# Application of the Soil and Water Assessment Tool (SWAT) Model for hydrological modelling in the Upper Oum Er Rbia watershed (Middle Atlas, Morocco)

Tarik El Orfi, Mohamed El Ghachi, Sébastien Lebaut, Ionel Haidu

► To cite this version:

Tarik El Orfi, Mohamed El Ghachi, Sébastien Lebaut, Ionel Haidu. Application of the Soil and Water Assessment Tool (SWAT) Model for hydrological modelling in the Upper Oum Er Rbia watershed (Middle Atlas, Morocco). 2nd Environment and Natural Resources: Challenges and Solutions, May 2023, Khénifra (Morocco), France. pp.012014, 10.1088/1755-1315/1398/1/012014. [hal-04757550](https://hal.science/hal-04757550v1)

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PAPER • OPEN ACCESS

Application of the Soil and Water Assessment Tool (SWAT) Model for hydrological modelling in the Upper Oum Er Rbia watershed (Middle Atlas, Morocco)

To cite this article: Tarik El Orfi et al 2024 IOP Conf. Ser.: Earth Environ. Sci. 1398 012014

View the [article online](#) for updates and enhancements.

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# Application of the Soil and Water Assessment Tool (SWAT) Model for hydrological modelling in the Upper Oum Er Rbia watershed (Middle Atlas, Morocco)

Tarik El Orfi<fnref n="1" />,<fnref n="2" />*, Mohamed El Ghachi<fnref n="1" />, Sébastien Lebaut<fnref n="2" /> & Ionel Haidu<fnref n="2" />

**Abstract.** In the study assessing the impact of global change on hydrosystems, particularly water resources, calibrated and validated tools are deemed crucial for accurate representation of the area's characteristics. The Soil and Water Assessment Tool “SWAT” model is identified through literature review as well-suited for evaluating changes in land use and climate effects on water balance components. Applied to the Upper Oum Er Rbia watershed and its sub-basins, namely Oum Er Rbia, Srou, and Ouaoumana, the SWAT model demonstrates its effectiveness over the 1982-2019 period. Employing the sequential uncertainty adjustment method (SUFI-2) in SWAT-CUP software for calibration and validation, the model achieves good performance in simulating river flows. Statistical tests, including "R<fnref n="2" />," "NSE," "PBIAS," and "RSR," reveal consistent accuracy across various stations. The positive outcomes affirm the SWAT model's operational capability for diagnostic assessments, risk evaluation, adaptation measures, and potential future projections in these three sub-basins.

**Keywords:** SWAT model, Hydrosystems, Water balance, Oum Er Rbia, Middle Atlas.

## 1. Introduction

Human activities, primarily greenhouse gas emissions, have unequivocally caused global warming, leading to a 1.1°C increase in global surface temperature from 1850-1900 to 2011-2020 [1]. This global phenomenon significantly affects water resource availability by increasing evaporation rates. Population growth further compounds the situation, especially in water-scarce regions. The Middle East and North Africa (MENA) region grapples with water scarcity, aggravated by population growth and extensive water use, particularly for irrigation, exceeding annual renewable reserves [2]. The trend of global warming, along with irregular rainfall and heightened extreme events, has been verified, disrupting the seasonal cycle [3].

Due to its location on the southern shore of the Mediterranean, Morocco is typically a semi-arid country that has consistently faced extreme climatic events. Since the 1980s, Morocco has been grappling with water stress, marked by an increased frequency and intensity of droughts [4]. Concurrently, water resources are overexploited and nearing the threshold of ecologically sustainable groundwater and surface water extraction [5]. Water availability has dropped from 2,500 m³ per person in 1960 to 645 m³ per person in 2015, significantly below the "water poverty line" of 1,000

<figure><img src="image_3.png" /></figure>

<footnote n="1">¹ “DPRP” Laboratory, Sultan Moulay Slimane University, Beni Mellal, Morocco.</footnote>

<footnote n="2">² “LOTERR” Research Unit, University of Lorraine, Metz, France.</footnote>

<footnote>* Corresponding author: elorfitarik@gmail.com</footnote>
$m^3$/person/year. Even without alterations in water resource availability, a projected population of nearly 44 million by 2050 would result in a ratio of 510 $m^3$/person/year, approaching the "extreme water scarcity threshold" of 500 $m^3$/person/year [6].

In response to this situation, significant efforts have been undertaken at the national level, encompassing a reinforced dam policy, structured and organized water management planning, and the implementation of adaptation measures and actions [3]. Morocco is embracing three key strategic plans related to water: the National Water Plan, the National Water Saving Plan, and the Green Morocco Plan, each with a primary focus on agriculture [7]. Various planning tools aimed at facilitating water resource management have been established, including the Integrated Water Resources Management Master Plan, Participatory Water Management Contract, and others [8]. However, these institutional tools face limitations due to a lack of in-depth knowledge about available water resources and the underlying physical processes. To address this knowledge gap, hydrological models are employed to simulate hydrological processes and simplify intricate rainfall-runoff reactions at the catchment scale. In this context, diverse models have been developed, such as TOPMODEL [9], GR4J [10], HyMod [11], HEC HMS [12], SWAT [13], among others. Notably, SWAT has demonstrated effectiveness [14].

The agro-hydrological model SWAT (USGS Soil and Water Assessment Tool) is a semi-distributed model designed to simulate surface runoff, soil erosion, nutrient, and sediment transport [15]. Its reliability has been validated in various global regions. In Morocco, SWAT has been applied to the Sebou watershed, including the Upper Sebou [4], R'dom [16], Mikkes [5], M'diz [17], the north-western watershed encompassing Nakhla [18], Laou [19], Kalaya [20], Tleta [21], and the Bouregreg watershed, covering Mazer and El Himer [23]. It has also been utilized in the Oum Er Rbia watershed [24] for both downstream [25] and upstream [26] areas, as well as in the Tensift watershed, specifically for N'Fis [27].

The Oum Er Rbia watershed is abundant in renewable resources, contributing over 3.45 $Mm^3$, equivalent to 20% of Morocco's resources, primarily surface water [28]. With agricultural water demand estimated at over 80% of withdrawals, it holds crucial significance in water management [29]. At the scale of the upper Oum Er Rbia "Up-OER" watershed, no prior studies have employed SWAT to assess water resource functionality; only one study has focused on quantifying the impact of erosion and evaluating soil losses. Our objective is to present a model that estimates the water resource availability supplying the Ahmed El Hansali dam; the primary water source for the Tadla irrigated perimeter, one of Morocco's most crucial perimeters.

## 2. Materials and methods

### 2.1. Study area

The Up-OER watershed is situated within the central plateau and Middle Atlas Mountains of Morocco. It is demarcated by the Moulouya watershed to the east, the Sebou watershed to the north, and the Bouregreg watershed to the west (Figure 1). This study area typifies the Middle Atlas, featuring an average altitude of 1,350 m and peaks reaching up to 2,400 m above sea level. Owing to its elevation, geographical positioning, and exposure to oceanic influences, the climate is Mediterranean, characterized by wet winters and dry summers [30]. The average annual rainfall stands at approximately 481 mm at the Chacha N'Amellah station and 564 mm at the Tarhat station over the period 1982-83/2018-19.

From a geological perspective, the Paleozoic outcrops at the base of the Middle Atlas along the entire southeastern edge of the Central Plateau, generally forming schistose massifs. Above the Primary basement, there are Permo-Triassic formations of gypsiferous marl and clay. The Lower Jurassic (Lias) is marked by calcareous-dolomitic sedimentation. During the Cretaceous to the Paleocene period, the sea's return led to sedimentation with alternating layers of marl, marly limestone, and yellowish limestone. Finally, there are basalts and alluvium dating from the Quaternary period.

The Oum Er Rbia river originates from the Oued Fellat. The Oued Fellat is nourished by a cluster of springs known as the " Oum Er Rbia springs". It only takes the confluence of the Oued Fellat with the Oued Bou Idji for the river to be named Oum Er Rbia [31]. The flow of the Oum Er Rbia river is
continuous, and the average annual flow at the Tarhat station is approximately 15 m³/s. The Oued Srou serves as the primary tributary of the Up-OER watershed, boasting a mean annual flow of 7 m³/s.

<figure><img src="image_4.png" /><figcaption>Figure 1. Geographical positioning of the Up-OER watershed.</figcaption></figure>

## 2.2. SWAT Model

### 2.2.1. Model description

The SWAT (Soil and Water Assessment Tool) model is a continuous, semi-distributed hydrological model operating on a daily time step. This model was developed by [13] for the Agricultural Research Service of the United States Department of Agriculture (USDA). SWAT integrates climatic data, hydrological and biological processes, as well as complex phenomena such as erosion, infiltration, and agricultural practices [32]. It is employed to predict the impacts of land management practices on surface water, sediments, and agricultural chemical yields over an extended period in large watersheds [33].

SWAT provides the capability to simulate numerous physical processes in a watershed with high spatial resolution [34]. To achieve this, the watershed is divided into several sub-basins, which are further subdivided into Hydrological Response Units (HRUs) [32]. These HRUs are homogeneous spatial units characterized by soil, vegetation cover, and slope, from which model calculations are derived.

The hydrological cycle of the SWAT model is based on the following water balance equation [33]:

$$SW_t = SW_0 + \sum_{i=1}^{t} (R_{day} - Q_{surf} - E_a - W_{seep} - Q_{gw}) \quad (1)$$

where t is time in days, $SW_t$ (mm) and $SW_0$ (mm) respectively represent the soil moisture content at time t and at the initial time, $R_{day}$ (mm) is the precipitation amount at time i, $W_{seep}$ (mm) is the water infiltrated into the soil from the surface at time i, $Q_{surf}$ (mm) is the water available for runoff at time i, $Q_{gw}$ (mm) is the base flow from the groundwater table at time i, and $E_a$ (mm) is the evapotranspiration at time i.

To estimate evapotranspiration, SWAT adopts three methods: Hargreaves, Priestley and Taylor, and Penman-Monteith. Given the data availability, evaporation has been estimated using the Penman method
(1956). Among the two methods for estimating runoff and infiltration, the "Curve Number" (CN) method and Green and Ampt's infiltration method, the "CN" method developed by the Soil Conservation Service (SCS) [36] was selected. Finally, to estimate runoff across the hydrographic network, the variable storage coefficient method developed by Williams (1969) was chosen in this study.

The main steps in building the SWAT model are: i) creating the model using a hydrological program, such as ArcSWAT, ii) performing a sensitivity analysis, iii) conducting a calibration and uncertainty analysis, iv) validation [38].

### 2.2.2. Data

In this study, topography and slopes were derived from the Digital Terrain Model "TanDEM-X DEM" (© DLR, 2021), which was produced by the German Aerospace Center at a spatial resolution of 12 m (Figure 2a).

The land cover of the watershed is derived from a Landsat-8 satellite image with a spatial resolution of 30 m, using a supervised "Random Forest" classification through SNAP software (Figure 2b). The "RF" algorithm, a combination of tree predictors, allows each tree to depend on the values of a randomly sampled vector, enhancing classification performance by incorporating various remote sensing data sources with biophysical spatial data [40]. Applying the "RF" algorithm enabled the production of a land cover map with four classes: forests, cultivated areas and rangelands, bare soil, and water.

Accurate soil characteristics (horizon, depth, and texture) play a crucial role in hydrological modeling, influencing the distribution of runoff and infiltration from rainfall. However, there is limited pedological information specific to the Up-OER watershed. To address this gap, we utilized the "HWSD" harmonized world soil database (Figure 2c) [41]. Developed in collaboration between the FAO (Food and Agriculture Organization of the United Nations) and IIASA (International Institute for Applied Systems Analysis), and in partnership with ISRIC (International Soil Reference and Information Centre), EISBN (European Soil Bureau Network), and ISC-CAS (Institute of Soil Science - Chinese Academy of Sciences), this database, with a spatial resolution of 1 km², has proven successful in SWAT modeling [42].

Each combination of land use, soil type, and slope class is represented as a distinct Hydrological Response Unit (HRU) within each sub-basin (Figure 2d).

Daily hydro-climatic data for the Taghzoute, Chacha n'Amellah, and Tarhat stations were collected from the Oum Er Rbia Water Basin Agency. Missing daily (Max-Min) temperature data were obtained from the National Aeronautics and Space Administration's (NASA) Langley Research Center (LaRC) World Energy Resources Prediction (POWER) project, funded by NASA's Earth and Applied Sciences Program. The data cover a common period of 37 years from 1982 to 2019.

<figure><img src="image_5.png" /></figure>
<figure><img src="image_6.png" /><figcaption>Figure 2. (a) Digital terrain model at 12 m; (b) Land use; (c) Soil map & (d) Sub-basins map in the Up-OER watershed.</figcaption></figure>

### 2.2.3. Sensitivity analysis, calibration and validation

The calibration of hydrological models poses a global challenge due to the inherent uncertainty associated with a multitude of parameters [43]. The SWAT model is no stranger to this challenge, given the extensive number of parameters that require calibration. In addition to pedological, topographical, climatic, and land-use data, over 600 parameters can be calibrated in SWAT. Parameters related to surface runoff, such as "CN2," those influencing evapotranspiration, and base flow parameters are typically the most adjusted [32]. Among the five algorithmic procedures available in SWAT-CUP (SUF12, PSO, GLUE, ParaSol, and MCMC), we opted for the SUFI-2 program, "Sequential Uncertainty Fitting V2" [44], for model calibration and validation.

The uncertainty of input parameters is represented by uniform distributions, while the uncertainty of model results is quantified by the 95% prediction uncertainty (95PPU), calculated at the 2.5% and 97.5% levels of the cumulative distribution of the output variables [45]. In this study, the model underwent calibration and validation using flow datasets from the Taghzoute, Chacha n'Amellah, and Tarhat stations. Each station was independently calibrated by adjusting parameters associated with its sub-basins. Once optimal parameters were validated, their values were set before proceeding to the next stations [46].

The performance of the SWAT model is assessed initially through graphical analysis and subsequently using statistical indices. A compilation of performance measures, as recommended by [47], includes several statistical coefficients, with four being the most utilized: coefficient of determination (R²), Nash Sutcliffe coefficient (NSE), bias (PBIAS), and squared error (RSR) (table 1).

$$R^2 = \frac{\left[ \sum_i (Q_{m,i} - \bar{Q}_m) (Q_{s,i} - \bar{Q}_s) \right]^2}{\sum_i (Q_{m,i} - \bar{Q}_m)^2 \sum_i (Q_{s,i} - \bar{Q}_s)^2} \quad (2)$$

$$NSE = 1 - \frac{\sum_i (Q_{m,i} - Q_s)_i^2}{\sum_i (Q_{m,i} - Q_s)_i^2} \quad (3)$$

$$PBAIS = 100 \times \frac{\sum_{i=1}^{n} (Q_{m,i} - Q_s)_i}{\sum_{i=1}^{n} Q_{m,i}} \quad (4)$$

$$RSR = \frac{\sqrt{\sum_{i=1}^{n} (Q_{m,i} - Q_s)_i^2}}{\sqrt{\sum_{i=1}^{n} (Q_{m,i} - \bar{Q}_m)^2}} \quad (5)$$

In this study, the period from 1982 to 1984 was employed for model initialization, with the sub-periods 1985-95 and 2007-12 utilized for calibration. The periods 1996-06 and 2013-19 were designated for validation. The selection of these periods was grounded in the homogeneity of dry and wet periods, as well as the mean and standard deviation of the chronicles.
<table><caption>**Table 1.** The performance of the statistical indices employed [47].</caption><thead><tr><th>Performance rating</th><th>R<sup>2</sup></th><th>NSE</th><th>PBIAS</th><th>RSR</th></tr></thead><tbody><tr><td>Unsatisfactory</td><td>&le; 0.5</td><td>&le; 0.5</td><td>&ge; &plusmn;25</td><td>&gt; 0.7</td></tr><tr><td>Satisfactory</td><td>0.5 &lt; R<sup>2</sup> &le; 0.7</td><td>0.5 &lt; NSE &le; 0.65</td><td>&plusmn;15 &le; PBIAS &lt; &plusmn;25</td><td>0.6 &lt; RSR &le; 0.7</td></tr><tr><td>Good</td><td>0.7 &lt; R<sup>2</sup> &le; 0.8</td><td>0.65 &lt; NSE &le; 0.75</td><td>&plusmn;10 &le; PBIAS &lt; &plusmn;15</td><td>0.5 &lt; RSR &le; 0.6</td></tr><tr><td>Very good</td><td>&gt; 0.8</td><td>0.75 &lt; NSE &le; 1</td><td>&lt; &plusmn;10</td><td>0 &lt; RSR &le; 0.5</td></tr></tbody></table>

# 3. Results

## 3.1. Sensitivity analysis

In mountain watersheds, precipitation and temperature exhibit a close relationship with altitude. To consider orographic effects, altitude bands were generated for each Hydrological Response Unit (HRU). The parameters PLAPS and TLAPS were then uniformly applied to all HRUs to adjust precipitation and temperature. For other parameters, a list of approximately twenty was compiled, drawing insights from the findings of [32] and studies on the application of the SWAT model, particularly within the Oum Er Rbia watershed [2,24,25]. Sensitivity analysis was conducted using the SUFI-2 program, where a higher absolute value of the t-stat and a smaller p-value indicated greater parameter sensitivity [38]. The analysis identified the nine most sensitive parameters for Hab-OER (table 2).

This study underscores that the key parameters influencing the hydrology of the study area are the "curve number" (CN2), soil parameters, specifically soil layer water capacity "SOL_AWC," saturated hydraulic conductivity "SOL_K," and soil wet bulk density "SOL_BD." Additionally, two compensating factors, soil evaporation "ESCO" and plant uptake "EPCO," play a significant role.

<table><caption>**Table 2.** The performance of the statistical indices employed [47].</caption><thead><tr><th>Parameters</th><th>Signification</th><th>t-Stat</th><th>p-Value</th></tr></thead><tbody><tr><td>R_CN2.mgt</td><td>SCS runoff curve number</td><td>-96.03</td><td>0.00E+00</td></tr><tr><td>R_SOL_AWC(..).sol</td><td>Available water capacity of the soil layer</td><td>7.24</td><td>0.00E+00</td></tr><tr><td>R_SOL_K(..).sol</td><td>Saturated hydraulic conductivity</td><td>-5.52</td><td>3.90E-08</td></tr><tr><td>V_ESCO.hru</td><td>Soil evaporation compensation factor</td><td>-5.39</td><td>7.90E-08</td></tr><tr><td>R_SOL_BD(..).sol</td><td>Moist bulk density</td><td>-2.87</td><td>4.19E-03</td></tr><tr><td>R_HRU_SLP.hru</td><td>Average slope steepness</td><td>-2.68</td><td>7.53E-03</td></tr><tr><td>V_CH_K2.rte</td><td>Effective hydraulic conductivity in main channel alluvium</td><td>1.94</td><td>5.19E-02</td></tr><tr><td>V_EPCO.hru</td><td>Plant uptake compensation factor</td><td>1.93</td><td>5.41E-02</td></tr><tr><td>V_RCHRG_DP.gw</td><td>Deep aquifer percolation fraction</td><td>1.68</td><td>9.33E-02</td></tr></tbody></table>

## 3.2. Calibration and validation

During the calibration process, the model underwent multiple runs with the most sensitive parameters to ascertain their optimal values. Over ten iterations involving 350 simulations, satisfactory objective functions were obtained using SUFI-2. The best parameter values derived were then employed to validate the model for a distinct period not used in the calibration. A comparison between observed and simulated flows was conducted monthly.

The results were scrutinized through the calculation of the statistical coefficients mentioned earlier (equations 2, 3, 4, and 5). All statistical values consistently fall within the "very good" and "good" categories. The significance of the simulation results implies that the model adequately represents the hydrological processes in the Hab-OER (table 3).

<table><caption>**Table 3.** Performance outcomes of the model at various stations.</caption><thead><tr><th rowspan="2">Station</th><th colspan="4">Calibration</th><th colspan="3">Validation</th></tr><tr><th>R<sup>2</sup></th><th>Pbias</th><th>NSE</th><th>RSR</th><th>R<sup>2</sup></th><th>Pbias</th><th>NSE</th><th>RSR</th></tr></thead><tbody><tr><td>Tarhat</td><td>0.87</td><td>4.11</td><td>0.86</td><td>0.37</td><td>0.81</td><td>-14.18</td><td>0.68</td><td>0.56</td></tr><tr><td>Cha. n'Amellah</td><td>0.89</td><td>1.23</td><td>0.89</td><td>0.34</td><td>0.82</td><td>-6.07</td><td>0.67</td><td>0.57</td></tr><tr><td>Taghzoute</td><td>0.72</td><td>3.64</td><td>0.71</td><td>0.54</td><td>0.72</td><td>-14.94</td><td>0.68</td><td>0.56</td></tr></tbody></table>
Monthly R2 values surpassed 0.72 at all three stations for both calibration and validation periods. Pbias is deemed very good for all three stations during calibration, exhibiting a slight decrease during validation for the Tarhat and Taghzoute stations, yet remaining within the good range. Regarding NSE and RSR, they achieved very good ratings for the two stations of Tarhat and Chacha n'Amellah and good ratings for the Taghzout station during the calibration period. In the validation period, all values for these two indices are considered good.

Furthermore, the graphical comparison revealed a commendable alignment between observed and simulated flows. Hydrographs of simulated and observed flows at the three selected stations demonstrated the SWAT model's effective tracking of recorded flows. Indeed, most peak, and low flows exhibited a close match with the observed data during the calibration and validation periods (figure 3). This robust agreement between simulations and observations underscores the model's calibration performance and its adeptness in representing diverse climatic situations.

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 3.** Observed and simulated monthly flow hydrographs.</figcaption></figure>
### 3.3. Water balance

The water balance computed by the SWAT model for Hab-OER indicates that the average annual precipitation from 1982 to 2019 is 543 mm. Consistent with numerous studies conducted in arid and semi-arid regions, the components of the balance are predominantly influenced by evapotranspiration, which accounts for 322 mm or 60% of the precipitation. Surface runoff represents 58 mm or 10% of the precipitation, contributing to 32% of the total discharge of approximately 181 mm. The precipitation that infiltrates or percolates amounts to 163 mm, constituting 30% of the total precipitation. The combined contribution of percolated and infiltrated water to the flow is 68% (figure 4). Subterranean inflow plays a crucial role in regulating and sustaining flow throughout the year. In general, these findings align with the outcomes of the study by [48], illustrating that about 70% of the precipitation over the Hab-OER either evaporates, intercepts, or infiltrates, while the remaining 30% contributes to runoff, with 24% feeding base runoff and 6% flood runoff.

<figure><img src="image_10.png" /><figcaption>**Figure 4.** Up-OER watershed water balance for the period 1982-2019.</figcaption></figure>

## 4. Conclusion

Water resource management necessitates a model capable of assessing various development or intervention scenarios. This study aims to calibrate and validate the SWAT hydrological model in a watershed recognized as one of the richest in water resources in Morocco. The application of the SWAT model focused on the three sub-watersheds characterizing the Hab-OER to facilitate the management of the Ahmed El Hansali dam over the period 1982-2019. Achieving model success entailed a meticulous process of preparing and structuring the diverse data essential for SWAT execution. The ArcSWAT interface effectively modeled the Oum Er Rbia, Srou, and Ouaoumana rivers. The watershed was intricately subdivided into 17 sub-basins and 142 HRUs, each HRU distinguished by a unique combination of land use, soil type, and slope class. Calibration aimed to optimize the most sensitive parameters through the SUFI-2 procedure under SWAT-CUP software.

The calibration spanned the sub-periods 1985-95 and 2007-12, with validation occurring between 1996-06 and 2013-19. Model performance assessed using the widely used statistical coefficients R², NSE, PBIAS, and RSR, revealed values surpassing 0.7 for R² and 0.65 for NSE, while staying within ±15 for PBIAS and 0.15 for RSR during both calibration and validation. These results affirm the model's
effectiveness, demonstrating its suitability for reproducing the dynamics of the Oum Er Rbia, Srou, and Ouaoumana sub-basins. Furthermore, graphical comparisons of observed and simulated flows underscored the model's proficiency in replicating monthly flow patterns.

Analysis of the water balance reveals that the average annual precipitation over 37 years is 543 mm. Evapotranspiration dominates the balance, accounting for 60% of precipitation, while surface runoff contributes 10%, and 30% infiltrates or percolates to recharge the water table or replenish streams. Total runoff amounts to approximately 181 mm, with 32% as surface runoff and 68% as subsurface runoff.

In conclusion, the reliability of the SWAT model positions it as a valuable tool for exploring climate change, agricultural practices, and water resource management. The SWAT model calibrated and validated for Hab-OER could be useful to local decision-makers in developing water resource management strategies to enhance conservation and environmental policy development.

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