the difference in the characteristics of the baseflow recession decreases by 0.15875. This graphical model shows that the shift of farmland to forest impacts reducing the baseflow recession less than the increase in farmland remains farmland. The model curve's slope is gentler than the model curve (the change remains to farmland as presented in Figure 8.
The graphical model of the change of agricultural land into settlements, Qmodel= 0.00014189*Exp(- 0.18402*ts), states that if the change of agricultural land into settlements increases by 1, then the change in the characteristics of the base flow recession decreases
by 0.18402. This graphical model shows that the shift in farmland into settlements has less impact on reducing the baseflow recession compared to the shift in farmland to farmland, but more significant than the change of farmland to forest land. as presented in Figure 9.
The third model curve slope of variation in agricultural land change shows that the model curve for the change of agricultural land into forest land is gentler compared to the model curve of change that remains farmland, and the change of agricultural land remains agrarian land. This can be seen in the coefficients of each model. The combination of coefficient values and residual values of the MSE model describes the curve of the slope model of each class of agricultural land change.
Open Access 3879 Figure 8. The graphical model of the change of agricultural land into forests on baseflow recession.
Figure 9. The graphical model of the change of agricultural land into settlements on baseflow recession.
Visualization of the slope of the three curves The model of agricultural land variation depicts agricultural land continuing to increase significantly, while the change from farmland to forest Back is relatively slowing down. Conservation efforts in the research watershed have somewhat not changed considerably.
The model curve's slope has implications for the characteristics of recessions of the baseflow of the research watershed hydrologically. The gentle slope of the model curve describes the characteristic condition of the baseflow recession, moving slowly and causing the availability of flow to remain assured for a relatively long time. At the same time, the steep slope
of the model curve describes the condition characteristic of the baseflow recession hurrying and causing the flow deposit condition to be relatively drained (wasteful).
The results of this study are also relevant to the baseflow recession research that is taking place in several regions. Wang and Cai (2010) explored the shape and slope of long-term base flow recession curves during winter and summer based on climate factors and human activity in the American watershed.
They showed that the sloping trend of the long-term base flow recession curve in all watersheds results from human intervention in changing land use. In urban watersheds, the slope of the recession curve
Open Access 3880 decreases over winter and summer due to waste
disposal. In the Kankakee watershed with irrigation, the pitch of the recession curve seasons in winter but increases in summer, and vice versa in winter and summer trends are caused by seasonal water use of agricultural irrigation areas (Wang and Cai, 2010). In the Embarras watershed with rain-dredged agriculture, the slope of the recession curve decreases in winter but does not show changes in the summer. The source of the land water intake versus surface water also has a different impact on the shape and slope of the base flow recession curve-the human and climate influence the processes of the recession of river baseflows in almost all world regions. Analysis of land use changes has a significant relationship to the characteristics of
baseflow recessions over the long term (Sannigrahi et al., 2020).
Graphical model of residential land use against the characteristics of base flow recession
The graphical model of the change of settlements into settlements, Qmodel= 0.0035004*Exp(-0.2172*ts) with MSE 4.261e-07, states that if the change in settlements remains settlements,, increasing by 1, then the difference in the characteristics of the baseflow recession decreases by 0.2172. This graphical model shows that changes in settlements are increasing significantly, affecting the characteristics of baseflow recessions, particularly compared to other land use changes presented in Figure 10.
Figure 10. The graphical model of the change of settlements into settlements on baseflow recession.
The calculation results of the model coefficients that vary from the smallest to the largest are forests that remain forests (0.030599), changes of agriculture to forests (0.15875), changes of forests into settlements (0.17188), changes of agricultural land into settlements (0.18402), changes of forests to agricultural land (0.18477), settlements remain settlements (0.2172), and finally, changes of agrarian land remains agricultural land (0.27346). Based on the coefficients of the model, visualizing the seven curves of the land use change model against the characteristics of the baseflow recession describes the slope of the seven graphic models that vary from gentle to steep; namely, the curve of the forest model remains to a more gentle forest, followed by the model curve of agricultural change into forests, the curve of the model of forest change into settlements, the curve of the model of change of agricultural land into settlements, the change of forests into agricultural
land, settlements remain settlements and the curve of the model of change of agricultural land remains agrarian land. Variations in the slope of the seven model curves have implications for the characteristics of baseflow recessions, namely variations in baseflow deposit conditions for overall land use change.
Changes in the use of agricultural land in settlements are increasing significantly. Land changes are inevitable due to economic growth and increasing population (Huang et al., 2016; Sannigrahi et al., 2020) and urbanization (Bao dan Fang, 2007; Hauser et al., 2017). The increased rate of settlement change can be attributed to hydrological components such as residence time. The study results (Buytaert et al., 2004) simulated a stay time on an undisturbed watershed having a rapid response with a time of 5.4 hours, followed by a slower response of T 44.3 hours.
The base stream has an average T value of 360 hours.
The reaction from farmland is similar to the T value of
Open Access 3881 3.6 hours, 27.2 hours, and 175 hours, respectively. As
a result, flow release in a disrupted watershed is 40%
faster than in undisturbed catchment areas, so base flow decreases rapidly at a lower rate.
Simulations of land use change in some regions of the world show that urbanization contributes the most to the increase in the rate of surface runoff and water yields within 25 years (Nejadhashemi et al., 2010; Kepner and Yuan, 2011). The conversion of shrubs into built-up land is the most substantial contributor to decreased base flow and percolation and increases evapotranspiration rates. This then harms water resources on the watershed scale (Wang et al., 2009; Hauser et al., 2017).
Conclusion
The calibration of the seven graphical models of land use change against the characteristics of the Wuryantoro watershed baseflow recession shows that the seven curves of the land use change graphic model against having varying model coefficients. The graphic model of land use change against baseflow recession characteristics with the smallest to largest model coefficients depicts the slope of the model curve ranging from gentle to steep namely, the curve of the forest model remains to be a more gentle forest, followed by the model curve of agricultural change into the forest, the model curve of forest change into settlements, the model curve of changing agricultural land into settlements, the change of forests into agricultural land, settlements remain settlements. The curve of the farmland change model remains farmland.
Variations in the slope of the seven model curves show that the sloping model curves have flow deposit conditions that are slowed down in nature and last for a long time. At the same time, the steeper slope of the model curve has flow conditions that are quickly depleted and last for a short time. The slope of the model curve that varies from gentle to steep describes the baseflow storage condition over time. The condition of water storage in the forest class remains a better and longer forest, followed by the change of agriculture into forests, forests into settlements, changes of agricultural land into settlements, changes of forests into agricultural land, settlements remain settlements, and changes of agricultural land remain agrarian land. Thus, land use change to the characteristics of a baseflow recession requires significant environmentally friendly soil and water conservation efforts in the research watershed.
Acknowledgements
The authors would like to thank the heads and staff of the Research and Technology Management Station in Surakarta (BPTPDAS), Ministry of Environment and Forestry, for permitting the collection of secondary data (daily discharge during the research years) for the completion of this paper.
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