PATUXENT MODEL SCENARIOS

The goal of the linked ecological economic model development is to test alternative scenarios of land use management. A wide range of future and historical scenarios may be explored using the calibrated model. We have developed scenarios based on the concerns of county, state and federal government agencies, local stakeholders and researchers. The following set of initial scenarios was considered:

A group of historical scenarios based on the USGS reconstruction (Buchanan, et al. 1998) of land use in the Patuxent watershed:

• (1) 1650 — pre-development era. Most of the area forested, zero emissions.
• (2) 1850 — agro-development. Almost all the area under agricultural use, traditional fertilizers (marl, river mud, manure, etc.), low emissions.
• (3) 1953 — decline of agriculture, start of reforestation and fast urbanization.
• (4) 1972 — maximal reforestation, intensive agriculture, high emissions.

(5) Baseline scenario. We use 1990 as a baseline to compare the modeling results. The 1990-1991 climatic patterns and nutrient loadings were used.

(6) 1997 land use pattern. This data set has just recently been released and we used it with the 1990-1991 forcings to estimate the effect of landuse change alone.

(7) Buildout scenario. With the existing zoning regulations, we assumed that all the possible development in the area occurred. This may be considered as the worst case scenario in terms of urbanization and it’s associated loadings.

(8) Best Management Practices (BMP) — 1997 land use with lowered fertilizer application and crop rotation. These management practices were also assumed in the remaining scenarios.

A group of scenarios of change in land use over the 5 years following 1997 (i.e. for 2003) developed based on the Economic Land Use Conversion (ELUC) Model by N. Bockstael:

• (9) Development as usual
• (10) Development with all projected sewer systems in place
• (11) Development with no new sewers but contiguous patches of forest 500 acres and more protected
• (12) Development with all sewers in place and contiguous forest protected

Another group of hypothetical scenarios to study more dramatic change in land use patterns using the 1997 land use as the starting point:

• (13) Conversion of all agricultural land into residential
• (14) Conversion of all agricultural land into forested
• (15) Conversion of all residential land into forested
• (16) Conversion of all forested land into residential
• (17) Residential clustering — conversion of all low density residential land use into urban around 3 major centers
• (18) Residential sprawl — conversion of all high density urban into residential randomly spread across the watershed.

The scenarios were driven by changes in the Landuse map, the Sewers map, patterns of fertilizer application, amounts of atmospheric deposition, and location and number of dwelling units. We compare the model output in the different scenarios looking at nitrogen concentration in the Patuxent River as an indicator of water quality, changes in the hydrologic flow and changes in the net primary productivity of the landscape.

Table. Some results of scenario runs for the Patuxent Model. The buildout conditions (LUBO) were estimated based on the existing zoning maps and the average population densities for particular land use types. The buildout conditions represent the "worst case" scenario. The ELUC scenarios (LUB1-4) are based on the model by N. Bockstael. The historical scenarios (LU1650 - 1972) are a reconstruction based on estimates done by USGS. Total NPP (g/m²/day) presents the average across the whole watershed productivity of the plant ecosystem. It well represents the approximate proportion of forested and agricultural land use types, which have a larger NPP than the residential ones. N gw.c. is the average concentration on N in the ground water. Since ground water is fairly slow variable in the model and the model had only 1 year of relaxation time in the experiments performed, it is most likely that this parameter does not adapt fast enough to track the changes assumed under different scenarios. Wmax is the total of the 10% of the flow that is maximal over one year period. This represents the peak flow. Wmin is the total of the 50% of flow that is minimal over one year period. This is an indicator of the baseflow.

		Forest	Resid	Urban	Agro	Popul.	Atmos	Fertil	Decomp	Septic	N average	N max	N min	Wmax	Wmin	N gw conc.	NPP
			number of cells			mln.		kg/ha/year				mg/l				g/m3	kg/m2/y
1	1650	2386	0	0	56	0	3.00	0.00	161.00	0.00	3.02	10.63	0.05	100.926	34.377	0.023	2.181
2	1850	348	7	0	2087	0.018	7.00	106.00	63.00	0.00	6.69	38.52	0.25	148.034	22.508	0.251	0.333
3	1950	911	111	28	1391	0.167	124.00	111.00	99.00	7.00	13.24	36.05	1.14	128.002	18.906	0.301	1.161
4	1972	1252	223	83	884	0.397	111.00	149.00	118.00	7.00	16.41	58.23	1.09	126.580	19.676	0.294	1.782
5	1990	1315	311	92	724	0.486	111.00	106.00	113.00	13.00	12.59	36.85	1.53	138.273	18.502	0.28	1.715
6	1997	1195	460	115	672	0.660	117.00	100.00	105.00	18.00	12.37	56.00	1.33	147.360	18.087	0.307	1.627
7	BuildOut	312	729	216	1185	1.137	124.00	165.00	62.00	21.00	15.36	87.83	2.56	174.472	11.231	0.476	0.578
8	BMP	1195	460	115	672	0.660	104.00	44.00	103.00	18.00	8.17	20.76	0.13	147.066	16.452	0.243	1.579
9	LUB1	1129	575	134	604	0.797	111.00	92.00	99.00	8.00	15.26	81.75	1.08	151.690	19.666	0.295	1.553
10	LUB2	1147	538	134	623	0.769	111.00	94.00	101.00	11.00	12.53	51.67	1.79	149.256	18.695	0.295	1.577
11	LUB3	1129	577	134	602	0.798	111.00	91.00	100.00	24.00	11.68	51.69	0.45	149.378	19.013	0.316	1.565
12	LUB4	1133	564	135	610	0.791	111.00	92.00	100.00	12.00	11.71	51.46	0.41	149.318	18.574	0.297	1.566
13	agro2res	1195	1132	115	0	1.160	111.00	16.00	95.00	39.00	8.75	22.76	0.56	168.196	17.012	0.316	1.78
14	agro2frst	1867	460	115	0	0.656	111.00	6.00	133.00	18.00	6.82	16.81	0.15	137.995	21.496	0.154	2.351
15	res2frst	1655	0	115	672	0.315	111.00	94.00	130.00	7.00	10.24	26.97	0.48	122.177	23.036	0.197	2.037
16	frst2res	0	1655	115	672	1.556	111.00	102.00	36.00	54.00	12.37	48.81	4.51	182.190	9.423	0.535	0.451
17	cluster	1528	0	276	638	0.749	111.00	89.00	121.00	17.00	10.50	30.06	1.37	167.607	19.406	0.233	1.868
18	sprawl	811	993	0	638	0.749	111.00	101.00	84.00	27.00	13.50	45.14	3.55	140.712	18.403	0.381	1.271

Comparing the effect of various land use change scenarios on the water quality in the river shows that there is no obvious connection between the nutrient loading to the watershed and the nutrient concentration in the river. However some conclusions can be drawn. The effects of loadings which are distributed more evenly over the year are much less pronounced than those which occur sporadically . For example, fertilizer applications that occur once or twice a year increase the average nutrient content and especially the maximum nutrient concentration quite significantly, whereas the effect of, say, atmospheric deposition is much more obscure. The difference in atmospheric loading between Scenarios (1) and (3) is almost 2 orders of magnitude, yet the nutrient response is only 5-6 times higher, even though loadings from other sources also increase. Similarly the effect of septic loadings that are occurring continuously is not so large.

The average N concentration is well correlated (corr=0.87) with the total amount of nutrients loaded. The effect of fertilizers is most pronounced among the individual factors (corr=0.82), while the effect of other sources is much less (septic corr=0.02; decomposition corr=-0.40; atmosphere corr=0.71). The fertilizer application rate determines the maximum nutrient concentrations (corr=0.76), with the total nutrient input also playing an important role (corr=0.55). Even the groundwater concentration of nutrients is related to fertilizer applications (corr=0.64), however in this case the septic loadings are more important (corr=0.68), even a more important one than the total N loading (corr=0.52).

The hydrologic response is quite strongly driven by the land use patterns. The peak flow (max 10% of flow) is determined by the degree of urbanization (corr=0.61). The baseflow (min 50% of flow) is very much related to the number of forested cells (corr=0.78), but in both cases there are obviously other factors involved.

Different land use patterns result in quite significant variations in the net primary productivity (NPP) of the watershed, both in the temporal and in the spatial domains. The predevelopment 1650 conditions produce the largest NPP, while under Build Out conditions NPP is the lowest. In the latter case the dynamics of NPP are more representative of the agricultural landuse with higher NPP values attained later in the year as crops mature. Interestingly under the BMP scenario with lower fertilizer applications we still get a higher NPP than under reference conditions of 1997, because of the crop rotation and growth of winter wheat that matures earlier in the season than corn.

The major result of the analysis performed thus far is that the model behaves well and produces plausible output under significant variations in forcing functions and land use patterns. It can therefore be instrumental for analysis and comparisons of very diverse environmental conditions that can be formulated as scenarios of change and further studied and refined as additional data and information are obtained. The real power of the model comes from its ability to link hydrology, nutrients, plant dynamics and economic behavior via land use change. The model allows fairly site specific effects to be examined as well as regional impacts so that both local water quality and Chesapeake Bay inputs can be considered. The linked ecological economic model is a potentially important tool for addressing issues of land use change. The model integrates our current understanding of certain ecological and economic processes to give best available estimates of effects of land use or land management change. The model also highlights areas where knowledge is lacking and where further research could be targeted for the most impact.

  to see the discussion on scenarios