Incorporating an Urban Irrigation Module into the Noah Land Surface Model Coupled with an Urban Canopy Model

Abstract

The current research examines the influence of irrigation on urban hydrological cycles through the development of an irrigation scheme within the Noah land surface model (LSM)-Single Layer Urban Canopy Model (SLUCM) system. The model is run at a 30-m resolution for a 2-yr period over a 49 km 2 urban domain in the Los Angeles metropolitan area. A sensitivity analysis indicates significant sensitivity relative to both the amount and timing of irrigation on diurnal and monthly energy budgets, hydrological fluxes, and state variables. Monthly residential water use data and three estimates of outdoor water consumption are used to calibrate the developed irrigation scheme. Model performance is evaluated using a previously developed MODIS-Landsat evapotranspiration (ET) and Landsat land surface temperature (LST) products as well as hourly ET observations through the California Irrigation Management Information System (CIMIS). Results show that the Noah LSM-SLUCM realistically simulates the diurnal and seasonal variations of ET when the irrigation module is incorporated. However, without irrigation, the model produces large biases in ET simulations. The ET errors for the nonirrigation simulations are 256 and 290 mm month 21 for July 2003 and 2004, respectively, while these values decline to 26 and 211 mm month 21 over the same 2 months when the proposed irrigation scheme is adopted. Results show that the irrigation-induced increase in latent heat flux leads to a decrease in LST of about 28C in urban parks. The developed modeling framework can be utilized for a number of applications, ranging from outdoor water use estimation to climate change impact assessments.

Urban irrigation effects on WRF‐UCM summertime forecast skill over the Los Angeles metropolitan area

“…This module runs within the Noah LSM and simulates urban irrigation by updating the topsoil layer moisture content for irrigated pixels, to a certain percentage of soil saturation (irrigation demand factor) at a specified irrigation interval. Irrigated soil moisture (SMC IRR ; m 3 m −3 ), soil moisture deficit (DEF; m 3 m −3 ), and irrigation water (IRR; kg m −2 s −1 ) are calculated for the pervious portion of each grid pixel using equations (adopted from Vahmani and Hogue []). where SMC 1 (m 3 m −3 ) and SMC max (m 3 m −3 ) are actual and saturation soil moisture contents, respectively.…”

“…In the current analysis, irrigation is assumed to be automatic and with no water loss (fully effective), and irrigation water is added at 02:00 A.M. (local time) to avoid large solar radiation flux periods. Over the Los Angeles metropolitan area, Vahmani and Hogue [] reported that an irrigation demand factor of 65% and an application interval of 3 days represent Los Angeles irrigation behavior with a reasonable accuracy based on prior analysis of monthly water consumption records and monthly outdoor water use estimates for 2003 and 2004. In the current study, an irrigation demand factor of 0.65 with an irrigation rate of 3 times per week is adopted, which is consistent with water restrictions implemented by the Los Angeles Department of Water and Power in 2010 [ Mini et al ., ].…”

Assessment of irrigation physics in a land surface modeling framework using non-traditional and human-practice datasets

“…For example, a flood irrigation parameterization with two different triggering thresholds resulted in up to 80 W m −2 difference in average seasonal latent heat flux increase in the US central Great Plains (Lawston et al, 2015). In another case, Vahmani and Hogue (2014) tested several irrigation demand factors and irrigation timing in their urban irrigation module, finding fluxes, runoff, and irrigation water are sensitive to both inputs. Additionally, the same parameterization used in a different model (Kueppers et al, 2008;Tuinenburg et al, 2014) or in the same model but at a different resolution (Sorooshian et al, 2011) has also produced different coupled atmospheric impacts.…”

“…However, datasets required for evaluation, such as irrigation amount, irrigation timing, and co-located continuous soil moisture observations, are not widely available, making it difficult to evaluate irrigation schemes (Kueppers et al, 2007). Modeling studies that have included some assessment of the irrigation scheme have used comparisons to annual water withdrawals for irrigation (Lobell et al, 2009;Pokhrel et al, 2012), outdoor water use (Vahmani and Hogue, 2014), recommended amounts of irrigation (Sorooshian et al, 2011(Sorooshian et al, , 2012, or irrigation water usage reported by the US Geological Survey (Ozdogan et al, 2010). Bulk estimates such as these are often not used for robust evaluation but rather indicate that the simulated results are reasonable.…”

“…In response, the Global Energy and Water Cycle Exchanges project's (GEWEX) Hydroclimatology Panel (GHP) and Global Land/Atmosphere System Study (GLASS) have begun a joint effort to advance the representation of human water resources management in land surface and coupled models (van Oevelen, 2016). To effectively meet these challenges, new, non-traditional datasets are needed to evaluate and improve representation of irrigation in models and to assess the processes by which simulated irrigation impacts the water cycle.…”