Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real‐time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8° to 1/8° spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel‐nearest neighbor (K‐NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS‐2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS‐2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991–2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.
Despite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infiltration Capacity model is used over the Apalachicola River at Chattahoochee in the southeastern United States. The model is forced with a combination of daily precipitation forcings (temporally disaggregated observed precipitation, spatially downscaled and temporally disaggregated observed precipitation, ESP, ECHAM4.5 forecasts, and observed) and IHCs [simulated and climatological ensemble reverse ESP (RESP)] but with observed air temperature and wind speed at 1 /88 resolution. Then, errors in forecasting monthly streamflow at up to a 3-month lead time are decomposed by comparing the forecasted streamflow to simulated streamflow under observed forcings. Results indicate that the errors due to temporal disaggregation are much higher than the spatial downscaling errors. During winter and early spring, the increasing order of errors at a 1-month lead time is spatial downscaling, model, temporal disaggregation, RESP, large-scale precipitation forecasts, and ESP.
With an increasing number of continental-scale hydrologic models, the ability to evaluate performance is key to understanding uncertainty in prediction and making improvements to the model(s). In 2016, the NOAA National Water Model (NWM) was put into operations to improve the spatial and temporal resolution of hydrologic prediction in the U.S. Here, we evaluate the NWM 2.0 historical streamflow record in natural and controlled basins using the Nash Sutcliffe Efficiency metric decomposed into relative error, conditional, and unconditional bias. Each of these is evaluated in the contexts of categorized meteorologic, landscape, and anthropogenic characteristics to assess model performance and diagnose error types. Broadly speaking greater rainfall and snow coverage leads to improved performance while larger potential evapotranspiration (PET), aridity, and phase correlation reduce performance. More rainfall and phase correlation reduce overall bias, while increasing PET, aridity, snow coverage/fraction increase model bias. With respect to landscape traits, more barren and agricultural land yeild improved performance while more forest, shrubland, grassland and imperviousness tend to decrease performance. Lastly, more barren and herbaceous land tend to decrease bias, while greater imperviousness, urban, forest, and shrubland cover increase bias. The insights gained can help identify key hydrological factors in NWM predictions; enforce the need for regionalized physics and modeling; and help develop hybid post-processing methods to improve prediction. Finally, we demonstrate how the NOAA Next Generation Water Resource Modeling Framework can help reduce the structural bias through the application of heterogenous model processes and highlight opportunities for ongoing development and evaluation.
Abstract. Global hydroclimatic conditions have been substantially altered over the past century by anthropogenic influences that arise from the warming global climate and from local/regional anthropogenic disturbances. Traditionally, studies have used coupling of multiple models to understand how land-surface water fluxes vary due to changes in global climatic patterns and local land-use changes. We argue that because the basis of the Budyko framework relies on the supply and demand concept, the framework could be effectively adapted and extended to quantify the role of drivers – both changing climate and local human disturbances – in altering the land-surface response across the globe. We review the Budyko framework, along with these potential extensions, with the intent of furthering the applicability of the framework to emerging hydrologic questions. Challenges in extending the Budyko framework over various spatio-temporal scales and the use of global datasets to evaluate the water balance at these various scales are also discussed.
Urbanization has been shown to locally increase the nighttime temperatures creating urban heat islands, which partly arise due to evapotranspiration (ET) reduction. It is unclear how the direction and magnitude of the change in local ET due to urbanization varies globally across different climatic regimes. This knowledge gap is critical, both for the key role of ET in the energy and water balance accounting for the majority of local precipitation, and for reducing the urban heat island effect. We explore and assess the impacts of urbanization on monthly and mean annual ET across a range of landscapes from local to global spatial scales. Remotely sensed land cover and ET available at 1-km resolution are used to quantify the differences in ET between urban and surrounding non-urban areas across the globe. The observed patterns show that the statistically significant difference between urban and non-urban ET can be estimated to first order as a function of local hydroclimate, with arid regions seeing increased ET, and humid regions showing decreased ET. Cities under cold climates also evaporate more than their non-urban surroundings during the winter, as the urban micro-climate has increased energy availability resulting from human activities. Increased ET in arid cities arises from municipal water withdrawals and increased irrigation during drought conditions. These results can help inform planners to improve the integration of environmental conditions into the design and management of urban landscapes.
Statistical information from ensembles of climate forecasts can be utilized in improving the streamflow predictions by using different downscaling methods. This study investigates the use of multinomial logistic regression (MLR) in downscaling large-scale ensemble climate forecasts into basin-scale probabilistic streamflow forecasts of categorical events over major river basins across the U.S. Sun Belt. The performance of MLR is then compared with the categorical forecasts estimated from the traditional approach, principal component regression (PCR). Results from both cross validation and split sampling reveal that in general, the probabilistic categorical forecasts from the MLR model have more accuracy and exhibit higher rank probability skill score (RPSS) compared to the PCR probabilistic forecasts. MLR forecasts are also more skillful than PCR forecasts during the winter season as well as for basins that exhibit high interannual variability in streamflows. The role of ensemble size of precipitation forecasts in developing MLR-based streamflow forecasts was also investigated. Because of its simplicity, MLR offers an alternate, reliable approach to developing categorical streamflow forecasts.
Abstract. Providing accurate soil moisture (SM) conditions is a critical step in model initialization in weather forecasting, agricultural planning, and water resources management. This study develops monthly-to-seasonal (M2S) top layer SM forecasts by forcing 1- to 3-month-ahead precipitation forecasts with Noah3.2 Land Surface Model. The SM forecasts are developed over the southeastern US (SEUS), and the SM forecasting skill is evaluated in comparison with the remotely sensed SM observations collected by the Soil Moisture Active Passive (SMAP) satellite. Our results indicate potential in developing real-time SM forecasts. The retrospective 18-month (April 2015–September 2016) comparison between SM forecasts and the SMAP observations shows statistically significant correlations of 0.62, 0.57, and 0.58 over 1-, 2-, and 3-month lead times respectively.
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